From 6625b62c3cc2a5b7f75c059549b5b9235f51e0cf Mon Sep 17 00:00:00 2001
From: kun <1819123358@qq.com>
Date: Thu, 8 Jun 2023 13:11:42 +0800
Subject: [PATCH] yolov5
---
yolov5-6.2/.dockerignore | 222 ----
yolov5-6.2/.gitattributes | 2 -
yolov5-6.2/.github/CODE_OF_CONDUCT.md | 128 --
.../.github/ISSUE_TEMPLATE/bug-report.yml | 85 --
yolov5-6.2/.github/ISSUE_TEMPLATE/config.yml | 8 -
.../ISSUE_TEMPLATE/feature-request.yml | 50 -
.../.github/ISSUE_TEMPLATE/question.yml | 33 -
yolov5-6.2/.github/PULL_REQUEST_TEMPLATE.md | 9 -
yolov5-6.2/.github/README_cn.md | 353 -----
yolov5-6.2/.github/SECURITY.md | 7 -
yolov5-6.2/.github/dependabot.yml | 23 -
yolov5-6.2/.github/workflows/ci-testing.yml | 135 --
.../.github/workflows/codeql-analysis.yml | 54 -
yolov5-6.2/.github/workflows/docker.yml | 54 -
yolov5-6.2/.github/workflows/greetings.yml | 63 -
yolov5-6.2/.github/workflows/rebase.yml | 21 -
yolov5-6.2/.github/workflows/stale.yml | 40 -
yolov5-6.2/.gitignore | 256 ----
yolov5-6.2/.pre-commit-config.yaml | 64 -
yolov5-6.2/CONTRIBUTING.md | 98 --
yolov5-6.2/LICENSE | 674 ----------
yolov5-6.2/README.md | 363 ------
yolov5-6.2/classify/predict.py | 109 --
yolov5-6.2/classify/train.py | 325 -----
yolov5-6.2/classify/val.py | 158 ---
yolov5-6.2/data/Argoverse.yaml | 67 -
yolov5-6.2/data/GlobalWheat2020.yaml | 54 -
yolov5-6.2/data/ImageNet.yaml | 156 ---
yolov5-6.2/data/Objects365.yaml | 114 --
yolov5-6.2/data/SKU-110K.yaml | 53 -
yolov5-6.2/data/VOC.yaml | 81 --
yolov5-6.2/data/VisDrone.yaml | 61 -
yolov5-6.2/data/ball.yaml | 5 -
yolov5-6.2/data/coco.yaml | 45 -
yolov5-6.2/data/coco128.yaml | 30 -
yolov5-6.2/data/hyps/hyp.Objects365.yaml | 34 -
yolov5-6.2/data/hyps/hyp.VOC.yaml | 40 -
yolov5-6.2/data/hyps/hyp.scratch-high.yaml | 34 -
yolov5-6.2/data/hyps/hyp.scratch-low.yaml | 34 -
yolov5-6.2/data/hyps/hyp.scratch-med.yaml | 34 -
yolov5-6.2/data/images/bus.jpg | Bin 487438 -> 0 bytes
yolov5-6.2/data/images/zidane.jpg | Bin 168949 -> 0 bytes
yolov5-6.2/data/scripts/download_weights.sh | 21 -
yolov5-6.2/data/scripts/get_coco.sh | 56 -
yolov5-6.2/data/scripts/get_coco128.sh | 17 -
yolov5-6.2/data/scripts/get_imagenet.sh | 51 -
yolov5-6.2/data/xView.yaml | 102 --
yolov5-6.2/detect.py | 260 ----
yolov5-6.2/export.py | 616 ---------
yolov5-6.2/hubconf.py | 160 ---
yolov5-6.2/ip.py | 21 -
yolov5-6.2/models/__init__.py | 0
yolov5-6.2/models/common.py | 771 -----------
yolov5-6.2/models/experimental.py | 107 --
yolov5-6.2/models/hub/anchors.yaml | 59 -
yolov5-6.2/models/hub/yolov3-spp.yaml | 51 -
yolov5-6.2/models/hub/yolov3-tiny.yaml | 41 -
yolov5-6.2/models/hub/yolov3.yaml | 51 -
yolov5-6.2/models/hub/yolov5-bifpn.yaml | 48 -
yolov5-6.2/models/hub/yolov5-fpn.yaml | 42 -
yolov5-6.2/models/hub/yolov5-p2.yaml | 54 -
yolov5-6.2/models/hub/yolov5-p34.yaml | 41 -
yolov5-6.2/models/hub/yolov5-p6.yaml | 56 -
yolov5-6.2/models/hub/yolov5-p7.yaml | 67 -
yolov5-6.2/models/hub/yolov5-panet.yaml | 48 -
yolov5-6.2/models/hub/yolov5l6.yaml | 60 -
yolov5-6.2/models/hub/yolov5m6.yaml | 60 -
yolov5-6.2/models/hub/yolov5n6.yaml | 60 -
yolov5-6.2/models/hub/yolov5s-ghost.yaml | 48 -
.../models/hub/yolov5s-transformer.yaml | 48 -
yolov5-6.2/models/hub/yolov5s6.yaml | 60 -
yolov5-6.2/models/hub/yolov5x6.yaml | 60 -
yolov5-6.2/models/tf.py | 574 --------
yolov5-6.2/models/yolo.py | 360 -----
yolov5-6.2/models/yolov5l.yaml | 48 -
yolov5-6.2/models/yolov5m.yaml | 48 -
yolov5-6.2/models/yolov5n.yaml | 48 -
yolov5-6.2/models/yolov5s.yaml | 48 -
yolov5-6.2/models/yolov5x.yaml | 48 -
yolov5-6.2/requirements.txt | 43 -
yolov5-6.2/setup.cfg | 59 -
yolov5-6.2/test.py | 300 -----
yolov5-6.2/test/test.py | 12 -
yolov5-6.2/train.py | 632 ---------
yolov5-6.2/tutorial.ipynb | 1141 ----------------
yolov5-6.2/utils/__init__.py | 36 -
yolov5-6.2/utils/activations.py | 103 --
yolov5-6.2/utils/augmentations.py | 348 -----
yolov5-6.2/utils/autoanchor.py | 170 ---
yolov5-6.2/utils/autobatch.py | 66 -
yolov5-6.2/utils/aws/__init__.py | 0
yolov5-6.2/utils/aws/mime.sh | 26 -
yolov5-6.2/utils/aws/resume.py | 40 -
yolov5-6.2/utils/aws/userdata.sh | 27 -
yolov5-6.2/utils/benchmarks.py | 157 ---
yolov5-6.2/utils/callbacks.py | 71 -
yolov5-6.2/utils/dataloaders.py | 1156 -----------------
yolov5-6.2/utils/docker/Dockerfile | 68 -
yolov5-6.2/utils/docker/Dockerfile-arm64 | 42 -
yolov5-6.2/utils/docker/Dockerfile-cpu | 39 -
yolov5-6.2/utils/downloads.py | 180 ---
yolov5-6.2/utils/flask_rest_api/README.md | 73 --
.../utils/flask_rest_api/example_request.py | 19 -
yolov5-6.2/utils/flask_rest_api/restapi.py | 48 -
yolov5-6.2/utils/general.py | 1050 ---------------
yolov5-6.2/utils/google_app_engine/Dockerfile | 25 -
.../additional_requirements.txt | 4 -
yolov5-6.2/utils/google_app_engine/app.yaml | 14 -
yolov5-6.2/utils/loggers/__init__.py | 308 -----
yolov5-6.2/utils/loggers/clearml/README.md | 222 ----
yolov5-6.2/utils/loggers/clearml/__init__.py | 0
.../utils/loggers/clearml/clearml_utils.py | 156 ---
yolov5-6.2/utils/loggers/clearml/hpo.py | 84 --
yolov5-6.2/utils/loggers/wandb/README.md | 162 ---
yolov5-6.2/utils/loggers/wandb/__init__.py | 0
yolov5-6.2/utils/loggers/wandb/log_dataset.py | 27 -
yolov5-6.2/utils/loggers/wandb/sweep.py | 41 -
yolov5-6.2/utils/loggers/wandb/sweep.yaml | 143 --
yolov5-6.2/utils/loggers/wandb/wandb_utils.py | 584 ---------
yolov5-6.2/utils/loss.py | 234 ----
yolov5-6.2/utils/metrics.py | 364 ------
yolov5-6.2/utils/plots.py | 522 --------
yolov5-6.2/utils/torch_utils.py | 454 -------
yolov5-6.2/val.py | 396 ------
124 files changed, 18132 deletions(-)
delete mode 100644 yolov5-6.2/.dockerignore
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delete mode 100644 yolov5-6.2/.github/CODE_OF_CONDUCT.md
delete mode 100644 yolov5-6.2/.github/ISSUE_TEMPLATE/bug-report.yml
delete mode 100644 yolov5-6.2/.github/ISSUE_TEMPLATE/config.yml
delete mode 100644 yolov5-6.2/.github/ISSUE_TEMPLATE/feature-request.yml
delete mode 100644 yolov5-6.2/.github/ISSUE_TEMPLATE/question.yml
delete mode 100644 yolov5-6.2/.github/PULL_REQUEST_TEMPLATE.md
delete mode 100644 yolov5-6.2/.github/README_cn.md
delete mode 100644 yolov5-6.2/.github/SECURITY.md
delete mode 100644 yolov5-6.2/.github/dependabot.yml
delete mode 100644 yolov5-6.2/.github/workflows/ci-testing.yml
delete mode 100644 yolov5-6.2/.github/workflows/codeql-analysis.yml
delete mode 100644 yolov5-6.2/.github/workflows/docker.yml
delete mode 100644 yolov5-6.2/.github/workflows/greetings.yml
delete mode 100644 yolov5-6.2/.github/workflows/rebase.yml
delete mode 100644 yolov5-6.2/.github/workflows/stale.yml
delete mode 100644 yolov5-6.2/.gitignore
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delete mode 100644 yolov5-6.2/CONTRIBUTING.md
delete mode 100644 yolov5-6.2/LICENSE
delete mode 100644 yolov5-6.2/README.md
delete mode 100644 yolov5-6.2/classify/predict.py
delete mode 100644 yolov5-6.2/classify/train.py
delete mode 100644 yolov5-6.2/classify/val.py
delete mode 100644 yolov5-6.2/data/Argoverse.yaml
delete mode 100644 yolov5-6.2/data/GlobalWheat2020.yaml
delete mode 100644 yolov5-6.2/data/ImageNet.yaml
delete mode 100644 yolov5-6.2/data/Objects365.yaml
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delete mode 100644 yolov5-6.2/data/VisDrone.yaml
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delete mode 100644 yolov5-6.2/data/hyps/hyp.VOC.yaml
delete mode 100644 yolov5-6.2/data/hyps/hyp.scratch-high.yaml
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delete mode 100644 yolov5-6.2/models/__init__.py
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delete mode 100644 yolov5-6.2/models/experimental.py
delete mode 100644 yolov5-6.2/models/hub/anchors.yaml
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delete mode 100644 yolov5-6.2/utils/google_app_engine/Dockerfile
delete mode 100644 yolov5-6.2/utils/google_app_engine/additional_requirements.txt
delete mode 100644 yolov5-6.2/utils/google_app_engine/app.yaml
delete mode 100644 yolov5-6.2/utils/loggers/__init__.py
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delete mode 100644 yolov5-6.2/utils/loggers/clearml/hpo.py
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delete mode 100644 yolov5-6.2/utils/loggers/wandb/sweep.yaml
delete mode 100644 yolov5-6.2/utils/loggers/wandb/wandb_utils.py
delete mode 100644 yolov5-6.2/utils/loss.py
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delete mode 100644 yolov5-6.2/utils/plots.py
delete mode 100644 yolov5-6.2/utils/torch_utils.py
delete mode 100644 yolov5-6.2/val.py
diff --git a/yolov5-6.2/.dockerignore b/yolov5-6.2/.dockerignore
deleted file mode 100644
index 3b669254..00000000
--- a/yolov5-6.2/.dockerignore
+++ /dev/null
@@ -1,222 +0,0 @@
-# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------
-.git
-.cache
-.idea
-runs
-output
-coco
-storage.googleapis.com
-
-data/samples/*
-**/results*.csv
-*.jpg
-
-# Neural Network weights -----------------------------------------------------------------------------------------------
-**/*.pt
-**/*.pth
-**/*.onnx
-**/*.engine
-**/*.mlmodel
-**/*.torchscript
-**/*.torchscript.pt
-**/*.tflite
-**/*.h5
-**/*.pb
-*_saved_model/
-*_web_model/
-*_openvino_model/
-
-# Below Copied From .gitignore -----------------------------------------------------------------------------------------
-# Below Copied From .gitignore -----------------------------------------------------------------------------------------
-
-
-# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-wandb/
-.installed.cfg
-*.egg
-
-# PyInstaller
-# Usually these files are written by a python script from a template
-# before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv*
-venv*/
-ENV*/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-
-# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
-
-# General
-.DS_Store
-.AppleDouble
-.LSOverride
-
-# Icon must end with two \r
-Icon
-Icon?
-
-# Thumbnails
-._*
-
-# Files that might appear in the root of a volume
-.DocumentRevisions-V100
-.fseventsd
-.Spotlight-V100
-.TemporaryItems
-.Trashes
-.VolumeIcon.icns
-.com.apple.timemachine.donotpresent
-
-# Directories potentially created on remote AFP share
-.AppleDB
-.AppleDesktop
-Network Trash Folder
-Temporary Items
-.apdisk
-
-
-# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
-# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
-# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
-
-# User-specific stuff:
-.idea/*
-.idea/**/workspace.xml
-.idea/**/tasks.xml
-.idea/dictionaries
-.html # Bokeh Plots
-.pg # TensorFlow Frozen Graphs
-.avi # videos
-
-# Sensitive or high-churn files:
-.idea/**/dataSources/
-.idea/**/dataSources.ids
-.idea/**/dataSources.local.xml
-.idea/**/sqlDataSources.xml
-.idea/**/dynamic.xml
-.idea/**/uiDesigner.xml
-
-# Gradle:
-.idea/**/gradle.xml
-.idea/**/libraries
-
-# CMake
-cmake-build-debug/
-cmake-build-release/
-
-# Mongo Explorer plugin:
-.idea/**/mongoSettings.xml
-
-## File-based project format:
-*.iws
-
-## Plugin-specific files:
-
-# IntelliJ
-out/
-
-# mpeltonen/sbt-idea plugin
-.idea_modules/
-
-# JIRA plugin
-atlassian-ide-plugin.xml
-
-# Cursive Clojure plugin
-.idea/replstate.xml
-
-# Crashlytics plugin (for Android Studio and IntelliJ)
-com_crashlytics_export_strings.xml
-crashlytics.properties
-crashlytics-build.properties
-fabric.properties
diff --git a/yolov5-6.2/.gitattributes b/yolov5-6.2/.gitattributes
deleted file mode 100644
index dad4239e..00000000
--- a/yolov5-6.2/.gitattributes
+++ /dev/null
@@ -1,2 +0,0 @@
-# this drop notebooks from GitHub language stats
-*.ipynb linguist-vendored
diff --git a/yolov5-6.2/.github/CODE_OF_CONDUCT.md b/yolov5-6.2/.github/CODE_OF_CONDUCT.md
deleted file mode 100644
index 27e59e9a..00000000
--- a/yolov5-6.2/.github/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,128 +0,0 @@
-# YOLOv5 🚀 Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-- Demonstrating empathy and kindness toward other people
-- Being respectful of differing opinions, viewpoints, and experiences
-- Giving and gracefully accepting constructive feedback
-- Accepting responsibility and apologizing to those affected by our mistakes,
- and learning from the experience
-- Focusing on what is best not just for us as individuals, but for the
- overall community
-
-Examples of unacceptable behavior include:
-
-- The use of sexualized language or imagery, and sexual attention or
- advances of any kind
-- Trolling, insulting or derogatory comments, and personal or political attacks
-- Public or private harassment
-- Publishing others' private information, such as a physical or email
- address, without their explicit permission
-- Other conduct which could reasonably be considered inappropriate in a
- professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-hello@ultralytics.com.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
-
-[homepage]: https://www.contributor-covenant.org
diff --git a/yolov5-6.2/.github/ISSUE_TEMPLATE/bug-report.yml b/yolov5-6.2/.github/ISSUE_TEMPLATE/bug-report.yml
deleted file mode 100644
index fcb64138..00000000
--- a/yolov5-6.2/.github/ISSUE_TEMPLATE/bug-report.yml
+++ /dev/null
@@ -1,85 +0,0 @@
-name: 🐛 Bug Report
-# title: " "
-description: Problems with YOLOv5
-labels: [bug, triage]
-body:
- - type: markdown
- attributes:
- value: |
- Thank you for submitting a YOLOv5 🐛 Bug Report!
-
- - type: checkboxes
- attributes:
- label: Search before asking
- description: >
- Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar bug report already exists.
- options:
- - label: >
- I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report.
- required: true
-
- - type: dropdown
- attributes:
- label: YOLOv5 Component
- description: |
- Please select the part of YOLOv5 where you found the bug.
- multiple: true
- options:
- - "Training"
- - "Validation"
- - "Detection"
- - "Export"
- - "PyTorch Hub"
- - "Multi-GPU"
- - "Evolution"
- - "Integrations"
- - "Other"
- validations:
- required: false
-
- - type: textarea
- attributes:
- label: Bug
- description: Provide console output with error messages and/or screenshots of the bug.
- placeholder: |
- 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Environment
- description: Please specify the software and hardware you used to produce the bug.
- placeholder: |
- - YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)
- - OS: Ubuntu 20.04
- - Python: 3.9.0
- validations:
- required: false
-
- - type: textarea
- attributes:
- label: Minimal Reproducible Example
- description: >
- When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem.
- This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example).
- placeholder: |
- ```
- # Code to reproduce your issue here
- ```
- validations:
- required: false
-
- - type: textarea
- attributes:
- label: Additional
- description: Anything else you would like to share?
-
- - type: checkboxes
- attributes:
- label: Are you willing to submit a PR?
- description: >
- (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature.
- See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started.
- options:
- - label: Yes I'd like to help by submitting a PR!
diff --git a/yolov5-6.2/.github/ISSUE_TEMPLATE/config.yml b/yolov5-6.2/.github/ISSUE_TEMPLATE/config.yml
deleted file mode 100644
index 4db7cefb..00000000
--- a/yolov5-6.2/.github/ISSUE_TEMPLATE/config.yml
+++ /dev/null
@@ -1,8 +0,0 @@
-blank_issues_enabled: true
-contact_links:
- - name: 💬 Forum
- url: https://community.ultralytics.com/
- about: Ask on Ultralytics Community Forum
- - name: Stack Overflow
- url: https://stackoverflow.com/search?q=YOLOv5
- about: Ask on Stack Overflow with 'YOLOv5' tag
diff --git a/yolov5-6.2/.github/ISSUE_TEMPLATE/feature-request.yml b/yolov5-6.2/.github/ISSUE_TEMPLATE/feature-request.yml
deleted file mode 100644
index 68ef9851..00000000
--- a/yolov5-6.2/.github/ISSUE_TEMPLATE/feature-request.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-name: 🚀 Feature Request
-description: Suggest a YOLOv5 idea
-# title: " "
-labels: [enhancement]
-body:
- - type: markdown
- attributes:
- value: |
- Thank you for submitting a YOLOv5 🚀 Feature Request!
-
- - type: checkboxes
- attributes:
- label: Search before asking
- description: >
- Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar feature request already exists.
- options:
- - label: >
- I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests.
- required: true
-
- - type: textarea
- attributes:
- label: Description
- description: A short description of your feature.
- placeholder: |
- What new feature would you like to see in YOLOv5?
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Use case
- description: |
- Describe the use case of your feature request. It will help us understand and prioritize the feature request.
- placeholder: |
- How would this feature be used, and who would use it?
-
- - type: textarea
- attributes:
- label: Additional
- description: Anything else you would like to share?
-
- - type: checkboxes
- attributes:
- label: Are you willing to submit a PR?
- description: >
- (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature.
- See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started.
- options:
- - label: Yes I'd like to help by submitting a PR!
diff --git a/yolov5-6.2/.github/ISSUE_TEMPLATE/question.yml b/yolov5-6.2/.github/ISSUE_TEMPLATE/question.yml
deleted file mode 100644
index 8e0993c6..00000000
--- a/yolov5-6.2/.github/ISSUE_TEMPLATE/question.yml
+++ /dev/null
@@ -1,33 +0,0 @@
-name: ❓ Question
-description: Ask a YOLOv5 question
-# title: " "
-labels: [question]
-body:
- - type: markdown
- attributes:
- value: |
- Thank you for asking a YOLOv5 ❓ Question!
-
- - type: checkboxes
- attributes:
- label: Search before asking
- description: >
- Please search the [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) to see if a similar question already exists.
- options:
- - label: >
- I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
- required: true
-
- - type: textarea
- attributes:
- label: Question
- description: What is your question?
- placeholder: |
- 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Additional
- description: Anything else you would like to share?
diff --git a/yolov5-6.2/.github/PULL_REQUEST_TEMPLATE.md b/yolov5-6.2/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index f25b017a..00000000
--- a/yolov5-6.2/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,9 +0,0 @@
-<!--
-Thank you for submitting a YOLOv5 🚀 Pull Request! We want to make contributing to YOLOv5 as easy and transparent as possible. A few tips to get you started:
-
-- Search existing YOLOv5 [PRs](https://github.com/ultralytics/yolov5/pull) to see if a similar PR already exists.
-- Link this PR to a YOLOv5 [issue](https://github.com/ultralytics/yolov5/issues) to help us understand what bug fix or feature is being implemented.
-- Provide before and after profiling/inference/training results to help us quantify the improvement your PR provides (if applicable).
-
-Please see our ✅ [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) for more details.
--->
diff --git a/yolov5-6.2/.github/README_cn.md b/yolov5-6.2/.github/README_cn.md
deleted file mode 100644
index 86b502df..00000000
--- a/yolov5-6.2/.github/README_cn.md
+++ /dev/null
@@ -1,353 +0,0 @@
-<div align="center">
-<p>
- <a align="left" href="https://ultralytics.com/yolov5" target="_blank">
- <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
-</p>
-<br>
-
-[English](../README.md) | 简体中文
-<div>
- <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
- <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
- <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
- <br>
- <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
- <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
-</div>
-
-<br>
-<p>
-YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了<a href="https://ultralytics.com">Ultralytics</a>对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。
-</p>
-
-<div align="center">
- <a href="https://github.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
-</div>
-
-<!--
-<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
-<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
--->
-
-</div>
-
-## <div align="center">文件</div>
-
-请参阅[YOLOv5 Docs](https://docs.ultralytics.com)，了解有关训练、测试和部署的完整文件。
-
-## <div align="center">快速开始案例</div>
-
-<details open>
-<summary>安装</summary>
-
-在[**Python>=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。
-```bash
-git clone https://github.com/ultralytics/yolov5 # 克隆
-cd yolov5
-pip install -r requirements.txt # 安装
-```
-
-</details>
-
-<details open>
-<summary>推理</summary>
-
-YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。
-
-```python
-import torch
-
-# 模型
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom
-
-# 图像
-img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
-
-# 推理
-results = model(img)
-
-# 结果
-results.print() # or .show(), .save(), .crop(), .pandas(), etc.
-```
-
-</details>
-
-<details>
-<summary>用 detect.py 进行推理</summary>
-
-`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。
-
-```bash
-python detect.py --source 0 # 网络摄像头
- img.jpg # 图像
- vid.mp4 # 视频
- path/ # 文件夹
- 'path/*.jpg' # glob
- 'https://youtu.be/Zgi9g1ksQHc' # YouTube
- 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP 流
-```
-
-</details>
-
-<details>
-<summary>训练</summary>
-
-以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
-数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。
-
-```bash
-python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
- yolov5s 64
- yolov5m 40
- yolov5l 24
- yolov5x 16
-```
-
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
-
-</details>
-
-<details open>
-<summary>教程</summary>
-
-- [训练自定义数据](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐
-- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ 推荐
-- [使用 Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289) 🌟 新
-- [Roboflow：数据集、标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新
-- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475)
-- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ 新
-- [TFLite, ONNX, CoreML, TensorRT 导出](https://github.com/ultralytics/yolov5/issues/251) 🚀
-- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303)
-- [模型集成](https://github.com/ultralytics/yolov5/issues/318)
-- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304)
-- [超参数进化](https://github.com/ultralytics/yolov5/issues/607)
-- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314) ⭐ 新
-- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) ⭐ 新
-
-</details>
-
-## <div align="center">环境</div>
-
-使用经过我们验证的环境，几秒钟就可以开始。点击下面的每个图标了解详情。
-
-<div align="center">
- <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
- </a>
- <a href="https://www.kaggle.com/ultralytics/yolov5">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
- </a>
- <a href="https://hub.docker.com/r/ultralytics/yolov5">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
- </a>
- <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
- </a>
- <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
- </a>
-</div>
-
-## <div align="center">如何与第三方集成</div>
-
-<div align="center">
- <a href="https://bit.ly/yolov5-deci-platform">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
- <a href="https://cutt.ly/yolov5-readme-clearml">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
- <a href="https://roboflow.com/?ref=ultralytics">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
- <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width="10%" /></a>
-</div>
-
-|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
-|:-:|:-:|:-:|:-:|
-|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)
-
-
-## <div align="center">为什么选择 YOLOv5</div>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
-<details>
- <summary>YOLOv5-P5 640 图像 (点击扩展)</summary>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
-</details>
-<details>
- <summary>图片注释 (点击扩展)</summary>
-
-- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。
-- **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。
-- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。
-- 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
-</details>
-
-### 预训练检查点
-
-| Model | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
-| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
-| | | | | | | | | |
-| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
-| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
-| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
-| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)<br>+ [TTA][TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
-
-<details>
- <summary>表格注释 (点击扩展)</summary>
-
-- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
-- **mAP<sup>val</sup>** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。
-<br>复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img)
-<br>复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
-- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.
-<br>复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-
-## <div align="center">Classification ⭐ NEW</div>
-
-YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started.
-
-<details>
- <summary>Classification Checkpoints (click to expand)</summary>
-
-<br>
-
-We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
-
-| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
-|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
-| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
-| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
-| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
-| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
-| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
-| |
-| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
-| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
-| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
-| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
-| |
-| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
-| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
-| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
-| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
-
-<details>
- <summary>Table Notes (click to expand)</summary>
-
-- All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 at image size 224 and all default settings. Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2.
-- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
-- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
-- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
-</details>
-</details>
-
-<details>
- <summary>Classification Usage Examples (click to expand)</summary>
-
-### Train
-YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
-
-```bash
-# Single-GPU
-python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
-
-# Multi-GPU DDP
-python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
-```
-
-### Val
-Validate accuracy on a pretrained model. To validate YOLOv5s-cls accuracy on ImageNet.
-```bash
-bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
-python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224
-```
-
-### Predict
-Run a classification prediction on an image.
-```bash
-python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
-```
-```python
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub
-```
-
-### Export
-Export a group of trained YOLOv5-cls, ResNet and EfficientNet models to ONNX and TensorRT.
-```bash
-python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
-```
-</details>
-
-
-## <div align="center">贡献</div>
-
-我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!
-
-<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
-<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png" /></a>
-
-## <div align="center">联系</div>
-
-关于YOLOv5的漏洞和功能问题，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。商业咨询或技术支持服务请访问[https://ultralytics.com/contact](https://ultralytics.com/contact)。
-
-<br>
-<div align="center">
- <a href="https://github.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="3%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
- <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="3%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
- <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="3%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
- <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="3%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
- <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="3%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
- <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="3%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
- <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="3%" alt="" /></a>
-</div>
-
-[assets]: https://github.com/ultralytics/yolov5/releases
-[tta]: https://github.com/ultralytics/yolov5/issues/303
diff --git a/yolov5-6.2/.github/SECURITY.md b/yolov5-6.2/.github/SECURITY.md
deleted file mode 100644
index aa3e8409..00000000
--- a/yolov5-6.2/.github/SECURITY.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Security Policy
-
-We aim to make YOLOv5 🚀 as secure as possible! If you find potential vulnerabilities or have any concerns please let us know so we can investigate and take corrective action if needed.
-
-### Reporting a Vulnerability
-
-To report vulnerabilities please email us at hello@ultralytics.com or visit https://ultralytics.com/contact. Thank you!
diff --git a/yolov5-6.2/.github/dependabot.yml b/yolov5-6.2/.github/dependabot.yml
deleted file mode 100644
index c1b3d5d5..00000000
--- a/yolov5-6.2/.github/dependabot.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-version: 2
-updates:
- - package-ecosystem: pip
- directory: "/"
- schedule:
- interval: weekly
- time: "04:00"
- open-pull-requests-limit: 10
- reviewers:
- - glenn-jocher
- labels:
- - dependencies
-
- - package-ecosystem: github-actions
- directory: "/"
- schedule:
- interval: weekly
- time: "04:00"
- open-pull-requests-limit: 5
- reviewers:
- - glenn-jocher
- labels:
- - dependencies
diff --git a/yolov5-6.2/.github/workflows/ci-testing.yml b/yolov5-6.2/.github/workflows/ci-testing.yml
deleted file mode 100644
index aa797c44..00000000
--- a/yolov5-6.2/.github/workflows/ci-testing.yml
+++ /dev/null
@@ -1,135 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# YOLOv5 Continuous Integration (CI) GitHub Actions tests
-
-name: YOLOv5 CI
-
-on:
- push:
- branches: [ master ]
- pull_request:
- branches: [ master ]
- schedule:
- - cron: '0 0 * * *' # runs at 00:00 UTC every day
-
-jobs:
- Benchmarks:
- runs-on: ${{ matrix.os }}
- strategy:
- matrix:
- os: [ ubuntu-latest ]
- python-version: [ '3.9' ] # requires python<=3.9
- model: [ yolov5n ]
- steps:
- - uses: actions/checkout@v3
- - uses: actions/setup-python@v4
- with:
- python-version: ${{ matrix.python-version }}
- #- name: Cache pip
- # uses: actions/cache@v3
- # with:
- # path: ~/.cache/pip
- # key: ${{ runner.os }}-Benchmarks-${{ hashFiles('requirements.txt') }}
- # restore-keys: ${{ runner.os }}-Benchmarks-
- - name: Install requirements
- run: |
- python -m pip install --upgrade pip wheel
- pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu
- python --version
- pip --version
- pip list
- - name: Run benchmarks
- run: |
- python utils/benchmarks.py --weights ${{ matrix.model }}.pt --img 320 --hard-fail
-
- Tests:
- timeout-minutes: 60
- runs-on: ${{ matrix.os }}
- strategy:
- fail-fast: false
- matrix:
- os: [ ubuntu-latest, macos-latest, windows-latest ]
- python-version: [ '3.10' ]
- model: [ yolov5n ]
- include:
- - os: ubuntu-latest
- python-version: '3.7' # '3.6.8' min
- model: yolov5n
- - os: ubuntu-latest
- python-version: '3.8'
- model: yolov5n
- - os: ubuntu-latest
- python-version: '3.9'
- model: yolov5n
- - os: ubuntu-latest
- python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8
- model: yolov5n
- torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/
- steps:
- - uses: actions/checkout@v3
- - uses: actions/setup-python@v4
- with:
- python-version: ${{ matrix.python-version }}
- - name: Get cache dir
- # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
- id: pip-cache
- run: echo "::set-output name=dir::$(pip cache dir)"
- - name: Cache pip
- uses: actions/cache@v3
- with:
- path: ${{ steps.pip-cache.outputs.dir }}
- key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
- restore-keys: ${{ runner.os }}-${{ matrix.python-version }}-pip-
- - name: Install requirements
- run: |
- python -m pip install --upgrade pip wheel
- if [ "${{ matrix.torch }}" == "1.7.0" ]; then
- pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu
- else
- pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
- fi
- shell: bash # for Windows compatibility
- - name: Check environment
- run: |
- python -c "import utils; utils.notebook_init()"
- echo "RUNNER_OS is ${{ runner.os }}"
- echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
- echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
- echo "GITHUB_ACTOR is ${{ github.actor }}"
- echo "GITHUB_REPOSITORY is ${{ github.repository }}"
- echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}"
- python --version
- pip --version
- pip list
- - name: Test detection
- shell: bash # for Windows compatibility
- run: |
- # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories
- m=${{ matrix.model }} # official weights
- b=runs/train/exp/weights/best # best.pt checkpoint
- python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train
- for d in cpu; do # devices
- for w in $m $b; do # weights
- python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val
- python detect.py --imgsz 64 --weights $w.pt --device $d # detect
- done
- done
- python hubconf.py --model $m # hub
- # python models/tf.py --weights $m.pt # build TF model
- python models/yolo.py --cfg $m.yaml # build PyTorch model
- python export.py --weights $m.pt --img 64 --include torchscript # export
- python - <<EOF
- import torch
- for path in '$m', '$b':
- model = torch.hub.load('.', 'custom', path=path, source='local')
- print(model('data/images/bus.jpg'))
- EOF
- - name: Test classification
- shell: bash # for Windows compatibility
- run: |
- m=${{ matrix.model }}-cls.pt # official weights
- b=runs/train-cls/exp/weights/best.pt # best.pt checkpoint
- python classify/train.py --imgsz 32 --model $m --data mnist2560 --epochs 1 # train
- python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist2560 # val
- python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist2560/test/7/60.png # predict
- python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict
- python export.py --weights $b --img 64 --imgsz 224 --include torchscript # export
diff --git a/yolov5-6.2/.github/workflows/codeql-analysis.yml b/yolov5-6.2/.github/workflows/codeql-analysis.yml
deleted file mode 100644
index b6f75109..00000000
--- a/yolov5-6.2/.github/workflows/codeql-analysis.yml
+++ /dev/null
@@ -1,54 +0,0 @@
-# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
-# https://github.com/github/codeql-action
-
-name: "CodeQL"
-
-on:
- schedule:
- - cron: '0 0 1 * *' # Runs at 00:00 UTC on the 1st of every month
-
-jobs:
- analyze:
- name: Analyze
- runs-on: ubuntu-latest
-
- strategy:
- fail-fast: false
- matrix:
- language: ['python']
- # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
- # Learn more:
- # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
-
- steps:
- - name: Checkout repository
- uses: actions/checkout@v3
-
- # Initializes the CodeQL tools for scanning.
- - name: Initialize CodeQL
- uses: github/codeql-action/init@v2
- with:
- languages: ${{ matrix.language }}
- # If you wish to specify custom queries, you can do so here or in a config file.
- # By default, queries listed here will override any specified in a config file.
- # Prefix the list here with "+" to use these queries and those in the config file.
- # queries: ./path/to/local/query, your-org/your-repo/queries@main
-
- # Autobuild attempts to build any compiled languages (C/C++, C#, or Java).
- # If this step fails, then you should remove it and run the build manually (see below)
- - name: Autobuild
- uses: github/codeql-action/autobuild@v2
-
- # ℹ️ Command-line programs to run using the OS shell.
- # 📚 https://git.io/JvXDl
-
- # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
- # and modify them (or add more) to build your code if your project
- # uses a compiled language
-
- #- run: |
- # make bootstrap
- # make release
-
- - name: Perform CodeQL Analysis
- uses: github/codeql-action/analyze@v2
diff --git a/yolov5-6.2/.github/workflows/docker.yml b/yolov5-6.2/.github/workflows/docker.yml
deleted file mode 100644
index c89d0ada..00000000
--- a/yolov5-6.2/.github/workflows/docker.yml
+++ /dev/null
@@ -1,54 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Builds ultralytics/yolov5:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov5
-
-name: Publish Docker Images
-
-on:
- push:
- branches: [ master ]
-
-jobs:
- docker:
- if: github.repository == 'ultralytics/yolov5'
- name: Push Docker image to Docker Hub
- runs-on: ubuntu-latest
- steps:
- - name: Checkout repo
- uses: actions/checkout@v3
-
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v2
-
- - name: Set up Docker Buildx
- uses: docker/setup-buildx-action@v2
-
- - name: Login to Docker Hub
- uses: docker/login-action@v2
- with:
- username: ${{ secrets.DOCKERHUB_USERNAME }}
- password: ${{ secrets.DOCKERHUB_TOKEN }}
-
- - name: Build and push arm64 image
- uses: docker/build-push-action@v3
- with:
- context: .
- platforms: linux/arm64
- file: utils/docker/Dockerfile-arm64
- push: true
- tags: ultralytics/yolov5:latest-arm64
-
- - name: Build and push CPU image
- uses: docker/build-push-action@v3
- with:
- context: .
- file: utils/docker/Dockerfile-cpu
- push: true
- tags: ultralytics/yolov5:latest-cpu
-
- - name: Build and push GPU image
- uses: docker/build-push-action@v3
- with:
- context: .
- file: utils/docker/Dockerfile
- push: true
- tags: ultralytics/yolov5:latest
diff --git a/yolov5-6.2/.github/workflows/greetings.yml b/yolov5-6.2/.github/workflows/greetings.yml
deleted file mode 100644
index d5dad7a2..00000000
--- a/yolov5-6.2/.github/workflows/greetings.yml
+++ /dev/null
@@ -1,63 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-name: Greetings
-
-on:
- pull_request_target:
- types: [opened]
- issues:
- types: [opened]
-
-jobs:
- greeting:
- runs-on: ubuntu-latest
- steps:
- - uses: actions/first-interaction@v1
- with:
- repo-token: ${{ secrets.GITHUB_TOKEN }}
- pr-message: |
- 👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
- - ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an automatic [GitHub Actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) merge may be attempted by writing /rebase in a new comment, or by running the following code, replacing 'feature' with the name of your local branch:
- ```bash
- git remote add upstream https://github.com/ultralytics/yolov5.git
- git fetch upstream
- # git checkout feature # <--- replace 'feature' with local branch name
- git merge upstream/master
- git push -u origin -f
- ```
- - ✅ Verify all Continuous Integration (CI) **checks are passing**.
- - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ -Bruce Lee
-
- issue-message: |
- 👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov5/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
-
- If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
-
- If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online [W&B logging](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#visualize) if available.
-
- For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.
-
- ## Requirements
-
- [**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started:
- ```bash
- git clone https://github.com/ultralytics/yolov5 # clone
- cd yolov5
- pip install -r requirements.txt # install
- ```
-
- ## Environments
-
- YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
- - **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-
-
- ## Status
-
- <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
-
- If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/yolov5-6.2/.github/workflows/rebase.yml b/yolov5-6.2/.github/workflows/rebase.yml
deleted file mode 100644
index a4dc9e50..00000000
--- a/yolov5-6.2/.github/workflows/rebase.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-# https://github.com/marketplace/actions/automatic-rebase
-
-name: Automatic Rebase
-on:
- issue_comment:
- types: [created]
-jobs:
- rebase:
- name: Rebase
- if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase')
- runs-on: ubuntu-latest
- steps:
- - name: Checkout the latest code
- uses: actions/checkout@v3
- with:
- token: ${{ secrets.ACTIONS_TOKEN }}
- fetch-depth: 0 # otherwise, you will fail to push refs to dest repo
- - name: Automatic Rebase
- uses: cirrus-actions/rebase@1.7
- env:
- GITHUB_TOKEN: ${{ secrets.ACTIONS_TOKEN }}
diff --git a/yolov5-6.2/.github/workflows/stale.yml b/yolov5-6.2/.github/workflows/stale.yml
deleted file mode 100644
index 03d99790..00000000
--- a/yolov5-6.2/.github/workflows/stale.yml
+++ /dev/null
@@ -1,40 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-name: Close stale issues
-on:
- schedule:
- - cron: '0 0 * * *' # Runs at 00:00 UTC every day
-
-jobs:
- stale:
- runs-on: ubuntu-latest
- steps:
- - uses: actions/stale@v5
- with:
- repo-token: ${{ secrets.GITHUB_TOKEN }}
- stale-issue-message: |
- 👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
-
- Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources:
- - **Wiki** – https://github.com/ultralytics/yolov5/wiki
- - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials
- - **Docs** – https://docs.ultralytics.com
-
- Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
- - **Ultralytics HUB** – https://ultralytics.com/hub
- - **Vision API** – https://ultralytics.com/yolov5
- - **About Us** – https://ultralytics.com/about
- - **Join Our Team** – https://ultralytics.com/work
- - **Contact Us** – https://ultralytics.com/contact
-
- Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
-
- Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!
-
- stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
- days-before-issue-stale: 30
- days-before-issue-close: 10
- days-before-pr-stale: 90
- days-before-pr-close: 30
- exempt-issue-labels: 'documentation,tutorial,TODO'
- operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.
diff --git a/yolov5-6.2/.gitignore b/yolov5-6.2/.gitignore
deleted file mode 100644
index 69a00843..00000000
--- a/yolov5-6.2/.gitignore
+++ /dev/null
@@ -1,256 +0,0 @@
-# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
-*.jpg
-*.jpeg
-*.png
-*.bmp
-*.tif
-*.tiff
-*.heic
-*.JPG
-*.JPEG
-*.PNG
-*.BMP
-*.TIF
-*.TIFF
-*.HEIC
-*.mp4
-*.mov
-*.MOV
-*.avi
-*.data
-*.json
-*.cfg
-!setup.cfg
-!cfg/yolov3*.cfg
-
-storage.googleapis.com
-runs/*
-data/*
-data/images/*
-!data/*.yaml
-!data/hyps
-!data/scripts
-!data/images
-!data/images/zidane.jpg
-!data/images/bus.jpg
-!data/*.sh
-
-results*.csv
-
-# Datasets -------------------------------------------------------------------------------------------------------------
-coco/
-coco128/
-VOC/
-
-# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
-*.m~
-*.mat
-!targets*.mat
-
-# Neural Network weights -----------------------------------------------------------------------------------------------
-*.weights
-*.pt
-*.pb
-*.onnx
-*.engine
-*.mlmodel
-*.torchscript
-*.tflite
-*.h5
-*_saved_model/
-*_web_model/
-*_openvino_model/
-darknet53.conv.74
-yolov3-tiny.conv.15
-
-# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-/wandb/
-.installed.cfg
-*.egg
-
-
-# PyInstaller
-# Usually these files are written by a python script from a template
-# before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv*
-venv*/
-ENV*/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-
-# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
-
-# General
-.DS_Store
-.AppleDouble
-.LSOverride
-
-# Icon must end with two \r
-Icon
-Icon?
-
-# Thumbnails
-._*
-
-# Files that might appear in the root of a volume
-.DocumentRevisions-V100
-.fseventsd
-.Spotlight-V100
-.TemporaryItems
-.Trashes
-.VolumeIcon.icns
-.com.apple.timemachine.donotpresent
-
-# Directories potentially created on remote AFP share
-.AppleDB
-.AppleDesktop
-Network Trash Folder
-Temporary Items
-.apdisk
-
-
-# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
-# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
-# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
-
-# User-specific stuff:
-.idea/*
-.idea/**/workspace.xml
-.idea/**/tasks.xml
-.idea/dictionaries
-.html # Bokeh Plots
-.pg # TensorFlow Frozen Graphs
-.avi # videos
-
-# Sensitive or high-churn files:
-.idea/**/dataSources/
-.idea/**/dataSources.ids
-.idea/**/dataSources.local.xml
-.idea/**/sqlDataSources.xml
-.idea/**/dynamic.xml
-.idea/**/uiDesigner.xml
-
-# Gradle:
-.idea/**/gradle.xml
-.idea/**/libraries
-
-# CMake
-cmake-build-debug/
-cmake-build-release/
-
-# Mongo Explorer plugin:
-.idea/**/mongoSettings.xml
-
-## File-based project format:
-*.iws
-
-## Plugin-specific files:
-
-# IntelliJ
-out/
-
-# mpeltonen/sbt-idea plugin
-.idea_modules/
-
-# JIRA plugin
-atlassian-ide-plugin.xml
-
-# Cursive Clojure plugin
-.idea/replstate.xml
-
-# Crashlytics plugin (for Android Studio and IntelliJ)
-com_crashlytics_export_strings.xml
-crashlytics.properties
-crashlytics-build.properties
-fabric.properties
diff --git a/yolov5-6.2/.pre-commit-config.yaml b/yolov5-6.2/.pre-commit-config.yaml
deleted file mode 100644
index 43aca019..00000000
--- a/yolov5-6.2/.pre-commit-config.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# Define hooks for code formations
-# Will be applied on any updated commit files if a user has installed and linked commit hook
-
-default_language_version:
- python: python3.8
-
-# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
-ci:
- autofix_prs: true
- autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
- autoupdate_schedule: monthly
- # submodules: true
-
-repos:
- - repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.3.0
- hooks:
- # - id: end-of-file-fixer
- - id: trailing-whitespace
- - id: check-case-conflict
- - id: check-yaml
- - id: check-toml
- - id: pretty-format-json
- - id: check-docstring-first
-
- - repo: https://github.com/asottile/pyupgrade
- rev: v2.37.3
- hooks:
- - id: pyupgrade
- name: Upgrade code
- args: [ --py37-plus ]
-
- - repo: https://github.com/PyCQA/isort
- rev: 5.10.1
- hooks:
- - id: isort
- name: Sort imports
-
- - repo: https://github.com/pre-commit/mirrors-yapf
- rev: v0.32.0
- hooks:
- - id: yapf
- name: YAPF formatting
-
- - repo: https://github.com/executablebooks/mdformat
- rev: 0.7.14
- hooks:
- - id: mdformat
- name: MD formatting
- additional_dependencies:
- - mdformat-gfm
- - mdformat-black
- exclude: "README.md|README_cn.md"
-
- - repo: https://github.com/asottile/yesqa
- rev: v1.3.0
- hooks:
- - id: yesqa
-
- - repo: https://github.com/PyCQA/flake8
- rev: 5.0.2
- hooks:
- - id: flake8
- name: PEP8
diff --git a/yolov5-6.2/CONTRIBUTING.md b/yolov5-6.2/CONTRIBUTING.md
deleted file mode 100644
index 13b9b73b..00000000
--- a/yolov5-6.2/CONTRIBUTING.md
+++ /dev/null
@@ -1,98 +0,0 @@
-## Contributing to YOLOv5 🚀
-
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
-
-- Reporting a bug
-- Discussing the current state of the code
-- Submitting a fix
-- Proposing a new feature
-- Becoming a maintainer
-
-YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be
-helping push the frontiers of what's possible in AI 😃!
-
-## Submitting a Pull Request (PR) 🛠️
-
-Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
-
-### 1. Select File to Update
-
-Select `requirements.txt` to update by clicking on it in GitHub.
-
-<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
-
-### 2. Click 'Edit this file'
-
-Button is in top-right corner.
-
-<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
-
-### 3. Make Changes
-
-Change `matplotlib` version from `3.2.2` to `3.3`.
-
-<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
-
-### 4. Preview Changes and Submit PR
-
-Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
-for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
-changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
-
-<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
-
-### PR recommendations
-
-To allow your work to be integrated as seamlessly as possible, we advise you to:
-
-- ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an
- automatic [GitHub Actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) merge may
- be attempted by writing /rebase in a new comment, or by running the following code, replacing 'feature' with the name
- of your local branch:
-
-```bash
-git remote add upstream https://github.com/ultralytics/yolov5.git
-git fetch upstream
-# git checkout feature # <--- replace 'feature' with local branch name
-git merge upstream/master
-git push -u origin -f
-```
-
-- ✅ Verify all Continuous Integration (CI) **checks are passing**.
-- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
- but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
-
-## Submitting a Bug Report 🐛
-
-If you spot a problem with YOLOv5 please submit a Bug Report!
-
-For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
-short guidelines below to help users provide what we need in order to get started.
-
-When asking a question, people will be better able to provide help if you provide **code** that they can easily
-understand and use to **reproduce** the problem. This is referred to by community members as creating
-a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
-the problem should be:
-
-- ✅ **Minimal** – Use as little code as possible that still produces the same problem
-- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
-- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
-
-In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
-should be:
-
-- ✅ **Current** – Verify that your code is up-to-date with current
- GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
- copy to ensure your problem has not already been resolved by previous commits.
-- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
- repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
-
-If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
-**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
-a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
-understand and diagnose your problem.
-
-## License
-
-By contributing, you agree that your contributions will be licensed under
-the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)
diff --git a/yolov5-6.2/LICENSE b/yolov5-6.2/LICENSE
deleted file mode 100644
index 92b370f0..00000000
--- a/yolov5-6.2/LICENSE
+++ /dev/null
@@ -1,674 +0,0 @@
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- IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
- 17. Interpretation of Sections 15 and 16.
-
- If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
- To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
- <one line to give the program's name and a brief idea of what it does.>
- Copyright (C) <year> <name of author>
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see <http://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
- If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
- <program> Copyright (C) <year> <name of author>
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
- You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
-
- The GNU General Public License does not permit incorporating your program
-into proprietary programs. If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library. If this is what you want to do, use the GNU Lesser General
-Public License instead of this License. But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/yolov5-6.2/README.md b/yolov5-6.2/README.md
deleted file mode 100644
index b368d1d6..00000000
--- a/yolov5-6.2/README.md
+++ /dev/null
@@ -1,363 +0,0 @@
-<div align="center">
-<p>
- <a align="left" href="https://ultralytics.com/yolov5" target="_blank">
- <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
-</p>
-
-English | [简体中文](.github/README_cn.md)
-<br>
-<div>
- <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
- <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
- <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
- <br>
- <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
- <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
-</div>
-
-<br>
-<p>
-YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
- open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
-</p>
-
-<div align="center">
- <a href="https://github.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
- <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
-</div>
-
-<!--
-<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
-<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
--->
-
-</div>
-
-## <div align="center">Documentation</div>
-
-See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
-
-## <div align="center">Quick Start Examples</div>
-
-<details open>
-<summary>Install</summary>
-
-Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
-[**Python>=3.7.0**](https://www.python.org/) environment, including
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
-
-```bash
-git clone https://github.com/ultralytics/yolov5 # clone
-cd yolov5
-pip install -r requirements.txt # install
-```
-
-</details>
-
-<details open>
-<summary>Inference</summary>
-
-YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
-YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
-
-```python
-import torch
-
-# Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom
-
-# Images
-img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
-
-# Inference
-results = model(img)
-
-# Results
-results.print() # or .show(), .save(), .crop(), .pandas(), etc.
-```
-
-</details>
-
-<details>
-<summary>Inference with detect.py</summary>
-
-`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
-the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
-
-```bash
-python detect.py --source 0 # webcam
- img.jpg # image
- vid.mp4 # video
- path/ # directory
- 'path/*.jpg' # glob
- 'https://youtu.be/Zgi9g1ksQHc' # YouTube
- 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
-```
-
-</details>
-
-<details>
-<summary>Training</summary>
-
-The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
-results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
-and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
-YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
-1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
-largest `--batch-size` possible, or pass `--batch-size -1` for
-YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
-
-```bash
-python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
- yolov5s 64
- yolov5m 40
- yolov5l 24
- yolov5x 16
-```
-
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
-
-</details>
-
-<details open>
-<summary>Tutorials</summary>
-
-- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED
-- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
- RECOMMENDED
-- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
-- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 NEW
-- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
-- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
-- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
-- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
-- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
-- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)
-- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) 🌟 NEW
-- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)
-- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
-- [ClearML Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 NEW
-- [Deci Platform](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 NEW
-
-</details>
-
-## <div align="center">Environments</div>
-
-Get started in seconds with our verified environments. Click each icon below for details.
-
-<div align="center">
- <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
- <a href="https://www.kaggle.com/ultralytics/yolov5">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
- <a href="https://hub.docker.com/r/ultralytics/yolov5">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
- <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
- <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="10%" /></a>
-</div>
-
-## <div align="center">Integrations</div>
-
-<div align="center">
- <a href="https://bit.ly/yolov5-deci-platform">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
- <a href="https://cutt.ly/yolov5-readme-clearml">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
- <a href="https://roboflow.com/?ref=ultralytics">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
- <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
- <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width="10%" /></a>
-</div>
-
-|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
-|:-:|:-:|:-:|:-:|
-|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)
-
-
-## <div align="center">Why YOLOv5</div>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
-<details>
- <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
-</details>
-<details>
- <summary>Figure Notes (click to expand)</summary>
-
-- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
-- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
-- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
-- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
-</details>
-
-### Pretrained Checkpoints
-
-| Model | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
-| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
-| | | | | | | | | |
-| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
-| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
-| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
-| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)<br>+ [TTA][TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
-
-<details>
- <summary>Table Notes (click to expand)</summary>
-
-- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
-- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
-- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-## <div align="center">Classification ⭐ NEW</div>
-
-YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started.
-
-<details>
- <summary>Classification Checkpoints (click to expand)</summary>
-
-<br>
-
-We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
-
-| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
-|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
-| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
-| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
-| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
-| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
-| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
-| |
-| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
-| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
-| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
-| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
-| |
-| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
-| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
-| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
-| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
-
-<details>
- <summary>Table Notes (click to expand)</summary>
-
-- All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 at image size 224 and all default settings. Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2.
-- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
-- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
-- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
-</details>
-</details>
-
-<details>
- <summary>Classification Usage Examples (click to expand)</summary>
-
-### Train
-YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
-
-```bash
-# Single-GPU
-python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
-
-# Multi-GPU DDP
-python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
-```
-
-### Val
-Validate accuracy on a pretrained model. To validate YOLOv5s-cls accuracy on ImageNet.
-```bash
-bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
-python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224
-```
-
-### Predict
-Run a classification prediction on an image.
-```bash
-python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
-```
-```python
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub
-```
-
-### Export
-Export a group of trained YOLOv5-cls, ResNet and EfficientNet models to ONNX and TensorRT.
-```bash
-python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
-```
-</details>
-
-
-## <div align="center">Contribute</div>
-
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
-
-<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
-<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png" /></a>
-
-## <div align="center">Contact</div>
-
-For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
-professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
-
-<br>
-<div align="center">
- <a href="https://github.com/ultralytics" style="text-decoration:none;">
- <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="3%" alt="" /></a>
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-</div>
-
-[assets]: https://github.com/ultralytics/yolov5/releases
-[tta]: https://github.com/ultralytics/yolov5/issues/303
diff --git a/yolov5-6.2/classify/predict.py b/yolov5-6.2/classify/predict.py
deleted file mode 100644
index 419830d4..00000000
--- a/yolov5-6.2/classify/predict.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run classification inference on images
-
-Usage:
- $ python classify/predict.py --weights yolov5s-cls.pt --source im.jpg
-"""
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-import cv2
-import torch.nn.functional as F
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from classify.train import imshow_cls
-from models.common import DetectMultiBackend
-from utils.augmentations import classify_transforms
-from utils.general import LOGGER, check_requirements, colorstr, increment_path, print_args
-from utils.torch_utils import select_device, smart_inference_mode, time_sync
-
-
-@smart_inference_mode()
-def run(
- weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
- source=ROOT / 'data/images/bus.jpg', # file/dir/URL/glob, 0 for webcam
- imgsz=224, # inference size
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- half=False, # use FP16 half-precision inference
- dnn=False, # use OpenCV DNN for ONNX inference
- show=True,
- project=ROOT / 'runs/predict-cls', # save to project/name
- name='exp', # save to project/name
- exist_ok=False, # existing project/name ok, do not increment
-):
- file = str(source)
- seen, dt = 1, [0.0, 0.0, 0.0]
- device = select_device(device)
-
- # Directories
- save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
- save_dir.mkdir(parents=True, exist_ok=True) # make dir
-
- # Transforms
- transforms = classify_transforms(imgsz)
-
- # Load model
- model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
- model.warmup(imgsz=(1, 3, imgsz, imgsz)) # warmup
-
- # Image
- t1 = time_sync()
- im = cv2.cvtColor(cv2.imread(file), cv2.COLOR_BGR2RGB)
- im = transforms(im).unsqueeze(0).to(device)
- im = im.half() if model.fp16 else im.float()
- t2 = time_sync()
- dt[0] += t2 - t1
-
- # Inference
- results = model(im)
- t3 = time_sync()
- dt[1] += t3 - t2
-
- p = F.softmax(results, dim=1) # probabilities
- i = p.argsort(1, descending=True)[:, :5].squeeze() # top 5 indices
- dt[2] += time_sync() - t3
- LOGGER.info(f"image 1/1 {file}: {imgsz}x{imgsz} {', '.join(f'{model.names[j]} {p[0, j]:.2f}' for j in i)}")
-
- # Print results
- t = tuple(x / seen * 1E3 for x in dt) # speeds per image
- shape = (1, 3, imgsz, imgsz)
- LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
- if show:
- imshow_cls(im, f=save_dir / Path(file).name, verbose=True)
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
- return p
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)')
- parser.add_argument('--source', type=str, default=ROOT / 'data/images/bus.jpg', help='file')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
- parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- opt = parser.parse_args()
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- check_requirements(exclude=('tensorboard', 'thop'))
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/classify/train.py b/yolov5-6.2/classify/train.py
deleted file mode 100644
index f2b46556..00000000
--- a/yolov5-6.2/classify/train.py
+++ /dev/null
@@ -1,325 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Train a YOLOv5 classifier model on a classification dataset
-Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/custom/dataset'
-
-Usage:
- $ python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 128
- $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
-"""
-
-import argparse
-import os
-import subprocess
-import sys
-import time
-from copy import deepcopy
-from datetime import datetime
-from pathlib import Path
-
-import torch
-import torch.distributed as dist
-import torch.hub as hub
-import torch.optim.lr_scheduler as lr_scheduler
-import torchvision
-from torch.cuda import amp
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from classify import val as validate
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, DetectionModel
-from utils.dataloaders import create_classification_dataloader
-from utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
- download, increment_path, init_seeds, print_args, yaml_save)
-from utils.loggers import GenericLogger
-from utils.plots import imshow_cls
-from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
- smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
-
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-
-
-def train(opt, device):
- init_seeds(opt.seed + 1 + RANK, deterministic=True)
- save_dir, data, bs, epochs, nw, imgsz, pretrained = \
- opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \
- opt.imgsz, str(opt.pretrained).lower() == 'true'
- cuda = device.type != 'cpu'
-
- # Directories
- wdir = save_dir / 'weights'
- wdir.mkdir(parents=True, exist_ok=True) # make dir
- last, best = wdir / 'last.pt', wdir / 'best.pt'
-
- # Save run settings
- yaml_save(save_dir / 'opt.yaml', vars(opt))
-
- # Logger
- logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
-
- # Download Dataset
- with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
- data_dir = data if data.is_dir() else (DATASETS_DIR / data)
- if not data_dir.is_dir():
- LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
- t = time.time()
- if str(data) == 'imagenet':
- subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
- else:
- url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
- download(url, dir=data_dir.parent)
- s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
- LOGGER.info(s)
-
- # Dataloaders
- nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes
- trainloader = create_classification_dataloader(path=data_dir / 'train',
- imgsz=imgsz,
- batch_size=bs // WORLD_SIZE,
- augment=True,
- cache=opt.cache,
- rank=LOCAL_RANK,
- workers=nw)
-
- test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val
- if RANK in {-1, 0}:
- testloader = create_classification_dataloader(path=test_dir,
- imgsz=imgsz,
- batch_size=bs // WORLD_SIZE * 2,
- augment=False,
- cache=opt.cache,
- rank=-1,
- workers=nw)
-
- # Model
- with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
- if Path(opt.model).is_file() or opt.model.endswith('.pt'):
- model = attempt_load(opt.model, device='cpu', fuse=False)
- elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
- model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None)
- else:
- m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models
- raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
- if isinstance(model, DetectionModel):
- LOGGER.warning("WARNING: pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
- model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model
- reshape_classifier_output(model, nc) # update class count
- for p in model.parameters():
- p.requires_grad = True # for training
- for m in model.modules():
- if not pretrained and hasattr(m, 'reset_parameters'):
- m.reset_parameters()
- if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
- m.p = opt.dropout # set dropout
- model = model.to(device)
- names = trainloader.dataset.classes # class names
- model.names = names # attach class names
-
- # Info
- if RANK in {-1, 0}:
- model_info(model)
- if opt.verbose:
- LOGGER.info(model)
- images, labels = next(iter(trainloader))
- file = imshow_cls(images[:25], labels[:25], names=names, f=save_dir / 'train_images.jpg')
- logger.log_images(file, name='Train Examples')
- logger.log_graph(model, imgsz) # log model
-
- # Optimizer
- optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=5e-5)
-
- # Scheduler
- lrf = 0.01 # final lr (fraction of lr0)
- # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine
- lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf # linear
- scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
- # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
- # final_div_factor=1 / 25 / lrf)
-
- # EMA
- ema = ModelEMA(model) if RANK in {-1, 0} else None
-
- # DDP mode
- if cuda and RANK != -1:
- model = smart_DDP(model)
-
- # Train
- t0 = time.time()
- criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function
- best_fitness = 0.0
- scaler = amp.GradScaler(enabled=cuda)
- val = test_dir.stem # 'val' or 'test'
- LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n'
- f'Using {nw * WORLD_SIZE} dataloader workers\n'
- f"Logging results to {colorstr('bold', save_dir)}\n"
- f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
- f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}")
- for epoch in range(epochs): # loop over the dataset multiple times
- tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness
- model.train()
- if RANK != -1:
- trainloader.sampler.set_epoch(epoch)
- pbar = enumerate(trainloader)
- if RANK in {-1, 0}:
- pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
- for i, (images, labels) in pbar: # progress bar
- images, labels = images.to(device, non_blocking=True), labels.to(device)
-
- # Forward
- with amp.autocast(enabled=cuda): # stability issues when enabled
- loss = criterion(model(images), labels)
-
- # Backward
- scaler.scale(loss).backward()
-
- # Optimize
- scaler.unscale_(optimizer) # unscale gradients
- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
- scaler.step(optimizer)
- scaler.update()
- optimizer.zero_grad()
- if ema:
- ema.update(model)
-
- if RANK in {-1, 0}:
- # Print
- tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses
- mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
- pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36
-
- # Test
- if i == len(pbar) - 1: # last batch
- top1, top5, vloss = validate.run(model=ema.ema,
- dataloader=testloader,
- criterion=criterion,
- pbar=pbar) # test accuracy, loss
- fitness = top1 # define fitness as top1 accuracy
-
- # Scheduler
- scheduler.step()
-
- # Log metrics
- if RANK in {-1, 0}:
- # Best fitness
- if fitness > best_fitness:
- best_fitness = fitness
-
- # Log
- metrics = {
- "train/loss": tloss,
- f"{val}/loss": vloss,
- "metrics/accuracy_top1": top1,
- "metrics/accuracy_top5": top5,
- "lr/0": optimizer.param_groups[0]['lr']} # learning rate
- logger.log_metrics(metrics, epoch)
-
- # Save model
- final_epoch = epoch + 1 == epochs
- if (not opt.nosave) or final_epoch:
- ckpt = {
- 'epoch': epoch,
- 'best_fitness': best_fitness,
- 'model': deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),
- 'ema': None, # deepcopy(ema.ema).half(),
- 'updates': ema.updates,
- 'optimizer': None, # optimizer.state_dict(),
- 'opt': vars(opt),
- 'date': datetime.now().isoformat()}
-
- # Save last, best and delete
- torch.save(ckpt, last)
- if best_fitness == fitness:
- torch.save(ckpt, best)
- del ckpt
-
- # Train complete
- if RANK in {-1, 0} and final_epoch:
- LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
- f"\nResults saved to {colorstr('bold', save_dir)}"
- f"\nPredict: python classify/predict.py --weights {best} --source im.jpg"
- f"\nValidate: python classify/val.py --weights {best} --data {data_dir}"
- f"\nExport: python export.py --weights {best} --include onnx"
- f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
- f"\nVisualize: https://netron.app\n")
-
- # Plot examples
- images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
- pred = torch.max(ema.ema((images.half() if cuda else images.float()).to(device)), 1)[1]
- file = imshow_cls(images, labels, pred, names, verbose=False, f=save_dir / 'test_images.jpg')
-
- # Log results
- meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
- logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
- logger.log_model(best, epochs, metadata=meta)
-
-
-def parse_opt(known=False):
- parser = argparse.ArgumentParser()
- parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path')
- parser.add_argument('--data', type=str, default='mnist', help='cifar10, cifar100, mnist, imagenet, etc.')
- parser.add_argument('--epochs', type=int, default=10)
- parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=128, help='train, val image size (pixels)')
- parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
- parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
- parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False')
- parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer')
- parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate')
- parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon')
- parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head')
- parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)')
- parser.add_argument('--verbose', action='store_true', help='Verbose mode')
- parser.add_argument('--seed', type=int, default=0, help='Global training seed')
- parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
- return parser.parse_known_args()[0] if known else parser.parse_args()
-
-
-def main(opt):
- # Checks
- if RANK in {-1, 0}:
- print_args(vars(opt))
- check_git_status()
- check_requirements()
-
- # DDP mode
- device = select_device(opt.device, batch_size=opt.batch_size)
- if LOCAL_RANK != -1:
- assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size'
- assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
- assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
- torch.cuda.set_device(LOCAL_RANK)
- device = torch.device('cuda', LOCAL_RANK)
- dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
-
- # Parameters
- opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
-
- # Train
- train(opt, device)
-
-
-def run(**kwargs):
- # Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
- opt = parse_opt(True)
- for k, v in kwargs.items():
- setattr(opt, k, v)
- main(opt)
- return opt
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/classify/val.py b/yolov5-6.2/classify/val.py
deleted file mode 100644
index 0930ba8c..00000000
--- a/yolov5-6.2/classify/val.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Validate a classification model on a dataset
-
-Usage:
- $ python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet
-"""
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-import torch
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import create_classification_dataloader
-from utils.general import LOGGER, check_img_size, check_requirements, colorstr, increment_path, print_args
-from utils.torch_utils import select_device, smart_inference_mode, time_sync
-
-
-@smart_inference_mode()
-def run(
- data=ROOT / '../datasets/mnist', # dataset dir
- weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
- batch_size=128, # batch size
- imgsz=224, # inference size (pixels)
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- workers=8, # max dataloader workers (per RANK in DDP mode)
- verbose=False, # verbose output
- project=ROOT / 'runs/val-cls', # save to project/name
- name='exp', # save to project/name
- exist_ok=False, # existing project/name ok, do not increment
- half=True, # use FP16 half-precision inference
- dnn=False, # use OpenCV DNN for ONNX inference
- model=None,
- dataloader=None,
- criterion=None,
- pbar=None,
-):
- # Initialize/load model and set device
- training = model is not None
- if training: # called by train.py
- device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
- half &= device.type != 'cpu' # half precision only supported on CUDA
- model.half() if half else model.float()
- else: # called directly
- device = select_device(device, batch_size=batch_size)
-
- # Directories
- save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
- save_dir.mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
- stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
- imgsz = check_img_size(imgsz, s=stride) # check image size
- half = model.fp16 # FP16 supported on limited backends with CUDA
- if engine:
- batch_size = model.batch_size
- else:
- device = model.device
- if not (pt or jit):
- batch_size = 1 # export.py models default to batch-size 1
- LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
-
- # Dataloader
- data = Path(data)
- test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val
- dataloader = create_classification_dataloader(path=test_dir,
- imgsz=imgsz,
- batch_size=batch_size,
- augment=False,
- rank=-1,
- workers=workers)
-
- model.eval()
- pred, targets, loss, dt = [], [], 0, [0.0, 0.0, 0.0]
- n = len(dataloader) # number of batches
- action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
- desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
- bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
- with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
- for images, labels in bar:
- t1 = time_sync()
- images, labels = images.to(device, non_blocking=True), labels.to(device)
- t2 = time_sync()
- dt[0] += t2 - t1
-
- y = model(images)
- t3 = time_sync()
- dt[1] += t3 - t2
-
- pred.append(y.argsort(1, descending=True)[:, :5])
- targets.append(labels)
- if criterion:
- loss += criterion(y, labels)
- dt[2] += time_sync() - t3
-
- loss /= n
- pred, targets = torch.cat(pred), torch.cat(targets)
- correct = (targets[:, None] == pred).float()
- acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
- top1, top5 = acc.mean(0).tolist()
-
- if pbar:
- pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
- if verbose: # all classes
- LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
- LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
- for i, c in enumerate(model.names):
- aci = acc[targets == i]
- top1i, top5i = aci.mean(0).tolist()
- LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
-
- # Print results
- t = tuple(x / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image
- shape = (1, 3, imgsz, imgsz)
- LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
-
- return top1, top5, loss
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')
- parser.add_argument('--batch-size', type=int, default=128, help='batch size')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
- parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')
- parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
- opt = parser.parse_args()
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- check_requirements(exclude=('tensorboard', 'thop'))
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/data/Argoverse.yaml b/yolov5-6.2/data/Argoverse.yaml
deleted file mode 100644
index 9d21296e..00000000
--- a/yolov5-6.2/data/Argoverse.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
-# Example usage: python train.py --data Argoverse.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── Argoverse ← downloads here (31.3 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Argoverse # dataset root dir
-train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
-val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
-test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
-
-# Classes
-nc: 8 # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- import json
-
- from tqdm import tqdm
- from utils.general import download, Path
-
-
- def argoverse2yolo(set):
- labels = {}
- a = json.load(open(set, "rb"))
- for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
- img_id = annot['image_id']
- img_name = a['images'][img_id]['name']
- img_label_name = f'{img_name[:-3]}txt'
-
- cls = annot['category_id'] # instance class id
- x_center, y_center, width, height = annot['bbox']
- x_center = (x_center + width / 2) / 1920.0 # offset and scale
- y_center = (y_center + height / 2) / 1200.0 # offset and scale
- width /= 1920.0 # scale
- height /= 1200.0 # scale
-
- img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
- if not img_dir.exists():
- img_dir.mkdir(parents=True, exist_ok=True)
-
- k = str(img_dir / img_label_name)
- if k not in labels:
- labels[k] = []
- labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
-
- for k in labels:
- with open(k, "w") as f:
- f.writelines(labels[k])
-
-
- # Download
- dir = Path('../datasets/Argoverse') # dataset root dir
- urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
- download(urls, dir=dir, delete=False)
-
- # Convert
- annotations_dir = 'Argoverse-HD/annotations/'
- (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
- for d in "train.json", "val.json":
- argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/yolov5-6.2/data/GlobalWheat2020.yaml b/yolov5-6.2/data/GlobalWheat2020.yaml
deleted file mode 100644
index 4c43693f..00000000
--- a/yolov5-6.2/data/GlobalWheat2020.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
-# Example usage: python train.py --data GlobalWheat2020.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── GlobalWheat2020 ← downloads here (7.0 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/GlobalWheat2020 # dataset root dir
-train: # train images (relative to 'path') 3422 images
- - images/arvalis_1
- - images/arvalis_2
- - images/arvalis_3
- - images/ethz_1
- - images/rres_1
- - images/inrae_1
- - images/usask_1
-val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
- - images/ethz_1
-test: # test images (optional) 1276 images
- - images/utokyo_1
- - images/utokyo_2
- - images/nau_1
- - images/uq_1
-
-# Classes
-nc: 1 # number of classes
-names: ['wheat_head'] # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- from utils.general import download, Path
-
-
- # Download
- dir = Path(yaml['path']) # dataset root dir
- urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
- download(urls, dir=dir)
-
- # Make Directories
- for p in 'annotations', 'images', 'labels':
- (dir / p).mkdir(parents=True, exist_ok=True)
-
- # Move
- for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
- 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
- (dir / p).rename(dir / 'images' / p) # move to /images
- f = (dir / p).with_suffix('.json') # json file
- if f.exists():
- f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
diff --git a/yolov5-6.2/data/ImageNet.yaml b/yolov5-6.2/data/ImageNet.yaml
deleted file mode 100644
index 9f89b426..00000000
--- a/yolov5-6.2/data/ImageNet.yaml
+++ /dev/null
@@ -1,156 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
-# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
-# Example usage: python classify/train.py --data imagenet
-# parent
-# ├── yolov5
-# └── datasets
-# └── imagenet ← downloads here (144 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/imagenet # dataset root dir
-train: train # train images (relative to 'path') 1281167 images
-val: val # val images (relative to 'path') 50000 images
-test: # test images (optional)
-
-# Classes
-nc: 1000 # number of classes
-names: ['tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead shark', 'electric ray', 'stingray', 'cock',
- 'hen', 'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco', 'indigo bunting', 'American robin',
- 'bulbul', 'jay', 'magpie', 'chickadee', 'American dipper', 'kite', 'bald eagle', 'vulture', 'great grey owl',
- 'fire salamander', 'smooth newt', 'newt', 'spotted salamander', 'axolotl', 'American bullfrog', 'tree frog',
- 'tailed frog', 'loggerhead sea turtle', 'leatherback sea turtle', 'mud turtle', 'terrapin', 'box turtle',
- 'banded gecko', 'green iguana', 'Carolina anole', 'desert grassland whiptail lizard', 'agama',
- 'frilled-necked lizard', 'alligator lizard', 'Gila monster', 'European green lizard', 'chameleon',
- 'Komodo dragon', 'Nile crocodile', 'American alligator', 'triceratops', 'worm snake', 'ring-necked snake',
- 'eastern hog-nosed snake', 'smooth green snake', 'kingsnake', 'garter snake', 'water snake', 'vine snake',
- 'night snake', 'boa constrictor', 'African rock python', 'Indian cobra', 'green mamba', 'sea snake',
- 'Saharan horned viper', 'eastern diamondback rattlesnake', 'sidewinder', 'trilobite', 'harvestman', 'scorpion',
- 'yellow garden spider', 'barn spider', 'European garden spider', 'southern black widow', 'tarantula',
- 'wolf spider', 'tick', 'centipede', 'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie grouse', 'peacock',
- 'quail', 'partridge', 'grey parrot', 'macaw', 'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater',
- 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'duck', 'red-breasted merganser', 'goose', 'black swan',
- 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea anemone', 'brain coral',
- 'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton', 'chambered nautilus', 'Dungeness crab',
- 'rock crab', 'fiddler crab', 'red king crab', 'American lobster', 'spiny lobster', 'crayfish', 'hermit crab',
- 'isopod', 'white stork', 'black stork', 'spoonbill', 'flamingo', 'little blue heron', 'great egret', 'bittern',
- 'crane (bird)', 'limpkin', 'common gallinule', 'American coot', 'bustard', 'ruddy turnstone', 'dunlin',
- 'common redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale',
- 'killer whale', 'dugong', 'sea lion', 'Chihuahua', 'Japanese Chin', 'Maltese', 'Pekingese', 'Shih Tzu',
- 'King Charles Spaniel', 'Papillon', 'toy terrier', 'Rhodesian Ridgeback', 'Afghan Hound', 'Basset Hound',
- 'Beagle', 'Bloodhound', 'Bluetick Coonhound', 'Black and Tan Coonhound', 'Treeing Walker Coonhound',
- 'English foxhound', 'Redbone Coonhound', 'borzoi', 'Irish Wolfhound', 'Italian Greyhound', 'Whippet',
- 'Ibizan Hound', 'Norwegian Elkhound', 'Otterhound', 'Saluki', 'Scottish Deerhound', 'Weimaraner',
- 'Staffordshire Bull Terrier', 'American Staffordshire Terrier', 'Bedlington Terrier', 'Border Terrier',
- 'Kerry Blue Terrier', 'Irish Terrier', 'Norfolk Terrier', 'Norwich Terrier', 'Yorkshire Terrier',
- 'Wire Fox Terrier', 'Lakeland Terrier', 'Sealyham Terrier', 'Airedale Terrier', 'Cairn Terrier',
- 'Australian Terrier', 'Dandie Dinmont Terrier', 'Boston Terrier', 'Miniature Schnauzer', 'Giant Schnauzer',
- 'Standard Schnauzer', 'Scottish Terrier', 'Tibetan Terrier', 'Australian Silky Terrier',
- 'Soft-coated Wheaten Terrier', 'West Highland White Terrier', 'Lhasa Apso', 'Flat-Coated Retriever',
- 'Curly-coated Retriever', 'Golden Retriever', 'Labrador Retriever', 'Chesapeake Bay Retriever',
- 'German Shorthaired Pointer', 'Vizsla', 'English Setter', 'Irish Setter', 'Gordon Setter', 'Brittany',
- 'Clumber Spaniel', 'English Springer Spaniel', 'Welsh Springer Spaniel', 'Cocker Spaniels', 'Sussex Spaniel',
- 'Irish Water Spaniel', 'Kuvasz', 'Schipperke', 'Groenendael', 'Malinois', 'Briard', 'Australian Kelpie',
- 'Komondor', 'Old English Sheepdog', 'Shetland Sheepdog', 'collie', 'Border Collie', 'Bouvier des Flandres',
- 'Rottweiler', 'German Shepherd Dog', 'Dobermann', 'Miniature Pinscher', 'Greater Swiss Mountain Dog',
- 'Bernese Mountain Dog', 'Appenzeller Sennenhund', 'Entlebucher Sennenhund', 'Boxer', 'Bullmastiff',
- 'Tibetan Mastiff', 'French Bulldog', 'Great Dane', 'St. Bernard', 'husky', 'Alaskan Malamute', 'Siberian Husky',
- 'Dalmatian', 'Affenpinscher', 'Basenji', 'pug', 'Leonberger', 'Newfoundland', 'Pyrenean Mountain Dog',
- 'Samoyed', 'Pomeranian', 'Chow Chow', 'Keeshond', 'Griffon Bruxellois', 'Pembroke Welsh Corgi',
- 'Cardigan Welsh Corgi', 'Toy Poodle', 'Miniature Poodle', 'Standard Poodle', 'Mexican hairless dog',
- 'grey wolf', 'Alaskan tundra wolf', 'red wolf', 'coyote', 'dingo', 'dhole', 'African wild dog', 'hyena',
- 'red fox', 'kit fox', 'Arctic fox', 'grey fox', 'tabby cat', 'tiger cat', 'Persian cat', 'Siamese cat',
- 'Egyptian Mau', 'cougar', 'lynx', 'leopard', 'snow leopard', 'jaguar', 'lion', 'tiger', 'cheetah', 'brown bear',
- 'American black bear', 'polar bear', 'sloth bear', 'mongoose', 'meerkat', 'tiger beetle', 'ladybug',
- 'ground beetle', 'longhorn beetle', 'leaf beetle', 'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee',
- 'ant', 'grasshopper', 'cricket', 'stick insect', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing',
- 'dragonfly', 'damselfly', 'red admiral', 'ringlet', 'monarch butterfly', 'small white', 'sulphur butterfly',
- 'gossamer-winged butterfly', 'starfish', 'sea urchin', 'sea cucumber', 'cottontail rabbit', 'hare',
- 'Angora rabbit', 'hamster', 'porcupine', 'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'common sorrel',
- 'zebra', 'pig', 'wild boar', 'warthog', 'hippopotamus', 'ox', 'water buffalo', 'bison', 'ram', 'bighorn sheep',
- 'Alpine ibex', 'hartebeest', 'impala', 'gazelle', 'dromedary', 'llama', 'weasel', 'mink', 'European polecat',
- 'black-footed ferret', 'otter', 'skunk', 'badger', 'armadillo', 'three-toed sloth', 'orangutan', 'gorilla',
- 'chimpanzee', 'gibbon', 'siamang', 'guenon', 'patas monkey', 'baboon', 'macaque', 'langur',
- 'black-and-white colobus', 'proboscis monkey', 'marmoset', 'white-headed capuchin', 'howler monkey', 'titi',
- "Geoffroy's spider monkey", 'common squirrel monkey', 'ring-tailed lemur', 'indri', 'Asian elephant',
- 'African bush elephant', 'red panda', 'giant panda', 'snoek', 'eel', 'coho salmon', 'rock beauty', 'clownfish',
- 'sturgeon', 'garfish', 'lionfish', 'pufferfish', 'abacus', 'abaya', 'academic gown', 'accordion',
- 'acoustic guitar', 'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibious vehicle',
- 'analog clock', 'apiary', 'apron', 'waste container', 'assault rifle', 'backpack', 'bakery', 'balance beam',
- 'balloon', 'ballpoint pen', 'Band-Aid', 'banjo', 'baluster', 'barbell', 'barber chair', 'barbershop', 'barn',
- 'barometer', 'barrel', 'wheelbarrow', 'baseball', 'basketball', 'bassinet', 'bassoon', 'swimming cap',
- 'bath towel', 'bathtub', 'station wagon', 'lighthouse', 'beaker', 'military cap', 'beer bottle', 'beer glass',
- 'bell-cot', 'bib', 'tandem bicycle', 'bikini', 'ring binder', 'binoculars', 'birdhouse', 'boathouse',
- 'bobsleigh', 'bolo tie', 'poke bonnet', 'bookcase', 'bookstore', 'bottle cap', 'bow', 'bow tie', 'brass', 'bra',
- 'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof vest', 'high-speed train',
- 'butcher shop', 'taxicab', 'cauldron', 'candle', 'cannon', 'canoe', 'can opener', 'cardigan', 'car mirror',
- 'carousel', 'tool kit', 'carton', 'car wheel', 'automated teller machine', 'cassette', 'cassette player',
- 'castle', 'catamaran', 'CD player', 'cello', 'mobile phone', 'chain', 'chain-link fence', 'chain mail',
- 'chainsaw', 'chest', 'chiffonier', 'chime', 'china cabinet', 'Christmas stocking', 'church', 'movie theater',
- 'cleaver', 'cliff dwelling', 'cloak', 'clogs', 'cocktail shaker', 'coffee mug', 'coffeemaker', 'coil',
- 'combination lock', 'computer keyboard', 'confectionery store', 'container ship', 'convertible', 'corkscrew',
- 'cornet', 'cowboy boot', 'cowboy hat', 'cradle', 'crane (machine)', 'crash helmet', 'crate', 'infant bed',
- 'Crock Pot', 'croquet ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop computer', 'rotary dial telephone',
- 'diaper', 'digital clock', 'digital watch', 'dining table', 'dishcloth', 'dishwasher', 'disc brake', 'dock',
- 'dog sled', 'dome', 'doormat', 'drilling rig', 'drum', 'drumstick', 'dumbbell', 'Dutch oven', 'electric fan',
- 'electric guitar', 'electric locomotive', 'entertainment center', 'envelope', 'espresso machine', 'face powder',
- 'feather boa', 'filing cabinet', 'fireboat', 'fire engine', 'fire screen sheet', 'flagpole', 'flute',
- 'folding chair', 'football helmet', 'forklift', 'fountain', 'fountain pen', 'four-poster bed', 'freight car',
- 'French horn', 'frying pan', 'fur coat', 'garbage truck', 'gas mask', 'gas pump', 'goblet', 'go-kart',
- 'golf ball', 'golf cart', 'gondola', 'gong', 'gown', 'grand piano', 'greenhouse', 'grille', 'grocery store',
- 'guillotine', 'barrette', 'hair spray', 'half-track', 'hammer', 'hamper', 'hair dryer', 'hand-held computer',
- 'handkerchief', 'hard disk drive', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home theater',
- 'honeycomb', 'hook', 'hoop skirt', 'horizontal bar', 'horse-drawn vehicle', 'hourglass', 'iPod', 'clothes iron',
- "jack-o'-lantern", 'jeans', 'jeep', 'T-shirt', 'jigsaw puzzle', 'pulled rickshaw', 'joystick', 'kimono',
- 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade', 'laptop computer', 'lawn mower', 'lens cap',
- 'paper knife', 'library', 'lifeboat', 'lighter', 'limousine', 'ocean liner', 'lipstick', 'slip-on shoe',
- 'lotion', 'speaker', 'loupe', 'sawmill', 'magnetic compass', 'mail bag', 'mailbox', 'tights', 'tank suit',
- 'manhole cover', 'maraca', 'marimba', 'mask', 'match', 'maypole', 'maze', 'measuring cup', 'medicine chest',
- 'megalith', 'microphone', 'microwave oven', 'military uniform', 'milk can', 'minibus', 'miniskirt', 'minivan',
- 'missile', 'mitten', 'mixing bowl', 'mobile home', 'Model T', 'modem', 'monastery', 'monitor', 'moped',
- 'mortar', 'square academic cap', 'mosque', 'mosquito net', 'scooter', 'mountain bike', 'tent', 'computer mouse',
- 'mousetrap', 'moving van', 'muzzle', 'nail', 'neck brace', 'necklace', 'nipple', 'notebook computer', 'obelisk',
- 'oboe', 'ocarina', 'odometer', 'oil filter', 'organ', 'oscilloscope', 'overskirt', 'bullock cart',
- 'oxygen mask', 'packet', 'paddle', 'paddle wheel', 'padlock', 'paintbrush', 'pajamas', 'palace', 'pan flute',
- 'paper towel', 'parachute', 'parallel bars', 'park bench', 'parking meter', 'passenger car', 'patio',
- 'payphone', 'pedestal', 'pencil case', 'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'plectrum',
- 'Pickelhaube', 'picket fence', 'pickup truck', 'pier', 'piggy bank', 'pill bottle', 'pillow', 'ping-pong ball',
- 'pinwheel', 'pirate ship', 'pitcher', 'hand plane', 'planetarium', 'plastic bag', 'plate rack', 'plow',
- 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho', 'billiard table', 'soda bottle', 'pot',
- "potter's wheel", 'power drill', 'prayer rug', 'printer', 'prison', 'projectile', 'projector', 'hockey puck',
- 'punching bag', 'purse', 'quill', 'quilt', 'race car', 'racket', 'radiator', 'radio', 'radio telescope',
- 'rain barrel', 'recreational vehicle', 'reel', 'reflex camera', 'refrigerator', 'remote control', 'restaurant',
- 'revolver', 'rifle', 'rocking chair', 'rotisserie', 'eraser', 'rugby ball', 'ruler', 'running shoe', 'safe',
- 'safety pin', 'salt shaker', 'sandal', 'sarong', 'saxophone', 'scabbard', 'weighing scale', 'school bus',
- 'schooner', 'scoreboard', 'CRT screen', 'screw', 'screwdriver', 'seat belt', 'sewing machine', 'shield',
- 'shoe store', 'shoji', 'shopping basket', 'shopping cart', 'shovel', 'shower cap', 'shower curtain', 'ski',
- 'ski mask', 'sleeping bag', 'slide rule', 'sliding door', 'slot machine', 'snorkel', 'snowmobile', 'snowplow',
- 'soap dispenser', 'soccer ball', 'sock', 'solar thermal collector', 'sombrero', 'soup bowl', 'space bar',
- 'space heater', 'space shuttle', 'spatula', 'motorboat', 'spider web', 'spindle', 'sports car', 'spotlight',
- 'stage', 'steam locomotive', 'through arch bridge', 'steel drum', 'stethoscope', 'scarf', 'stone wall',
- 'stopwatch', 'stove', 'strainer', 'tram', 'stretcher', 'couch', 'stupa', 'submarine', 'suit', 'sundial',
- 'sunglass', 'sunglasses', 'sunscreen', 'suspension bridge', 'mop', 'sweatshirt', 'swimsuit', 'swing', 'switch',
- 'syringe', 'table lamp', 'tank', 'tape player', 'teapot', 'teddy bear', 'television', 'tennis ball',
- 'thatched roof', 'front curtain', 'thimble', 'threshing machine', 'throne', 'tile roof', 'toaster',
- 'tobacco shop', 'toilet seat', 'torch', 'totem pole', 'tow truck', 'toy store', 'tractor', 'semi-trailer truck',
- 'tray', 'trench coat', 'tricycle', 'trimaran', 'tripod', 'triumphal arch', 'trolleybus', 'trombone', 'tub',
- 'turnstile', 'typewriter keyboard', 'umbrella', 'unicycle', 'upright piano', 'vacuum cleaner', 'vase', 'vault',
- 'velvet', 'vending machine', 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle iron', 'wall clock',
- 'wallet', 'wardrobe', 'military aircraft', 'sink', 'washing machine', 'water bottle', 'water jug',
- 'water tower', 'whiskey jug', 'whistle', 'wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle',
- 'wing', 'wok', 'wooden spoon', 'wool', 'split-rail fence', 'shipwreck', 'yawl', 'yurt', 'website', 'comic book',
- 'crossword', 'traffic sign', 'traffic light', 'dust jacket', 'menu', 'plate', 'guacamole', 'consomme',
- 'hot pot', 'trifle', 'ice cream', 'ice pop', 'baguette', 'bagel', 'pretzel', 'cheeseburger', 'hot dog',
- 'mashed potato', 'cabbage', 'broccoli', 'cauliflower', 'zucchini', 'spaghetti squash', 'acorn squash',
- 'butternut squash', 'cucumber', 'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith', 'strawberry',
- 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard apple', 'pomegranate', 'hay',
- 'carbonara', 'chocolate syrup', 'dough', 'meatloaf', 'pizza', 'pot pie', 'burrito', 'red wine', 'espresso',
- 'cup', 'eggnog', 'alp', 'bubble', 'cliff', 'coral reef', 'geyser', 'lakeshore', 'promontory', 'shoal',
- 'seashore', 'valley', 'volcano', 'baseball player', 'bridegroom', 'scuba diver', 'rapeseed', 'daisy',
- "yellow lady's slipper", 'corn', 'acorn', 'rose hip', 'horse chestnut seed', 'coral fungus', 'agaric',
- 'gyromitra', 'stinkhorn mushroom', 'earth star', 'hen-of-the-woods', 'bolete', 'ear',
- 'toilet paper'] # class names
-
-# Download script/URL (optional)
-download: data/scripts/get_imagenet.sh
diff --git a/yolov5-6.2/data/Objects365.yaml b/yolov5-6.2/data/Objects365.yaml
deleted file mode 100644
index 4cc94753..00000000
--- a/yolov5-6.2/data/Objects365.yaml
+++ /dev/null
@@ -1,114 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Objects365 dataset https://www.objects365.org/ by Megvii
-# Example usage: python train.py --data Objects365.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Objects365 # dataset root dir
-train: images/train # train images (relative to 'path') 1742289 images
-val: images/val # val images (relative to 'path') 80000 images
-test: # test images (optional)
-
-# Classes
-nc: 365 # number of classes
-names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
- 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
- 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
- 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
- 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
- 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
- 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
- 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
- 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
- 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
- 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
- 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
- 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
- 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
- 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
- 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
- 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
- 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
- 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
- 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
- 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
- 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
- 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
- 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
- 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
- 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
- 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
- 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
- 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
- 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
- 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
- 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
- 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
- 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
- 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
- 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
- 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
- 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
- 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
- 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
- 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- from tqdm import tqdm
-
- from utils.general import Path, check_requirements, download, np, xyxy2xywhn
-
- check_requirements(('pycocotools>=2.0',))
- from pycocotools.coco import COCO
-
- # Make Directories
- dir = Path(yaml['path']) # dataset root dir
- for p in 'images', 'labels':
- (dir / p).mkdir(parents=True, exist_ok=True)
- for q in 'train', 'val':
- (dir / p / q).mkdir(parents=True, exist_ok=True)
-
- # Train, Val Splits
- for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
- print(f"Processing {split} in {patches} patches ...")
- images, labels = dir / 'images' / split, dir / 'labels' / split
-
- # Download
- url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
- if split == 'train':
- download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
- download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
- elif split == 'val':
- download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
- download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
- download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
-
- # Move
- for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
- f.rename(images / f.name) # move to /images/{split}
-
- # Labels
- coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
- names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
- for cid, cat in enumerate(names):
- catIds = coco.getCatIds(catNms=[cat])
- imgIds = coco.getImgIds(catIds=catIds)
- for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
- width, height = im["width"], im["height"]
- path = Path(im["file_name"]) # image filename
- try:
- with open(labels / path.with_suffix('.txt').name, 'a') as file:
- annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
- for a in coco.loadAnns(annIds):
- x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
- xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
- x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
- file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
- except Exception as e:
- print(e)
diff --git a/yolov5-6.2/data/SKU-110K.yaml b/yolov5-6.2/data/SKU-110K.yaml
deleted file mode 100644
index 2acf34d1..00000000
--- a/yolov5-6.2/data/SKU-110K.yaml
+++ /dev/null
@@ -1,53 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
-# Example usage: python train.py --data SKU-110K.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── SKU-110K ← downloads here (13.6 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/SKU-110K # dataset root dir
-train: train.txt # train images (relative to 'path') 8219 images
-val: val.txt # val images (relative to 'path') 588 images
-test: test.txt # test images (optional) 2936 images
-
-# Classes
-nc: 1 # number of classes
-names: ['object'] # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- import shutil
- from tqdm import tqdm
- from utils.general import np, pd, Path, download, xyxy2xywh
-
-
- # Download
- dir = Path(yaml['path']) # dataset root dir
- parent = Path(dir.parent) # download dir
- urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
- download(urls, dir=parent, delete=False)
-
- # Rename directories
- if dir.exists():
- shutil.rmtree(dir)
- (parent / 'SKU110K_fixed').rename(dir) # rename dir
- (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
-
- # Convert labels
- names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
- for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
- x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
- images, unique_images = x[:, 0], np.unique(x[:, 0])
- with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
- f.writelines(f'./images/{s}\n' for s in unique_images)
- for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
- cls = 0 # single-class dataset
- with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
- for r in x[images == im]:
- w, h = r[6], r[7] # image width, height
- xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
- f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
diff --git a/yolov5-6.2/data/VOC.yaml b/yolov5-6.2/data/VOC.yaml
deleted file mode 100644
index 636ddc42..00000000
--- a/yolov5-6.2/data/VOC.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
-# Example usage: python train.py --data VOC.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── VOC ← downloads here (2.8 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VOC
-train: # train images (relative to 'path') 16551 images
- - images/train2012
- - images/train2007
- - images/val2012
- - images/val2007
-val: # val images (relative to 'path') 4952 images
- - images/test2007
-test: # test images (optional)
- - images/test2007
-
-# Classes
-nc: 20 # number of classes
-names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
- 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- import xml.etree.ElementTree as ET
-
- from tqdm import tqdm
- from utils.general import download, Path
-
-
- def convert_label(path, lb_path, year, image_id):
- def convert_box(size, box):
- dw, dh = 1. / size[0], 1. / size[1]
- x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
- return x * dw, y * dh, w * dw, h * dh
-
- in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
- out_file = open(lb_path, 'w')
- tree = ET.parse(in_file)
- root = tree.getroot()
- size = root.find('size')
- w = int(size.find('width').text)
- h = int(size.find('height').text)
-
- for obj in root.iter('object'):
- cls = obj.find('name').text
- if cls in yaml['names'] and not int(obj.find('difficult').text) == 1:
- xmlbox = obj.find('bndbox')
- bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
- cls_id = yaml['names'].index(cls) # class id
- out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
-
-
- # Download
- dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
- urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
- f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
- f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
- download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
-
- # Convert
- path = dir / 'images/VOCdevkit'
- for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
- imgs_path = dir / 'images' / f'{image_set}{year}'
- lbs_path = dir / 'labels' / f'{image_set}{year}'
- imgs_path.mkdir(exist_ok=True, parents=True)
- lbs_path.mkdir(exist_ok=True, parents=True)
-
- with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
- image_ids = f.read().strip().split()
- for id in tqdm(image_ids, desc=f'{image_set}{year}'):
- f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
- lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
- f.rename(imgs_path / f.name) # move image
- convert_label(path, lb_path, year, id) # convert labels to YOLO format
diff --git a/yolov5-6.2/data/VisDrone.yaml b/yolov5-6.2/data/VisDrone.yaml
deleted file mode 100644
index 10337b46..00000000
--- a/yolov5-6.2/data/VisDrone.yaml
+++ /dev/null
@@ -1,61 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
-# Example usage: python train.py --data VisDrone.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── VisDrone ← downloads here (2.3 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VisDrone # dataset root dir
-train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
-val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
-test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
-
-# Classes
-nc: 10 # number of classes
-names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- from utils.general import download, os, Path
-
- def visdrone2yolo(dir):
- from PIL import Image
- from tqdm import tqdm
-
- def convert_box(size, box):
- # Convert VisDrone box to YOLO xywh box
- dw = 1. / size[0]
- dh = 1. / size[1]
- return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
-
- (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
- pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
- for f in pbar:
- img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
- lines = []
- with open(f, 'r') as file: # read annotation.txt
- for row in [x.split(',') for x in file.read().strip().splitlines()]:
- if row[4] == '0': # VisDrone 'ignored regions' class 0
- continue
- cls = int(row[5]) - 1
- box = convert_box(img_size, tuple(map(int, row[:4])))
- lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
- with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
- fl.writelines(lines) # write label.txt
-
-
- # Download
- dir = Path(yaml['path']) # dataset root dir
- urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
- download(urls, dir=dir, curl=True, threads=4)
-
- # Convert
- for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
- visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/yolov5-6.2/data/ball.yaml b/yolov5-6.2/data/ball.yaml
deleted file mode 100644
index 6a0b7feb..00000000
--- a/yolov5-6.2/data/ball.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-train: ./data/ball/train/images
-val: ./data/ball/valid/images
-
-nc: 2
-names: ['Cricketball','Football']
\ No newline at end of file
diff --git a/yolov5-6.2/data/coco.yaml b/yolov5-6.2/data/coco.yaml
deleted file mode 100644
index 0c0c4ada..00000000
--- a/yolov5-6.2/data/coco.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org by Microsoft
-# Example usage: python train.py --data coco.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── coco ← downloads here (20.1 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco # dataset root dir
-train: train2017.txt # train images (relative to 'path') 118287 images
-val: val2017.txt # val images (relative to 'path') 5000 images
-test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
-
-# Classes
-nc: 80 # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
- 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
- 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
- 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
- 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
- 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
- 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
- 'hair drier', 'toothbrush'] # class names
-
-
-# Download script/URL (optional)
-download: |
- from utils.general import download, Path
-
-
- # Download labels
- segments = False # segment or box labels
- dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
- urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
- download(urls, dir=dir.parent)
-
- # Download data
- urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
- 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
- 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
- download(urls, dir=dir / 'images', threads=3)
diff --git a/yolov5-6.2/data/coco128.yaml b/yolov5-6.2/data/coco128.yaml
deleted file mode 100644
index 2517d207..00000000
--- a/yolov5-6.2/data/coco128.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
-# Example usage: python train.py --data coco128.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── coco128 ← downloads here (7 MB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128 # dataset root dir
-train: images/train2017 # train images (relative to 'path') 128 images
-val: images/train2017 # val images (relative to 'path') 128 images
-test: # test images (optional)
-
-# Classes
-nc: 80 # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
- 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
- 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
- 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
- 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
- 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
- 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
- 'hair drier', 'toothbrush'] # class names
-
-
-# Download script/URL (optional)
-download: https://ultralytics.com/assets/coco128.zip
diff --git a/yolov5-6.2/data/hyps/hyp.Objects365.yaml b/yolov5-6.2/data/hyps/hyp.Objects365.yaml
deleted file mode 100644
index 74971740..00000000
--- a/yolov5-6.2/data/hyps/hyp.Objects365.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for Objects365 training
-# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
-# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.00258
-lrf: 0.17
-momentum: 0.779
-weight_decay: 0.00058
-warmup_epochs: 1.33
-warmup_momentum: 0.86
-warmup_bias_lr: 0.0711
-box: 0.0539
-cls: 0.299
-cls_pw: 0.825
-obj: 0.632
-obj_pw: 1.0
-iou_t: 0.2
-anchor_t: 3.44
-anchors: 3.2
-fl_gamma: 0.0
-hsv_h: 0.0188
-hsv_s: 0.704
-hsv_v: 0.36
-degrees: 0.0
-translate: 0.0902
-scale: 0.491
-shear: 0.0
-perspective: 0.0
-flipud: 0.0
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.0
-copy_paste: 0.0
diff --git a/yolov5-6.2/data/hyps/hyp.VOC.yaml b/yolov5-6.2/data/hyps/hyp.VOC.yaml
deleted file mode 100644
index 0aa4e7d9..00000000
--- a/yolov5-6.2/data/hyps/hyp.VOC.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for VOC training
-# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
-# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
-
-# YOLOv5 Hyperparameter Evolution Results
-# Best generation: 467
-# Last generation: 996
-# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
-# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865
-
-lr0: 0.00334
-lrf: 0.15135
-momentum: 0.74832
-weight_decay: 0.00025
-warmup_epochs: 3.3835
-warmup_momentum: 0.59462
-warmup_bias_lr: 0.18657
-box: 0.02
-cls: 0.21638
-cls_pw: 0.5
-obj: 0.51728
-obj_pw: 0.67198
-iou_t: 0.2
-anchor_t: 3.3744
-fl_gamma: 0.0
-hsv_h: 0.01041
-hsv_s: 0.54703
-hsv_v: 0.27739
-degrees: 0.0
-translate: 0.04591
-scale: 0.75544
-shear: 0.0
-perspective: 0.0
-flipud: 0.0
-fliplr: 0.5
-mosaic: 0.85834
-mixup: 0.04266
-copy_paste: 0.0
-anchors: 3.412
diff --git a/yolov5-6.2/data/hyps/hyp.scratch-high.yaml b/yolov5-6.2/data/hyps/hyp.scratch-high.yaml
deleted file mode 100644
index 123cc840..00000000
--- a/yolov5-6.2/data/hyps/hyp.scratch-high.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for high-augmentation COCO training from scratch
-# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.3 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 0.7 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3 # anchors per output layer (0 to ignore)
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0.1 # image translation (+/- fraction)
-scale: 0.9 # image scale (+/- gain)
-shear: 0.0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.5 # image flip left-right (probability)
-mosaic: 1.0 # image mosaic (probability)
-mixup: 0.1 # image mixup (probability)
-copy_paste: 0.1 # segment copy-paste (probability)
diff --git a/yolov5-6.2/data/hyps/hyp.scratch-low.yaml b/yolov5-6.2/data/hyps/hyp.scratch-low.yaml
deleted file mode 100644
index b9ef1d55..00000000
--- a/yolov5-6.2/data/hyps/hyp.scratch-low.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for low-augmentation COCO training from scratch
-# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.5 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 1.0 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3 # anchors per output layer (0 to ignore)
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0.1 # image translation (+/- fraction)
-scale: 0.5 # image scale (+/- gain)
-shear: 0.0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.5 # image flip left-right (probability)
-mosaic: 1.0 # image mosaic (probability)
-mixup: 0.0 # image mixup (probability)
-copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/yolov5-6.2/data/hyps/hyp.scratch-med.yaml b/yolov5-6.2/data/hyps/hyp.scratch-med.yaml
deleted file mode 100644
index d6867d75..00000000
--- a/yolov5-6.2/data/hyps/hyp.scratch-med.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for medium-augmentation COCO training from scratch
-# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.3 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 0.7 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3 # anchors per output layer (0 to ignore)
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0.1 # image translation (+/- fraction)
-scale: 0.9 # image scale (+/- gain)
-shear: 0.0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.5 # image flip left-right (probability)
-mosaic: 1.0 # image mosaic (probability)
-mixup: 0.1 # image mixup (probability)
-copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/yolov5-6.2/data/images/bus.jpg b/yolov5-6.2/data/images/bus.jpg
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diff --git a/yolov5-6.2/data/scripts/download_weights.sh b/yolov5-6.2/data/scripts/download_weights.sh
deleted file mode 100644
index a4f3becf..00000000
--- a/yolov5-6.2/data/scripts/download_weights.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download latest models from https://github.com/ultralytics/yolov5/releases
-# Example usage: bash data/scripts/download_weights.sh
-# parent
-# └── yolov5
-# ├── yolov5s.pt ← downloads here
-# ├── yolov5m.pt
-# └── ...
-
-python - <<EOF
-from utils.downloads import attempt_download
-
-p5 = ['n', 's', 'm', 'l', 'x'] # P5 models
-p6 = [f'{x}6' for x in p5] # P6 models
-cls = [f'{x}-cls' for x in p5] # classification models
-
-for x in p5 + p6 + cls:
- attempt_download(f'weights/yolov5{x}.pt')
-
-EOF
diff --git a/yolov5-6.2/data/scripts/get_coco.sh b/yolov5-6.2/data/scripts/get_coco.sh
deleted file mode 100644
index 506d46df..00000000
--- a/yolov5-6.2/data/scripts/get_coco.sh
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO 2017 dataset http://cocodataset.org
-# Example usage: bash data/scripts/get_coco.sh
-# parent
-# ├── yolov5
-# └── datasets
-# └── coco ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
-if [ "$#" -gt 0 ]; then
- for opt in "$@"; do
- case "${opt}" in
- --train) train=true ;;
- --val) val=true ;;
- --test) test=true ;;
- --segments) segments=true ;;
- esac
- done
-else
- train=true
- val=true
- test=false
- segments=false
-fi
-
-# Download/unzip labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-if [ "$segments" == "true" ]; then
- f='coco2017labels-segments.zip' # 168 MB
-else
- f='coco2017labels.zip' # 168 MB
-fi
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-
-# Download/unzip images
-d='../datasets/coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-if [ "$train" == "true" ]; then
- f='train2017.zip' # 19G, 118k images
- echo 'Downloading' $url$f '...'
- curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$val" == "true" ]; then
- f='val2017.zip' # 1G, 5k images
- echo 'Downloading' $url$f '...'
- curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$test" == "true" ]; then
- f='test2017.zip' # 7G, 41k images (optional)
- echo 'Downloading' $url$f '...'
- curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-wait # finish background tasks
diff --git a/yolov5-6.2/data/scripts/get_coco128.sh b/yolov5-6.2/data/scripts/get_coco128.sh
deleted file mode 100644
index e7ddce89..00000000
--- a/yolov5-6.2/data/scripts/get_coco128.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
-# Example usage: bash data/scripts/get_coco128.sh
-# parent
-# ├── yolov5
-# └── datasets
-# └── coco128 ← downloads here
-
-# Download/unzip images and labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco128.zip' # or 'coco128-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-
-wait # finish background tasks
diff --git a/yolov5-6.2/data/scripts/get_imagenet.sh b/yolov5-6.2/data/scripts/get_imagenet.sh
deleted file mode 100644
index 6026d502..00000000
--- a/yolov5-6.2/data/scripts/get_imagenet.sh
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download ILSVRC2012 ImageNet dataset https://image-net.org
-# Example usage: bash data/scripts/get_imagenet.sh
-# parent
-# ├── yolov5
-# └── datasets
-# └── imagenet ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
-if [ "$#" -gt 0 ]; then
- for opt in "$@"; do
- case "${opt}" in
- --train) train=true ;;
- --val) val=true ;;
- esac
- done
-else
- train=true
- val=true
-fi
-
-# Make dir
-d='../datasets/imagenet' # unzip directory
-mkdir -p $d && cd $d
-
-# Download/unzip train
-if [ "$train" == "true" ]; then
- wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
- mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
- tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
- find . -name "*.tar" | while read NAME; do
- mkdir -p "${NAME%.tar}"
- tar -xf "${NAME}" -C "${NAME%.tar}"
- rm -f "${NAME}"
- done
- cd ..
-fi
-
-# Download/unzip val
-if [ "$val" == "true" ]; then
- wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
- mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
- wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
-fi
-
-# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
-# rm train/n04266014/n04266014_10835.JPEG
-
-# TFRecords (optional)
-# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
diff --git a/yolov5-6.2/data/xView.yaml b/yolov5-6.2/data/xView.yaml
deleted file mode 100644
index 3b38f1ff..00000000
--- a/yolov5-6.2/data/xView.yaml
+++ /dev/null
@@ -1,102 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
-# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
-# Example usage: python train.py --data xView.yaml
-# parent
-# ├── yolov5
-# └── datasets
-# └── xView ← downloads here (20.7 GB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/xView # dataset root dir
-train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
-val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
-
-# Classes
-nc: 60 # number of classes
-names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
- 'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
- 'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
- 'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
- 'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
- 'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
- 'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
- 'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
- 'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower'] # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
- import json
- import os
- from pathlib import Path
-
- import numpy as np
- from PIL import Image
- from tqdm import tqdm
-
- from utils.datasets import autosplit
- from utils.general import download, xyxy2xywhn
-
-
- def convert_labels(fname=Path('xView/xView_train.geojson')):
- # Convert xView geoJSON labels to YOLO format
- path = fname.parent
- with open(fname) as f:
- print(f'Loading {fname}...')
- data = json.load(f)
-
- # Make dirs
- labels = Path(path / 'labels' / 'train')
- os.system(f'rm -rf {labels}')
- labels.mkdir(parents=True, exist_ok=True)
-
- # xView classes 11-94 to 0-59
- xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
- 12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
- 29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
- 47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
-
- shapes = {}
- for feature in tqdm(data['features'], desc=f'Converting {fname}'):
- p = feature['properties']
- if p['bounds_imcoords']:
- id = p['image_id']
- file = path / 'train_images' / id
- if file.exists(): # 1395.tif missing
- try:
- box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
- assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
- cls = p['type_id']
- cls = xview_class2index[int(cls)] # xView class to 0-60
- assert 59 >= cls >= 0, f'incorrect class index {cls}'
-
- # Write YOLO label
- if id not in shapes:
- shapes[id] = Image.open(file).size
- box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
- with open((labels / id).with_suffix('.txt'), 'a') as f:
- f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
- except Exception as e:
- print(f'WARNING: skipping one label for {file}: {e}')
-
-
- # Download manually from https://challenge.xviewdataset.org
- dir = Path(yaml['path']) # dataset root dir
- # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
- # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
- # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
- # download(urls, dir=dir, delete=False)
-
- # Convert labels
- convert_labels(dir / 'xView_train.geojson')
-
- # Move images
- images = Path(dir / 'images')
- images.mkdir(parents=True, exist_ok=True)
- Path(dir / 'train_images').rename(dir / 'images' / 'train')
- Path(dir / 'val_images').rename(dir / 'images' / 'val')
-
- # Split
- autosplit(dir / 'images' / 'train')
diff --git a/yolov5-6.2/detect.py b/yolov5-6.2/detect.py
deleted file mode 100644
index 0a88608d..00000000
--- a/yolov5-6.2/detect.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run inference on images, videos, directories, streams, etc.
-
-Usage - sources:
- $ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
- img.jpg # image
- vid.mp4 # video
- path/ # directory
- path/*.jpg # glob
- 'https://youtu.be/Zgi9g1ksQHc' # YouTube
- 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
-
-Usage - formats:
- $ python path/to/detect.py --weights yolov5s.pt # PyTorch
- yolov5s.torchscript # TorchScript
- yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s.xml # OpenVINO
- yolov5s.engine # TensorRT
- yolov5s.mlmodel # CoreML (macOS-only)
- yolov5s_saved_model # TensorFlow SavedModel
- yolov5s.pb # TensorFlow GraphDef
- yolov5s.tflite # TensorFlow Lite
- yolov5s_edgetpu.tflite # TensorFlow Edge TPU
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-import torch.backends.cudnn as cudnn
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
-from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
- increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, smart_inference_mode, time_sync
-
-
-@smart_inference_mode()
-def run(
- weights=ROOT / 'yolov5s.pt', # model.pt path(s)
- source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
- data=ROOT / 'data/coco128.yaml', # dataset.yaml path
- imgsz=(640, 640), # inference size (height, width)
- conf_thres=0.25, # confidence threshold
- iou_thres=0.45, # NMS IOU threshold
- max_det=1000, # maximum detections per image
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- view_img=False, # show results
- save_txt=False, # save results to *.txt
- save_conf=False, # save confidences in --save-txt labels
- save_crop=False, # save cropped prediction boxes
- nosave=False, # do not save images/videos
- classes=None, # filter by class: --class 0, or --class 0 2 3
- agnostic_nms=False, # class-agnostic NMS
- augment=False, # augmented inference
- visualize=False, # visualize features
- update=False, # update all models
- project=ROOT / 'runs/detect', # save results to project/name
- name='exp', # save results to project/name
- exist_ok=False, # existing project/name ok, do not increment
- line_thickness=3, # bounding box thickness (pixels)
- hide_labels=False, # hide labels
- hide_conf=False, # hide confidences
- half=False, # use FP16 half-precision inference
- dnn=False, # use OpenCV DNN for ONNX inference
-):
- source = str(source)
- save_img = not nosave and not source.endswith('.txt') # save inference images
- is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
- is_url = source.lower().startswith(('rtsp://'))#, 'rtmp://', 'http://', 'https://'))
- webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
- if is_url and is_file:
- source = check_file(source) # download
-
- # Directories
- save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- device = select_device(device)
- model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
- stride, names, pt = model.stride, model.names, model.pt
- imgsz = check_img_size(imgsz, s=stride) # check image size
-
- # Dataloader
- if webcam:
- view_img = check_imshow()
- cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
- bs = len(dataset) # batch_size
- else:
- dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
- bs = 1 # batch_size
- vid_path, vid_writer = [None] * bs, [None] * bs
-
- # Run inference
- model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
- seen, windows, dt = 0, [], [0.0, 0.0, 0.0]
- for path, im, im0s, vid_cap, s in dataset:
- t1 = time_sync()
- im = torch.from_numpy(im).to(device)
- im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
- im /= 255 # 0 - 255 to 0.0 - 1.0
- if len(im.shape) == 3:
- im = im[None] # expand for batch dim
- t2 = time_sync()
- dt[0] += t2 - t1
-
- # Inference
- visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
- pred = model(im, augment=augment, visualize=visualize)
- t3 = time_sync()
- dt[1] += t3 - t2
-
- # NMS
- pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
- dt[2] += time_sync() - t3
-
- # Second-stage classifier (optional)
- # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
- # Process predictions
- for i, det in enumerate(pred): # per image
- seen += 1
- if webcam: # batch_size >= 1
- p, im0, frame = path[i], im0s[i].copy(), dataset.count
- s += f'{i}: '
- else:
- p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
-
- p = Path(p) # to Path
- save_path = str(save_dir / p.name) # im.jpg
- txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
- s += '%gx%g ' % im.shape[2:] # print string
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- imc = im0.copy() if save_crop else im0 # for save_crop
- annotator = Annotator(im0, line_width=line_thickness, example=str(names))
- if len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
-
- # Print results
- for c in det[:, -1].unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
-
- # Write results
- for *xyxy, conf, cls in reversed(det):
- if save_txt: # Write to file
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(f'{txt_path}.txt', 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- if save_img or save_crop or view_img: # Add bbox to image
- c = int(cls) # integer class
- label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
- annotator.box_label(xyxy, label, color=colors(c, True))
-
- if save_crop:
- save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
-
- # Stream results
- im0 = annotator.result()
- if view_img:
- if platform.system() == 'Linux' and p not in windows:
- windows.append(p)
- cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
- cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
- cv2.imshow(str(p), im0)
- cv2.waitKey(1) # 1 millisecond
-
- # Save results (image with detections)
- if save_img:
- if dataset.mode == 'image':
- cv2.imwrite(save_path, im0)
- else: # 'video' or 'stream'
- if vid_path[i] != save_path: # new video
- vid_path[i] = save_path
- if isinstance(vid_writer[i], cv2.VideoWriter):
- vid_writer[i].release() # release previous video writer
- if vid_cap: # video
- fps = vid_cap.get(cv2.CAP_PROP_FPS)
- w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- else: # stream
- fps, w, h = 30, im0.shape[1], im0.shape[0]
- save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
- vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
- vid_writer[i].write(im0)
-
- # Print time (inference-only)
- LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
-
- # Print results
- t = tuple(x / seen * 1E3 for x in dt) # speeds per image
- LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
- if save_txt or save_img:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
- if update:
- strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
- parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
- parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
- parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
- parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--view-img', action='store_true', help='show results')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
- parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
- parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
- parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--visualize', action='store_true', help='visualize features')
- parser.add_argument('--update', action='store_true', help='update all models')
- parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
- parser.add_argument('--name', default='exp', help='save results to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-
- parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
- parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
- parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
-
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
- opt = parser.parse_args()
- opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- check_requirements(exclude=('tensorboard', 'thop'))
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/export.py b/yolov5-6.2/export.py
deleted file mode 100644
index 595039b2..00000000
--- a/yolov5-6.2/export.py
+++ /dev/null
@@ -1,616 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
-
-Format | `export.py --include` | Model
---- | --- | ---
-PyTorch | - | yolov5s.pt
-TorchScript | `torchscript` | yolov5s.torchscript
-ONNX | `onnx` | yolov5s.onnx
-OpenVINO | `openvino` | yolov5s_openvino_model/
-TensorRT | `engine` | yolov5s.engine
-CoreML | `coreml` | yolov5s.mlmodel
-TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
-TensorFlow GraphDef | `pb` | yolov5s.pb
-TensorFlow Lite | `tflite` | yolov5s.tflite
-TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
-TensorFlow.js | `tfjs` | yolov5s_web_model/
-
-Requirements:
- $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
- $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
-
-Usage:
- $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
-
-Inference:
- $ python path/to/detect.py --weights yolov5s.pt # PyTorch
- yolov5s.torchscript # TorchScript
- yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s.xml # OpenVINO
- yolov5s.engine # TensorRT
- yolov5s.mlmodel # CoreML (macOS-only)
- yolov5s_saved_model # TensorFlow SavedModel
- yolov5s.pb # TensorFlow GraphDef
- yolov5s.tflite # TensorFlow Lite
- yolov5s_edgetpu.tflite # TensorFlow Edge TPU
-
-TensorFlow.js:
- $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
- $ npm install
- $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
- $ npm start
-"""
-
-import argparse
-import json
-import os
-import platform
-import subprocess
-import sys
-import time
-import warnings
-from pathlib import Path
-
-import pandas as pd
-import torch
-import yaml
-from torch.utils.mobile_optimizer import optimize_for_mobile
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-if platform.system() != 'Windows':
- ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.experimental import attempt_load
-from models.yolo import Detect
-from utils.dataloaders import LoadImages
-from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, check_yaml,
- colorstr, file_size, print_args, url2file)
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-def export_formats():
- # YOLOv5 export formats
- x = [
- ['PyTorch', '-', '.pt', True, True],
- ['TorchScript', 'torchscript', '.torchscript', True, True],
- ['ONNX', 'onnx', '.onnx', True, True],
- ['OpenVINO', 'openvino', '_openvino_model', True, False],
- ['TensorRT', 'engine', '.engine', False, True],
- ['CoreML', 'coreml', '.mlmodel', True, False],
- ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
- ['TensorFlow GraphDef', 'pb', '.pb', True, True],
- ['TensorFlow Lite', 'tflite', '.tflite', True, False],
- ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
- ['TensorFlow.js', 'tfjs', '_web_model', False, False],]
- return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
-
-
-def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
- # YOLOv5 TorchScript model export
- try:
- LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
- f = file.with_suffix('.torchscript')
-
- ts = torch.jit.trace(model, im, strict=False)
- d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
- extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
- if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
- optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
- else:
- ts.save(str(f), _extra_files=extra_files)
-
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'{prefix} export failure: {e}')
-
-
-def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
- # YOLOv5 ONNX export
- try:
- check_requirements(('onnx',))
- import onnx
-
- LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
- f = file.with_suffix('.onnx')
-
- torch.onnx.export(
- model.cpu() if dynamic else model, # --dynamic only compatible with cpu
- im.cpu() if dynamic else im,
- f,
- verbose=False,
- opset_version=opset,
- training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
- do_constant_folding=not train,
- input_names=['images'],
- output_names=['output'],
- dynamic_axes={
- 'images': {
- 0: 'batch',
- 2: 'height',
- 3: 'width'}, # shape(1,3,640,640)
- 'output': {
- 0: 'batch',
- 1: 'anchors'} # shape(1,25200,85)
- } if dynamic else None)
-
- # Checks
- model_onnx = onnx.load(f) # load onnx model
- onnx.checker.check_model(model_onnx) # check onnx model
-
- # Metadata
- d = {'stride': int(max(model.stride)), 'names': model.names}
- for k, v in d.items():
- meta = model_onnx.metadata_props.add()
- meta.key, meta.value = k, str(v)
- onnx.save(model_onnx, f)
-
- # Simplify
- if simplify:
- try:
- cuda = torch.cuda.is_available()
- check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
- import onnxsim
-
- LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
- model_onnx, check = onnxsim.simplify(model_onnx)
- assert check, 'assert check failed'
- onnx.save(model_onnx, f)
- except Exception as e:
- LOGGER.info(f'{prefix} simplifier failure: {e}')
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'{prefix} export failure: {e}')
-
-
-def export_openvino(model, file, half, prefix=colorstr('OpenVINO:')):
- # YOLOv5 OpenVINO export
- try:
- check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
- import openvino.inference_engine as ie
-
- LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
- f = str(file).replace('.pt', f'_openvino_model{os.sep}')
-
- cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
- subprocess.check_output(cmd.split()) # export
- with open(Path(f) / file.with_suffix('.yaml').name, 'w') as g:
- yaml.dump({'stride': int(max(model.stride)), 'names': model.names}, g) # add metadata.yaml
-
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
- # YOLOv5 CoreML export
- try:
- check_requirements(('coremltools',))
- import coremltools as ct
-
- LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
- f = file.with_suffix('.mlmodel')
-
- ts = torch.jit.trace(model, im, strict=False) # TorchScript model
- ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
- bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
- if bits < 32:
- if platform.system() == 'Darwin': # quantization only supported on macOS
- with warnings.catch_warnings():
- warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning
- ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
- else:
- print(f'{prefix} quantization only supported on macOS, skipping...')
- ct_model.save(f)
-
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return ct_model, f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
- return None, None
-
-
-def export_engine(model, im, file, train, half, dynamic, simplify, workspace=4, verbose=False):
- # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
- prefix = colorstr('TensorRT:')
- try:
- assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
- try:
- import tensorrt as trt
- except Exception:
- if platform.system() == 'Linux':
- check_requirements(('nvidia-tensorrt',), cmds=('-U --index-url https://pypi.ngc.nvidia.com',))
- import tensorrt as trt
-
- if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
- grid = model.model[-1].anchor_grid
- model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
- export_onnx(model, im, file, 12, train, dynamic, simplify) # opset 12
- model.model[-1].anchor_grid = grid
- else: # TensorRT >= 8
- check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
- export_onnx(model, im, file, 13, train, dynamic, simplify) # opset 13
- onnx = file.with_suffix('.onnx')
-
- LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
- assert onnx.exists(), f'failed to export ONNX file: {onnx}'
- f = file.with_suffix('.engine') # TensorRT engine file
- logger = trt.Logger(trt.Logger.INFO)
- if verbose:
- logger.min_severity = trt.Logger.Severity.VERBOSE
-
- builder = trt.Builder(logger)
- config = builder.create_builder_config()
- config.max_workspace_size = workspace * 1 << 30
- # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
-
- flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
- network = builder.create_network(flag)
- parser = trt.OnnxParser(network, logger)
- if not parser.parse_from_file(str(onnx)):
- raise RuntimeError(f'failed to load ONNX file: {onnx}')
-
- inputs = [network.get_input(i) for i in range(network.num_inputs)]
- outputs = [network.get_output(i) for i in range(network.num_outputs)]
- LOGGER.info(f'{prefix} Network Description:')
- for inp in inputs:
- LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
- for out in outputs:
- LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
-
- if dynamic:
- if im.shape[0] <= 1:
- LOGGER.warning(f"{prefix}WARNING: --dynamic model requires maximum --batch-size argument")
- profile = builder.create_optimization_profile()
- for inp in inputs:
- profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
- config.add_optimization_profile(profile)
-
- LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
- if builder.platform_has_fast_fp16 and half:
- config.set_flag(trt.BuilderFlag.FP16)
- with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
- t.write(engine.serialize())
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_saved_model(model,
- im,
- file,
- dynamic,
- tf_nms=False,
- agnostic_nms=False,
- topk_per_class=100,
- topk_all=100,
- iou_thres=0.45,
- conf_thres=0.25,
- keras=False,
- prefix=colorstr('TensorFlow SavedModel:')):
- # YOLOv5 TensorFlow SavedModel export
- try:
- import tensorflow as tf
- from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
-
- from models.tf import TFDetect, TFModel
-
- LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
- f = str(file).replace('.pt', '_saved_model')
- batch_size, ch, *imgsz = list(im.shape) # BCHW
-
- tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
- im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
- _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
- inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
- outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
- keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
- keras_model.trainable = False
- keras_model.summary()
- if keras:
- keras_model.save(f, save_format='tf')
- else:
- spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
- m = tf.function(lambda x: keras_model(x)) # full model
- m = m.get_concrete_function(spec)
- frozen_func = convert_variables_to_constants_v2(m)
- tfm = tf.Module()
- tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x)[0], [spec])
- tfm.__call__(im)
- tf.saved_model.save(tfm,
- f,
- options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)
- if check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return keras_model, f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
- return None, None
-
-
-def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
- # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
- try:
- import tensorflow as tf
- from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
-
- LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
- f = file.with_suffix('.pb')
-
- m = tf.function(lambda x: keras_model(x)) # full model
- m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
- frozen_func = convert_variables_to_constants_v2(m)
- frozen_func.graph.as_graph_def()
- tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
-
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
- # YOLOv5 TensorFlow Lite export
- try:
- import tensorflow as tf
-
- LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
- batch_size, ch, *imgsz = list(im.shape) # BCHW
- f = str(file).replace('.pt', '-fp16.tflite')
-
- converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
- converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
- converter.target_spec.supported_types = [tf.float16]
- converter.optimizations = [tf.lite.Optimize.DEFAULT]
- if int8:
- from models.tf import representative_dataset_gen
- dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
- converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
- converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
- converter.target_spec.supported_types = []
- converter.inference_input_type = tf.uint8 # or tf.int8
- converter.inference_output_type = tf.uint8 # or tf.int8
- converter.experimental_new_quantizer = True
- f = str(file).replace('.pt', '-int8.tflite')
- if nms or agnostic_nms:
- converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
-
- tflite_model = converter.convert()
- open(f, "wb").write(tflite_model)
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
- # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
- try:
- cmd = 'edgetpu_compiler --version'
- help_url = 'https://coral.ai/docs/edgetpu/compiler/'
- assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
- if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
- LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
- sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
- for c in (
- 'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
- 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
- 'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
- subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
- ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
-
- LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
- f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
- f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
-
- cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
- subprocess.run(cmd.split(), check=True)
-
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
- # YOLOv5 TensorFlow.js export
- try:
- check_requirements(('tensorflowjs',))
- import re
-
- import tensorflowjs as tfjs
-
- LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
- f = str(file).replace('.pt', '_web_model') # js dir
- f_pb = file.with_suffix('.pb') # *.pb path
- f_json = f'{f}/model.json' # *.json path
-
- cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
- f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
- subprocess.run(cmd.split())
-
- with open(f_json) as j:
- json = j.read()
- with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
- subst = re.sub(
- r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
- r'"Identity.?.?": {"name": "Identity.?.?"}, '
- r'"Identity.?.?": {"name": "Identity.?.?"}, '
- r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
- r'"Identity_1": {"name": "Identity_1"}, '
- r'"Identity_2": {"name": "Identity_2"}, '
- r'"Identity_3": {"name": "Identity_3"}}}', json)
- j.write(subst)
-
- LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- return f
- except Exception as e:
- LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-@smart_inference_mode()
-def run(
- data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
- weights=ROOT / 'yolov5s.pt', # weights path
- imgsz=(640, 640), # image (height, width)
- batch_size=1, # batch size
- device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- include=('torchscript', 'onnx'), # include formats
- half=False, # FP16 half-precision export
- inplace=False, # set YOLOv5 Detect() inplace=True
- train=False, # model.train() mode
- keras=False, # use Keras
- optimize=False, # TorchScript: optimize for mobile
- int8=False, # CoreML/TF INT8 quantization
- dynamic=False, # ONNX/TF/TensorRT: dynamic axes
- simplify=False, # ONNX: simplify model
- opset=12, # ONNX: opset version
- verbose=False, # TensorRT: verbose log
- workspace=4, # TensorRT: workspace size (GB)
- nms=False, # TF: add NMS to model
- agnostic_nms=False, # TF: add agnostic NMS to model
- topk_per_class=100, # TF.js NMS: topk per class to keep
- topk_all=100, # TF.js NMS: topk for all classes to keep
- iou_thres=0.45, # TF.js NMS: IoU threshold
- conf_thres=0.25, # TF.js NMS: confidence threshold
-):
- t = time.time()
- include = [x.lower() for x in include] # to lowercase
- fmts = tuple(export_formats()['Argument'][1:]) # --include arguments
- flags = [x in include for x in fmts]
- assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
- jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags # export booleans
- file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights
-
- # Load PyTorch model
- device = select_device(device)
- if half:
- assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
- assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
- model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model
-
- # Checks
- imgsz *= 2 if len(imgsz) == 1 else 1 # expand
- if optimize:
- assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
-
- # Input
- gs = int(max(model.stride)) # grid size (max stride)
- imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
- im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
-
- # Update model
- model.train() if train else model.eval() # training mode = no Detect() layer grid construction
- for k, m in model.named_modules():
- if isinstance(m, Detect):
- m.inplace = inplace
- m.onnx_dynamic = dynamic
- m.export = True
-
- for _ in range(2):
- y = model(im) # dry runs
- if half and not coreml:
- im, model = im.half(), model.half() # to FP16
- shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
- LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
-
- # Exports
- f = [''] * 10 # exported filenames
- warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
- if jit:
- f[0] = export_torchscript(model, im, file, optimize)
- if engine: # TensorRT required before ONNX
- f[1] = export_engine(model, im, file, train, half, dynamic, simplify, workspace, verbose)
- if onnx or xml: # OpenVINO requires ONNX
- f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
- if xml: # OpenVINO
- f[3] = export_openvino(model, file, half)
- if coreml:
- _, f[4] = export_coreml(model, im, file, int8, half)
-
- # TensorFlow Exports
- if any((saved_model, pb, tflite, edgetpu, tfjs)):
- if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
- check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
- assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
- model, f[5] = export_saved_model(model.cpu(),
- im,
- file,
- dynamic,
- tf_nms=nms or agnostic_nms or tfjs,
- agnostic_nms=agnostic_nms or tfjs,
- topk_per_class=topk_per_class,
- topk_all=topk_all,
- iou_thres=iou_thres,
- conf_thres=conf_thres,
- keras=keras)
- if pb or tfjs: # pb prerequisite to tfjs
- f[6] = export_pb(model, file)
- if tflite or edgetpu:
- f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
- if edgetpu:
- f[8] = export_edgetpu(file)
- if tfjs:
- f[9] = export_tfjs(file)
-
- # Finish
- f = [str(x) for x in f if x] # filter out '' and None
- if any(f):
- h = '--half' if half else '' # --half FP16 inference arg
- LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
- f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
- f"\nDetect: python detect.py --weights {f[-1]} {h}"
- f"\nValidate: python val.py --weights {f[-1]} {h}"
- f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')"
- f"\nVisualize: https://netron.app")
- return f # return list of exported files/dirs
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
- parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
- parser.add_argument('--batch-size', type=int, default=1, help='batch size')
- parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
- parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
- parser.add_argument('--train', action='store_true', help='model.train() mode')
- parser.add_argument('--keras', action='store_true', help='TF: use Keras')
- parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
- parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
- parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
- parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
- parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
- parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
- parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
- parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
- parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
- parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
- parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
- parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
- parser.add_argument('--include',
- nargs='+',
- default=['torchscript', 'onnx'],
- help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
- opt = parser.parse_args()
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/hubconf.py b/yolov5-6.2/hubconf.py
deleted file mode 100644
index 011eaa57..00000000
--- a/yolov5-6.2/hubconf.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
-
-Usage:
- import torch
- model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
- model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch
-"""
-
-import torch
-
-
-def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
- """Creates or loads a YOLOv5 model
-
- Arguments:
- name (str): model name 'yolov5s' or path 'path/to/best.pt'
- pretrained (bool): load pretrained weights into the model
- channels (int): number of input channels
- classes (int): number of model classes
- autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
- verbose (bool): print all information to screen
- device (str, torch.device, None): device to use for model parameters
-
- Returns:
- YOLOv5 model
- """
- from pathlib import Path
-
- from models.common import AutoShape, DetectMultiBackend
- from models.experimental import attempt_load
- from models.yolo import Model
- from utils.downloads import attempt_download
- from utils.general import LOGGER, check_requirements, intersect_dicts, logging
- from utils.torch_utils import select_device
-
- if not verbose:
- LOGGER.setLevel(logging.WARNING)
- check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
- name = Path(name)
- path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path
- try:
- device = select_device(device)
- if pretrained and channels == 3 and classes == 80:
- try:
- model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
- if autoshape:
- model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
- except Exception:
- model = attempt_load(path, device=device, fuse=False) # arbitrary model
- else:
- cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
- model = Model(cfg, channels, classes) # create model
- if pretrained:
- ckpt = torch.load(attempt_download(path), map_location=device) # load
- csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
- csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect
- model.load_state_dict(csd, strict=False) # load
- if len(ckpt['model'].names) == classes:
- model.names = ckpt['model'].names # set class names attribute
- if not verbose:
- LOGGER.setLevel(logging.INFO) # reset to default
- return model.to(device)
-
- except Exception as e:
- help_url = 'https://github.com/ultralytics/yolov5/issues/36'
- s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
- raise Exception(s) from e
-
-
-def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
- # YOLOv5 custom or local model
- return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
-
-
-def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-nano model https://github.com/ultralytics/yolov5
- return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-small model https://github.com/ultralytics/yolov5
- return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-medium model https://github.com/ultralytics/yolov5
- return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-large model https://github.com/ultralytics/yolov5
- return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
- return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
- return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
- return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
- return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
- return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
- # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
- return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-if __name__ == '__main__':
- import argparse
- from pathlib import Path
-
- import numpy as np
- from PIL import Image
-
- from utils.general import cv2, print_args
-
- # Argparser
- parser = argparse.ArgumentParser()
- parser.add_argument('--model', type=str, default='yolov5s', help='model name')
- opt = parser.parse_args()
- print_args(vars(opt))
-
- # Model
- model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
- # model = custom(path='path/to/model.pt') # custom
-
- # Images
- imgs = [
- 'data/images/zidane.jpg', # filename
- Path('data/images/zidane.jpg'), # Path
- 'https://ultralytics.com/images/zidane.jpg', # URI
- cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
- Image.open('data/images/bus.jpg'), # PIL
- np.zeros((320, 640, 3))] # numpy
-
- # Inference
- results = model(imgs, size=320) # batched inference
-
- # Results
- results.print()
- results.save()
diff --git a/yolov5-6.2/ip.py b/yolov5-6.2/ip.py
deleted file mode 100644
index 4ca5f9cf..00000000
--- a/yolov5-6.2/ip.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import socket
-import sys,json
-import numpy as np
-# 创建 socket 对象
-serversocket = socket.socket(
- socket.AF_INET, socket.SOCK_STREAM)
-
-# 获取本地主机名，本机的ip
-host = '192.168.220.151'
-port = 9999
-
-# 绑定端口号
-serversocket.connect((host, port))
-
-# 设置最大连接数，超过后排队
-while True:
- # 建立客户端连接
- msg = serversocket.recv(4096)
- msg = msg.decode('utf-8')
- recvmsg = json.loads(msg)
- print(recvmsg)
diff --git a/yolov5-6.2/models/__init__.py b/yolov5-6.2/models/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/yolov5-6.2/models/common.py b/yolov5-6.2/models/common.py
deleted file mode 100644
index 17e40e60..00000000
--- a/yolov5-6.2/models/common.py
+++ /dev/null
@@ -1,771 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Common modules
-"""
-
-import json
-import math
-import platform
-import warnings
-from collections import OrderedDict, namedtuple
-from copy import copy
-from pathlib import Path
-
-import cv2
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-from PIL import Image
-from torch.cuda import amp
-
-from utils.dataloaders import exif_transpose, letterbox
-from utils.general import (LOGGER, ROOT, check_requirements, check_suffix, check_version, colorstr, increment_path,
- make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh, yaml_load)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import copy_attr, smart_inference_mode, time_sync
-
-
-def autopad(k, p=None): # kernel, padding
- # Pad to 'same'
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
- return p
-
-
-class Conv(nn.Module):
- # Standard convolution
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
- def forward(self, x):
- return self.act(self.bn(self.conv(x)))
-
- def forward_fuse(self, x):
- return self.act(self.conv(x))
-
-
-class DWConv(Conv):
- # Depth-wise convolution class
- def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class DWConvTranspose2d(nn.ConvTranspose2d):
- # Depth-wise transpose convolution class
- def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out
- super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
-
-
-class TransformerLayer(nn.Module):
- # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
- def __init__(self, c, num_heads):
- super().__init__()
- self.q = nn.Linear(c, c, bias=False)
- self.k = nn.Linear(c, c, bias=False)
- self.v = nn.Linear(c, c, bias=False)
- self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
- self.fc1 = nn.Linear(c, c, bias=False)
- self.fc2 = nn.Linear(c, c, bias=False)
-
- def forward(self, x):
- x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
- x = self.fc2(self.fc1(x)) + x
- return x
-
-
-class TransformerBlock(nn.Module):
- # Vision Transformer https://arxiv.org/abs/2010.11929
- def __init__(self, c1, c2, num_heads, num_layers):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
- self.linear = nn.Linear(c2, c2) # learnable position embedding
- self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
- self.c2 = c2
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- b, _, w, h = x.shape
- p = x.flatten(2).permute(2, 0, 1)
- return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
-
-
-class Bottleneck(nn.Module):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2, 3, 1, g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
- self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
- self.act = nn.SiLU()
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
-
-
-class CrossConv(nn.Module):
- # Cross Convolution Downsample
- def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
- # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, (1, k), (1, s))
- self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class C3(nn.Module):
- # CSP Bottleneck with 3 convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
- def forward(self, x):
- return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
-
-
-class C3x(C3):
- # C3 module with cross-convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e)
- self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
-
-
-class C3TR(C3):
- # C3 module with TransformerBlock()
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e)
- self.m = TransformerBlock(c_, c_, 4, n)
-
-
-class C3SPP(C3):
- # C3 module with SPP()
- def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e)
- self.m = SPP(c_, c_, k)
-
-
-class C3Ghost(C3):
- # C3 module with GhostBottleneck()
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
-
-
-class SPP(nn.Module):
- # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
- def __init__(self, c1, c2, k=(5, 9, 13)):
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
- def forward(self, x):
- x = self.cv1(x)
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
- return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class SPPF(nn.Module):
- # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
- def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * 4, c2, 1, 1)
- self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
- def forward(self, x):
- x = self.cv1(x)
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
- y1 = self.m(x)
- y2 = self.m(y1)
- return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
-
-
-class Focus(nn.Module):
- # Focus wh information into c-space
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
- # self.contract = Contract(gain=2)
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
- # return self.conv(self.contract(x))
-
-
-class GhostConv(nn.Module):
- # Ghost Convolution https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
- super().__init__()
- c_ = c2 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, k, s, None, g, act)
- self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
-
- def forward(self, x):
- y = self.cv1(x)
- return torch.cat((y, self.cv2(y)), 1)
-
-
-class GhostBottleneck(nn.Module):
- # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
- super().__init__()
- c_ = c2 // 2
- self.conv = nn.Sequential(
- GhostConv(c1, c_, 1, 1), # pw
- DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
- GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
- self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
- act=False)) if s == 2 else nn.Identity()
-
- def forward(self, x):
- return self.conv(x) + self.shortcut(x)
-
-
-class Contract(nn.Module):
- # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
- s = self.gain
- x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
- x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
- return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
-
-
-class Expand(nn.Module):
- # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
- s = self.gain
- x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
- x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
- return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
-
-
-class Concat(nn.Module):
- # Concatenate a list of tensors along dimension
- def __init__(self, dimension=1):
- super().__init__()
- self.d = dimension
-
- def forward(self, x):
- return torch.cat(x, self.d)
-
-
-class DetectMultiBackend(nn.Module):
- # YOLOv5 MultiBackend class for python inference on various backends
- def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
- # Usage:
- # PyTorch: weights = *.pt
- # TorchScript: *.torchscript
- # ONNX Runtime: *.onnx
- # ONNX OpenCV DNN: *.onnx with --dnn
- # OpenVINO: *.xml
- # CoreML: *.mlmodel
- # TensorRT: *.engine
- # TensorFlow SavedModel: *_saved_model
- # TensorFlow GraphDef: *.pb
- # TensorFlow Lite: *.tflite
- # TensorFlow Edge TPU: *_edgetpu.tflite
- from models.experimental import attempt_download, attempt_load # scoped to avoid circular import
-
- super().__init__()
- w = str(weights[0] if isinstance(weights, list) else weights)
- pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self._model_type(w) # get backend
- w = attempt_download(w) # download if not local
- fp16 &= pt or jit or onnx or engine # FP16
- stride = 32 # default stride
-
- if pt: # PyTorch
- model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
- stride = max(int(model.stride.max()), 32) # model stride
- names = model.module.names if hasattr(model, 'module') else model.names # get class names
- model.half() if fp16 else model.float()
- self.model = model # explicitly assign for to(), cpu(), cuda(), half()
- elif jit: # TorchScript
- LOGGER.info(f'Loading {w} for TorchScript inference...')
- extra_files = {'config.txt': ''} # model metadata
- model = torch.jit.load(w, _extra_files=extra_files)
- model.half() if fp16 else model.float()
- if extra_files['config.txt']:
- d = json.loads(extra_files['config.txt']) # extra_files dict
- stride, names = int(d['stride']), d['names']
- elif dnn: # ONNX OpenCV DNN
- LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
- check_requirements(('opencv-python>=4.5.4',))
- net = cv2.dnn.readNetFromONNX(w)
- elif onnx: # ONNX Runtime
- LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
- cuda = torch.cuda.is_available() and device.type != 'cpu'
- check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
- import onnxruntime
- providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
- session = onnxruntime.InferenceSession(w, providers=providers)
- meta = session.get_modelmeta().custom_metadata_map # metadata
- if 'stride' in meta:
- stride, names = int(meta['stride']), eval(meta['names'])
- elif xml: # OpenVINO
- LOGGER.info(f'Loading {w} for OpenVINO inference...')
- check_requirements(('openvino',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
- from openvino.runtime import Core, Layout, get_batch
- ie = Core()
- if not Path(w).is_file(): # if not *.xml
- w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir
- network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
- if network.get_parameters()[0].get_layout().empty:
- network.get_parameters()[0].set_layout(Layout("NCHW"))
- batch_dim = get_batch(network)
- if batch_dim.is_static:
- batch_size = batch_dim.get_length()
- executable_network = ie.compile_model(network, device_name="CPU") # device_name="MYRIAD" for Intel NCS2
- output_layer = next(iter(executable_network.outputs))
- meta = Path(w).with_suffix('.yaml')
- if meta.exists():
- stride, names = self._load_metadata(meta) # load metadata
- elif engine: # TensorRT
- LOGGER.info(f'Loading {w} for TensorRT inference...')
- import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
- check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
- if device.type == 'cpu':
- device = torch.device('cuda:0')
- Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
- logger = trt.Logger(trt.Logger.INFO)
- with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
- model = runtime.deserialize_cuda_engine(f.read())
- context = model.create_execution_context()
- bindings = OrderedDict()
- fp16 = False # default updated below
- dynamic = False
- for index in range(model.num_bindings):
- name = model.get_binding_name(index)
- dtype = trt.nptype(model.get_binding_dtype(index))
- if model.binding_is_input(index):
- if -1 in tuple(model.get_binding_shape(index)): # dynamic
- dynamic = True
- context.set_binding_shape(index, tuple(model.get_profile_shape(0, index)[2]))
- if dtype == np.float16:
- fp16 = True
- shape = tuple(context.get_binding_shape(index))
- im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
- bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
- binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
- batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size
- elif coreml: # CoreML
- LOGGER.info(f'Loading {w} for CoreML inference...')
- import coremltools as ct
- model = ct.models.MLModel(w)
- else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
- if saved_model: # SavedModel
- LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
- import tensorflow as tf
- keras = False # assume TF1 saved_model
- model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
- elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
- LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
- import tensorflow as tf
-
- def wrap_frozen_graph(gd, inputs, outputs):
- x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
- ge = x.graph.as_graph_element
- return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
-
- gd = tf.Graph().as_graph_def() # graph_def
- with open(w, 'rb') as f:
- gd.ParseFromString(f.read())
- frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0")
- elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
- try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
- from tflite_runtime.interpreter import Interpreter, load_delegate
- except ImportError:
- import tensorflow as tf
- Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
- if edgetpu: # Edge TPU https://coral.ai/software/#edgetpu-runtime
- LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
- delegate = {
- 'Linux': 'libedgetpu.so.1',
- 'Darwin': 'libedgetpu.1.dylib',
- 'Windows': 'edgetpu.dll'}[platform.system()]
- interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
- else: # Lite
- LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
- interpreter = Interpreter(model_path=w) # load TFLite model
- interpreter.allocate_tensors() # allocate
- input_details = interpreter.get_input_details() # inputs
- output_details = interpreter.get_output_details() # outputs
- elif tfjs:
- raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
- else:
- raise NotImplementedError(f'ERROR: {w} is not a supported format')
-
- # class names
- if 'names' not in locals():
- names = yaml_load(data)['names'] if data else [f'class{i}' for i in range(999)]
- if names[0] == 'n01440764' and len(names) == 1000: # ImageNet
- names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names
-
- self.__dict__.update(locals()) # assign all variables to self
-
- def forward(self, im, augment=False, visualize=False, val=False):
- # YOLOv5 MultiBackend inference
- b, ch, h, w = im.shape # batch, channel, height, width
- if self.fp16 and im.dtype != torch.float16:
- im = im.half() # to FP16
-
- if self.pt: # PyTorch
- y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
- if isinstance(y, tuple):
- y = y[0]
- elif self.jit: # TorchScript
- y = self.model(im)[0]
- elif self.dnn: # ONNX OpenCV DNN
- im = im.cpu().numpy() # torch to numpy
- self.net.setInput(im)
- y = self.net.forward()
- elif self.onnx: # ONNX Runtime
- im = im.cpu().numpy() # torch to numpy
- y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
- elif self.xml: # OpenVINO
- im = im.cpu().numpy() # FP32
- y = self.executable_network([im])[self.output_layer]
- elif self.engine: # TensorRT
- if self.dynamic and im.shape != self.bindings['images'].shape:
- i_in, i_out = (self.model.get_binding_index(x) for x in ('images', 'output'))
- self.context.set_binding_shape(i_in, im.shape) # reshape if dynamic
- self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
- self.bindings['output'].data.resize_(tuple(self.context.get_binding_shape(i_out)))
- s = self.bindings['images'].shape
- assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
- self.binding_addrs['images'] = int(im.data_ptr())
- self.context.execute_v2(list(self.binding_addrs.values()))
- y = self.bindings['output'].data
- elif self.coreml: # CoreML
- im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
- im = Image.fromarray((im[0] * 255).astype('uint8'))
- # im = im.resize((192, 320), Image.ANTIALIAS)
- y = self.model.predict({'image': im}) # coordinates are xywh normalized
- if 'confidence' in y:
- box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
- conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
- y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
- else:
- k = 'var_' + str(sorted(int(k.replace('var_', '')) for k in y)[-1]) # output key
- y = y[k] # output
- else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
- im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
- if self.saved_model: # SavedModel
- y = (self.model(im, training=False) if self.keras else self.model(im)).numpy()
- elif self.pb: # GraphDef
- y = self.frozen_func(x=self.tf.constant(im)).numpy()
- else: # Lite or Edge TPU
- input, output = self.input_details[0], self.output_details[0]
- int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model
- if int8:
- scale, zero_point = input['quantization']
- im = (im / scale + zero_point).astype(np.uint8) # de-scale
- self.interpreter.set_tensor(input['index'], im)
- self.interpreter.invoke()
- y = self.interpreter.get_tensor(output['index'])
- if int8:
- scale, zero_point = output['quantization']
- y = (y.astype(np.float32) - zero_point) * scale # re-scale
- y[..., :4] *= [w, h, w, h] # xywh normalized to pixels
-
- if isinstance(y, np.ndarray):
- y = torch.tensor(y, device=self.device)
- return (y, []) if val else y
-
- def warmup(self, imgsz=(1, 3, 640, 640)):
- # Warmup model by running inference once
- warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb
- if any(warmup_types) and self.device.type != 'cpu':
- im = torch.zeros(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input
- for _ in range(2 if self.jit else 1): #
- self.forward(im) # warmup
-
- @staticmethod
- def _model_type(p='path/to/model.pt'):
- # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
- from export import export_formats
- suffixes = list(export_formats().Suffix) + ['.xml'] # export suffixes
- check_suffix(p, suffixes) # checks
- p = Path(p).name # eliminate trailing separators
- pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes)
- xml |= xml2 # *_openvino_model or *.xml
- tflite &= not edgetpu # *.tflite
- return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs
-
- @staticmethod
- def _load_metadata(f='path/to/meta.yaml'):
- # Load metadata from meta.yaml if it exists
- d = yaml_load(f)
- return d['stride'], d['names'] # assign stride, names
-
-
-class AutoShape(nn.Module):
- # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
- conf = 0.25 # NMS confidence threshold
- iou = 0.45 # NMS IoU threshold
- agnostic = False # NMS class-agnostic
- multi_label = False # NMS multiple labels per box
- classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
- max_det = 1000 # maximum number of detections per image
- amp = False # Automatic Mixed Precision (AMP) inference
-
- def __init__(self, model, verbose=True):
- super().__init__()
- if verbose:
- LOGGER.info('Adding AutoShape... ')
- copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
- self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance
- self.pt = not self.dmb or model.pt # PyTorch model
- self.model = model.eval()
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
- m.inplace = False # Detect.inplace=False for safe multithread inference
-
- def _apply(self, fn):
- # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
- self = super()._apply(fn)
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
- m.stride = fn(m.stride)
- m.grid = list(map(fn, m.grid))
- if isinstance(m.anchor_grid, list):
- m.anchor_grid = list(map(fn, m.anchor_grid))
- return self
-
- @smart_inference_mode()
- def forward(self, imgs, size=640, augment=False, profile=False):
- # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
- # file: imgs = 'data/images/zidane.jpg' # str or PosixPath
- # URI: = 'https://ultralytics.com/images/zidane.jpg'
- # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
- # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
- # numpy: = np.zeros((640,1280,3)) # HWC
- # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
- # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
-
- t = [time_sync()]
- p = next(self.model.parameters()) if self.pt else torch.zeros(1, device=self.model.device) # for device, type
- autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
- if isinstance(imgs, torch.Tensor): # torch
- with amp.autocast(autocast):
- return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
-
- # Pre-process
- n, imgs = (len(imgs), list(imgs)) if isinstance(imgs, (list, tuple)) else (1, [imgs]) # number, list of images
- shape0, shape1, files = [], [], [] # image and inference shapes, filenames
- for i, im in enumerate(imgs):
- f = f'image{i}' # filename
- if isinstance(im, (str, Path)): # filename or uri
- im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
- im = np.asarray(exif_transpose(im))
- elif isinstance(im, Image.Image): # PIL Image
- im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
- files.append(Path(f).with_suffix('.jpg').name)
- if im.shape[0] < 5: # image in CHW
- im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
- im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input
- s = im.shape[:2] # HWC
- shape0.append(s) # image shape
- g = (size / max(s)) # gain
- shape1.append([y * g for y in s])
- imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
- shape1 = [make_divisible(x, self.stride) if self.pt else size for x in np.array(shape1).max(0)] # inf shape
- x = [letterbox(im, shape1, auto=False)[0] for im in imgs] # pad
- x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
- t.append(time_sync())
-
- with amp.autocast(autocast):
- # Inference
- y = self.model(x, augment, profile) # forward
- t.append(time_sync())
-
- # Post-process
- y = non_max_suppression(y if self.dmb else y[0],
- self.conf,
- self.iou,
- self.classes,
- self.agnostic,
- self.multi_label,
- max_det=self.max_det) # NMS
- for i in range(n):
- scale_coords(shape1, y[i][:, :4], shape0[i])
-
- t.append(time_sync())
- return Detections(imgs, y, files, t, self.names, x.shape)
-
-
-class Detections:
- # YOLOv5 detections class for inference results
- def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None):
- super().__init__()
- d = pred[0].device # device
- gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations
- self.imgs = imgs # list of images as numpy arrays
- self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
- self.names = names # class names
- self.files = files # image filenames
- self.times = times # profiling times
- self.xyxy = pred # xyxy pixels
- self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
- self.n = len(self.pred) # number of images (batch size)
- self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
- self.s = shape # inference BCHW shape
-
- def display(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
- crops = []
- for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
- s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
- if pred.shape[0]:
- for c in pred[:, -1].unique():
- n = (pred[:, -1] == c).sum() # detections per class
- s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
- if show or save or render or crop:
- annotator = Annotator(im, example=str(self.names))
- for *box, conf, cls in reversed(pred): # xyxy, confidence, class
- label = f'{self.names[int(cls)]} {conf:.2f}'
- if crop:
- file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
- crops.append({
- 'box': box,
- 'conf': conf,
- 'cls': cls,
- 'label': label,
- 'im': save_one_box(box, im, file=file, save=save)})
- else: # all others
- annotator.box_label(box, label if labels else '', color=colors(cls))
- im = annotator.im
- else:
- s += '(no detections)'
-
- im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
- if pprint:
- print(s.rstrip(', '))
- if show:
- im.show(self.files[i]) # show
- if save:
- f = self.files[i]
- im.save(save_dir / f) # save
- if i == self.n - 1:
- LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
- if render:
- self.imgs[i] = np.asarray(im)
- if crop:
- if save:
- LOGGER.info(f'Saved results to {save_dir}\n')
- return crops
-
- def print(self):
- self.display(pprint=True) # print results
- print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
-
- def show(self, labels=True):
- self.display(show=True, labels=labels) # show results
-
- def save(self, labels=True, save_dir='runs/detect/exp'):
- save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
- self.display(save=True, labels=labels, save_dir=save_dir) # save results
-
- def crop(self, save=True, save_dir='runs/detect/exp'):
- save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
- return self.display(crop=True, save=save, save_dir=save_dir) # crop results
-
- def render(self, labels=True):
- self.display(render=True, labels=labels) # render results
- return self.imgs
-
- def pandas(self):
- # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
- new = copy(self) # return copy
- ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
- cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
- for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
- a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
- setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
- return new
-
- def tolist(self):
- # return a list of Detections objects, i.e. 'for result in results.tolist():'
- r = range(self.n) # iterable
- x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
- # for d in x:
- # for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
- # setattr(d, k, getattr(d, k)[0]) # pop out of list
- return x
-
- def __len__(self):
- return self.n # override len(results)
-
- def __str__(self):
- self.print() # override print(results)
- return ''
-
-
-class Classify(nn.Module):
- # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- c_ = 1280 # efficientnet_b0 size
- self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
- self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1)
- self.drop = nn.Dropout(p=0.0, inplace=True)
- self.linear = nn.Linear(c_, c2) # to x(b,c2)
-
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, 1)
- return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
diff --git a/yolov5-6.2/models/experimental.py b/yolov5-6.2/models/experimental.py
deleted file mode 100644
index cb32d01b..00000000
--- a/yolov5-6.2/models/experimental.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Experimental modules
-"""
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from models.common import Conv
-from utils.downloads import attempt_download
-
-
-class Sum(nn.Module):
- # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
- def __init__(self, n, weight=False): # n: number of inputs
- super().__init__()
- self.weight = weight # apply weights boolean
- self.iter = range(n - 1) # iter object
- if weight:
- self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights
-
- def forward(self, x):
- y = x[0] # no weight
- if self.weight:
- w = torch.sigmoid(self.w) * 2
- for i in self.iter:
- y = y + x[i + 1] * w[i]
- else:
- for i in self.iter:
- y = y + x[i + 1]
- return y
-
-
-class MixConv2d(nn.Module):
- # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
- def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy
- super().__init__()
- n = len(k) # number of convolutions
- if equal_ch: # equal c_ per group
- i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices
- c_ = [(i == g).sum() for g in range(n)] # intermediate channels
- else: # equal weight.numel() per group
- b = [c2] + [0] * n
- a = np.eye(n + 1, n, k=-1)
- a -= np.roll(a, 1, axis=1)
- a *= np.array(k) ** 2
- a[0] = 1
- c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
-
- self.m = nn.ModuleList([
- nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
- self.bn = nn.BatchNorm2d(c2)
- self.act = nn.SiLU()
-
- def forward(self, x):
- return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
-
-
-class Ensemble(nn.ModuleList):
- # Ensemble of models
- def __init__(self):
- super().__init__()
-
- def forward(self, x, augment=False, profile=False, visualize=False):
- y = [module(x, augment, profile, visualize)[0] for module in self]
- # y = torch.stack(y).max(0)[0] # max ensemble
- # y = torch.stack(y).mean(0) # mean ensemble
- y = torch.cat(y, 1) # nms ensemble
- return y, None # inference, train output
-
-
-def attempt_load(weights, device=None, inplace=True, fuse=True):
- # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
- from models.yolo import Detect, Model
-
- model = Ensemble()
- for w in weights if isinstance(weights, list) else [weights]:
- ckpt = torch.load(attempt_download(w), map_location='cpu') # load
- ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
- if not hasattr(ckpt, 'stride'):
- ckpt.stride = torch.tensor([32.]) # compatibility update for ResNet etc.
- model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
-
- # Compatibility updates
- for m in model.modules():
- t = type(m)
- if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
- m.inplace = inplace # torch 1.7.0 compatibility
- if t is Detect and not isinstance(m.anchor_grid, list):
- delattr(m, 'anchor_grid')
- setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
- elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
- m.recompute_scale_factor = None # torch 1.11.0 compatibility
-
- # Return model
- if len(model) == 1:
- return model[-1]
-
- # Return detection ensemble
- print(f'Ensemble created with {weights}\n')
- for k in 'names', 'nc', 'yaml':
- setattr(model, k, getattr(model[0], k))
- model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
- assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
- return model
diff --git a/yolov5-6.2/models/hub/anchors.yaml b/yolov5-6.2/models/hub/anchors.yaml
deleted file mode 100644
index e4d7beb0..00000000
--- a/yolov5-6.2/models/hub/anchors.yaml
+++ /dev/null
@@ -1,59 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Default anchors for COCO data
-
-
-# P5 -------------------------------------------------------------------------------------------------------------------
-# P5-640:
-anchors_p5_640:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-
-# P6 -------------------------------------------------------------------------------------------------------------------
-# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387
-anchors_p6_640:
- - [9,11, 21,19, 17,41] # P3/8
- - [43,32, 39,70, 86,64] # P4/16
- - [65,131, 134,130, 120,265] # P5/32
- - [282,180, 247,354, 512,387] # P6/64
-
-# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792
-anchors_p6_1280:
- - [19,27, 44,40, 38,94] # P3/8
- - [96,68, 86,152, 180,137] # P4/16
- - [140,301, 303,264, 238,542] # P5/32
- - [436,615, 739,380, 925,792] # P6/64
-
-# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187
-anchors_p6_1920:
- - [28,41, 67,59, 57,141] # P3/8
- - [144,103, 129,227, 270,205] # P4/16
- - [209,452, 455,396, 358,812] # P5/32
- - [653,922, 1109,570, 1387,1187] # P6/64
-
-
-# P7 -------------------------------------------------------------------------------------------------------------------
-# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372
-anchors_p7_640:
- - [11,11, 13,30, 29,20] # P3/8
- - [30,46, 61,38, 39,92] # P4/16
- - [78,80, 146,66, 79,163] # P5/32
- - [149,150, 321,143, 157,303] # P6/64
- - [257,402, 359,290, 524,372] # P7/128
-
-# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818
-anchors_p7_1280:
- - [19,22, 54,36, 32,77] # P3/8
- - [70,83, 138,71, 75,173] # P4/16
- - [165,159, 148,334, 375,151] # P5/32
- - [334,317, 251,626, 499,474] # P6/64
- - [750,326, 534,814, 1079,818] # P7/128
-
-# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227
-anchors_p7_1920:
- - [29,34, 81,55, 47,115] # P3/8
- - [105,124, 207,107, 113,259] # P4/16
- - [247,238, 222,500, 563,227] # P5/32
- - [501,476, 376,939, 749,711] # P6/64
- - [1126,489, 801,1222, 1618,1227] # P7/128
diff --git a/yolov5-6.2/models/hub/yolov3-spp.yaml b/yolov5-6.2/models/hub/yolov3-spp.yaml
deleted file mode 100644
index c6698215..00000000
--- a/yolov5-6.2/models/hub/yolov3-spp.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# darknet53 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [32, 3, 1]], # 0
- [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
- [-1, 1, Bottleneck, [64]],
- [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
- [-1, 2, Bottleneck, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
- [-1, 8, Bottleneck, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
- [-1, 8, Bottleneck, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
- [-1, 4, Bottleneck, [1024]], # 10
- ]
-
-# YOLOv3-SPP head
-head:
- [[-1, 1, Bottleneck, [1024, False]],
- [-1, 1, SPP, [512, [5, 9, 13]]],
- [-1, 1, Conv, [1024, 3, 1]],
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
-
- [-2, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P4
- [-1, 1, Bottleneck, [512, False]],
- [-1, 1, Bottleneck, [512, False]],
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
-
- [-2, 1, Conv, [128, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P3
- [-1, 1, Bottleneck, [256, False]],
- [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
-
- [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov3-tiny.yaml b/yolov5-6.2/models/hub/yolov3-tiny.yaml
deleted file mode 100644
index b28b4431..00000000
--- a/yolov5-6.2/models/hub/yolov3-tiny.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,14, 23,27, 37,58] # P4/16
- - [81,82, 135,169, 344,319] # P5/32
-
-# YOLOv3-tiny backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [16, 3, 1]], # 0
- [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
- [-1, 1, Conv, [32, 3, 1]],
- [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
- [-1, 1, Conv, [64, 3, 1]],
- [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
- [-1, 1, Conv, [128, 3, 1]],
- [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
- [-1, 1, Conv, [256, 3, 1]],
- [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
- [-1, 1, Conv, [512, 3, 1]],
- [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
- [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
- ]
-
-# YOLOv3-tiny head
-head:
- [[-1, 1, Conv, [1024, 3, 1]],
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
-
- [-2, 1, Conv, [128, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P4
- [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
-
- [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov3.yaml b/yolov5-6.2/models/hub/yolov3.yaml
deleted file mode 100644
index d1ef9129..00000000
--- a/yolov5-6.2/models/hub/yolov3.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# darknet53 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [32, 3, 1]], # 0
- [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
- [-1, 1, Bottleneck, [64]],
- [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
- [-1, 2, Bottleneck, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
- [-1, 8, Bottleneck, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
- [-1, 8, Bottleneck, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
- [-1, 4, Bottleneck, [1024]], # 10
- ]
-
-# YOLOv3 head
-head:
- [[-1, 1, Bottleneck, [1024, False]],
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, Conv, [1024, 3, 1]],
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
-
- [-2, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P4
- [-1, 1, Bottleneck, [512, False]],
- [-1, 1, Bottleneck, [512, False]],
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
-
- [-2, 1, Conv, [128, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P3
- [-1, 1, Bottleneck, [256, False]],
- [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
-
- [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-bifpn.yaml b/yolov5-6.2/models/hub/yolov5-bifpn.yaml
deleted file mode 100644
index 504815f5..00000000
--- a/yolov5-6.2/models/hub/yolov5-bifpn.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 BiFPN head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-fpn.yaml b/yolov5-6.2/models/hub/yolov5-fpn.yaml
deleted file mode 100644
index a23e9c6f..00000000
--- a/yolov5-6.2/models/hub/yolov5-fpn.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 FPN head
-head:
- [[-1, 3, C3, [1024, False]], # 10 (P5/32-large)
-
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 3, C3, [512, False]], # 14 (P4/16-medium)
-
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 3, C3, [256, False]], # 18 (P3/8-small)
-
- [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-p2.yaml b/yolov5-6.2/models/hub/yolov5-p2.yaml
deleted file mode 100644
index 554117dd..00000000
--- a/yolov5-6.2/models/hub/yolov5-p2.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [128, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 2], 1, Concat, [1]], # cat backbone P2
- [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall)
-
- [-1, 1, Conv, [128, 3, 2]],
- [[-1, 18], 1, Concat, [1]], # cat head P3
- [-1, 3, C3, [256, False]], # 24 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 27 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 30 (P5/32-large)
-
- [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-p34.yaml b/yolov5-6.2/models/hub/yolov5-p34.yaml
deleted file mode 100644
index dbf0f850..00000000
--- a/yolov5-6.2/models/hub/yolov5-p34.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.50 # layer channel multiple
-anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2
- [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
- [ -1, 3, C3, [ 128 ] ],
- [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
- [ -1, 6, C3, [ 256 ] ],
- [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
- [ -1, 9, C3, [ 512 ] ],
- [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
- [ -1, 3, C3, [ 1024 ] ],
- [ -1, 1, SPPF, [ 1024, 5 ] ], # 9
- ]
-
-# YOLOv5 v6.0 head with (P3, P4) outputs
-head:
- [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
- [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
- [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
- [ -1, 3, C3, [ 512, False ] ], # 13
-
- [ -1, 1, Conv, [ 256, 1, 1 ] ],
- [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
- [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
- [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small)
-
- [ -1, 1, Conv, [ 256, 3, 2 ] ],
- [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
- [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium)
-
- [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-p6.yaml b/yolov5-6.2/models/hub/yolov5-p6.yaml
deleted file mode 100644
index a17202f2..00000000
--- a/yolov5-6.2/models/hub/yolov5-p6.yaml
+++ /dev/null
@@ -1,56 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 11
- ]
-
-# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs
-head:
- [[-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 15
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 19
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 23 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 20], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 16], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 29 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 12], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
-
- [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-p7.yaml b/yolov5-6.2/models/hub/yolov5-p7.yaml
deleted file mode 100644
index edd7d13a..00000000
--- a/yolov5-6.2/models/hub/yolov5-p7.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
- [-1, 3, C3, [1280]],
- [-1, 1, SPPF, [1280, 5]], # 13
- ]
-
-# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs
-head:
- [[-1, 1, Conv, [1024, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 10], 1, Concat, [1]], # cat backbone P6
- [-1, 3, C3, [1024, False]], # 17
-
- [-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 21
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 25
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 29 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 26], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 32 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 22], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 35 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 18], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge)
-
- [-1, 1, Conv, [1024, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P7
- [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge)
-
- [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5-panet.yaml b/yolov5-6.2/models/hub/yolov5-panet.yaml
deleted file mode 100644
index ccfbf900..00000000
--- a/yolov5-6.2/models/hub/yolov5-panet.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 PANet head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5l6.yaml b/yolov5-6.2/models/hub/yolov5l6.yaml
deleted file mode 100644
index 632c2cb6..00000000
--- a/yolov5-6.2/models/hub/yolov5l6.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [19,27, 44,40, 38,94] # P3/8
- - [96,68, 86,152, 180,137] # P4/16
- - [140,301, 303,264, 238,542] # P5/32
- - [436,615, 739,380, 925,792] # P6/64
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 11
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 15
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 19
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 23 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 20], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 16], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 29 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 12], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
-
- [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5m6.yaml b/yolov5-6.2/models/hub/yolov5m6.yaml
deleted file mode 100644
index ecc53fd6..00000000
--- a/yolov5-6.2/models/hub/yolov5m6.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.67 # model depth multiple
-width_multiple: 0.75 # layer channel multiple
-anchors:
- - [19,27, 44,40, 38,94] # P3/8
- - [96,68, 86,152, 180,137] # P4/16
- - [140,301, 303,264, 238,542] # P5/32
- - [436,615, 739,380, 925,792] # P6/64
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 11
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 15
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 19
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 23 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 20], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 16], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 29 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 12], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
-
- [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5n6.yaml b/yolov5-6.2/models/hub/yolov5n6.yaml
deleted file mode 100644
index 0c0c71d3..00000000
--- a/yolov5-6.2/models/hub/yolov5n6.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.25 # layer channel multiple
-anchors:
- - [19,27, 44,40, 38,94] # P3/8
- - [96,68, 86,152, 180,137] # P4/16
- - [140,301, 303,264, 238,542] # P5/32
- - [436,615, 739,380, 925,792] # P6/64
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 11
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 15
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 19
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 23 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 20], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 16], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 29 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 12], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
-
- [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5s-ghost.yaml b/yolov5-6.2/models/hub/yolov5s-ghost.yaml
deleted file mode 100644
index ff9519c3..00000000
--- a/yolov5-6.2/models/hub/yolov5s-ghost.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.50 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3Ghost, [128]],
- [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3Ghost, [256]],
- [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3Ghost, [512]],
- [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3Ghost, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, GhostConv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3Ghost, [512, False]], # 13
-
- [-1, 1, GhostConv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, GhostConv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, GhostConv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5s-transformer.yaml b/yolov5-6.2/models/hub/yolov5s-transformer.yaml
deleted file mode 100644
index 100d7c44..00000000
--- a/yolov5-6.2/models/hub/yolov5s-transformer.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.50 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5s6.yaml b/yolov5-6.2/models/hub/yolov5s6.yaml
deleted file mode 100644
index a28fb559..00000000
--- a/yolov5-6.2/models/hub/yolov5s6.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.50 # layer channel multiple
-anchors:
- - [19,27, 44,40, 38,94] # P3/8
- - [96,68, 86,152, 180,137] # P4/16
- - [140,301, 303,264, 238,542] # P5/32
- - [436,615, 739,380, 925,792] # P6/64
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 11
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 15
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 19
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 23 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 20], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 16], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 29 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 12], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
-
- [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
- ]
diff --git a/yolov5-6.2/models/hub/yolov5x6.yaml b/yolov5-6.2/models/hub/yolov5x6.yaml
deleted file mode 100644
index ba795c4a..00000000
--- a/yolov5-6.2/models/hub/yolov5x6.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.33 # model depth multiple
-width_multiple: 1.25 # layer channel multiple
-anchors:
- - [19,27, 44,40, 38,94] # P3/8
- - [96,68, 86,152, 180,137] # P4/16
- - [140,301, 303,264, 238,542] # P5/32
- - [436,615, 739,380, 925,792] # P6/64
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
- [-1, 3, C3, [768]],
- [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 11
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [768, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 8], 1, Concat, [1]], # cat backbone P5
- [-1, 3, C3, [768, False]], # 15
-
- [-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 19
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 23 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 20], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 16], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [768, False]], # 29 (P5/32-large)
-
- [-1, 1, Conv, [768, 3, 2]],
- [[-1, 12], 1, Concat, [1]], # cat head P6
- [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
-
- [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
- ]
diff --git a/yolov5-6.2/models/tf.py b/yolov5-6.2/models/tf.py
deleted file mode 100644
index b0d98cc2..00000000
--- a/yolov5-6.2/models/tf.py
+++ /dev/null
@@ -1,574 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-TensorFlow, Keras and TFLite versions of YOLOv5
-Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
-
-Usage:
- $ python models/tf.py --weights yolov5s.pt
-
-Export:
- $ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
-"""
-
-import argparse
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-# ROOT = ROOT.relative_to(Path.cwd()) # relative
-
-import numpy as np
-import tensorflow as tf
-import torch
-import torch.nn as nn
-from tensorflow import keras
-
-from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv,
- DWConvTranspose2d, Focus, autopad)
-from models.experimental import MixConv2d, attempt_load
-from models.yolo import Detect
-from utils.activations import SiLU
-from utils.general import LOGGER, make_divisible, print_args
-
-
-class TFBN(keras.layers.Layer):
- # TensorFlow BatchNormalization wrapper
- def __init__(self, w=None):
- super().__init__()
- self.bn = keras.layers.BatchNormalization(
- beta_initializer=keras.initializers.Constant(w.bias.numpy()),
- gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
- moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
- moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
- epsilon=w.eps)
-
- def call(self, inputs):
- return self.bn(inputs)
-
-
-class TFPad(keras.layers.Layer):
- # Pad inputs in spatial dimensions 1 and 2
- def __init__(self, pad):
- super().__init__()
- if isinstance(pad, int):
- self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
- else: # tuple/list
- self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]])
-
- def call(self, inputs):
- return tf.pad(inputs, self.pad, mode='constant', constant_values=0)
-
-
-class TFConv(keras.layers.Layer):
- # Standard convolution
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
- # ch_in, ch_out, weights, kernel, stride, padding, groups
- super().__init__()
- assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
- # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
- # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
- conv = keras.layers.Conv2D(
- filters=c2,
- kernel_size=k,
- strides=s,
- padding='SAME' if s == 1 else 'VALID',
- use_bias=not hasattr(w, 'bn'),
- kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
- bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
- self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
- self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
- self.act = activations(w.act) if act else tf.identity
-
- def call(self, inputs):
- return self.act(self.bn(self.conv(inputs)))
-
-
-class TFDWConv(keras.layers.Layer):
- # Depthwise convolution
- def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
- # ch_in, ch_out, weights, kernel, stride, padding, groups
- super().__init__()
- assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels'
- conv = keras.layers.DepthwiseConv2D(
- kernel_size=k,
- depth_multiplier=c2 // c1,
- strides=s,
- padding='SAME' if s == 1 else 'VALID',
- use_bias=not hasattr(w, 'bn'),
- depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
- bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
- self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
- self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
- self.act = activations(w.act) if act else tf.identity
-
- def call(self, inputs):
- return self.act(self.bn(self.conv(inputs)))
-
-
-class TFDWConvTranspose2d(keras.layers.Layer):
- # Depthwise ConvTranspose2d
- def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
- # ch_in, ch_out, weights, kernel, stride, padding, groups
- super().__init__()
- assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels'
- assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1'
- weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy()
- self.c1 = c1
- self.conv = [
- keras.layers.Conv2DTranspose(filters=1,
- kernel_size=k,
- strides=s,
- padding='VALID',
- output_padding=p2,
- use_bias=True,
- kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]),
- bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)]
-
- def call(self, inputs):
- return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1]
-
-
-class TFFocus(keras.layers.Layer):
- # Focus wh information into c-space
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
- # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
-
- def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c)
- # inputs = inputs / 255 # normalize 0-255 to 0-1
- inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]
- return self.conv(tf.concat(inputs, 3))
-
-
-class TFBottleneck(keras.layers.Layer):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
- self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
- self.add = shortcut and c1 == c2
-
- def call(self, inputs):
- return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
-
-
-class TFCrossConv(keras.layers.Layer):
- # Cross Convolution
- def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1)
- self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2)
- self.add = shortcut and c1 == c2
-
- def call(self, inputs):
- return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
-
-
-class TFConv2d(keras.layers.Layer):
- # Substitution for PyTorch nn.Conv2D
- def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
- super().__init__()
- assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
- self.conv = keras.layers.Conv2D(filters=c2,
- kernel_size=k,
- strides=s,
- padding='VALID',
- use_bias=bias,
- kernel_initializer=keras.initializers.Constant(
- w.weight.permute(2, 3, 1, 0).numpy()),
- bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None)
-
- def call(self, inputs):
- return self.conv(inputs)
-
-
-class TFBottleneckCSP(keras.layers.Layer):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
- # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
- self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
- self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
- self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
- self.bn = TFBN(w.bn)
- self.act = lambda x: keras.activations.swish(x)
- self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
-
- def call(self, inputs):
- y1 = self.cv3(self.m(self.cv1(inputs)))
- y2 = self.cv2(inputs)
- return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
-
-
-class TFC3(keras.layers.Layer):
- # CSP Bottleneck with 3 convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
- # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
- self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
- self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
- self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
-
- def call(self, inputs):
- return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
-
-
-class TFC3x(keras.layers.Layer):
- # 3 module with cross-convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
- # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
- self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
- self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
- self.m = keras.Sequential([
- TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)])
-
- def call(self, inputs):
- return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
-
-
-class TFSPP(keras.layers.Layer):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
- def __init__(self, c1, c2, k=(5, 9, 13), w=None):
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
- self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
- self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k]
-
- def call(self, inputs):
- x = self.cv1(inputs)
- return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
-
-
-class TFSPPF(keras.layers.Layer):
- # Spatial pyramid pooling-Fast layer
- def __init__(self, c1, c2, k=5, w=None):
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
- self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
- self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')
-
- def call(self, inputs):
- x = self.cv1(inputs)
- y1 = self.m(x)
- y2 = self.m(y1)
- return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
-
-
-class TFDetect(keras.layers.Layer):
- # TF YOLOv5 Detect layer
- def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer
- super().__init__()
- self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
- self.nc = nc # number of classes
- self.no = nc + 5 # number of outputs per anchor
- self.nl = len(anchors) # number of detection layers
- self.na = len(anchors[0]) // 2 # number of anchors
- self.grid = [tf.zeros(1)] * self.nl # init grid
- self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
- self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2])
- self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
- self.training = False # set to False after building model
- self.imgsz = imgsz
- for i in range(self.nl):
- ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
- self.grid[i] = self._make_grid(nx, ny)
-
- def call(self, inputs):
- z = [] # inference output
- x = []
- for i in range(self.nl):
- x.append(self.m[i](inputs[i]))
- # x(bs,20,20,255) to x(bs,3,20,20,85)
- ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
- x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])
-
- if not self.training: # inference
- y = tf.sigmoid(x[i])
- grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5
- anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4
- xy = (y[..., 0:2] * 2 + grid) * self.stride[i] # xy
- wh = y[..., 2:4] ** 2 * anchor_grid
- # Normalize xywh to 0-1 to reduce calibration error
- xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
- wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
- y = tf.concat([xy, wh, y[..., 4:]], -1)
- z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
-
- return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), x)
-
- @staticmethod
- def _make_grid(nx=20, ny=20):
- # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
- # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
- xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
- return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
-
-
-class TFUpsample(keras.layers.Layer):
- # TF version of torch.nn.Upsample()
- def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w'
- super().__init__()
- assert scale_factor == 2, "scale_factor must be 2"
- self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
- # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
- # with default arguments: align_corners=False, half_pixel_centers=False
- # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
- # size=(x.shape[1] * 2, x.shape[2] * 2))
-
- def call(self, inputs):
- return self.upsample(inputs)
-
-
-class TFConcat(keras.layers.Layer):
- # TF version of torch.concat()
- def __init__(self, dimension=1, w=None):
- super().__init__()
- assert dimension == 1, "convert only NCHW to NHWC concat"
- self.d = 3
-
- def call(self, inputs):
- return tf.concat(inputs, self.d)
-
-
-def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
- LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
- anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
- na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
- no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
-
- layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
- for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
- m_str = m
- m = eval(m) if isinstance(m, str) else m # eval strings
- for j, a in enumerate(args):
- try:
- args[j] = eval(a) if isinstance(a, str) else a # eval strings
- except NameError:
- pass
-
- n = max(round(n * gd), 1) if n > 1 else n # depth gain
- if m in [
- nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv,
- BottleneckCSP, C3, C3x]:
- c1, c2 = ch[f], args[0]
- c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
- args = [c1, c2, *args[1:]]
- if m in [BottleneckCSP, C3, C3x]:
- args.insert(2, n)
- n = 1
- elif m is nn.BatchNorm2d:
- args = [ch[f]]
- elif m is Concat:
- c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
- elif m is Detect:
- args.append([ch[x + 1] for x in f])
- if isinstance(args[1], int): # number of anchors
- args[1] = [list(range(args[1] * 2))] * len(f)
- args.append(imgsz)
- else:
- c2 = ch[f]
-
- tf_m = eval('TF' + m_str.replace('nn.', ''))
- m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
- else tf_m(*args, w=model.model[i]) # module
-
- torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
- t = str(m)[8:-2].replace('__main__.', '') # module type
- np = sum(x.numel() for x in torch_m_.parameters()) # number params
- m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
- LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print
- save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
- layers.append(m_)
- ch.append(c2)
- return keras.Sequential(layers), sorted(save)
-
-
-class TFModel:
- # TF YOLOv5 model
- def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes
- super().__init__()
- if isinstance(cfg, dict):
- self.yaml = cfg # model dict
- else: # is *.yaml
- import yaml # for torch hub
- self.yaml_file = Path(cfg).name
- with open(cfg) as f:
- self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
-
- # Define model
- if nc and nc != self.yaml['nc']:
- LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
- self.yaml['nc'] = nc # override yaml value
- self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
-
- def predict(self,
- inputs,
- tf_nms=False,
- agnostic_nms=False,
- topk_per_class=100,
- topk_all=100,
- iou_thres=0.45,
- conf_thres=0.25):
- y = [] # outputs
- x = inputs
- for m in self.model.layers:
- if m.f != -1: # if not from previous layer
- x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
-
- x = m(x) # run
- y.append(x if m.i in self.savelist else None) # save output
-
- # Add TensorFlow NMS
- if tf_nms:
- boxes = self._xywh2xyxy(x[0][..., :4])
- probs = x[0][:, :, 4:5]
- classes = x[0][:, :, 5:]
- scores = probs * classes
- if agnostic_nms:
- nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
- else:
- boxes = tf.expand_dims(boxes, 2)
- nms = tf.image.combined_non_max_suppression(boxes,
- scores,
- topk_per_class,
- topk_all,
- iou_thres,
- conf_thres,
- clip_boxes=False)
- return nms, x[1]
- return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
- # x = x[0][0] # [x(1,6300,85), ...] to x(6300,85)
- # xywh = x[..., :4] # x(6300,4) boxes
- # conf = x[..., 4:5] # x(6300,1) confidences
- # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes
- # return tf.concat([conf, cls, xywh], 1)
-
- @staticmethod
- def _xywh2xyxy(xywh):
- # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
- return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
-
-
-class AgnosticNMS(keras.layers.Layer):
- # TF Agnostic NMS
- def call(self, input, topk_all, iou_thres, conf_thres):
- # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
- return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres),
- input,
- fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
- name='agnostic_nms')
-
- @staticmethod
- def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS
- boxes, classes, scores = x
- class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
- scores_inp = tf.reduce_max(scores, -1)
- selected_inds = tf.image.non_max_suppression(boxes,
- scores_inp,
- max_output_size=topk_all,
- iou_threshold=iou_thres,
- score_threshold=conf_thres)
- selected_boxes = tf.gather(boxes, selected_inds)
- padded_boxes = tf.pad(selected_boxes,
- paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
- mode="CONSTANT",
- constant_values=0.0)
- selected_scores = tf.gather(scores_inp, selected_inds)
- padded_scores = tf.pad(selected_scores,
- paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
- mode="CONSTANT",
- constant_values=-1.0)
- selected_classes = tf.gather(class_inds, selected_inds)
- padded_classes = tf.pad(selected_classes,
- paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
- mode="CONSTANT",
- constant_values=-1.0)
- valid_detections = tf.shape(selected_inds)[0]
- return padded_boxes, padded_scores, padded_classes, valid_detections
-
-
-def activations(act=nn.SiLU):
- # Returns TF activation from input PyTorch activation
- if isinstance(act, nn.LeakyReLU):
- return lambda x: keras.activations.relu(x, alpha=0.1)
- elif isinstance(act, nn.Hardswish):
- return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
- elif isinstance(act, (nn.SiLU, SiLU)):
- return lambda x: keras.activations.swish(x)
- else:
- raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}')
-
-
-def representative_dataset_gen(dataset, ncalib=100):
- # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
- for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
- im = np.transpose(img, [1, 2, 0])
- im = np.expand_dims(im, axis=0).astype(np.float32)
- im /= 255
- yield [im]
- if n >= ncalib:
- break
-
-
-def run(
- weights=ROOT / 'yolov5s.pt', # weights path
- imgsz=(640, 640), # inference size h,w
- batch_size=1, # batch size
- dynamic=False, # dynamic batch size
-):
- # PyTorch model
- im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
- model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False)
- _ = model(im) # inference
- model.info()
-
- # TensorFlow model
- im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
- tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
- _ = tf_model.predict(im) # inference
-
- # Keras model
- im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
- keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
- keras_model.summary()
-
- LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.')
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
- parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
- parser.add_argument('--batch-size', type=int, default=1, help='batch size')
- parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
- opt = parser.parse_args()
- opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/models/yolo.py b/yolov5-6.2/models/yolo.py
deleted file mode 100644
index df420972..00000000
--- a/yolov5-6.2/models/yolo.py
+++ /dev/null
@@ -1,360 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-YOLO-specific modules
-
-Usage:
- $ python path/to/models/yolo.py --cfg yolov5s.yaml
-"""
-
-import argparse
-import contextlib
-import os
-import platform
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-if platform.system() != 'Windows':
- ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.common import *
-from models.experimental import *
-from utils.autoanchor import check_anchor_order
-from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
-from utils.plots import feature_visualization
-from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
- time_sync)
-
-try:
- import thop # for FLOPs computation
-except ImportError:
- thop = None
-
-
-class Detect(nn.Module):
- stride = None # strides computed during build
- onnx_dynamic = False # ONNX export parameter
- export = False # export mode
-
- def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
- super().__init__()
- self.nc = nc # number of classes
- self.no = nc + 5 # number of outputs per anchor
- self.nl = len(anchors) # number of detection layers
- self.na = len(anchors[0]) // 2 # number of anchors
- self.grid = [torch.zeros(1)] * self.nl # init grid
- self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
- self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
- self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
- self.inplace = inplace # use inplace ops (e.g. slice assignment)
-
- def forward(self, x):
- z = [] # inference output
- for i in range(self.nl):
- x[i] = self.m[i](x[i]) # conv
- bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
- x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
- if not self.training: # inference
- if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
- self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
-
- y = x[i].sigmoid()
- if self.inplace:
- y[..., 0:2] = (y[..., 0:2] * 2 + self.grid[i]) * self.stride[i] # xy
- y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
- else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
- xy, wh, conf = y.split((2, 2, self.nc + 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0
- xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy
- wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
- y = torch.cat((xy, wh, conf), 4)
- z.append(y.view(bs, -1, self.no))
-
- return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
-
- def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
- d = self.anchors[i].device
- t = self.anchors[i].dtype
- shape = 1, self.na, ny, nx, 2 # grid shape
- y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
- if torch_1_10: # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
- yv, xv = torch.meshgrid(y, x, indexing='ij')
- else:
- yv, xv = torch.meshgrid(y, x)
- grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
- anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
- return grid, anchor_grid
-
-
-class BaseModel(nn.Module):
- # YOLOv5 base model
- def forward(self, x, profile=False, visualize=False):
- return self._forward_once(x, profile, visualize) # single-scale inference, train
-
- def _forward_once(self, x, profile=False, visualize=False):
- y, dt = [], [] # outputs
- for m in self.model:
- if m.f != -1: # if not from previous layer
- x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
- if profile:
- self._profile_one_layer(m, x, dt)
- x = m(x) # run
- y.append(x if m.i in self.save else None) # save output
- if visualize:
- feature_visualization(x, m.type, m.i, save_dir=visualize)
- return x
-
- def _profile_one_layer(self, m, x, dt):
- c = m == self.model[-1] # is final layer, copy input as inplace fix
- o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
- t = time_sync()
- for _ in range(10):
- m(x.copy() if c else x)
- dt.append((time_sync() - t) * 100)
- if m == self.model[0]:
- LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
- LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
- if c:
- LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
-
- def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
- LOGGER.info('Fusing layers... ')
- for m in self.model.modules():
- if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
- m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
- delattr(m, 'bn') # remove batchnorm
- m.forward = m.forward_fuse # update forward
- self.info()
- return self
-
- def info(self, verbose=False, img_size=640): # print model information
- model_info(self, verbose, img_size)
-
- def _apply(self, fn):
- # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
- self = super()._apply(fn)
- m = self.model[-1] # Detect()
- if isinstance(m, Detect):
- m.stride = fn(m.stride)
- m.grid = list(map(fn, m.grid))
- if isinstance(m.anchor_grid, list):
- m.anchor_grid = list(map(fn, m.anchor_grid))
- return self
-
-
-class DetectionModel(BaseModel):
- # YOLOv5 detection model
- def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
- super().__init__()
- if isinstance(cfg, dict):
- self.yaml = cfg # model dict
- else: # is *.yaml
- import yaml # for torch hub
- self.yaml_file = Path(cfg).name
- with open(cfg, encoding='ascii', errors='ignore') as f:
- self.yaml = yaml.safe_load(f) # model dict
-
- # Define model
- ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
- if nc and nc != self.yaml['nc']:
- LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
- self.yaml['nc'] = nc # override yaml value
- if anchors:
- LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
- self.yaml['anchors'] = round(anchors) # override yaml value
- self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
- self.names = [str(i) for i in range(self.yaml['nc'])] # default names
- self.inplace = self.yaml.get('inplace', True)
-
- # Build strides, anchors
- m = self.model[-1] # Detect()
- if isinstance(m, Detect):
- s = 256 # 2x min stride
- m.inplace = self.inplace
- m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
- check_anchor_order(m) # must be in pixel-space (not grid-space)
- m.anchors /= m.stride.view(-1, 1, 1)
- self.stride = m.stride
- self._initialize_biases() # only run once
-
- # Init weights, biases
- initialize_weights(self)
- self.info()
- LOGGER.info('')
-
- def forward(self, x, augment=False, profile=False, visualize=False):
- if augment:
- return self._forward_augment(x) # augmented inference, None
- return self._forward_once(x, profile, visualize) # single-scale inference, train
-
- def _forward_augment(self, x):
- img_size = x.shape[-2:] # height, width
- s = [1, 0.83, 0.67] # scales
- f = [None, 3, None] # flips (2-ud, 3-lr)
- y = [] # outputs
- for si, fi in zip(s, f):
- xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
- yi = self._forward_once(xi)[0] # forward
- # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
- yi = self._descale_pred(yi, fi, si, img_size)
- y.append(yi)
- y = self._clip_augmented(y) # clip augmented tails
- return torch.cat(y, 1), None # augmented inference, train
-
- def _descale_pred(self, p, flips, scale, img_size):
- # de-scale predictions following augmented inference (inverse operation)
- if self.inplace:
- p[..., :4] /= scale # de-scale
- if flips == 2:
- p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
- elif flips == 3:
- p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
- else:
- x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
- if flips == 2:
- y = img_size[0] - y # de-flip ud
- elif flips == 3:
- x = img_size[1] - x # de-flip lr
- p = torch.cat((x, y, wh, p[..., 4:]), -1)
- return p
-
- def _clip_augmented(self, y):
- # Clip YOLOv5 augmented inference tails
- nl = self.model[-1].nl # number of detection layers (P3-P5)
- g = sum(4 ** x for x in range(nl)) # grid points
- e = 1 # exclude layer count
- i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices
- y[0] = y[0][:, :-i] # large
- i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
- y[-1] = y[-1][:, i:] # small
- return y
-
- def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
- # https://arxiv.org/abs/1708.02002 section 3.3
- # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
- m = self.model[-1] # Detect() module
- for mi, s in zip(m.m, m.stride): # from
- b = mi.bias.view(m.na, -1).detach() # conv.bias(255) to (3,85)
- b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
- b[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls
- mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-
-Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility
-
-
-class ClassificationModel(BaseModel):
- # YOLOv5 classification model
- def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index
- super().__init__()
- self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
-
- def _from_detection_model(self, model, nc=1000, cutoff=10):
- # Create a YOLOv5 classification model from a YOLOv5 detection model
- if isinstance(model, DetectMultiBackend):
- model = model.model # unwrap DetectMultiBackend
- model.model = model.model[:cutoff] # backbone
- m = model.model[-1] # last layer
- ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module
- c = Classify(ch, nc) # Classify()
- c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type
- model.model[-1] = c # replace
- self.model = model.model
- self.stride = model.stride
- self.save = []
- self.nc = nc
-
- def _from_yaml(self, cfg):
- # Create a YOLOv5 classification model from a *.yaml file
- self.model = None
-
-
-def parse_model(d, ch): # model_dict, input_channels(3)
- LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
- anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
- na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
- no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
-
- layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
- for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
- m = eval(m) if isinstance(m, str) else m # eval strings
- for j, a in enumerate(args):
- with contextlib.suppress(NameError):
- args[j] = eval(a) if isinstance(a, str) else a # eval strings
-
- n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
- if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
- BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
- c1, c2 = ch[f], args[0]
- if c2 != no: # if not output
- c2 = make_divisible(c2 * gw, 8)
-
- args = [c1, c2, *args[1:]]
- if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:
- args.insert(2, n) # number of repeats
- n = 1
- elif m is nn.BatchNorm2d:
- args = [ch[f]]
- elif m is Concat:
- c2 = sum(ch[x] for x in f)
- elif m is Detect:
- args.append([ch[x] for x in f])
- if isinstance(args[1], int): # number of anchors
- args[1] = [list(range(args[1] * 2))] * len(f)
- elif m is Contract:
- c2 = ch[f] * args[0] ** 2
- elif m is Expand:
- c2 = ch[f] // args[0] ** 2
- else:
- c2 = ch[f]
-
- m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
- t = str(m)[8:-2].replace('__main__.', '') # module type
- np = sum(x.numel() for x in m_.parameters()) # number params
- m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
- LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print
- save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
- layers.append(m_)
- if i == 0:
- ch = []
- ch.append(c2)
- return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
- parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--profile', action='store_true', help='profile model speed')
- parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
- parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
- opt = parser.parse_args()
- opt.cfg = check_yaml(opt.cfg) # check YAML
- print_args(vars(opt))
- device = select_device(opt.device)
-
- # Create model
- im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
- model = Model(opt.cfg).to(device)
-
- # Options
- if opt.line_profile: # profile layer by layer
- model(im, profile=True)
-
- elif opt.profile: # profile forward-backward
- results = profile(input=im, ops=[model], n=3)
-
- elif opt.test: # test all models
- for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
- try:
- _ = Model(cfg)
- except Exception as e:
- print(f'Error in {cfg}: {e}')
-
- else: # report fused model summary
- model.fuse()
diff --git a/yolov5-6.2/models/yolov5l.yaml b/yolov5-6.2/models/yolov5l.yaml
deleted file mode 100644
index ce8a5de4..00000000
--- a/yolov5-6.2/models/yolov5l.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.0 # model depth multiple
-width_multiple: 1.0 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/yolov5m.yaml b/yolov5-6.2/models/yolov5m.yaml
deleted file mode 100644
index ad13ab37..00000000
--- a/yolov5-6.2/models/yolov5m.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.67 # model depth multiple
-width_multiple: 0.75 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/yolov5n.yaml b/yolov5-6.2/models/yolov5n.yaml
deleted file mode 100644
index 8a28a40d..00000000
--- a/yolov5-6.2/models/yolov5n.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.25 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/yolov5s.yaml b/yolov5-6.2/models/yolov5s.yaml
deleted file mode 100644
index f35beabb..00000000
--- a/yolov5-6.2/models/yolov5s.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 0.33 # model depth multiple
-width_multiple: 0.50 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/models/yolov5x.yaml b/yolov5-6.2/models/yolov5x.yaml
deleted file mode 100644
index f617a027..00000000
--- a/yolov5-6.2/models/yolov5x.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Parameters
-nc: 80 # number of classes
-depth_multiple: 1.33 # model depth multiple
-width_multiple: 1.25 # layer channel multiple
-anchors:
- - [10,13, 16,30, 33,23] # P3/8
- - [30,61, 62,45, 59,119] # P4/16
- - [116,90, 156,198, 373,326] # P5/32
-
-# YOLOv5 v6.0 backbone
-backbone:
- # [from, number, module, args]
- [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
- [-1, 3, C3, [128]],
- [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
- [-1, 6, C3, [256]],
- [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
- [-1, 9, C3, [512]],
- [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
- [-1, 3, C3, [1024]],
- [-1, 1, SPPF, [1024, 5]], # 9
- ]
-
-# YOLOv5 v6.0 head
-head:
- [[-1, 1, Conv, [512, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 6], 1, Concat, [1]], # cat backbone P4
- [-1, 3, C3, [512, False]], # 13
-
- [-1, 1, Conv, [256, 1, 1]],
- [-1, 1, nn.Upsample, [None, 2, 'nearest']],
- [[-1, 4], 1, Concat, [1]], # cat backbone P3
- [-1, 3, C3, [256, False]], # 17 (P3/8-small)
-
- [-1, 1, Conv, [256, 3, 2]],
- [[-1, 14], 1, Concat, [1]], # cat head P4
- [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
-
- [-1, 1, Conv, [512, 3, 2]],
- [[-1, 10], 1, Concat, [1]], # cat head P5
- [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
-
- [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
- ]
diff --git a/yolov5-6.2/requirements.txt b/yolov5-6.2/requirements.txt
deleted file mode 100644
index 10620566..00000000
--- a/yolov5-6.2/requirements.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# YOLOv5 requirements
-# Usage: pip install -r requirements.txt
-
-# Base ----------------------------------------
-matplotlib>=3.2.2
-numpy>=1.18.5
-opencv-python>=4.1.1
-Pillow>=7.1.2
-PyYAML>=5.3.1
-requests>=2.23.0
-scipy>=1.4.1
-torch>=1.7.0
-torchvision>=0.8.1
-tqdm>=4.64.0
-protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012
-
-# Logging -------------------------------------
-tensorboard>=2.4.1
-# wandb
-# clearml
-
-# Plotting ------------------------------------
-pandas>=1.1.4
-seaborn>=0.11.0
-
-# Export --------------------------------------
-# coremltools>=5.2 # CoreML export
-# onnx>=1.9.0 # ONNX export
-# onnx-simplifier>=0.4.1 # ONNX simplifier
-# nvidia-pyindex # TensorRT export
-# nvidia-tensorrt # TensorRT export
-# scikit-learn==0.19.2 # CoreML quantization
-# tensorflow>=2.4.1 # TFLite export (or tensorflow-cpu, tensorflow-aarch64)
-# tensorflowjs>=3.9.0 # TF.js export
-# openvino-dev # OpenVINO export
-
-# Extras --------------------------------------
-ipython # interactive notebook
-psutil # system utilization
-thop>=0.1.1 # FLOPs computation
-# albumentations>=1.0.3
-# pycocotools>=2.0 # COCO mAP
-# roboflow
diff --git a/yolov5-6.2/setup.cfg b/yolov5-6.2/setup.cfg
deleted file mode 100644
index 020a7574..00000000
--- a/yolov5-6.2/setup.cfg
+++ /dev/null
@@ -1,59 +0,0 @@
-# Project-wide configuration file, can be used for package metadata and other toll configurations
-# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
-# Local usage: pip install pre-commit, pre-commit run --all-files
-
-[metadata]
-license_file = LICENSE
-description_file = README.md
-
-
-[tool:pytest]
-norecursedirs =
- .git
- dist
- build
-addopts =
- --doctest-modules
- --durations=25
- --color=yes
-
-
-[flake8]
-max-line-length = 120
-exclude = .tox,*.egg,build,temp
-select = E,W,F
-doctests = True
-verbose = 2
-# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
-format = pylint
-# see: https://www.flake8rules.com/
-ignore =
- E731 # Do not assign a lambda expression, use a def
- F405 # name may be undefined, or defined from star imports: module
- E402 # module level import not at top of file
- F401 # module imported but unused
- W504 # line break after binary operator
- E127 # continuation line over-indented for visual indent
- W504 # line break after binary operator
- E231 # missing whitespace after ‘,’, ‘;’, or ‘:’
- E501 # line too long
- F403 # ‘from module import *’ used; unable to detect undefined names
-
-
-[isort]
-# https://pycqa.github.io/isort/docs/configuration/options.html
-line_length = 120
-# see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html
-multi_line_output = 0
-
-
-[yapf]
-based_on_style = pep8
-spaces_before_comment = 2
-COLUMN_LIMIT = 120
-COALESCE_BRACKETS = True
-SPACES_AROUND_POWER_OPERATOR = True
-SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
-SPLIT_BEFORE_CLOSING_BRACKET = False
-SPLIT_BEFORE_FIRST_ARGUMENT = False
-# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False
diff --git a/yolov5-6.2/test.py b/yolov5-6.2/test.py
deleted file mode 100644
index d0be671a..00000000
--- a/yolov5-6.2/test.py
+++ /dev/null
@@ -1,300 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run inference on images, videos, directories, streams, etc.
-
-Usage - sources:
- $ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
- img.jpg # image
- vid.mp4 # video
- path/ # directory
- path/*.jpg # glob
- 'https://youtu.be/Zgi9g1ksQHc' # YouTube
- 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
-
-Usage - formats:
- $ python path/to/detect.py --weights yolov5s.pt # PyTorch
- yolov5s.torchscript # TorchScript
- yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s.xml # OpenVINO
- yolov5s.engine # TensorRT
- yolov5s.mlmodel # CoreML (macOS-only)
- yolov5s_saved_model # TensorFlow SavedModel
- yolov5s.pb # TensorFlow GraphDef
- yolov5s.tflite # TensorFlow Lite
- yolov5s_edgetpu.tflite # TensorFlow Edge TPU
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-import torch.backends.cudnn as cudnn
-
-import json
-import socket
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
-from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
- increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, smart_inference_mode, time_sync
-
-UDP_IP = '192.168.43.58' # change to desired IP address
-UDP_PORT = 1900 # change to desired port number
-sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-sock.bind((UDP_IP, UDP_PORT))
-sock.listen(2)
-clientsocket, addr = sock.accept()
-
-test = 0
-x_pos = 0.0
-y_pos = 0.0
-
-@smart_inference_mode()
-def run(
- weights=ROOT / 'yolov5s.pt', # model.pt path(s)
- source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
- data=ROOT / 'data/coco128.yaml', # dataset.yaml path
- imgsz=(640, 640), # inference size (height, width)
- conf_thres=0.25, # confidence threshold
- iou_thres=0.45, # NMS IOU threshold
- max_det=1000, # maximum detections per image
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- view_img=False, # show results
- save_txt=False, # save results to *.txt
- save_conf=False, # save confidences in --save-txt labels
- save_crop=False, # save cropped prediction boxes
- nosave=False, # do not save images/videos
- classes=None, # filter by class: --class 0, or --class 0 2 3
- agnostic_nms=False, # class-agnostic NMS
- augment=False, # augmented inference
- visualize=False, # visualize features
- update=False, # update all models
- project=ROOT / 'runs/detect', # save results to project/name
- name='exp', # save results to project/name
- exist_ok=False, # existing project/name ok, do not increment
- line_thickness=3, # bounding box thickness (pixels)
- hide_labels=False, # hide labels
- hide_conf=False, # hide confidences
- half=False, # use FP16 half-precision inference
- dnn=False, # use OpenCV DNN for ONNX inference
-):
- source = str(source)
- save_img = not nosave and not source.endswith('.txt') # save inference images
- is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
- is_url = source.lower().startswith(('rtsp://'))#, 'rtmp://', 'http://', 'https://'))
- webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
- if is_url and is_file:
- source = check_file(source) # download
-
- # Directories
- save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- device = select_device(device)
- model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
- stride, names, pt = model.stride, model.names, model.pt
- imgsz = check_img_size(imgsz, s=stride) # check image size
-
- # Dataloader
- if webcam:
- view_img = check_imshow()
- cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
- bs = len(dataset) # batch_size
- else:
- dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
- bs = 1 # batch_size
- vid_path, vid_writer = [None] * bs, [None] * bs
-
- # Run inference
- model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
- seen, windows, dt = 0, [], [0.0, 0.0, 0.0]
- for path, im, im0s, vid_cap, s in dataset:
- t1 = time_sync()
- im = torch.from_numpy(im).to(device)
- im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
- im /= 255 # 0 - 255 to 0.0 - 1.0
- if len(im.shape) == 3:
- im = im[None] # expand for batch dim
- t2 = time_sync()
- dt[0] += t2 - t1
-
- # Inference
- visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
- pred = model(im, augment=augment, visualize=visualize)
- t3 = time_sync()
- dt[1] += t3 - t2
-
- # NMS
- pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
- dt[2] += time_sync() - t3
-
- # Second-stage classifier (optional)
- # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
- # Process predictions
- for i, det in enumerate(pred): # per image
- seen += 1
- if webcam: # batch_size >= 1
- p, im0, frame = path[i], im0s[i].copy(), dataset.count
- s += f'{i}: '
- else:
- p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
-
- p = Path(p) # to Path
- save_path = str(save_dir / p.name) # im.jpg
-
- img_location = os.path.abspath(p)
-
- txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
- s += '%gx%g ' % im.shape[2:] # print string
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- imc = im0.copy() if save_crop else im0 # for save_crop
- annotator = Annotator(im0, line_width=line_thickness, example=str(names))
- if len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
-
- # Print results
- for c in det[:, -1].unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
-
- # Write results
- for *xyxy, conf, cls in reversed(det):
- if save_txt: # Write to file
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(f'{txt_path}.txt', 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- if save_img or save_crop or view_img: # Add bbox to image
- c = int(cls) # integer class
- label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
- annotator.box_label(xyxy, label, color=colors(c, True))
-
- if save_crop:
- save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
-
- # Stream results
- im0 = annotator.result()
- if view_img:
- if platform.system() == 'Linux' and p not in windows:
- windows.append(p)
- cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
- cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
- cv2.imshow(str(p), im0)
- cv2.waitKey(1) # 1 millisecond
-
- # Save results (image with detections)
- if save_img:
- if dataset.mode == 'image':
- cv2.imwrite(save_path, im0)
- else: # 'video' or 'stream'
- if vid_path[i] != save_path: # new video
- vid_path[i] = save_path
- if isinstance(vid_writer[i], cv2.VideoWriter):
- vid_writer[i].release() # release previous video writer
- if vid_cap: # video
- fps = vid_cap.get(cv2.CAP_PROP_FPS)
- w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- else: # stream
- fps, w, h = 30, im0.shape[1], im0.shape[0]
- save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
- vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
- vid_writer[i].write(im0)
-
- # Print time (inference-only)
- LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
-
- #Encode targets as JSON string and send via UDP socket
- targets = []
- for *xyxy, conf, cls in reversed(det):
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- targets.append({'class': names[int(cls)], 'conf': float(conf), 'bbox': [float(val) for val in xywh]})
- json_str = json.dumps({'location': img_location, 'targets': targets, 'x_pos':x_pos, 'y_pos':y_pos})
-
- clientsocket.send(json_str.encode('utf-8')) # change DEST_IP and DEST_PORT to desired values
- print(json_str)
- client()
-
- # Print results
- t = tuple(x / seen * 1E3 for x in dt) # speeds per image
- LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
- if save_txt or save_img:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
- if update:
- strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
-
-def client():
- global test
- global x_pos
- global y_pos
- if test == 0:
- msg = clientsocket.recv(4096)
- msg = msg.decode('utf-8')
- recvmsg = json.loads(msg)
- print(recvmsg)
- x_pos = recvmsg['x_pos']
- y_pos = recvmsg['y_pos']
- if (x_pos != 0.0 and y_pos != 0.0):
- test = 1
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
- parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
- parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
- parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
- parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--view-img', action='store_true', help='show results')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
- parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
- parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
- parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--visualize', action='store_true', help='visualize features')
- parser.add_argument('--update', action='store_true', help='update all models')
- parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
- parser.add_argument('--name', default='exp', help='save results to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-
- parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
- parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
- parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
-
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
- opt = parser.parse_args()
- opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- check_requirements(exclude=('tensorboard', 'thop'))
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/test/test.py b/yolov5-6.2/test/test.py
deleted file mode 100644
index 5edda545..00000000
--- a/yolov5-6.2/test/test.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import cv2
-url = "rtsp://192.168.144.108:8000/165506"
-cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)
-cap.set(cv2.CAP_PROP_FFMPEG_PARAM,'-strict -2')
-ret, frame = cap.read()
-while ret:
- ret, frame = cap.read()
- cv2.imshow("frame",frame)
- if cv2.waitKey(1) & 0xFF == ord('q'):
- break
-cv2.destroyAllWindows()
-cap.release()
\ No newline at end of file
diff --git a/yolov5-6.2/train.py b/yolov5-6.2/train.py
deleted file mode 100644
index bbb26cde..00000000
--- a/yolov5-6.2/train.py
+++ /dev/null
@@ -1,632 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Train a YOLOv5 model on a custom dataset.
-
-Models and datasets download automatically from the latest YOLOv5 release.
-Models: https://github.com/ultralytics/yolov5/tree/master/models
-Datasets: https://github.com/ultralytics/yolov5/tree/master/data
-Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
-
-Usage:
- $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (RECOMMENDED)
- $ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
-"""
-
-import argparse
-import math
-import os
-import random
-import sys
-import time
-from copy import deepcopy
-from datetime import datetime
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import yaml
-from torch.optim import lr_scheduler
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-import val # for end-of-epoch mAP
-from models.experimental import attempt_load
-from models.yolo import Model
-from utils.autoanchor import check_anchors
-from utils.autobatch import check_train_batch_size
-from utils.callbacks import Callbacks
-from utils.dataloaders import create_dataloader
-from utils.downloads import attempt_download, is_url
-from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
- check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
- init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
- one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
-from utils.loggers import Loggers
-from utils.loggers.wandb.wandb_utils import check_wandb_resume
-from utils.loss import ComputeLoss
-from utils.metrics import fitness
-from utils.plots import plot_evolve, plot_labels
-from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
- smart_resume, torch_distributed_zero_first)
-
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-
-
-def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary
- save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
- Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
- opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
- callbacks.run('on_pretrain_routine_start')
-
- # Directories
- w = save_dir / 'weights' # weights dir
- (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
- last, best = w / 'last.pt', w / 'best.pt'
-
- # Hyperparameters
- if isinstance(hyp, str):
- with open(hyp, errors='ignore') as f:
- hyp = yaml.safe_load(f) # load hyps dict
- LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
- opt.hyp = hyp.copy() # for saving hyps to checkpoints
-
- # Save run settings
- if not evolve:
- yaml_save(save_dir / 'hyp.yaml', hyp)
- yaml_save(save_dir / 'opt.yaml', vars(opt))
-
- # Loggers
- data_dict = None
- if RANK in {-1, 0}:
- loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
- if loggers.clearml:
- data_dict = loggers.clearml.data_dict # None if no ClearML dataset or filled in by ClearML
- if loggers.wandb:
- data_dict = loggers.wandb.data_dict
- if resume:
- weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
-
- # Register actions
- for k in methods(loggers):
- callbacks.register_action(k, callback=getattr(loggers, k))
-
- # Config
- plots = not evolve and not opt.noplots # create plots
- cuda = device.type != 'cpu'
- init_seeds(opt.seed + 1 + RANK, deterministic=True)
- with torch_distributed_zero_first(LOCAL_RANK):
- data_dict = data_dict or check_dataset(data) # check if None
- train_path, val_path = data_dict['train'], data_dict['val']
- nc = 1 if single_cls else int(data_dict['nc']) # number of classes
- names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
- assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
- is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset
-
- # Model
- check_suffix(weights, '.pt') # check weights
- pretrained = weights.endswith('.pt')
- if pretrained:
- with torch_distributed_zero_first(LOCAL_RANK):
- weights = attempt_download(weights) # download if not found locally
- ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
- model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
- exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
- csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
- csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
- model.load_state_dict(csd, strict=False) # load
- LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
- else:
- model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
- amp = check_amp(model) # check AMP
-
- # Freeze
- freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
- for k, v in model.named_parameters():
- v.requires_grad = True # train all layers
- # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
- if any(x in k for x in freeze):
- LOGGER.info(f'freezing {k}')
- v.requires_grad = False
-
- # Image size
- gs = max(int(model.stride.max()), 32) # grid size (max stride)
- imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
-
- # Batch size
- if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
- batch_size = check_train_batch_size(model, imgsz, amp)
- loggers.on_params_update({"batch_size": batch_size})
-
- # Optimizer
- nbs = 64 # nominal batch size
- accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
- hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
- optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
-
- # Scheduler
- if opt.cos_lr:
- lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
- else:
- lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
- scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
-
- # EMA
- ema = ModelEMA(model) if RANK in {-1, 0} else None
-
- # Resume
- best_fitness, start_epoch = 0.0, 0
- if pretrained:
- if resume:
- best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
- del ckpt, csd
-
- # DP mode
- if cuda and RANK == -1 and torch.cuda.device_count() > 1:
- LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
- 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
- model = torch.nn.DataParallel(model)
-
- # SyncBatchNorm
- if opt.sync_bn and cuda and RANK != -1:
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
- LOGGER.info('Using SyncBatchNorm()')
-
- # Trainloader
- train_loader, dataset = create_dataloader(train_path,
- imgsz,
- batch_size // WORLD_SIZE,
- gs,
- single_cls,
- hyp=hyp,
- augment=True,
- cache=None if opt.cache == 'val' else opt.cache,
- rect=opt.rect,
- rank=LOCAL_RANK,
- workers=workers,
- image_weights=opt.image_weights,
- quad=opt.quad,
- prefix=colorstr('train: '),
- shuffle=True)
- labels = np.concatenate(dataset.labels, 0)
- mlc = int(labels[:, 0].max()) # max label class
- assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
-
- # Process 0
- if RANK in {-1, 0}:
- val_loader = create_dataloader(val_path,
- imgsz,
- batch_size // WORLD_SIZE * 2,
- gs,
- single_cls,
- hyp=hyp,
- cache=None if noval else opt.cache,
- rect=True,
- rank=-1,
- workers=workers * 2,
- pad=0.5,
- prefix=colorstr('val: '))[0]
-
- if not resume:
- if plots:
- plot_labels(labels, names, save_dir)
-
- # Anchors
- if not opt.noautoanchor:
- check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
- model.half().float() # pre-reduce anchor precision
-
- callbacks.run('on_pretrain_routine_end')
-
- # DDP mode
- if cuda and RANK != -1:
- model = smart_DDP(model)
-
- # Model attributes
- nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
- hyp['box'] *= 3 / nl # scale to layers
- hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
- hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
- hyp['label_smoothing'] = opt.label_smoothing
- model.nc = nc # attach number of classes to model
- model.hyp = hyp # attach hyperparameters to model
- model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
- model.names = names
-
- # Start training
- t0 = time.time()
- nb = len(train_loader) # number of batches
- nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
- # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
- last_opt_step = -1
- maps = np.zeros(nc) # mAP per class
- results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
- scheduler.last_epoch = start_epoch - 1 # do not move
- scaler = torch.cuda.amp.GradScaler(enabled=amp)
- stopper, stop = EarlyStopping(patience=opt.patience), False
- compute_loss = ComputeLoss(model) # init loss class
- callbacks.run('on_train_start')
- LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
- f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
- f"Logging results to {colorstr('bold', save_dir)}\n"
- f'Starting training for {epochs} epochs...')
- for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
- callbacks.run('on_train_epoch_start')
- model.train()
-
- # Update image weights (optional, single-GPU only)
- if opt.image_weights:
- cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
- iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
- dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
-
- # Update mosaic border (optional)
- # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
- # dataset.mosaic_border = [b - imgsz, -b] # height, width borders
-
- mloss = torch.zeros(3, device=device) # mean losses
- if RANK != -1:
- train_loader.sampler.set_epoch(epoch)
- pbar = enumerate(train_loader)
- LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
- if RANK in {-1, 0}:
- pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
- optimizer.zero_grad()
- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
- callbacks.run('on_train_batch_start')
- ni = i + nb * epoch # number integrated batches (since train start)
- imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
-
- # Warmup
- if ni <= nw:
- xi = [0, nw] # x interp
- # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
- accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
- for j, x in enumerate(optimizer.param_groups):
- # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
- x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)])
- if 'momentum' in x:
- x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
-
- # Multi-scale
- if opt.multi_scale:
- sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
- sf = sz / max(imgs.shape[2:]) # scale factor
- if sf != 1:
- ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
- imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
-
- # Forward
- with torch.cuda.amp.autocast(amp):
- pred = model(imgs) # forward
- loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
- if RANK != -1:
- loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
- if opt.quad:
- loss *= 4.
-
- # Backward
- scaler.scale(loss).backward()
-
- # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
- if ni - last_opt_step >= accumulate:
- scaler.unscale_(optimizer) # unscale gradients
- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
- scaler.step(optimizer) # optimizer.step
- scaler.update()
- optimizer.zero_grad()
- if ema:
- ema.update(model)
- last_opt_step = ni
-
- # Log
- if RANK in {-1, 0}:
- mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
- mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
- pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
- (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
- callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots)
- if callbacks.stop_training:
- return
- # end batch ------------------------------------------------------------------------------------------------
-
- # Scheduler
- lr = [x['lr'] for x in optimizer.param_groups] # for loggers
- scheduler.step()
-
- if RANK in {-1, 0}:
- # mAP
- callbacks.run('on_train_epoch_end', epoch=epoch)
- ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
- final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
- if not noval or final_epoch: # Calculate mAP
- results, maps, _ = val.run(data_dict,
- batch_size=batch_size // WORLD_SIZE * 2,
- imgsz=imgsz,
- half=amp,
- model=ema.ema,
- single_cls=single_cls,
- dataloader=val_loader,
- save_dir=save_dir,
- plots=False,
- callbacks=callbacks,
- compute_loss=compute_loss)
-
- # Update best mAP
- fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
- stop = stopper(epoch=epoch, fitness=fi) # early stop check
- if fi > best_fitness:
- best_fitness = fi
- log_vals = list(mloss) + list(results) + lr
- callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
-
- # Save model
- if (not nosave) or (final_epoch and not evolve): # if save
- ckpt = {
- 'epoch': epoch,
- 'best_fitness': best_fitness,
- 'model': deepcopy(de_parallel(model)).half(),
- 'ema': deepcopy(ema.ema).half(),
- 'updates': ema.updates,
- 'optimizer': optimizer.state_dict(),
- 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
- 'opt': vars(opt),
- 'date': datetime.now().isoformat()}
-
- # Save last, best and delete
- torch.save(ckpt, last)
- if best_fitness == fi:
- torch.save(ckpt, best)
- if opt.save_period > 0 and epoch % opt.save_period == 0:
- torch.save(ckpt, w / f'epoch{epoch}.pt')
- del ckpt
- callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
-
- # EarlyStopping
- if RANK != -1: # if DDP training
- broadcast_list = [stop if RANK == 0 else None]
- dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
- if RANK != 0:
- stop = broadcast_list[0]
- if stop:
- break # must break all DDP ranks
-
- # end epoch ----------------------------------------------------------------------------------------------------
- # end training -----------------------------------------------------------------------------------------------------
- if RANK in {-1, 0}:
- LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
- for f in last, best:
- if f.exists():
- strip_optimizer(f) # strip optimizers
- if f is best:
- LOGGER.info(f'\nValidating {f}...')
- results, _, _ = val.run(
- data_dict,
- batch_size=batch_size // WORLD_SIZE * 2,
- imgsz=imgsz,
- model=attempt_load(f, device).half(),
- iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65
- single_cls=single_cls,
- dataloader=val_loader,
- save_dir=save_dir,
- save_json=is_coco,
- verbose=True,
- plots=plots,
- callbacks=callbacks,
- compute_loss=compute_loss) # val best model with plots
- if is_coco:
- callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
-
- callbacks.run('on_train_end', last, best, plots, epoch, results)
-
- torch.cuda.empty_cache()
- return results
-
-
-def parse_opt(known=False):
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
- parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
- parser.add_argument('--epochs', type=int, default=300)
- parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
- parser.add_argument('--rect', action='store_true', help='rectangular training')
- parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
- parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
- parser.add_argument('--noval', action='store_true', help='only validate final epoch')
- parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
- parser.add_argument('--noplots', action='store_true', help='save no plot files')
- parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
- parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
- parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
- parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
- parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
- parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
- parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
- parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
- parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--quad', action='store_true', help='quad dataloader')
- parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
- parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
- parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
- parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
- parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
- parser.add_argument('--seed', type=int, default=0, help='Global training seed')
- parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
-
- # Weights & Biases arguments
- parser.add_argument('--entity', default=None, help='W&B: Entity')
- parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
- parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
- parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
-
- return parser.parse_known_args()[0] if known else parser.parse_args()
-
-
-def main(opt, callbacks=Callbacks()):
- # Checks
- if RANK in {-1, 0}:
- print_args(vars(opt))
- check_git_status()
- check_requirements()
-
- # Resume
- if opt.resume and not (check_wandb_resume(opt) or opt.evolve): # resume from specified or most recent last.pt
- last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
- opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml
- opt_data = opt.data # original dataset
- if opt_yaml.is_file():
- with open(opt_yaml, errors='ignore') as f:
- d = yaml.safe_load(f)
- else:
- d = torch.load(last, map_location='cpu')['opt']
- opt = argparse.Namespace(**d) # replace
- opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate
- if is_url(opt_data):
- opt.data = check_file(opt_data) # avoid HUB resume auth timeout
- else:
- opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
- check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
- assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
- if opt.evolve:
- if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve
- opt.project = str(ROOT / 'runs/evolve')
- opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
- if opt.name == 'cfg':
- opt.name = Path(opt.cfg).stem # use model.yaml as name
- opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
-
- # DDP mode
- device = select_device(opt.device, batch_size=opt.batch_size)
- if LOCAL_RANK != -1:
- msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
- assert not opt.image_weights, f'--image-weights {msg}'
- assert not opt.evolve, f'--evolve {msg}'
- assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
- assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
- assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
- torch.cuda.set_device(LOCAL_RANK)
- device = torch.device('cuda', LOCAL_RANK)
- dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
-
- # Train
- if not opt.evolve:
- train(opt.hyp, opt, device, callbacks)
-
- # Evolve hyperparameters (optional)
- else:
- # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
- meta = {
- 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
- 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
- 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
- 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
- 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
- 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
- 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
- 'box': (1, 0.02, 0.2), # box loss gain
- 'cls': (1, 0.2, 4.0), # cls loss gain
- 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
- 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
- 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
- 'iou_t': (0, 0.1, 0.7), # IoU training threshold
- 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
- 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
- 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
- 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
- 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
- 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
- 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
- 'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
- 'scale': (1, 0.0, 0.9), # image scale (+/- gain)
- 'shear': (1, 0.0, 10.0), # image shear (+/- deg)
- 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
- 'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
- 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
- 'mosaic': (1, 0.0, 1.0), # image mixup (probability)
- 'mixup': (1, 0.0, 1.0), # image mixup (probability)
- 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
-
- with open(opt.hyp, errors='ignore') as f:
- hyp = yaml.safe_load(f) # load hyps dict
- if 'anchors' not in hyp: # anchors commented in hyp.yaml
- hyp['anchors'] = 3
- if opt.noautoanchor:
- del hyp['anchors'], meta['anchors']
- opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
- # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
- evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
- if opt.bucket:
- os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists
-
- for _ in range(opt.evolve): # generations to evolve
- if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
- # Select parent(s)
- parent = 'single' # parent selection method: 'single' or 'weighted'
- x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
- n = min(5, len(x)) # number of previous results to consider
- x = x[np.argsort(-fitness(x))][:n] # top n mutations
- w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
- if parent == 'single' or len(x) == 1:
- # x = x[random.randint(0, n - 1)] # random selection
- x = x[random.choices(range(n), weights=w)[0]] # weighted selection
- elif parent == 'weighted':
- x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
-
- # Mutate
- mp, s = 0.8, 0.2 # mutation probability, sigma
- npr = np.random
- npr.seed(int(time.time()))
- g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
- ng = len(meta)
- v = np.ones(ng)
- while all(v == 1): # mutate until a change occurs (prevent duplicates)
- v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
- for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
- hyp[k] = float(x[i + 7] * v[i]) # mutate
-
- # Constrain to limits
- for k, v in meta.items():
- hyp[k] = max(hyp[k], v[1]) # lower limit
- hyp[k] = min(hyp[k], v[2]) # upper limit
- hyp[k] = round(hyp[k], 5) # significant digits
-
- # Train mutation
- results = train(hyp.copy(), opt, device, callbacks)
- callbacks = Callbacks()
- # Write mutation results
- print_mutation(results, hyp.copy(), save_dir, opt.bucket)
-
- # Plot results
- plot_evolve(evolve_csv)
- LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
- f"Results saved to {colorstr('bold', save_dir)}\n"
- f'Usage example: $ python train.py --hyp {evolve_yaml}')
-
-
-def run(**kwargs):
- # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
- opt = parse_opt(True)
- for k, v in kwargs.items():
- setattr(opt, k, v)
- main(opt)
- return opt
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/tutorial.ipynb b/yolov5-6.2/tutorial.ipynb
deleted file mode 100644
index 61641bab..00000000
--- a/yolov5-6.2/tutorial.ipynb
+++ /dev/null
@@ -1,1141 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "YOLOv5 Tutorial",
- "provenance": [],
- "collapsed_sections": [],
- "machine_shape": "hm",
- "toc_visible": true,
- "include_colab_link": true
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "accelerator": "GPU",
- "widgets": {
- "application/vnd.jupyter.widget-state+json": {
- "c31d2039ccf74c22b67841f4877d1186": {
- "model_module": "@jupyter-widgets/controls",
- "model_name": "HBoxModel",
- "model_module_version": "1.5.0",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
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- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "t6MPjfT5NrKQ"
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- "source": [
- "<a align=\"left\" href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
- "<img width=\"1024\", src=\"https://user-images.githubusercontent.com/26833433/125273437-35b3fc00-e30d-11eb-9079-46f313325424.png\"></a>\n",
- "\n",
- "This is the **official YOLOv5 🚀 notebook** by **Ultralytics**, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n",
- "For more information please visit https://github.com/ultralytics/yolov5 and https://ultralytics.com. Thank you!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7mGmQbAO5pQb"
- },
- "source": [
- "# Setup\n",
- "\n",
- "Clone repo, install dependencies and check PyTorch and GPU."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "wbvMlHd_QwMG",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "185d0979-edcd-4860-e6fb-b8a27dbf5096"
- },
- "source": [
- "!git clone https://github.com/ultralytics/yolov5 # clone\n",
- "%cd yolov5\n",
- "%pip install -qr requirements.txt # install\n",
- "\n",
- "import torch\n",
- "import utils\n",
- "display = utils.notebook_init() # checks"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "YOLOv5 🚀 v6.1-370-g20f1b7e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Setup complete ✅ (8 CPUs, 51.0 GB RAM, 37.4/166.8 GB disk)\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4JnkELT0cIJg"
- },
- "source": [
- "# 1. Inference\n",
- "\n",
- "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",
- "\n",
- "```shell\n",
- "python detect.py --source 0 # webcam\n",
- " img.jpg # image \n",
- " vid.mp4 # video\n",
- " path/ # directory\n",
- " 'path/*.jpg' # glob\n",
- " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n",
- " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "zR9ZbuQCH7FX",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "4b13989f-32a4-4ef0-b403-06ff3aac255c"
- },
- "source": [
- "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
- "#display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
- "YOLOv5 🚀 v6.1-370-g20f1b7e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
- "\n",
- "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt to yolov5s.pt...\n",
- "100% 14.1M/14.1M [00:00<00:00, 53.9MB/s]\n",
- "\n",
- "Fusing layers... \n",
- "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
- "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.016s)\n",
- "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.021s)\n",
- "Speed: 0.6ms pre-process, 18.6ms inference, 25.0ms NMS per image at shape (1, 3, 640, 640)\n",
- "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hkAzDWJ7cWTr"
- },
- "source": [
- " \n",
- "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0eq1SMWl6Sfn"
- },
- "source": [
- "# 2. Validate\n",
- "Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "eyTZYGgRjnMc"
- },
- "source": [
- "## COCO val\n",
- "Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."
- ]
- },
- {
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- "colab": {
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- "height": 49,
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- "outputId": "a9004b06-37a6-41ed-a1f2-ac956f3963b3"
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- "source": [
- "# Download COCO val\n",
- "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
- "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
- ],
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- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- " 0%| | 0.00/780M [00:00<?, ?B/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
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- },
- "outputId": "c0f29758-4ec8-4def-893d-0efd6ed5b7f4"
- },
- "source": [
- "# Run YOLOv5x on COCO val\n",
- "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
- "YOLOv5 🚀 v6.1-370-g20f1b7e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
- "\n",
- "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt to yolov5x.pt...\n",
- "100% 166M/166M [00:35<00:00, 4.97MB/s]\n",
- "\n",
- "Fusing layers... \n",
- "YOLOv5x summary: 444 layers, 86705005 parameters, 0 gradients\n",
- "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
- "100% 755k/755k [00:00<00:00, 49.4MB/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 10716.86it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
- " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:08<00:00, 2.28it/s]\n",
- " all 5000 36335 0.743 0.625 0.683 0.504\n",
- "Speed: 0.1ms pre-process, 4.6ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
- "\n",
- "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
- "loading annotations into memory...\n",
- "Done (t=0.41s)\n",
- "creating index...\n",
- "index created!\n",
- "Loading and preparing results...\n",
- "DONE (t=5.64s)\n",
- "creating index...\n",
- "index created!\n",
- "Running per image evaluation...\n",
- "Evaluate annotation type *bbox*\n",
- "DONE (t=72.86s).\n",
- "Accumulating evaluation results...\n",
- "DONE (t=14.20s).\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506\n",
- " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688\n",
- " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.549\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.340\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.631\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.684\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.528\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.737\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.833\n",
- "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rc_KbFk0juX2"
- },
- "source": [
- "## COCO test\n",
- "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "V0AJnSeCIHyJ"
- },
- "source": [
- "# Download COCO test-dev2017\n",
- "!bash data/scripts/get_coco.sh --test"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "29GJXAP_lPrt"
- },
- "source": [
- "# Run YOLOv5x on COCO test\n",
- "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ZY2VXXXu74w5"
- },
- "source": [
- "# 3. Train\n",
- "\n",
- "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png\"/></a></p>\n",
- "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
- "<br><br>\n",
- "\n",
- "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n",
- "\n",
- "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
- "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
- "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
- "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
- "<br><br>\n",
- "\n",
- "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
- "\n",
- "## Train on Custom Data with Roboflow 🌟 NEW\n",
- "\n",
- "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
- "\n",
- "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n",
- "- Custom Training Notebook: [](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n",
- "<br>\n",
- "\n",
- "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "bOy5KI2ncnWd"
- },
- "source": [
- "# Tensorboard (optional)\n",
- "%load_ext tensorboard\n",
- "%tensorboard --logdir runs/train"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# ClearML (optional)\n",
- "%pip install -q clearml\n",
- "!clearml-init"
- ],
- "metadata": {
- "id": "DQhI6vvaRWjR"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "2fLAV42oNb7M"
- },
- "source": [
- "# Weights & Biases (optional)\n",
- "%pip install -q wandb\n",
- "import wandb\n",
- "wandb.login()"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "1NcFxRcFdJ_O",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "bce1b4bd-1a14-4c07-aebd-6c11e91ad24b"
- },
- "source": [
- "# Train YOLOv5s on COCO128 for 3 epochs\n",
- "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
- "YOLOv5 🚀 v6.1-370-g20f1b7e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
- "\n",
- "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
- "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
- "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 runs in ClearML\n",
- "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
- "\n",
- "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
- "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
- "100% 6.66M/6.66M [00:00<00:00, 75.2MB/s]\n",
- "Dataset download success ✅ (0.7s), saved to \u001b[1m/content/datasets\u001b[0m\n",
- "\n",
- " from n params module arguments \n",
- " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
- " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
- " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
- " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
- " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
- " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
- " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
- " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
- " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
- " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
- " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
- " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
- " 12 [-1, 6] 1 0 models.common.Concat [1] \n",
- " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
- " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
- " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
- " 16 [-1, 4] 1 0 models.common.Concat [1] \n",
- " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
- " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
- " 19 [-1, 14] 1 0 models.common.Concat [1] \n",
- " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
- " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
- " 22 [-1, 10] 1 0 models.common.Concat [1] \n",
- " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
- " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
- "Model summary: 270 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
- "\n",
- "Transferred 349/349 items from yolov5s.pt\n",
- "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
- "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
- "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(always_apply=False, p=0.01, blur_limit=(3, 7)), MedianBlur(always_apply=False, p=0.01, blur_limit=(3, 7)), ToGray(always_apply=False, p=0.01), CLAHE(always_apply=False, p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 7926.40it/s]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 975.81it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 258.62it/s]\n",
- "Plotting labels to runs/train/exp/labels.jpg... \n",
- "\n",
- "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
- "Image sizes 640 train, 640 val\n",
- "Using 8 dataloader workers\n",
- "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
- "Starting training for 3 epochs...\n",
- "\n",
- " Epoch gpu_mem box obj cls labels img_size\n",
- " 0/2 3.76G 0.04529 0.06712 0.01835 323 640: 100% 8/8 [00:05<00:00, 1.59it/s]\n",
- " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.05it/s]\n",
- " all 128 929 0.806 0.593 0.718 0.472\n",
- "\n",
- " Epoch gpu_mem box obj cls labels img_size\n",
- " 1/2 4.79G 0.04244 0.06423 0.01611 236 640: 100% 8/8 [00:00<00:00, 8.11it/s]\n",
- " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.20it/s]\n",
- " all 128 929 0.811 0.615 0.74 0.493\n",
- "\n",
- " Epoch gpu_mem box obj cls labels img_size\n",
- " 2/2 4.79G 0.04695 0.06875 0.0173 189 640: 100% 8/8 [00:00<00:00, 9.12it/s]\n",
- " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.24it/s]\n",
- " all 128 929 0.784 0.634 0.747 0.502\n",
- "\n",
- "3 epochs completed in 0.003 hours.\n",
- "Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
- "Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB\n",
- "\n",
- "Validating runs/train/exp/weights/best.pt...\n",
- "Fusing layers... \n",
- "Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
- " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.20it/s]\n",
- " all 128 929 0.781 0.637 0.747 0.502\n",
- " person 128 254 0.872 0.693 0.81 0.534\n",
- " bicycle 128 6 1 0.407 0.68 0.425\n",
- " car 128 46 0.743 0.413 0.581 0.247\n",
- " motorcycle 128 5 1 0.988 0.995 0.692\n",
- " airplane 128 6 0.965 1 0.995 0.717\n",
- " bus 128 7 0.706 0.714 0.814 0.697\n",
- " train 128 3 1 0.582 0.806 0.477\n",
- " truck 128 12 0.602 0.417 0.495 0.271\n",
- " boat 128 6 0.961 0.333 0.464 0.224\n",
- " traffic light 128 14 0.517 0.155 0.364 0.216\n",
- " stop sign 128 2 0.782 1 0.995 0.821\n",
- " bench 128 9 0.829 0.539 0.701 0.288\n",
- " bird 128 16 0.924 1 0.995 0.655\n",
- " cat 128 4 0.891 1 0.995 0.809\n",
- " dog 128 9 1 0.659 0.883 0.604\n",
- " horse 128 2 0.808 1 0.995 0.672\n",
- " elephant 128 17 0.973 0.882 0.936 0.733\n",
- " bear 128 1 0.692 1 0.995 0.995\n",
- " zebra 128 4 0.872 1 0.995 0.922\n",
- " giraffe 128 9 0.865 0.889 0.975 0.736\n",
- " backpack 128 6 1 0.547 0.787 0.372\n",
- " umbrella 128 18 0.823 0.667 0.889 0.504\n",
- " handbag 128 19 0.516 0.105 0.304 0.153\n",
- " tie 128 7 0.696 0.714 0.741 0.482\n",
- " suitcase 128 4 0.716 1 0.995 0.553\n",
- " frisbee 128 5 0.715 0.8 0.8 0.71\n",
- " skis 128 1 0.694 1 0.995 0.398\n",
- " snowboard 128 7 0.893 0.714 0.855 0.569\n",
- " sports ball 128 6 0.659 0.667 0.602 0.307\n",
- " kite 128 10 0.683 0.434 0.611 0.242\n",
- " baseball bat 128 4 0.838 0.5 0.55 0.146\n",
- " baseball glove 128 7 0.572 0.429 0.463 0.294\n",
- " skateboard 128 5 0.697 0.6 0.702 0.476\n",
- " tennis racket 128 7 0.62 0.429 0.544 0.29\n",
- " bottle 128 18 0.591 0.402 0.572 0.295\n",
- " wine glass 128 16 0.747 0.921 0.913 0.529\n",
- " cup 128 36 0.824 0.639 0.826 0.535\n",
- " fork 128 6 1 0.319 0.518 0.353\n",
- " knife 128 16 0.768 0.62 0.654 0.374\n",
- " spoon 128 22 0.824 0.427 0.65 0.382\n",
- " bowl 128 28 0.8 0.643 0.726 0.525\n",
- " banana 128 1 0.878 1 0.995 0.208\n",
- " sandwich 128 2 1 0 0.62 0.546\n",
- " orange 128 4 1 0.896 0.995 0.691\n",
- " broccoli 128 11 0.586 0.364 0.481 0.349\n",
- " carrot 128 24 0.702 0.589 0.722 0.475\n",
- " hot dog 128 2 0.524 1 0.828 0.795\n",
- " pizza 128 5 0.811 0.865 0.962 0.695\n",
- " donut 128 14 0.653 1 0.964 0.853\n",
- " cake 128 4 0.852 1 0.995 0.822\n",
- " chair 128 35 0.536 0.571 0.593 0.31\n",
- " couch 128 6 1 0.63 0.75 0.518\n",
- " potted plant 128 14 0.775 0.738 0.839 0.478\n",
- " bed 128 3 1 0 0.72 0.423\n",
- " dining table 128 13 0.817 0.348 0.592 0.381\n",
- " toilet 128 2 0.782 1 0.995 0.895\n",
- " tv 128 2 0.711 1 0.995 0.821\n",
- " laptop 128 3 1 0 0.789 0.42\n",
- " mouse 128 2 1 0 0.0798 0.0399\n",
- " remote 128 8 1 0.611 0.63 0.549\n",
- " cell phone 128 8 0.685 0.375 0.428 0.245\n",
- " microwave 128 3 0.803 1 0.995 0.767\n",
- " oven 128 5 0.42 0.4 0.444 0.306\n",
- " sink 128 6 0.288 0.167 0.34 0.247\n",
- " refrigerator 128 5 0.632 0.8 0.805 0.572\n",
- " book 128 29 0.494 0.207 0.332 0.161\n",
- " clock 128 9 0.791 0.889 0.93 0.75\n",
- " vase 128 2 0.355 1 0.995 0.895\n",
- " scissors 128 1 1 0 0.332 0.0663\n",
- " teddy bear 128 21 0.839 0.571 0.767 0.487\n",
- " toothbrush 128 5 0.829 0.974 0.962 0.644\n",
- "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "15glLzbQx5u0"
- },
- "source": [
- "# 4. Visualize"
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## ClearML Logging and Automation 🌟 NEW\n",
- "\n",
- "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
- "\n",
- "- `pip install clearml`\n",
- "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
- "\n",
- "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
- "\n",
- "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
- "\n",
- "<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
- "<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
- ],
- "metadata": {
- "id": "Lay2WsTjNJzP"
- }
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DLI1JmHU7B0l"
- },
- "source": [
- "## Weights & Biases Logging\n",
- "\n",
- "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
- "\n",
- "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
- "\n",
- "<a href=\"https://wandb.ai/glenn-jocher/yolov5_tutorial\">\n",
- "<img alt=\"Weights & Biases dashboard\" src=\"https://user-images.githubusercontent.com/26833433/182482859-288a9622-4661-48db-99de-650d1dead5c6.jpg\" width=\"1280\"/></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-WPvRbS5Swl6"
- },
- "source": [
- "## Local Logging\n",
- "\n",
- "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
- "\n",
- "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
- "\n",
- "<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Zelyeqbyt3GD"
- },
- "source": [
- "# Environments\n",
- "\n",
- "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
- "\n",
- "- **Google Colab and Kaggle** notebooks with free GPU: <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
- "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
- "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
- "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6Qu7Iesl0p54"
- },
- "source": [
- "# Status\n",
- "\n",
- "\n",
- "\n",
- "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IEijrePND_2I"
- },
- "source": [
- "# Appendix\n",
- "\n",
- "Additional content below for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "GMusP4OAxFu6"
- },
- "source": [
- "import torch\n",
- "\n",
- "# PyTorch Hub Model\n",
- "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom\n",
- "\n",
- "# Images\n",
- "img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list\n",
- "\n",
- "# Inference\n",
- "results = model(img)\n",
- "\n",
- "# Results\n",
- "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "FGH0ZjkGjejy"
- },
- "source": [
- "# YOLOv5 CI\n",
- "%%shell\n",
- "rm -rf runs # remove runs/\n",
- "m=yolov5n # official weights\n",
- "b=runs/train/exp/weights/best # best.pt checkpoint\n",
- "python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0 # train\n",
- "for d in 0 cpu; do # devices\n",
- " for w in $m $b; do # weights\n",
- " python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val\n",
- " python detect.py --imgsz 64 --weights $w.pt --device $d # detect\n",
- " done\n",
- "done\n",
- "python hubconf.py --model $m # hub\n",
- "python models/tf.py --weights $m.pt # build TF model\n",
- "python models/yolo.py --cfg $m.yaml # build PyTorch model\n",
- "python export.py --weights $m.pt --img 64 --include torchscript # export"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "mcKoSIK2WSzj"
- },
- "source": [
- "# Reproduce\n",
- "for x in (f'yolov5{x}' for x in 'nsmlx'):\n",
- " !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed # speed\n",
- " !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "gogI-kwi3Tye"
- },
- "source": [
- "# Profile\n",
- "from utils.torch_utils import profile\n",
- "\n",
- "m1 = lambda x: x * torch.sigmoid(x)\n",
- "m2 = torch.nn.SiLU()\n",
- "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "BSgFCAcMbk1R"
- },
- "source": [
- "# VOC\n",
- "for b, m in zip([64, 64, 64, 32, 16], [f'yolov5{x}' for x in 'nsmlx']): # batch, model\n",
- " !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Classification\n",
- "for m in [*(f'yolov5{x}.pt' for x in 'nsmlx'), 'resnet50.pt', 'efficientnet_b0.pt']:\n",
- " for d in 'mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'imagenette160', 'imagenette320', 'imagenette', 'imagewoof160', 'imagewoof320', 'imagewoof':\n",
- " !python classify/train.py --model {m} --data {d} --epochs 10 --project YOLOv5-cls --name {m}-{d}"
- ],
- "metadata": {
- "id": "UWGH7H6yakVl"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "VTRwsvA9u7ln"
- },
- "source": [
- "# TensorRT \n",
- "!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # install\n",
- "!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0 # export\n",
- "!python detect.py --weights yolov5s.engine --imgsz 640 --device 0 # inference"
- ],
- "execution_count": null,
- "outputs": []
- }
- ]
-}
\ No newline at end of file
diff --git a/yolov5-6.2/utils/__init__.py b/yolov5-6.2/utils/__init__.py
deleted file mode 100644
index da53a4d2..00000000
--- a/yolov5-6.2/utils/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-utils/initialization
-"""
-
-
-def notebook_init(verbose=True):
- # Check system software and hardware
- print('Checking setup...')
-
- import os
- import shutil
-
- from utils.general import check_requirements, emojis, is_colab
- from utils.torch_utils import select_device # imports
-
- check_requirements(('psutil', 'IPython'))
- import psutil
- from IPython import display # to display images and clear console output
-
- if is_colab():
- shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory
-
- # System info
- if verbose:
- gb = 1 << 30 # bytes to GiB (1024 ** 3)
- ram = psutil.virtual_memory().total
- total, used, free = shutil.disk_usage("/")
- display.clear_output()
- s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)'
- else:
- s = ''
-
- select_device(newline=False)
- print(emojis(f'Setup complete ✅ {s}'))
- return display
diff --git a/yolov5-6.2/utils/activations.py b/yolov5-6.2/utils/activations.py
deleted file mode 100644
index 084ce8c4..00000000
--- a/yolov5-6.2/utils/activations.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Activation functions
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SiLU(nn.Module):
- # SiLU activation https://arxiv.org/pdf/1606.08415.pdf
- @staticmethod
- def forward(x):
- return x * torch.sigmoid(x)
-
-
-class Hardswish(nn.Module):
- # Hard-SiLU activation
- @staticmethod
- def forward(x):
- # return x * F.hardsigmoid(x) # for TorchScript and CoreML
- return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX
-
-
-class Mish(nn.Module):
- # Mish activation https://github.com/digantamisra98/Mish
- @staticmethod
- def forward(x):
- return x * F.softplus(x).tanh()
-
-
-class MemoryEfficientMish(nn.Module):
- # Mish activation memory-efficient
- class F(torch.autograd.Function):
-
- @staticmethod
- def forward(ctx, x):
- ctx.save_for_backward(x)
- return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
-
- @staticmethod
- def backward(ctx, grad_output):
- x = ctx.saved_tensors[0]
- sx = torch.sigmoid(x)
- fx = F.softplus(x).tanh()
- return grad_output * (fx + x * sx * (1 - fx * fx))
-
- def forward(self, x):
- return self.F.apply(x)
-
-
-class FReLU(nn.Module):
- # FReLU activation https://arxiv.org/abs/2007.11824
- def __init__(self, c1, k=3): # ch_in, kernel
- super().__init__()
- self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
- self.bn = nn.BatchNorm2d(c1)
-
- def forward(self, x):
- return torch.max(x, self.bn(self.conv(x)))
-
-
-class AconC(nn.Module):
- r""" ACON activation (activate or not)
- AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
- according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
- """
-
- def __init__(self, c1):
- super().__init__()
- self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
-
- def forward(self, x):
- dpx = (self.p1 - self.p2) * x
- return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
-
-
-class MetaAconC(nn.Module):
- r""" ACON activation (activate or not)
- MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
- according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
- """
-
- def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r
- super().__init__()
- c2 = max(r, c1 // r)
- self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
- self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
- # self.bn1 = nn.BatchNorm2d(c2)
- # self.bn2 = nn.BatchNorm2d(c1)
-
- def forward(self, x):
- y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
- # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
- # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable
- beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed
- dpx = (self.p1 - self.p2) * x
- return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
diff --git a/yolov5-6.2/utils/augmentations.py b/yolov5-6.2/utils/augmentations.py
deleted file mode 100644
index 498776a6..00000000
--- a/yolov5-6.2/utils/augmentations.py
+++ /dev/null
@@ -1,348 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-import torchvision.transforms as T
-import torchvision.transforms.functional as TF
-
-from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
-from utils.metrics import bbox_ioa
-
-IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
-IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
-
-
-class Albumentations:
- # YOLOv5 Albumentations class (optional, only used if package is installed)
- def __init__(self):
- self.transform = None
- prefix = colorstr('albumentations: ')
- try:
- import albumentations as A
- check_version(A.__version__, '1.0.3', hard=True) # version requirement
-
- T = [
- A.Blur(p=0.01),
- A.MedianBlur(p=0.01),
- A.ToGray(p=0.01),
- A.CLAHE(p=0.01),
- A.RandomBrightnessContrast(p=0.0),
- A.RandomGamma(p=0.0),
- A.ImageCompression(quality_lower=75, p=0.0)] # transforms
- self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
-
- LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
- except ImportError: # package not installed, skip
- pass
- except Exception as e:
- LOGGER.info(f'{prefix}{e}')
-
- def __call__(self, im, labels, p=1.0):
- if self.transform and random.random() < p:
- new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
- im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
- return im, labels
-
-
-def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
- # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
- return TF.normalize(x, mean, std, inplace=inplace)
-
-
-def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
- # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
- for i in range(3):
- x[:, i] = x[:, i] * std[i] + mean[i]
- return x
-
-
-def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
- # HSV color-space augmentation
- if hgain or sgain or vgain:
- r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
- hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
- dtype = im.dtype # uint8
-
- x = np.arange(0, 256, dtype=r.dtype)
- lut_hue = ((x * r[0]) % 180).astype(dtype)
- lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
- lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
- im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
- cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
-
-
-def hist_equalize(im, clahe=True, bgr=False):
- # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
- yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
- if clahe:
- c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
- yuv[:, :, 0] = c.apply(yuv[:, :, 0])
- else:
- yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
- return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
-
-
-def replicate(im, labels):
- # Replicate labels
- h, w = im.shape[:2]
- boxes = labels[:, 1:].astype(int)
- x1, y1, x2, y2 = boxes.T
- s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
- for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
- x1b, y1b, x2b, y2b = boxes[i]
- bh, bw = y2b - y1b, x2b - x1b
- yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
- x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
- im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]
- labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
-
- return im, labels
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
- # Resize and pad image while meeting stride-multiple constraints
- shape = im.shape[:2] # current shape [height, width]
- if isinstance(new_shape, int):
- new_shape = (new_shape, new_shape)
-
- # Scale ratio (new / old)
- r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
- if not scaleup: # only scale down, do not scale up (for better val mAP)
- r = min(r, 1.0)
-
- # Compute padding
- ratio = r, r # width, height ratios
- new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
- dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
- if auto: # minimum rectangle
- dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
- elif scaleFill: # stretch
- dw, dh = 0.0, 0.0
- new_unpad = (new_shape[1], new_shape[0])
- ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
-
- dw /= 2 # divide padding into 2 sides
- dh /= 2
-
- if shape[::-1] != new_unpad: # resize
- im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
- top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
- left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
- im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
- return (im, ratio, (dw, dh))
-
-
-def random_perspective(im,
- targets=(),
- segments=(),
- degrees=10,
- translate=.1,
- scale=.1,
- shear=10,
- perspective=0.0,
- border=(0, 0)):
- # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
- # targets = [cls, xyxy]
-
- height = im.shape[0] + border[0] * 2 # shape(h,w,c)
- width = im.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
-
- # Perspective
- P = np.eye(3)
- P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
- P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(1 - scale, 1 + scale)
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
- T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
- else: # affine
- im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- if n:
- use_segments = any(x.any() for x in segments)
- new = np.zeros((n, 4))
- if use_segments: # warp segments
- segments = resample_segments(segments) # upsample
- for i, segment in enumerate(segments):
- xy = np.ones((len(segment), 3))
- xy[:, :2] = segment
- xy = xy @ M.T # transform
- xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
-
- # clip
- new[i] = segment2box(xy, width, height)
-
- else: # warp boxes
- xy = np.ones((n * 4, 3))
- xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
- xy = xy @ M.T # transform
- xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
-
- # create new boxes
- x = xy[:, [0, 2, 4, 6]]
- y = xy[:, [1, 3, 5, 7]]
- new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
- # clip
- new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
- new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
- # filter candidates
- i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
- targets = targets[i]
- targets[:, 1:5] = new[i]
-
- return im, targets
-
-
-def copy_paste(im, labels, segments, p=0.5):
- # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
- n = len(segments)
- if p and n:
- h, w, c = im.shape # height, width, channels
- im_new = np.zeros(im.shape, np.uint8)
- for j in random.sample(range(n), k=round(p * n)):
- l, s = labels[j], segments[j]
- box = w - l[3], l[2], w - l[1], l[4]
- ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
- if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
- labels = np.concatenate((labels, [[l[0], *box]]), 0)
- segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
- cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
- result = cv2.bitwise_and(src1=im, src2=im_new)
- result = cv2.flip(result, 1) # augment segments (flip left-right)
- i = result > 0 # pixels to replace
- # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
- im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
-
- return im, labels, segments
-
-
-def cutout(im, labels, p=0.5):
- # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
- if random.random() < p:
- h, w = im.shape[:2]
- scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
- for s in scales:
- mask_h = random.randint(1, int(h * s)) # create random masks
- mask_w = random.randint(1, int(w * s))
-
- # box
- xmin = max(0, random.randint(0, w) - mask_w // 2)
- ymin = max(0, random.randint(0, h) - mask_h // 2)
- xmax = min(w, xmin + mask_w)
- ymax = min(h, ymin + mask_h)
-
- # apply random color mask
- im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
-
- # return unobscured labels
- if len(labels) and s > 0.03:
- box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
- ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
- labels = labels[ioa < 0.60] # remove >60% obscured labels
-
- return labels
-
-
-def mixup(im, labels, im2, labels2):
- # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
- r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
- im = (im * r + im2 * (1 - r)).astype(np.uint8)
- labels = np.concatenate((labels, labels2), 0)
- return im, labels
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
- # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
- w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
- w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
- ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
- return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
-
-
-def classify_albumentations(augment=True,
- size=224,
- scale=(0.08, 1.0),
- hflip=0.5,
- vflip=0.0,
- jitter=0.4,
- mean=IMAGENET_MEAN,
- std=IMAGENET_STD,
- auto_aug=False):
- # YOLOv5 classification Albumentations (optional, only used if package is installed)
- prefix = colorstr('albumentations: ')
- try:
- import albumentations as A
- from albumentations.pytorch import ToTensorV2
- check_version(A.__version__, '1.0.3', hard=True) # version requirement
- if augment: # Resize and crop
- T = [A.RandomResizedCrop(height=size, width=size, scale=scale)]
- if auto_aug:
- # TODO: implement AugMix, AutoAug & RandAug in albumentation
- LOGGER.info(f'{prefix}auto augmentations are currently not supported')
- else:
- if hflip > 0:
- T += [A.HorizontalFlip(p=hflip)]
- if vflip > 0:
- T += [A.VerticalFlip(p=vflip)]
- if jitter > 0:
- color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
- T += [A.ColorJitter(*color_jitter, 0)]
- else: # Use fixed crop for eval set (reproducibility)
- T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
- T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
- LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
- return A.Compose(T)
-
- except ImportError: # package not installed, skip
- pass
- except Exception as e:
- LOGGER.info(f'{prefix}{e}')
-
-
-def classify_transforms(size=224):
- # Transforms to apply if albumentations not installed
- return T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
-
diff --git a/yolov5-6.2/utils/autoanchor.py b/yolov5-6.2/utils/autoanchor.py
deleted file mode 100644
index f2222203..00000000
--- a/yolov5-6.2/utils/autoanchor.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-AutoAnchor utils
-"""
-
-import random
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from utils.general import LOGGER, colorstr
-
-PREFIX = colorstr('AutoAnchor: ')
-
-
-def check_anchor_order(m):
- # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
- a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer
- da = a[-1] - a[0] # delta a
- ds = m.stride[-1] - m.stride[0] # delta s
- if da and (da.sign() != ds.sign()): # same order
- LOGGER.info(f'{PREFIX}Reversing anchor order')
- m.anchors[:] = m.anchors.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
- # Check anchor fit to data, recompute if necessary
- m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect()
- shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
- scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale
- wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh
-
- def metric(k): # compute metric
- r = wh[:, None] / k[None]
- x = torch.min(r, 1 / r).min(2)[0] # ratio metric
- best = x.max(1)[0] # best_x
- aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold
- bpr = (best > 1 / thr).float().mean() # best possible recall
- return bpr, aat
-
- stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides
- anchors = m.anchors.clone() * stride # current anchors
- bpr, aat = metric(anchors.cpu().view(-1, 2))
- s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
- if bpr > 0.98: # threshold to recompute
- LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅')
- else:
- LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')
- na = m.anchors.numel() // 2 # number of anchors
- try:
- anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
- except Exception as e:
- LOGGER.info(f'{PREFIX}ERROR: {e}')
- new_bpr = metric(anchors)[0]
- if new_bpr > bpr: # replace anchors
- anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
- m.anchors[:] = anchors.clone().view_as(m.anchors)
- check_anchor_order(m) # must be in pixel-space (not grid-space)
- m.anchors /= stride
- s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)'
- else:
- s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
- LOGGER.info(s)
-
-
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
- """ Creates kmeans-evolved anchors from training dataset
-
- Arguments:
- dataset: path to data.yaml, or a loaded dataset
- n: number of anchors
- img_size: image size used for training
- thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
- gen: generations to evolve anchors using genetic algorithm
- verbose: print all results
-
- Return:
- k: kmeans evolved anchors
-
- Usage:
- from utils.autoanchor import *; _ = kmean_anchors()
- """
- from scipy.cluster.vq import kmeans
-
- npr = np.random
- thr = 1 / thr
-
- def metric(k, wh): # compute metrics
- r = wh[:, None] / k[None]
- x = torch.min(r, 1 / r).min(2)[0] # ratio metric
- # x = wh_iou(wh, torch.tensor(k)) # iou metric
- return x, x.max(1)[0] # x, best_x
-
- def anchor_fitness(k): # mutation fitness
- _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
- return (best * (best > thr).float()).mean() # fitness
-
- def print_results(k, verbose=True):
- k = k[np.argsort(k.prod(1))] # sort small to large
- x, best = metric(k, wh0)
- bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
- s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
- f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
- f'past_thr={x[x > thr].mean():.3f}-mean: '
- for x in k:
- s += '%i,%i, ' % (round(x[0]), round(x[1]))
- if verbose:
- LOGGER.info(s[:-2])
- return k
-
- if isinstance(dataset, str): # *.yaml file
- with open(dataset, errors='ignore') as f:
- data_dict = yaml.safe_load(f) # model dict
- from utils.dataloaders import LoadImagesAndLabels
- dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-
- # Get label wh
- shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
- wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh
-
- # Filter
- i = (wh0 < 3.0).any(1).sum()
- if i:
- LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
- wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
- # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
-
- # Kmeans init
- try:
- LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
- assert n <= len(wh) # apply overdetermined constraint
- s = wh.std(0) # sigmas for whitening
- k = kmeans(wh / s, n, iter=30)[0] * s # points
- assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar
- except Exception:
- LOGGER.warning(f'{PREFIX}WARNING: switching strategies from kmeans to random init')
- k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init
- wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
- k = print_results(k, verbose=False)
-
- # Plot
- # k, d = [None] * 20, [None] * 20
- # for i in tqdm(range(1, 21)):
- # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance
- # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
- # ax = ax.ravel()
- # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
- # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh
- # ax[0].hist(wh[wh[:, 0]<100, 0],400)
- # ax[1].hist(wh[wh[:, 1]<100, 1],400)
- # fig.savefig('wh.png', dpi=200)
-
- # Evolve
- f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
- pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
- for _ in pbar:
- v = np.ones(sh)
- while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
- v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
- kg = (k.copy() * v).clip(min=2.0)
- fg = anchor_fitness(kg)
- if fg > f:
- f, k = fg, kg.copy()
- pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
- if verbose:
- print_results(k, verbose)
-
- return print_results(k)
diff --git a/yolov5-6.2/utils/autobatch.py b/yolov5-6.2/utils/autobatch.py
deleted file mode 100644
index c231d24c..00000000
--- a/yolov5-6.2/utils/autobatch.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-batch utils
-"""
-
-from copy import deepcopy
-
-import numpy as np
-import torch
-
-from utils.general import LOGGER, colorstr
-from utils.torch_utils import profile
-
-
-def check_train_batch_size(model, imgsz=640, amp=True):
- # Check YOLOv5 training batch size
- with torch.cuda.amp.autocast(amp):
- return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
-
-
-def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
- # Automatically estimate best batch size to use `fraction` of available CUDA memory
- # Usage:
- # import torch
- # from utils.autobatch import autobatch
- # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
- # print(autobatch(model))
-
- # Check device
- prefix = colorstr('AutoBatch: ')
- LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
- device = next(model.parameters()).device # get model device
- if device.type == 'cpu':
- LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
- return batch_size
-
- # Inspect CUDA memory
- gb = 1 << 30 # bytes to GiB (1024 ** 3)
- d = str(device).upper() # 'CUDA:0'
- properties = torch.cuda.get_device_properties(device) # device properties
- t = properties.total_memory / gb # GiB total
- r = torch.cuda.memory_reserved(device) / gb # GiB reserved
- a = torch.cuda.memory_allocated(device) / gb # GiB allocated
- f = t - (r + a) # GiB free
- LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
-
- # Profile batch sizes
- batch_sizes = [1, 2, 4, 8, 16]
- try:
- img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes]
- results = profile(img, model, n=3, device=device)
- except Exception as e:
- LOGGER.warning(f'{prefix}{e}')
-
- # Fit a solution
- y = [x[2] for x in results if x] # memory [2]
- p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
- b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
- if None in results: # some sizes failed
- i = results.index(None) # first fail index
- if b >= batch_sizes[i]: # y intercept above failure point
- b = batch_sizes[max(i - 1, 0)] # select prior safe point
-
- fraction = np.polyval(p, b) / t # actual fraction predicted
- LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
- return b
diff --git a/yolov5-6.2/utils/aws/__init__.py b/yolov5-6.2/utils/aws/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/yolov5-6.2/utils/aws/mime.sh b/yolov5-6.2/utils/aws/mime.sh
deleted file mode 100644
index c319a83c..00000000
--- a/yolov5-6.2/utils/aws/mime.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
-# This script will run on every instance restart, not only on first start
-# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
-
-Content-Type: multipart/mixed; boundary="//"
-MIME-Version: 1.0
-
---//
-Content-Type: text/cloud-config; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="cloud-config.txt"
-
-#cloud-config
-cloud_final_modules:
-- [scripts-user, always]
-
---//
-Content-Type: text/x-shellscript; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="userdata.txt"
-
-#!/bin/bash
-# --- paste contents of userdata.sh here ---
---//
diff --git a/yolov5-6.2/utils/aws/resume.py b/yolov5-6.2/utils/aws/resume.py
deleted file mode 100644
index b21731c9..00000000
--- a/yolov5-6.2/utils/aws/resume.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Resume all interrupted trainings in yolov5/ dir including DDP trainings
-# Usage: $ python utils/aws/resume.py
-
-import os
-import sys
-from pathlib import Path
-
-import torch
-import yaml
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[2] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-
-port = 0 # --master_port
-path = Path('').resolve()
-for last in path.rglob('*/**/last.pt'):
- ckpt = torch.load(last)
- if ckpt['optimizer'] is None:
- continue
-
- # Load opt.yaml
- with open(last.parent.parent / 'opt.yaml', errors='ignore') as f:
- opt = yaml.safe_load(f)
-
- # Get device count
- d = opt['device'].split(',') # devices
- nd = len(d) # number of devices
- ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel
-
- if ddp: # multi-GPU
- port += 1
- cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
- else: # single-GPU
- cmd = f'python train.py --resume {last}'
-
- cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread
- print(cmd)
- os.system(cmd)
diff --git a/yolov5-6.2/utils/aws/userdata.sh b/yolov5-6.2/utils/aws/userdata.sh
deleted file mode 100644
index 5fc1332a..00000000
--- a/yolov5-6.2/utils/aws/userdata.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash
-# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
-# This script will run only once on first instance start (for a re-start script see mime.sh)
-# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
-# Use >300 GB SSD
-
-cd home/ubuntu
-if [ ! -d yolov5 ]; then
- echo "Running first-time script." # install dependencies, download COCO, pull Docker
- git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5
- cd yolov5
- bash data/scripts/get_coco.sh && echo "COCO done." &
- sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
- python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
- wait && echo "All tasks done." # finish background tasks
-else
- echo "Running re-start script." # resume interrupted runs
- i=0
- list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
- while IFS= read -r id; do
- ((i++))
- echo "restarting container $i: $id"
- sudo docker start $id
- # sudo docker exec -it $id python train.py --resume # single-GPU
- sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
- done <<<"$list"
-fi
diff --git a/yolov5-6.2/utils/benchmarks.py b/yolov5-6.2/utils/benchmarks.py
deleted file mode 100644
index d412653c..00000000
--- a/yolov5-6.2/utils/benchmarks.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run YOLOv5 benchmarks on all supported export formats
-
-Format | `export.py --include` | Model
---- | --- | ---
-PyTorch | - | yolov5s.pt
-TorchScript | `torchscript` | yolov5s.torchscript
-ONNX | `onnx` | yolov5s.onnx
-OpenVINO | `openvino` | yolov5s_openvino_model/
-TensorRT | `engine` | yolov5s.engine
-CoreML | `coreml` | yolov5s.mlmodel
-TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
-TensorFlow GraphDef | `pb` | yolov5s.pb
-TensorFlow Lite | `tflite` | yolov5s.tflite
-TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
-TensorFlow.js | `tfjs` | yolov5s_web_model/
-
-Requirements:
- $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
- $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
- $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
-
-Usage:
- $ python utils/benchmarks.py --weights yolov5s.pt --img 640
-"""
-
-import argparse
-import platform
-import sys
-import time
-from pathlib import Path
-
-import pandas as pd
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-# ROOT = ROOT.relative_to(Path.cwd()) # relative
-
-import export
-import val
-from utils import notebook_init
-from utils.general import LOGGER, check_yaml, file_size, print_args
-from utils.torch_utils import select_device
-
-
-def run(
- weights=ROOT / 'yolov5s.pt', # weights path
- imgsz=640, # inference size (pixels)
- batch_size=1, # batch size
- data=ROOT / 'data/coco128.yaml', # dataset.yaml path
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- half=False, # use FP16 half-precision inference
- test=False, # test exports only
- pt_only=False, # test PyTorch only
- hard_fail=False, # throw error on benchmark failure
-):
- y, t = [], time.time()
- device = select_device(device)
- for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU)
- try:
- assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
- assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
- if 'cpu' in device.type:
- assert cpu, 'inference not supported on CPU'
- if 'cuda' in device.type:
- assert gpu, 'inference not supported on GPU'
-
- # Export
- if f == '-':
- w = weights # PyTorch format
- else:
- w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others
- assert suffix in str(w), 'export failed'
-
- # Validate
- result = val.run(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half)
- metrics = result[0] # metrics (mp, mr, map50, map, *losses(box, obj, cls))
- speeds = result[2] # times (preprocess, inference, postprocess)
- y.append([name, round(file_size(w), 1), round(metrics[3], 4), round(speeds[1], 2)]) # MB, mAP, t_inference
- except Exception as e:
- if hard_fail:
- assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
- LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}')
- y.append([name, None, None, None]) # mAP, t_inference
- if pt_only and i == 0:
- break # break after PyTorch
-
- # Print results
- LOGGER.info('\n')
- parse_opt()
- notebook_init() # print system info
- c = ['Format', 'Size (MB)', 'mAP@0.5:0.95', 'Inference time (ms)'] if map else ['Format', 'Export', '', '']
- py = pd.DataFrame(y, columns=c)
- LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)')
- LOGGER.info(str(py if map else py.iloc[:, :2]))
- return py
-
-
-def test(
- weights=ROOT / 'yolov5s.pt', # weights path
- imgsz=640, # inference size (pixels)
- batch_size=1, # batch size
- data=ROOT / 'data/coco128.yaml', # dataset.yaml path
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- half=False, # use FP16 half-precision inference
- test=False, # test exports only
- pt_only=False, # test PyTorch only
- hard_fail=False, # throw error on benchmark failure
-):
- y, t = [], time.time()
- device = select_device(device)
- for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
- try:
- w = weights if f == '-' else \
- export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights
- assert suffix in str(w), 'export failed'
- y.append([name, True])
- except Exception:
- y.append([name, False]) # mAP, t_inference
-
- # Print results
- LOGGER.info('\n')
- parse_opt()
- notebook_init() # print system info
- py = pd.DataFrame(y, columns=['Format', 'Export'])
- LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)')
- LOGGER.info(str(py))
- return py
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--batch-size', type=int, default=1, help='batch size')
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--test', action='store_true', help='test exports only')
- parser.add_argument('--pt-only', action='store_true', help='test PyTorch only')
- parser.add_argument('--hard-fail', action='store_true', help='throw error on benchmark failure')
- opt = parser.parse_args()
- opt.data = check_yaml(opt.data) # check YAML
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- test(**vars(opt)) if opt.test else run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/yolov5-6.2/utils/callbacks.py b/yolov5-6.2/utils/callbacks.py
deleted file mode 100644
index 2b32df0b..00000000
--- a/yolov5-6.2/utils/callbacks.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Callback utils
-"""
-
-
-class Callbacks:
- """"
- Handles all registered callbacks for YOLOv5 Hooks
- """
-
- def __init__(self):
- # Define the available callbacks
- self._callbacks = {
- 'on_pretrain_routine_start': [],
- 'on_pretrain_routine_end': [],
- 'on_train_start': [],
- 'on_train_epoch_start': [],
- 'on_train_batch_start': [],
- 'optimizer_step': [],
- 'on_before_zero_grad': [],
- 'on_train_batch_end': [],
- 'on_train_epoch_end': [],
- 'on_val_start': [],
- 'on_val_batch_start': [],
- 'on_val_image_end': [],
- 'on_val_batch_end': [],
- 'on_val_end': [],
- 'on_fit_epoch_end': [], # fit = train + val
- 'on_model_save': [],
- 'on_train_end': [],
- 'on_params_update': [],
- 'teardown': [],}
- self.stop_training = False # set True to interrupt training
-
- def register_action(self, hook, name='', callback=None):
- """
- Register a new action to a callback hook
-
- Args:
- hook: The callback hook name to register the action to
- name: The name of the action for later reference
- callback: The callback to fire
- """
- assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
- assert callable(callback), f"callback '{callback}' is not callable"
- self._callbacks[hook].append({'name': name, 'callback': callback})
-
- def get_registered_actions(self, hook=None):
- """"
- Returns all the registered actions by callback hook
-
- Args:
- hook: The name of the hook to check, defaults to all
- """
- return self._callbacks[hook] if hook else self._callbacks
-
- def run(self, hook, *args, **kwargs):
- """
- Loop through the registered actions and fire all callbacks
-
- Args:
- hook: The name of the hook to check, defaults to all
- args: Arguments to receive from YOLOv5
- kwargs: Keyword Arguments to receive from YOLOv5
- """
-
- assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
-
- for logger in self._callbacks[hook]:
- logger['callback'](*args, **kwargs)
diff --git a/yolov5-6.2/utils/dataloaders.py b/yolov5-6.2/utils/dataloaders.py
deleted file mode 100644
index 2c04040b..00000000
--- a/yolov5-6.2/utils/dataloaders.py
+++ /dev/null
@@ -1,1156 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Dataloaders and dataset utils
-"""
-
-import contextlib
-import glob
-import hashlib
-import json
-import math
-import os
-import random
-import shutil
-import time
-from itertools import repeat
-from multiprocessing.pool import Pool, ThreadPool
-from pathlib import Path
-from threading import Thread
-from urllib.parse import urlparse
-from zipfile import ZipFile
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchvision
-import yaml
-from PIL import ExifTags, Image, ImageOps
-from torch.utils.data import DataLoader, Dataset, dataloader, distributed
-from tqdm import tqdm
-
-from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
- letterbox, mixup, random_perspective)
-from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
- cv2, is_colab, is_kaggle, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
-from utils.torch_utils import torch_distributed_zero_first
-
-# Parameters
-HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
-IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp' # include image suffixes
-VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
-BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}' # tqdm bar format
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
-
-# Get orientation exif tag
-for orientation in ExifTags.TAGS.keys():
- if ExifTags.TAGS[orientation] == 'Orientation':
- break
-
-
-def get_hash(paths):
- # Returns a single hash value of a list of paths (files or dirs)
- size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
- h = hashlib.md5(str(size).encode()) # hash sizes
- h.update(''.join(paths).encode()) # hash paths
- return h.hexdigest() # return hash
-
-
-def exif_size(img):
- # Returns exif-corrected PIL size
- s = img.size # (width, height)
- with contextlib.suppress(Exception):
- rotation = dict(img._getexif().items())[orientation]
- if rotation in [6, 8]: # rotation 270 or 90
- s = (s[1], s[0])
- return s
-
-
-def exif_transpose(image):
- """
- Transpose a PIL image accordingly if it has an EXIF Orientation tag.
- Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
-
- :param image: The image to transpose.
- :return: An image.
- """
- exif = image.getexif()
- orientation = exif.get(0x0112, 1) # default 1
- if orientation > 1:
- method = {
- 2: Image.FLIP_LEFT_RIGHT,
- 3: Image.ROTATE_180,
- 4: Image.FLIP_TOP_BOTTOM,
- 5: Image.TRANSPOSE,
- 6: Image.ROTATE_270,
- 7: Image.TRANSVERSE,
- 8: Image.ROTATE_90,}.get(orientation)
- if method is not None:
- image = image.transpose(method)
- del exif[0x0112]
- image.info["exif"] = exif.tobytes()
- return image
-
-
-def seed_worker(worker_id):
- # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
- worker_seed = torch.initial_seed() % 2 ** 32
- np.random.seed(worker_seed)
- random.seed(worker_seed)
-
-
-def create_dataloader(path,
- imgsz,
- batch_size,
- stride,
- single_cls=False,
- hyp=None,
- augment=False,
- cache=False,
- pad=0.0,
- rect=False,
- rank=-1,
- workers=8,
- image_weights=False,
- quad=False,
- prefix='',
- shuffle=False):
- if rect and shuffle:
- LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False')
- shuffle = False
- with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
- dataset = LoadImagesAndLabels(
- path,
- imgsz,
- batch_size,
- augment=augment, # augmentation
- hyp=hyp, # hyperparameters
- rect=rect, # rectangular batches
- cache_images=cache,
- single_cls=single_cls,
- stride=int(stride),
- pad=pad,
- image_weights=image_weights,
- prefix=prefix)
-
- batch_size = min(batch_size, len(dataset))
- nd = torch.cuda.device_count() # number of CUDA devices
- nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
- sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
- loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
- generator = torch.Generator()
- generator.manual_seed(0)
- return loader(dataset,
- batch_size=batch_size,
- shuffle=shuffle and sampler is None,
- num_workers=nw,
- sampler=sampler,
- pin_memory=True,
- collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
- worker_init_fn=seed_worker,
- generator=generator), dataset
-
-
-class InfiniteDataLoader(dataloader.DataLoader):
- """ Dataloader that reuses workers
-
- Uses same syntax as vanilla DataLoader
- """
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
- self.iterator = super().__iter__()
-
- def __len__(self):
- return len(self.batch_sampler.sampler)
-
- def __iter__(self):
- for _ in range(len(self)):
- yield next(self.iterator)
-
-
-class _RepeatSampler:
- """ Sampler that repeats forever
-
- Args:
- sampler (Sampler)
- """
-
- def __init__(self, sampler):
- self.sampler = sampler
-
- def __iter__(self):
- while True:
- yield from iter(self.sampler)
-
-
-class LoadImages:
- # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
- def __init__(self, path, img_size=640, stride=32, auto=True):
- files = []
- for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
- p = str(Path(p).resolve())
- if '*' in p:
- files.extend(sorted(glob.glob(p, recursive=True))) # glob
- elif os.path.isdir(p):
- files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir
- elif os.path.isfile(p):
- files.append(p) # files
- else:
- raise FileNotFoundError(f'{p} does not exist')
-
- images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
- videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
- ni, nv = len(images), len(videos)
-
- self.img_size = img_size
- self.stride = stride
- self.files = images + videos
- self.nf = ni + nv # number of files
- self.video_flag = [False] * ni + [True] * nv
- self.mode = 'image'
- self.auto = auto
- if any(videos):
- self.new_video(videos[0]) # new video
- else:
- self.cap = None
- assert self.nf > 0, f'No images or videos found in {p}. ' \
- f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
-
- def __iter__(self):
- self.count = 0
- return self
-
- def __next__(self):
- if self.count == self.nf:
- raise StopIteration
- path = self.files[self.count]
-
- if self.video_flag[self.count]:
- # Read video
- self.mode = 'video'
- ret_val, img0 = self.cap.read()
- while not ret_val:
- self.count += 1
- self.cap.release()
- if self.count == self.nf: # last video
- raise StopIteration
- path = self.files[self.count]
- self.new_video(path)
- ret_val, img0 = self.cap.read()
-
- self.frame += 1
- s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
-
- else:
- # Read image
- self.count += 1
- img0 = cv2.imread(path) # BGR
- assert img0 is not None, f'Image Not Found {path}'
- s = f'image {self.count}/{self.nf} {path}: '
-
- # Padded resize
- img = letterbox(img0, self.img_size, stride=self.stride, auto=self.auto)[0]
-
- # Convert
- img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
- img = np.ascontiguousarray(img)
-
- return path, img, img0, self.cap, s
-
- def new_video(self, path):
- self.frame = 0
- self.cap = cv2.VideoCapture(path)
- self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
- def __len__(self):
- return self.nf # number of files
-
-
-class LoadWebcam: # for inference
- # YOLOv5 local webcam dataloader, i.e. `python detect.py --source 0`
- def __init__(self, pipe='0', img_size=640, stride=32):
- self.img_size = img_size
- self.stride = stride
- self.pipe = eval(pipe) if pipe.isnumeric() else pipe
- self.cap = cv2.VideoCapture(self.pipe) # video capture object
- self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
-
- def __iter__(self):
- self.count = -1
- return self
-
- def __next__(self):
- self.count += 1
- if cv2.waitKey(1) == ord('q'): # q to quit
- self.cap.release()
- cv2.destroyAllWindows()
- raise StopIteration
-
- # Read frame
- ret_val, img0 = self.cap.read()
- img0 = cv2.flip(img0, 1) # flip left-right
-
- # Print
- assert ret_val, f'Camera Error {self.pipe}'
- img_path = 'webcam.jpg'
- s = f'webcam {self.count}: '
-
- # Padded resize
- img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
- # Convert
- img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
- img = np.ascontiguousarray(img)
-
- return img_path, img, img0, None, s
-
- def __len__(self):
- return 0
-
-
-class LoadStreams:
- # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
- def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True):
- self.mode = 'stream'
- self.img_size = img_size
- self.stride = stride
-
- if os.path.isfile(sources):
- with open(sources) as f:
- sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
- else:
- sources = [sources]
-
- n = len(sources)
- self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
- self.sources = [clean_str(x) for x in sources] # clean source names for later
- self.auto = auto
- for i, s in enumerate(sources): # index, source
- # Start thread to read frames from video stream
- st = f'{i + 1}/{n}: {s}... '
- if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video
- check_requirements(('pafy', 'youtube_dl==2020.12.2'))
- import pafy
- s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
- s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
- if s == 0:
- assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
- assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.'
- cap = cv2.VideoCapture(s)
- assert cap.isOpened(), f'{st}Failed to open {s}'
- w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan
- self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback
- self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
-
- _, self.imgs[i] = cap.read() # guarantee first frame
- self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
- LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
- self.threads[i].start()
- LOGGER.info('') # newline
-
- # check for common shapes
- s = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0].shape for x in self.imgs])
- self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
- if not self.rect:
- LOGGER.warning('WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.')
-
- def update(self, i, cap, stream):
- # Read stream `i` frames in daemon thread
- n, f, read = 0, self.frames[i], 1 # frame number, frame array, inference every 'read' frame
- while cap.isOpened() and n < f:
- n += 1
- # _, self.imgs[index] = cap.read()
- cap.grab()
- if n % read == 0:
- success, im = cap.retrieve()
- if success:
- self.imgs[i] = im
- else:
- LOGGER.warning('WARNING: Video stream unresponsive, please check your IP camera connection.')
- self.imgs[i] = np.zeros_like(self.imgs[i])
- cap.open(stream) # re-open stream if signal was lost
- time.sleep(0.0) # wait time
-
- def __iter__(self):
- self.count = -1
- return self
-
- def __next__(self):
- self.count += 1
- if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit
- cv2.destroyAllWindows()
- raise StopIteration
-
- # Letterbox
- img0 = self.imgs.copy()
- img = [letterbox(x, self.img_size, stride=self.stride, auto=self.rect and self.auto)[0] for x in img0]
-
- # Stack
- img = np.stack(img, 0)
-
- # Convert
- img = img[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW
- img = np.ascontiguousarray(img)
-
- return self.sources, img, img0, None, ''
-
- def __len__(self):
- return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years
-
-
-def img2label_paths(img_paths):
- # Define label paths as a function of image paths
- sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings
- return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
-
-
-class LoadImagesAndLabels(Dataset):
- # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
- cache_version = 0.6 # dataset labels *.cache version
- rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
-
- def __init__(self,
- path,
- img_size=640,
- batch_size=16,
- augment=False,
- hyp=None,
- rect=False,
- image_weights=False,
- cache_images=False,
- single_cls=False,
- stride=32,
- pad=0.0,
- prefix=''):
- self.img_size = img_size
- self.augment = augment
- self.hyp = hyp
- self.image_weights = image_weights
- self.rect = False if image_weights else rect
- self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
- self.mosaic_border = [-img_size // 2, -img_size // 2]
- self.stride = stride
- self.path = path
- self.albumentations = Albumentations() if augment else None
-
- try:
- f = [] # image files
- for p in path if isinstance(path, list) else [path]:
- p = Path(p) # os-agnostic
- if p.is_dir(): # dir
- f += glob.glob(str(p / '**' / '*.*'), recursive=True)
- # f = list(p.rglob('*.*')) # pathlib
- elif p.is_file(): # file
- with open(p) as t:
- t = t.read().strip().splitlines()
- parent = str(p.parent) + os.sep
- f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
- # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
- else:
- raise FileNotFoundError(f'{prefix}{p} does not exist')
- self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
- # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib
- assert self.im_files, f'{prefix}No images found'
- except Exception as e:
- raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {HELP_URL}')
-
- # Check cache
- self.label_files = img2label_paths(self.im_files) # labels
- cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
- try:
- cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict
- assert cache['version'] == self.cache_version # matches current version
- assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash
- except Exception:
- cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops
-
- # Display cache
- nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total
- if exists and LOCAL_RANK in {-1, 0}:
- d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt"
- tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT) # display cache results
- if cache['msgs']:
- LOGGER.info('\n'.join(cache['msgs'])) # display warnings
- assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}'
-
- # Read cache
- [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items
- labels, shapes, self.segments = zip(*cache.values())
- self.labels = list(labels)
- self.shapes = np.array(shapes)
- self.im_files = list(cache.keys()) # update
- self.label_files = img2label_paths(cache.keys()) # update
- n = len(shapes) # number of images
- bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
- nb = bi[-1] + 1 # number of batches
- self.batch = bi # batch index of image
- self.n = n
- self.indices = range(n)
-
- # Update labels
- include_class = [] # filter labels to include only these classes (optional)
- include_class_array = np.array(include_class).reshape(1, -1)
- for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
- if include_class:
- j = (label[:, 0:1] == include_class_array).any(1)
- self.labels[i] = label[j]
- if segment:
- self.segments[i] = segment[j]
- if single_cls: # single-class training, merge all classes into 0
- self.labels[i][:, 0] = 0
- if segment:
- self.segments[i][:, 0] = 0
-
- # Rectangular Training
- if self.rect:
- # Sort by aspect ratio
- s = self.shapes # wh
- ar = s[:, 1] / s[:, 0] # aspect ratio
- irect = ar.argsort()
- self.im_files = [self.im_files[i] for i in irect]
- self.label_files = [self.label_files[i] for i in irect]
- self.labels = [self.labels[i] for i in irect]
- self.shapes = s[irect] # wh
- ar = ar[irect]
-
- # Set training image shapes
- shapes = [[1, 1]] * nb
- for i in range(nb):
- ari = ar[bi == i]
- mini, maxi = ari.min(), ari.max()
- if maxi < 1:
- shapes[i] = [maxi, 1]
- elif mini > 1:
- shapes[i] = [1, 1 / mini]
-
- self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
-
- # Cache images into RAM/disk for faster training (WARNING: large datasets may exceed system resources)
- self.ims = [None] * n
- self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
- if cache_images:
- gb = 0 # Gigabytes of cached images
- self.im_hw0, self.im_hw = [None] * n, [None] * n
- fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
- results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
- pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT, disable=LOCAL_RANK > 0)
- for i, x in pbar:
- if cache_images == 'disk':
- gb += self.npy_files[i].stat().st_size
- else: # 'ram'
- self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
- gb += self.ims[i].nbytes
- pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
- pbar.close()
-
- def cache_labels(self, path=Path('./labels.cache'), prefix=''):
- # Cache dataset labels, check images and read shapes
- x = {} # dict
- nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
- desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
- with Pool(NUM_THREADS) as pool:
- pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
- desc=desc,
- total=len(self.im_files),
- bar_format=BAR_FORMAT)
- for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
- nm += nm_f
- nf += nf_f
- ne += ne_f
- nc += nc_f
- if im_file:
- x[im_file] = [lb, shape, segments]
- if msg:
- msgs.append(msg)
- pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt"
-
- pbar.close()
- if msgs:
- LOGGER.info('\n'.join(msgs))
- if nf == 0:
- LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. See {HELP_URL}')
- x['hash'] = get_hash(self.label_files + self.im_files)
- x['results'] = nf, nm, ne, nc, len(self.im_files)
- x['msgs'] = msgs # warnings
- x['version'] = self.cache_version # cache version
- try:
- np.save(path, x) # save cache for next time
- path.with_suffix('.cache.npy').rename(path) # remove .npy suffix
- LOGGER.info(f'{prefix}New cache created: {path}')
- except Exception as e:
- LOGGER.warning(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}') # not writeable
- return x
-
- def __len__(self):
- return len(self.im_files)
-
- # def __iter__(self):
- # self.count = -1
- # print('ran dataset iter')
- # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
- # return self
-
- def __getitem__(self, index):
- index = self.indices[index] # linear, shuffled, or image_weights
-
- hyp = self.hyp
- mosaic = self.mosaic and random.random() < hyp['mosaic']
- if mosaic:
- # Load mosaic
- img, labels = self.load_mosaic(index)
- shapes = None
-
- # MixUp augmentation
- if random.random() < hyp['mixup']:
- img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))
-
- else:
- # Load image
- img, (h0, w0), (h, w) = self.load_image(index)
-
- # Letterbox
- shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
- img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
- shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
-
- labels = self.labels[index].copy()
- if labels.size: # normalized xywh to pixel xyxy format
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
-
- if self.augment:
- img, labels = random_perspective(img,
- labels,
- degrees=hyp['degrees'],
- translate=hyp['translate'],
- scale=hyp['scale'],
- shear=hyp['shear'],
- perspective=hyp['perspective'])
-
- nl = len(labels) # number of labels
- if nl:
- labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)
-
- if self.augment:
- # Albumentations
- img, labels = self.albumentations(img, labels)
- nl = len(labels) # update after albumentations
-
- # HSV color-space
- augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
- # Flip up-down
- if random.random() < hyp['flipud']:
- img = np.flipud(img)
- if nl:
- labels[:, 2] = 1 - labels[:, 2]
-
- # Flip left-right
- if random.random() < hyp['fliplr']:
- img = np.fliplr(img)
- if nl:
- labels[:, 1] = 1 - labels[:, 1]
-
- # Cutouts
- # labels = cutout(img, labels, p=0.5)
- # nl = len(labels) # update after cutout
-
- labels_out = torch.zeros((nl, 6))
- if nl:
- labels_out[:, 1:] = torch.from_numpy(labels)
-
- # Convert
- img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
- img = np.ascontiguousarray(img)
-
- return torch.from_numpy(img), labels_out, self.im_files[index], shapes
-
- def load_image(self, i):
- # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
- im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
- if im is None: # not cached in RAM
- if fn.exists(): # load npy
- im = np.load(fn)
- else: # read image
- im = cv2.imread(f) # BGR
- assert im is not None, f'Image Not Found {f}'
- h0, w0 = im.shape[:2] # orig hw
- r = self.img_size / max(h0, w0) # ratio
- if r != 1: # if sizes are not equal
- interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
- im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp)
- return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
- return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized
-
- def cache_images_to_disk(self, i):
- # Saves an image as an *.npy file for faster loading
- f = self.npy_files[i]
- if not f.exists():
- np.save(f.as_posix(), cv2.imread(self.im_files[i]))
-
- def load_mosaic(self, index):
- # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
- labels4, segments4 = [], []
- s = self.img_size
- yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y
- indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
- random.shuffle(indices)
- for i, index in enumerate(indices):
- # Load image
- img, _, (h, w) = self.load_image(index)
-
- # place img in img4
- if i == 0: # top left
- img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
- x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
- x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
- elif i == 1: # top right
- x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
- x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
- elif i == 2: # bottom left
- x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
- x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
- elif i == 3: # bottom right
- x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
- x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
- img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
- padw = x1a - x1b
- padh = y1a - y1b
-
- # Labels
- labels, segments = self.labels[index].copy(), self.segments[index].copy()
- if labels.size:
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
- segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
- labels4.append(labels)
- segments4.extend(segments)
-
- # Concat/clip labels
- labels4 = np.concatenate(labels4, 0)
- for x in (labels4[:, 1:], *segments4):
- np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
- # img4, labels4 = replicate(img4, labels4) # replicate
-
- # Augment
- img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
- img4, labels4 = random_perspective(img4,
- labels4,
- segments4,
- degrees=self.hyp['degrees'],
- translate=self.hyp['translate'],
- scale=self.hyp['scale'],
- shear=self.hyp['shear'],
- perspective=self.hyp['perspective'],
- border=self.mosaic_border) # border to remove
-
- return img4, labels4
-
- def load_mosaic9(self, index):
- # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
- labels9, segments9 = [], []
- s = self.img_size
- indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
- random.shuffle(indices)
- hp, wp = -1, -1 # height, width previous
- for i, index in enumerate(indices):
- # Load image
- img, _, (h, w) = self.load_image(index)
-
- # place img in img9
- if i == 0: # center
- img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
- h0, w0 = h, w
- c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
- elif i == 1: # top
- c = s, s - h, s + w, s
- elif i == 2: # top right
- c = s + wp, s - h, s + wp + w, s
- elif i == 3: # right
- c = s + w0, s, s + w0 + w, s + h
- elif i == 4: # bottom right
- c = s + w0, s + hp, s + w0 + w, s + hp + h
- elif i == 5: # bottom
- c = s + w0 - w, s + h0, s + w0, s + h0 + h
- elif i == 6: # bottom left
- c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
- elif i == 7: # left
- c = s - w, s + h0 - h, s, s + h0
- elif i == 8: # top left
- c = s - w, s + h0 - hp - h, s, s + h0 - hp
-
- padx, pady = c[:2]
- x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords
-
- # Labels
- labels, segments = self.labels[index].copy(), self.segments[index].copy()
- if labels.size:
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format
- segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
- labels9.append(labels)
- segments9.extend(segments)
-
- # Image
- img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
- hp, wp = h, w # height, width previous
-
- # Offset
- yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y
- img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
-
- # Concat/clip labels
- labels9 = np.concatenate(labels9, 0)
- labels9[:, [1, 3]] -= xc
- labels9[:, [2, 4]] -= yc
- c = np.array([xc, yc]) # centers
- segments9 = [x - c for x in segments9]
-
- for x in (labels9[:, 1:], *segments9):
- np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
- # img9, labels9 = replicate(img9, labels9) # replicate
-
- # Augment
- img9, labels9 = random_perspective(img9,
- labels9,
- segments9,
- degrees=self.hyp['degrees'],
- translate=self.hyp['translate'],
- scale=self.hyp['scale'],
- shear=self.hyp['shear'],
- perspective=self.hyp['perspective'],
- border=self.mosaic_border) # border to remove
-
- return img9, labels9
-
- @staticmethod
- def collate_fn(batch):
- im, label, path, shapes = zip(*batch) # transposed
- for i, lb in enumerate(label):
- lb[:, 0] = i # add target image index for build_targets()
- return torch.stack(im, 0), torch.cat(label, 0), path, shapes
-
- @staticmethod
- def collate_fn4(batch):
- img, label, path, shapes = zip(*batch) # transposed
- n = len(shapes) // 4
- im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
-
- ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
- wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
- s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale
- for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW
- i *= 4
- if random.random() < 0.5:
- im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear',
- align_corners=False)[0].type(img[i].type())
- lb = label[i]
- else:
- im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
- lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
- im4.append(im)
- label4.append(lb)
-
- for i, lb in enumerate(label4):
- lb[:, 0] = i # add target image index for build_targets()
-
- return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4
-
-
-# Ancillary functions --------------------------------------------------------------------------------------------------
-def flatten_recursive(path=DATASETS_DIR / 'coco128'):
- # Flatten a recursive directory by bringing all files to top level
- new_path = Path(f'{str(path)}_flat')
- if os.path.exists(new_path):
- shutil.rmtree(new_path) # delete output folder
- os.makedirs(new_path) # make new output folder
- for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)):
- shutil.copyfile(file, new_path / Path(file).name)
-
-
-def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders import *; extract_boxes()
- # Convert detection dataset into classification dataset, with one directory per class
- path = Path(path) # images dir
- shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None # remove existing
- files = list(path.rglob('*.*'))
- n = len(files) # number of files
- for im_file in tqdm(files, total=n):
- if im_file.suffix[1:] in IMG_FORMATS:
- # image
- im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB
- h, w = im.shape[:2]
-
- # labels
- lb_file = Path(img2label_paths([str(im_file)])[0])
- if Path(lb_file).exists():
- with open(lb_file) as f:
- lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
-
- for j, x in enumerate(lb):
- c = int(x[0]) # class
- f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename
- if not f.parent.is_dir():
- f.parent.mkdir(parents=True)
-
- b = x[1:] * [w, h, w, h] # box
- # b[2:] = b[2:].max() # rectangle to square
- b[2:] = b[2:] * 1.2 + 3 # pad
- b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
-
- b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
- b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
- assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
-
-
-def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
- """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
- Usage: from utils.dataloaders import *; autosplit()
- Arguments
- path: Path to images directory
- weights: Train, val, test weights (list, tuple)
- annotated_only: Only use images with an annotated txt file
- """
- path = Path(path) # images dir
- files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only
- n = len(files) # number of files
- random.seed(0) # for reproducibility
- indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split
-
- txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files
- [(path.parent / x).unlink(missing_ok=True) for x in txt] # remove existing
-
- print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
- for i, img in tqdm(zip(indices, files), total=n):
- if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label
- with open(path.parent / txt[i], 'a') as f:
- f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file
-
-
-def verify_image_label(args):
- # Verify one image-label pair
- im_file, lb_file, prefix = args
- nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments
- try:
- # verify images
- im = Image.open(im_file)
- im.verify() # PIL verify
- shape = exif_size(im) # image size
- assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
- assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
- if im.format.lower() in ('jpg', 'jpeg'):
- with open(im_file, 'rb') as f:
- f.seek(-2, 2)
- if f.read() != b'\xff\xd9': # corrupt JPEG
- ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
- msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'
-
- # verify labels
- if os.path.isfile(lb_file):
- nf = 1 # label found
- with open(lb_file) as f:
- lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
- if any(len(x) > 6 for x in lb): # is segment
- classes = np.array([x[0] for x in lb], dtype=np.float32)
- segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
- lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
- lb = np.array(lb, dtype=np.float32)
- nl = len(lb)
- if nl:
- assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
- assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
- assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
- _, i = np.unique(lb, axis=0, return_index=True)
- if len(i) < nl: # duplicate row check
- lb = lb[i] # remove duplicates
- if segments:
- segments = segments[i]
- msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
- else:
- ne = 1 # label empty
- lb = np.zeros((0, 5), dtype=np.float32)
- else:
- nm = 1 # label missing
- lb = np.zeros((0, 5), dtype=np.float32)
- return im_file, lb, shape, segments, nm, nf, ne, nc, msg
- except Exception as e:
- nc = 1
- msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'
- return [None, None, None, None, nm, nf, ne, nc, msg]
-
-
-class HUBDatasetStats():
- """ Return dataset statistics dictionary with images and instances counts per split per class
- To run in parent directory: export PYTHONPATH="$PWD/yolov5"
- Usage1: from utils.dataloaders import *; HUBDatasetStats('coco128.yaml', autodownload=True)
- Usage2: from utils.dataloaders import *; HUBDatasetStats('path/to/coco128_with_yaml.zip')
- Arguments
- path: Path to data.yaml or data.zip (with data.yaml inside data.zip)
- autodownload: Attempt to download dataset if not found locally
- """
-
- def __init__(self, path='coco128.yaml', autodownload=False):
- # Initialize class
- zipped, data_dir, yaml_path = self._unzip(Path(path))
- try:
- with open(check_yaml(yaml_path), errors='ignore') as f:
- data = yaml.safe_load(f) # data dict
- if zipped:
- data['path'] = data_dir
- except Exception as e:
- raise Exception("error/HUB/dataset_stats/yaml_load") from e
-
- check_dataset(data, autodownload) # download dataset if missing
- self.hub_dir = Path(data['path'] + '-hub')
- self.im_dir = self.hub_dir / 'images'
- self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images
- self.stats = {'nc': data['nc'], 'names': data['names']} # statistics dictionary
- self.data = data
-
- @staticmethod
- def _find_yaml(dir):
- # Return data.yaml file
- files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive
- assert files, f'No *.yaml file found in {dir}'
- if len(files) > 1:
- files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name
- assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed'
- assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}'
- return files[0]
-
- def _unzip(self, path):
- # Unzip data.zip
- if not str(path).endswith('.zip'): # path is data.yaml
- return False, None, path
- assert Path(path).is_file(), f'Error unzipping {path}, file not found'
- ZipFile(path).extractall(path=path.parent) # unzip
- dir = path.with_suffix('') # dataset directory == zip name
- assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
- return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path
-
- def _hub_ops(self, f, max_dim=1920):
- # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing
- f_new = self.im_dir / Path(f).name # dataset-hub image filename
- try: # use PIL
- im = Image.open(f)
- r = max_dim / max(im.height, im.width) # ratio
- if r < 1.0: # image too large
- im = im.resize((int(im.width * r), int(im.height * r)))
- im.save(f_new, 'JPEG', quality=50, optimize=True) # save
- except Exception as e: # use OpenCV
- print(f'WARNING: HUB ops PIL failure {f}: {e}')
- im = cv2.imread(f)
- im_height, im_width = im.shape[:2]
- r = max_dim / max(im_height, im_width) # ratio
- if r < 1.0: # image too large
- im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
- cv2.imwrite(str(f_new), im)
-
- def get_json(self, save=False, verbose=False):
- # Return dataset JSON for Ultralytics HUB
- def _round(labels):
- # Update labels to integer class and 6 decimal place floats
- return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]
-
- for split in 'train', 'val', 'test':
- if self.data.get(split) is None:
- self.stats[split] = None # i.e. no test set
- continue
- dataset = LoadImagesAndLabels(self.data[split]) # load dataset
- x = np.array([
- np.bincount(label[:, 0].astype(int), minlength=self.data['nc'])
- for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')]) # shape(128x80)
- self.stats[split] = {
- 'instance_stats': {
- 'total': int(x.sum()),
- 'per_class': x.sum(0).tolist()},
- 'image_stats': {
- 'total': dataset.n,
- 'unlabelled': int(np.all(x == 0, 1).sum()),
- 'per_class': (x > 0).sum(0).tolist()},
- 'labels': [{
- str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]}
-
- # Save, print and return
- if save:
- stats_path = self.hub_dir / 'stats.json'
- print(f'Saving {stats_path.resolve()}...')
- with open(stats_path, 'w') as f:
- json.dump(self.stats, f) # save stats.json
- if verbose:
- print(json.dumps(self.stats, indent=2, sort_keys=False))
- return self.stats
-
- def process_images(self):
- # Compress images for Ultralytics HUB
- for split in 'train', 'val', 'test':
- if self.data.get(split) is None:
- continue
- dataset = LoadImagesAndLabels(self.data[split]) # load dataset
- desc = f'{split} images'
- for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc):
- pass
- print(f'Done. All images saved to {self.im_dir}')
- return self.im_dir
-
-
-# Classification dataloaders -------------------------------------------------------------------------------------------
-class ClassificationDataset(torchvision.datasets.ImageFolder):
- """
- YOLOv5 Classification Dataset.
- Arguments
- root: Dataset path
- transform: torchvision transforms, used by default
- album_transform: Albumentations transforms, used if installed
- """
-
- def __init__(self, root, augment, imgsz, cache=False):
- super().__init__(root=root)
- self.torch_transforms = classify_transforms(imgsz)
- self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
- self.cache_ram = cache is True or cache == 'ram'
- self.cache_disk = cache == 'disk'
- self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im
-
- def __getitem__(self, i):
- f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image
- if self.album_transforms:
- if self.cache_ram and im is None:
- im = self.samples[i][3] = cv2.imread(f)
- elif self.cache_disk:
- if not fn.exists(): # load npy
- np.save(fn.as_posix(), cv2.imread(f))
- im = np.load(fn)
- else: # read image
- im = cv2.imread(f) # BGR
- sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
- else:
- sample = self.torch_transforms(self.loader(f))
- return sample, j
-
-
-def create_classification_dataloader(path,
- imgsz=224,
- batch_size=16,
- augment=True,
- cache=False,
- rank=-1,
- workers=8,
- shuffle=True):
- # Returns Dataloader object to be used with YOLOv5 Classifier
- with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
- dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
- batch_size = min(batch_size, len(dataset))
- nd = torch.cuda.device_count()
- nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
- sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
- generator = torch.Generator()
- generator.manual_seed(0)
- return InfiniteDataLoader(dataset,
- batch_size=batch_size,
- shuffle=shuffle and sampler is None,
- num_workers=nw,
- sampler=sampler,
- pin_memory=True,
- worker_init_fn=seed_worker,
- generator=generator) # or DataLoader(persistent_workers=True)
diff --git a/yolov5-6.2/utils/docker/Dockerfile b/yolov5-6.2/utils/docker/Dockerfile
deleted file mode 100644
index 2280f209..00000000
--- a/yolov5-6.2/utils/docker/Dockerfile
+++ /dev/null
@@ -1,68 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
-# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
-
-# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:22.07-py3
-RUN rm -rf /opt/pytorch # remove 1.2GB dir
-
-# Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
-
-# Install linux packages
-RUN apt update && apt install --no-install-recommends -y zip htop screen libgl1-mesa-glx
-
-# Install pip packages
-COPY requirements.txt .
-RUN python -m pip install --upgrade pip wheel
-RUN pip uninstall -y Pillow torchtext # torch torchvision
-RUN pip install --no-cache -r requirements.txt albumentations wandb gsutil notebook Pillow>=9.1.0 \
- 'opencv-python<4.6.0.66' \
- --extra-index-url https://download.pytorch.org/whl/cu113
-
-# Create working directory
-RUN mkdir -p /usr/src/app
-WORKDIR /usr/src/app
-
-# Copy contents
-# COPY . /usr/src/app (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-
-# Set environment variables
-ENV OMP_NUM_THREADS=8
-
-
-# Usage Examples -------------------------------------------------------------------------------------------------------
-
-# Build and Push
-# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
-
-# Pull and Run
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
-
-# Pull and Run with local directory access
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
-
-# Kill all
-# sudo docker kill $(sudo docker ps -q)
-
-# Kill all image-based
-# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
-
-# Bash into running container
-# sudo docker exec -it 5a9b5863d93d bash
-
-# Bash into stopped container
-# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
-
-# Clean up
-# docker system prune -a --volumes
-
-# Update Ubuntu drivers
-# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
-
-# DDP test
-# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
-
-# GCP VM from Image
-# docker.io/ultralytics/yolov5:latest
diff --git a/yolov5-6.2/utils/docker/Dockerfile-arm64 b/yolov5-6.2/utils/docker/Dockerfile-arm64
deleted file mode 100644
index fe92c8d5..00000000
--- a/yolov5-6.2/utils/docker/Dockerfile-arm64
+++ /dev/null
@@ -1,42 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
-# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
-
-# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM arm64v8/ubuntu:20.04
-
-# Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
-
-# Install linux packages
-RUN apt update
-RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc \
- libgl1-mesa-glx libglib2.0-0 libpython3.8-dev
-# RUN alias python=python3
-
-# Install pip packages
-COPY requirements.txt .
-RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -r requirements.txt gsutil notebook \
- tensorflow-aarch64
- # tensorflowjs \
- # onnx onnx-simplifier onnxruntime \
- # coremltools openvino-dev \
-
-# Create working directory
-RUN mkdir -p /usr/src/app
-WORKDIR /usr/src/app
-
-# Copy contents
-# COPY . /usr/src/app (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-
-
-# Usage Examples -------------------------------------------------------------------------------------------------------
-
-# Build and Push
-# t=ultralytics/yolov5:latest-M1 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
-
-# Pull and Run
-# t=ultralytics/yolov5:latest-M1 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
diff --git a/yolov5-6.2/utils/docker/Dockerfile-cpu b/yolov5-6.2/utils/docker/Dockerfile-cpu
deleted file mode 100644
index d61dfeff..00000000
--- a/yolov5-6.2/utils/docker/Dockerfile-cpu
+++ /dev/null
@@ -1,39 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
-# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
-
-# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:20.04
-
-# Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
-
-# Install linux packages
-RUN apt update
-RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3.8-dev
-# RUN alias python=python3
-
-# Install pip packages
-COPY requirements.txt .
-RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
- coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu tensorflowjs \
- --extra-index-url https://download.pytorch.org/whl/cpu
-
-# Create working directory
-RUN mkdir -p /usr/src/app
-WORKDIR /usr/src/app
-
-# Copy contents
-# COPY . /usr/src/app (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-
-
-# Usage Examples -------------------------------------------------------------------------------------------------------
-
-# Build and Push
-# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t
-
-# Pull and Run
-# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
diff --git a/yolov5-6.2/utils/downloads.py b/yolov5-6.2/utils/downloads.py
deleted file mode 100644
index 9d4780ad..00000000
--- a/yolov5-6.2/utils/downloads.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Download utils
-"""
-
-import logging
-import os
-import platform
-import subprocess
-import time
-import urllib
-from pathlib import Path
-from zipfile import ZipFile
-
-import requests
-import torch
-
-
-def is_url(url, check_online=True):
- # Check if online file exists
- try:
- url = str(url)
- result = urllib.parse.urlparse(url)
- assert all([result.scheme, result.netloc, result.path]) # check if is url
- return (urllib.request.urlopen(url).getcode() == 200) if check_online else True # check if exists online
- except (AssertionError, urllib.request.HTTPError):
- return False
-
-
-def gsutil_getsize(url=''):
- # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
- s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
- return eval(s.split(' ')[0]) if len(s) else 0 # bytes
-
-
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
- # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
- from utils.general import LOGGER
-
- file = Path(file)
- assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
- try: # url1
- LOGGER.info(f'Downloading {url} to {file}...')
- torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
- assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check
- except Exception as e: # url2
- file.unlink(missing_ok=True) # remove partial downloads
- LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
- os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -") # curl download, retry and resume on fail
- finally:
- if not file.exists() or file.stat().st_size < min_bytes: # check
- file.unlink(missing_ok=True) # remove partial downloads
- LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
- LOGGER.info('')
-
-
-def attempt_download(file, repo='ultralytics/yolov5', release='v6.1'):
- # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.1', etc.
- from utils.general import LOGGER
-
- def github_assets(repository, version='latest'):
- # Return GitHub repo tag (i.e. 'v6.1') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
- if version != 'latest':
- version = f'tags/{version}' # i.e. tags/v6.1
- response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api
- return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets
-
- file = Path(str(file).strip().replace("'", ''))
- if not file.exists():
- # URL specified
- name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc.
- if str(file).startswith(('http:/', 'https:/')): # download
- url = str(file).replace(':/', '://') # Pathlib turns :// -> :/
- file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth...
- if Path(file).is_file():
- LOGGER.info(f'Found {url} locally at {file}') # file already exists
- else:
- safe_download(file=file, url=url, min_bytes=1E5)
- return file
-
- # GitHub assets
- assets = [
- 'yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov5n6.pt', 'yolov5s6.pt',
- 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
- try:
- tag, assets = github_assets(repo, release)
- except Exception:
- try:
- tag, assets = github_assets(repo) # latest release
- except Exception:
- try:
- tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
- except Exception:
- tag = release
-
- file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
- if name in assets:
- url3 = 'https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl' # backup gdrive mirror
- safe_download(
- file,
- url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
- url2=f'https://storage.googleapis.com/{repo}/{tag}/{name}', # backup url (optional)
- min_bytes=1E5,
- error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
-
- return str(file)
-
-
-def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
- # Downloads a file from Google Drive. from yolov5.utils.downloads import *; gdrive_download()
- t = time.time()
- file = Path(file)
- cookie = Path('cookie') # gdrive cookie
- print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
- file.unlink(missing_ok=True) # remove existing file
- cookie.unlink(missing_ok=True) # remove existing cookie
-
- # Attempt file download
- out = "NUL" if platform.system() == "Windows" else "/dev/null"
- os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
- if os.path.exists('cookie'): # large file
- s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
- else: # small file
- s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
- r = os.system(s) # execute, capture return
- cookie.unlink(missing_ok=True) # remove existing cookie
-
- # Error check
- if r != 0:
- file.unlink(missing_ok=True) # remove partial
- print('Download error ') # raise Exception('Download error')
- return r
-
- # Unzip if archive
- if file.suffix == '.zip':
- print('unzipping... ', end='')
- ZipFile(file).extractall(path=file.parent) # unzip
- file.unlink() # remove zip
-
- print(f'Done ({time.time() - t:.1f}s)')
- return r
-
-
-def get_token(cookie="./cookie"):
- with open(cookie) as f:
- for line in f:
- if "download" in line:
- return line.split()[-1]
- return ""
-
-
-# Google utils: https://cloud.google.com/storage/docs/reference/libraries ----------------------------------------------
-#
-#
-# def upload_blob(bucket_name, source_file_name, destination_blob_name):
-# # Uploads a file to a bucket
-# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
-#
-# storage_client = storage.Client()
-# bucket = storage_client.get_bucket(bucket_name)
-# blob = bucket.blob(destination_blob_name)
-#
-# blob.upload_from_filename(source_file_name)
-#
-# print('File {} uploaded to {}.'.format(
-# source_file_name,
-# destination_blob_name))
-#
-#
-# def download_blob(bucket_name, source_blob_name, destination_file_name):
-# # Uploads a blob from a bucket
-# storage_client = storage.Client()
-# bucket = storage_client.get_bucket(bucket_name)
-# blob = bucket.blob(source_blob_name)
-#
-# blob.download_to_filename(destination_file_name)
-#
-# print('Blob {} downloaded to {}.'.format(
-# source_blob_name,
-# destination_file_name))
diff --git a/yolov5-6.2/utils/flask_rest_api/README.md b/yolov5-6.2/utils/flask_rest_api/README.md
deleted file mode 100644
index a726acbd..00000000
--- a/yolov5-6.2/utils/flask_rest_api/README.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Flask REST API
-
-[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are
-commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API
-created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
-
-## Requirements
-
-[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
-
-```shell
-$ pip install Flask
-```
-
-## Run
-
-After Flask installation run:
-
-```shell
-$ python3 restapi.py --port 5000
-```
-
-Then use [curl](https://curl.se/) to perform a request:
-
-```shell
-$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'
-```
-
-The model inference results are returned as a JSON response:
-
-```json
-[
- {
- "class": 0,
- "confidence": 0.8900438547,
- "height": 0.9318675399,
- "name": "person",
- "width": 0.3264600933,
- "xcenter": 0.7438579798,
- "ycenter": 0.5207948685
- },
- {
- "class": 0,
- "confidence": 0.8440024257,
- "height": 0.7155083418,
- "name": "person",
- "width": 0.6546785235,
- "xcenter": 0.427829951,
- "ycenter": 0.6334488392
- },
- {
- "class": 27,
- "confidence": 0.3771208823,
- "height": 0.3902671337,
- "name": "tie",
- "width": 0.0696444362,
- "xcenter": 0.3675483763,
- "ycenter": 0.7991207838
- },
- {
- "class": 27,
- "confidence": 0.3527112305,
- "height": 0.1540903747,
- "name": "tie",
- "width": 0.0336618312,
- "xcenter": 0.7814827561,
- "ycenter": 0.5065554976
- }
-]
-```
-
-An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given
-in `example_request.py`
diff --git a/yolov5-6.2/utils/flask_rest_api/example_request.py b/yolov5-6.2/utils/flask_rest_api/example_request.py
deleted file mode 100644
index 773ad893..00000000
--- a/yolov5-6.2/utils/flask_rest_api/example_request.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Perform test request
-"""
-
-import pprint
-
-import requests
-
-DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-IMAGE = "zidane.jpg"
-
-# Read image
-with open(IMAGE, "rb") as f:
- image_data = f.read()
-
-response = requests.post(DETECTION_URL, files={"image": image_data}).json()
-
-pprint.pprint(response)
diff --git a/yolov5-6.2/utils/flask_rest_api/restapi.py b/yolov5-6.2/utils/flask_rest_api/restapi.py
deleted file mode 100644
index 8482435c..00000000
--- a/yolov5-6.2/utils/flask_rest_api/restapi.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run a Flask REST API exposing one or more YOLOv5s models
-"""
-
-import argparse
-import io
-
-import torch
-from flask import Flask, request
-from PIL import Image
-
-app = Flask(__name__)
-models = {}
-
-DETECTION_URL = "/v1/object-detection/<model>"
-
-
-@app.route(DETECTION_URL, methods=["POST"])
-def predict(model):
- if request.method != "POST":
- return
-
- if request.files.get("image"):
- # Method 1
- # with request.files["image"] as f:
- # im = Image.open(io.BytesIO(f.read()))
-
- # Method 2
- im_file = request.files["image"]
- im_bytes = im_file.read()
- im = Image.open(io.BytesIO(im_bytes))
-
- if model in models:
- results = models[model](im, size=640) # reduce size=320 for faster inference
- return results.pandas().xyxy[0].to_json(orient="records")
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
- parser.add_argument("--port", default=5000, type=int, help="port number")
- parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
- opt = parser.parse_args()
-
- for m in opt.model:
- models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True)
-
- app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat
diff --git a/yolov5-6.2/utils/general.py b/yolov5-6.2/utils/general.py
deleted file mode 100644
index 1c525c45..00000000
--- a/yolov5-6.2/utils/general.py
+++ /dev/null
@@ -1,1050 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-General utils
-"""
-
-import contextlib
-import glob
-import inspect
-import logging
-import math
-import os
-import platform
-import random
-import re
-import shutil
-import signal
-import sys
-import threading
-import time
-import urllib
-from datetime import datetime
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from subprocess import check_output
-from typing import Optional
-from zipfile import ZipFile
-
-import cv2
-import numpy as np
-import pandas as pd
-import pkg_resources as pkg
-import torch
-import torchvision
-import yaml
-
-from utils.downloads import gsutil_getsize
-from utils.metrics import box_iou, fitness
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-RANK = int(os.getenv('RANK', -1))
-
-# Settings
-DATASETS_DIR = ROOT.parent / 'datasets' # YOLOv5 datasets directory
-NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
-AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
-VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode
-FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
-
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
-np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
-pd.options.display.max_columns = 10
-cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
-os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads
-os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy)
-
-
-def is_ascii(s=''):
- # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
- s = str(s) # convert list, tuple, None, etc. to str
- return len(s.encode().decode('ascii', 'ignore')) == len(s)
-
-
-def is_chinese(s='人工智能'):
- # Is string composed of any Chinese characters?
- return bool(re.search('[\u4e00-\u9fff]', str(s)))
-
-
-def is_colab():
- # Is environment a Google Colab instance?
- return 'COLAB_GPU' in os.environ
-
-
-def is_kaggle():
- # Is environment a Kaggle Notebook?
- return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'
-
-
-def is_docker() -> bool:
- """Check if the process runs inside a docker container."""
- if Path("/.dockerenv").exists():
- return True
- try: # check if docker is in control groups
- with open("/proc/self/cgroup") as file:
- return any("docker" in line for line in file)
- except OSError:
- return False
-
-
-def is_writeable(dir, test=False):
- # Return True if directory has write permissions, test opening a file with write permissions if test=True
- if not test:
- return os.access(dir, os.W_OK) # possible issues on Windows
- file = Path(dir) / 'tmp.txt'
- try:
- with open(file, 'w'): # open file with write permissions
- pass
- file.unlink() # remove file
- return True
- except OSError:
- return False
-
-
-def set_logging(name=None, verbose=VERBOSE):
- # Sets level and returns logger
- if is_kaggle() or is_colab():
- for h in logging.root.handlers:
- logging.root.removeHandler(h) # remove all handlers associated with the root logger object
- rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings
- level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
- log = logging.getLogger(name)
- log.setLevel(level)
- handler = logging.StreamHandler()
- handler.setFormatter(logging.Formatter("%(message)s"))
- handler.setLevel(level)
- log.addHandler(handler)
-
-
-set_logging() # run before defining LOGGER
-LOGGER = logging.getLogger("yolov5") # define globally (used in train.py, val.py, detect.py, etc.)
-if platform.system() == 'Windows':
- for fn in LOGGER.info, LOGGER.warning:
- setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging
-
-
-def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
- # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
- env = os.getenv(env_var)
- if env:
- path = Path(env) # use environment variable
- else:
- cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs
- path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir
- path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable
- path.mkdir(exist_ok=True) # make if required
- return path
-
-
-CONFIG_DIR = user_config_dir() # Ultralytics settings dir
-
-
-class Profile(contextlib.ContextDecorator):
- # Usage: @Profile() decorator or 'with Profile():' context manager
- def __enter__(self):
- self.start = time.time()
-
- def __exit__(self, type, value, traceback):
- print(f'Profile results: {time.time() - self.start:.5f}s')
-
-
-class Timeout(contextlib.ContextDecorator):
- # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
- def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
- self.seconds = int(seconds)
- self.timeout_message = timeout_msg
- self.suppress = bool(suppress_timeout_errors)
-
- def _timeout_handler(self, signum, frame):
- raise TimeoutError(self.timeout_message)
-
- def __enter__(self):
- if platform.system() != 'Windows': # not supported on Windows
- signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
- signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- if platform.system() != 'Windows':
- signal.alarm(0) # Cancel SIGALRM if it's scheduled
- if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
- return True
-
-
-class WorkingDirectory(contextlib.ContextDecorator):
- # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
- def __init__(self, new_dir):
- self.dir = new_dir # new dir
- self.cwd = Path.cwd().resolve() # current dir
-
- def __enter__(self):
- os.chdir(self.dir)
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- os.chdir(self.cwd)
-
-
-def try_except(func):
- # try-except function. Usage: @try_except decorator
- def handler(*args, **kwargs):
- try:
- func(*args, **kwargs)
- except Exception as e:
- print(e)
-
- return handler
-
-
-def threaded(func):
- # Multi-threads a target function and returns thread. Usage: @threaded decorator
- def wrapper(*args, **kwargs):
- thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
- thread.start()
- return thread
-
- return wrapper
-
-
-def methods(instance):
- # Get class/instance methods
- return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
-
-
-def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
- # Print function arguments (optional args dict)
- x = inspect.currentframe().f_back # previous frame
- file, _, fcn, _, _ = inspect.getframeinfo(x)
- if args is None: # get args automatically
- args, _, _, frm = inspect.getargvalues(x)
- args = {k: v for k, v in frm.items() if k in args}
- try:
- file = Path(file).resolve().relative_to(ROOT).with_suffix('')
- except ValueError:
- file = Path(file).stem
- s = (f'{file}: ' if show_file else '') + (f'{fcn}: ' if show_fcn else '')
- LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
-
-
-def init_seeds(seed=0, deterministic=False):
- # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
- # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible
- import torch.backends.cudnn as cudnn
-
- if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213
- torch.use_deterministic_algorithms(True)
- os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
- os.environ['PYTHONHASHSEED'] = str(seed)
-
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)
- torch.cuda.manual_seed(seed)
- torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
-
-
-def intersect_dicts(da, db, exclude=()):
- # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
- return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
-
-
-def get_latest_run(search_dir='.'):
- # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
- last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
- return max(last_list, key=os.path.getctime) if last_list else ''
-
-
-def emojis(str=''):
- # Return platform-dependent emoji-safe version of string
- return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-def file_age(path=__file__):
- # Return days since last file update
- dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta
- return dt.days # + dt.seconds / 86400 # fractional days
-
-
-def file_date(path=__file__):
- # Return human-readable file modification date, i.e. '2021-3-26'
- t = datetime.fromtimestamp(Path(path).stat().st_mtime)
- return f'{t.year}-{t.month}-{t.day}'
-
-
-def file_size(path):
- # Return file/dir size (MB)
- mb = 1 << 20 # bytes to MiB (1024 ** 2)
- path = Path(path)
- if path.is_file():
- return path.stat().st_size / mb
- elif path.is_dir():
- return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb
- else:
- return 0.0
-
-
-def check_online():
- # Check internet connectivity
- import socket
- try:
- socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
- return True
- except OSError:
- return False
-
-
-def git_describe(path=ROOT): # path must be a directory
- # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
- try:
- assert (Path(path) / '.git').is_dir()
- return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
- except Exception:
- return ''
-
-
-@try_except
-@WorkingDirectory(ROOT)
-def check_git_status(repo='ultralytics/yolov5'):
- # YOLOv5 status check, recommend 'git pull' if code is out of date
- url = f'https://github.com/{repo}'
- msg = f', for updates see {url}'
- s = colorstr('github: ') # string
- assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg
- assert check_online(), s + 'skipping check (offline)' + msg
-
- splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode())
- matches = [repo in s for s in splits]
- if any(matches):
- remote = splits[matches.index(True) - 1]
- else:
- remote = 'ultralytics'
- check_output(f'git remote add {remote} {url}', shell=True)
- check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch
- branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
- n = int(check_output(f'git rev-list {branch}..{remote}/master --count', shell=True)) # commits behind
- if n > 0:
- pull = 'git pull' if remote == 'origin' else f'git pull {remote} master'
- s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `{pull}` or `git clone {url}` to update."
- else:
- s += f'up to date with {url} ✅'
- LOGGER.info(s)
-
-
-def check_python(minimum='3.7.0'):
- # Check current python version vs. required python version
- check_version(platform.python_version(), minimum, name='Python ', hard=True)
-
-
-def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):
- # Check version vs. required version
- current, minimum = (pkg.parse_version(x) for x in (current, minimum))
- result = (current == minimum) if pinned else (current >= minimum) # bool
- s = f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed' # string
- if hard:
- assert result, s # assert min requirements met
- if verbose and not result:
- LOGGER.warning(s)
- return result
-
-
-@try_except
-def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=()):
- # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages)
- prefix = colorstr('red', 'bold', 'requirements:')
- check_python() # check python version
- if isinstance(requirements, (str, Path)): # requirements.txt file
- file = Path(requirements)
- assert file.exists(), f"{prefix} {file.resolve()} not found, check failed."
- with file.open() as f:
- requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
- else: # list or tuple of packages
- requirements = [x for x in requirements if x not in exclude]
-
- n = 0 # number of packages updates
- for i, r in enumerate(requirements):
- try:
- pkg.require(r)
- except Exception: # DistributionNotFound or VersionConflict if requirements not met
- s = f"{prefix} {r} not found and is required by YOLOv5"
- if install and AUTOINSTALL: # check environment variable
- LOGGER.info(f"{s}, attempting auto-update...")
- try:
- assert check_online(), f"'pip install {r}' skipped (offline)"
- LOGGER.info(check_output(f'pip install "{r}" {cmds[i] if cmds else ""}', shell=True).decode())
- n += 1
- except Exception as e:
- LOGGER.warning(f'{prefix} {e}')
- else:
- LOGGER.info(f'{s}. Please install and rerun your command.')
-
- if n: # if packages updated
- source = file.resolve() if 'file' in locals() else requirements
- s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
- f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
- LOGGER.info(s)
-
-
-def check_img_size(imgsz, s=32, floor=0):
- # Verify image size is a multiple of stride s in each dimension
- if isinstance(imgsz, int): # integer i.e. img_size=640
- new_size = max(make_divisible(imgsz, int(s)), floor)
- else: # list i.e. img_size=[640, 480]
- imgsz = list(imgsz) # convert to list if tuple
- new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
- if new_size != imgsz:
- LOGGER.warning(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
- return new_size
-
-
-def check_imshow():
- # Check if environment supports image displays
- try:
- assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
- assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
- cv2.imshow('test', np.zeros((1, 1, 3)))
- cv2.waitKey(1)
- cv2.destroyAllWindows()
- cv2.waitKey(1)
- return True
- except Exception as e:
- LOGGER.warning(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
- return False
-
-
-def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
- # Check file(s) for acceptable suffix
- if file and suffix:
- if isinstance(suffix, str):
- suffix = [suffix]
- for f in file if isinstance(file, (list, tuple)) else [file]:
- s = Path(f).suffix.lower() # file suffix
- if len(s):
- assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
-
-
-def check_yaml(file, suffix=('.yaml', '.yml')):
- # Search/download YAML file (if necessary) and return path, checking suffix
- return check_file(file, suffix)
-
-
-def check_file(file, suffix=''):
- # Search/download file (if necessary) and return path
- check_suffix(file, suffix) # optional
- file = str(file) # convert to str()
- if Path(file).is_file() or not file: # exists
- return file
- elif file.startswith(('http:/', 'https:/')): # download
- url = file # warning: Pathlib turns :// -> :/
- file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
- if Path(file).is_file():
- LOGGER.info(f'Found {url} locally at {file}') # file already exists
- else:
- LOGGER.info(f'Downloading {url} to {file}...')
- torch.hub.download_url_to_file(url, file)
- assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
- return file
- elif file.startswith('clearml://'): # ClearML Dataset ID
- assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
- return file
- else: # search
- files = []
- for d in 'data', 'models', 'utils': # search directories
- files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file
- assert len(files), f'File not found: {file}' # assert file was found
- assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
- return files[0] # return file
-
-
-def check_font(font=FONT, progress=False):
- # Download font to CONFIG_DIR if necessary
- font = Path(font)
- file = CONFIG_DIR / font.name
- if not font.exists() and not file.exists():
- url = "https://ultralytics.com/assets/" + font.name
- LOGGER.info(f'Downloading {url} to {file}...')
- torch.hub.download_url_to_file(url, str(file), progress=progress)
-
-
-def check_dataset(data, autodownload=True):
- # Download, check and/or unzip dataset if not found locally
-
- # Download (optional)
- extract_dir = ''
- if isinstance(data, (str, Path)) and str(data).endswith('.zip'): # i.e. gs://bucket/dir/coco128.zip
- download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
- data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
- extract_dir, autodownload = data.parent, False
-
- # Read yaml (optional)
- if isinstance(data, (str, Path)):
- with open(data, errors='ignore') as f:
- data = yaml.safe_load(f) # dictionary
-
- # Checks
- for k in 'train', 'val', 'nc':
- assert k in data, f"data.yaml '{k}:' field missing ❌"
- if 'names' not in data:
- LOGGER.warning("data.yaml 'names:' field missing ⚠️, assigning default names 'class0', 'class1', etc.")
- data['names'] = [f'class{i}' for i in range(data['nc'])] # default names
-
- # Resolve paths
- path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'
- if not path.is_absolute():
- path = (ROOT / path).resolve()
- for k in 'train', 'val', 'test':
- if data.get(k): # prepend path
- data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
-
- # Parse yaml
- train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
- if val:
- val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
- if not all(x.exists() for x in val):
- LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()])
- if not s or not autodownload:
- raise Exception('Dataset not found ❌')
- t = time.time()
- root = path.parent if 'path' in data else '..' # unzip directory i.e. '../'
- if s.startswith('http') and s.endswith('.zip'): # URL
- f = Path(s).name # filename
- LOGGER.info(f'Downloading {s} to {f}...')
- torch.hub.download_url_to_file(s, f)
- Path(root).mkdir(parents=True, exist_ok=True) # create root
- ZipFile(f).extractall(path=root) # unzip
- Path(f).unlink() # remove zip
- r = None # success
- elif s.startswith('bash '): # bash script
- LOGGER.info(f'Running {s} ...')
- r = os.system(s)
- else: # python script
- r = exec(s, {'yaml': data}) # return None
- dt = f'({round(time.time() - t, 1)}s)'
- s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
- LOGGER.info(f"Dataset download {s}")
- check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts
- return data # dictionary
-
-
-def check_amp(model):
- # Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation
- from models.common import AutoShape, DetectMultiBackend
-
- def amp_allclose(model, im):
- # All close FP32 vs AMP results
- m = AutoShape(model, verbose=False) # model
- a = m(im).xywhn[0] # FP32 inference
- m.amp = True
- b = m(im).xywhn[0] # AMP inference
- return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance
-
- prefix = colorstr('AMP: ')
- device = next(model.parameters()).device # get model device
- if device.type == 'cpu':
- return False # AMP disabled on CPU
- f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check
- im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3))
- try:
- assert amp_allclose(model, im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im)
- LOGGER.info(f'{prefix}checks passed ✅')
- return True
- except Exception:
- help_url = 'https://github.com/ultralytics/yolov5/issues/7908'
- LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}')
- return False
-
-
-def yaml_load(file='data.yaml'):
- # Single-line safe yaml loading
- with open(file, errors='ignore') as f:
- return yaml.safe_load(f)
-
-
-def yaml_save(file='data.yaml', data={}):
- # Single-line safe yaml saving
- with open(file, 'w') as f:
- yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
-
-
-def url2file(url):
- # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
- url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
- return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
-
-
-def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3):
- # Multi-threaded file download and unzip function, used in data.yaml for autodownload
- def download_one(url, dir):
- # Download 1 file
- success = True
- f = dir / Path(url).name # filename
- if Path(url).is_file(): # exists in current path
- Path(url).rename(f) # move to dir
- elif not f.exists():
- LOGGER.info(f'Downloading {url} to {f}...')
- for i in range(retry + 1):
- if curl:
- s = 'sS' if threads > 1 else '' # silent
- r = os.system(f'curl -{s}L "{url}" -o "{f}" --retry 9 -C -') # curl download with retry, continue
- success = r == 0
- else:
- torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download
- success = f.is_file()
- if success:
- break
- elif i < retry:
- LOGGER.warning(f'Download failure, retrying {i + 1}/{retry} {url}...')
- else:
- LOGGER.warning(f'Failed to download {url}...')
-
- if unzip and success and f.suffix in ('.zip', '.tar', '.gz'):
- LOGGER.info(f'Unzipping {f}...')
- if f.suffix == '.zip':
- ZipFile(f).extractall(path=dir) # unzip
- elif f.suffix == '.tar':
- os.system(f'tar xf {f} --directory {f.parent}') # unzip
- elif f.suffix == '.gz':
- os.system(f'tar xfz {f} --directory {f.parent}') # unzip
- if delete:
- f.unlink() # remove zip
-
- dir = Path(dir)
- dir.mkdir(parents=True, exist_ok=True) # make directory
- if threads > 1:
- pool = ThreadPool(threads)
- pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multi-threaded
- pool.close()
- pool.join()
- else:
- for u in [url] if isinstance(url, (str, Path)) else url:
- download_one(u, dir)
-
-
-def make_divisible(x, divisor):
- # Returns nearest x divisible by divisor
- if isinstance(divisor, torch.Tensor):
- divisor = int(divisor.max()) # to int
- return math.ceil(x / divisor) * divisor
-
-
-def clean_str(s):
- # Cleans a string by replacing special characters with underscore _
- return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
-
-
-def one_cycle(y1=0.0, y2=1.0, steps=100):
- # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
- return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
-
-
-def colorstr(*input):
- # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
- *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
- colors = {
- 'black': '\033[30m', # basic colors
- 'red': '\033[31m',
- 'green': '\033[32m',
- 'yellow': '\033[33m',
- 'blue': '\033[34m',
- 'magenta': '\033[35m',
- 'cyan': '\033[36m',
- 'white': '\033[37m',
- 'bright_black': '\033[90m', # bright colors
- 'bright_red': '\033[91m',
- 'bright_green': '\033[92m',
- 'bright_yellow': '\033[93m',
- 'bright_blue': '\033[94m',
- 'bright_magenta': '\033[95m',
- 'bright_cyan': '\033[96m',
- 'bright_white': '\033[97m',
- 'end': '\033[0m', # misc
- 'bold': '\033[1m',
- 'underline': '\033[4m'}
- return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
-
-
-def labels_to_class_weights(labels, nc=80):
- # Get class weights (inverse frequency) from training labels
- if labels[0] is None: # no labels loaded
- return torch.Tensor()
-
- labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
- classes = labels[:, 0].astype(int) # labels = [class xywh]
- weights = np.bincount(classes, minlength=nc) # occurrences per class
-
- # Prepend gridpoint count (for uCE training)
- # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
- # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
-
- weights[weights == 0] = 1 # replace empty bins with 1
- weights = 1 / weights # number of targets per class
- weights /= weights.sum() # normalize
- return torch.from_numpy(weights).float()
-
-
-def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
- # Produces image weights based on class_weights and image contents
- # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample
- class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
- return (class_weights.reshape(1, nc) * class_counts).sum(1)
-
-
-def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
- # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
- # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
- # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
- # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
- # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
- return [
- 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
- 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
- 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-
-
-def xyxy2xywh(x):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
- y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
- y[:, 2] = x[:, 2] - x[:, 0] # width
- y[:, 3] = x[:, 3] - x[:, 1] # height
- return y
-
-
-def xywh2xyxy(x):
- # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
- y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
- y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
- y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
- return y
-
-
-def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
- # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
- y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y
- y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x
- y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y
- return y
-
-
-def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
- if clip:
- clip_coords(x, (h - eps, w - eps)) # warning: inplace clip
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w # x center
- y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h # y center
- y[:, 2] = (x[:, 2] - x[:, 0]) / w # width
- y[:, 3] = (x[:, 3] - x[:, 1]) / h # height
- return y
-
-
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
- # Convert normalized segments into pixel segments, shape (n,2)
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = w * x[:, 0] + padw # top left x
- y[:, 1] = h * x[:, 1] + padh # top left y
- return y
-
-
-def segment2box(segment, width=640, height=640):
- # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
- x, y = segment.T # segment xy
- inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
- x, y, = x[inside], y[inside]
- return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
-
-
-def segments2boxes(segments):
- # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
- boxes = []
- for s in segments:
- x, y = s.T # segment xy
- boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
- return xyxy2xywh(np.array(boxes)) # cls, xywh
-
-
-def resample_segments(segments, n=1000):
- # Up-sample an (n,2) segment
- for i, s in enumerate(segments):
- s = np.concatenate((s, s[0:1, :]), axis=0)
- x = np.linspace(0, len(s) - 1, n)
- xp = np.arange(len(s))
- segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
- return segments
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
- # Rescale coords (xyxy) from img1_shape to img0_shape
- if ratio_pad is None: # calculate from img0_shape
- gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
- pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
- else:
- gain = ratio_pad[0][0]
- pad = ratio_pad[1]
-
- coords[:, [0, 2]] -= pad[0] # x padding
- coords[:, [1, 3]] -= pad[1] # y padding
- coords[:, :4] /= gain
- clip_coords(coords, img0_shape)
- return coords
-
-
-def clip_coords(boxes, shape):
- # Clip bounding xyxy bounding boxes to image shape (height, width)
- if isinstance(boxes, torch.Tensor): # faster individually
- boxes[:, 0].clamp_(0, shape[1]) # x1
- boxes[:, 1].clamp_(0, shape[0]) # y1
- boxes[:, 2].clamp_(0, shape[1]) # x2
- boxes[:, 3].clamp_(0, shape[0]) # y2
- else: # np.array (faster grouped)
- boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
- boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
-
-
-def non_max_suppression(prediction,
- conf_thres=0.25,
- iou_thres=0.45,
- classes=None,
- agnostic=False,
- multi_label=False,
- labels=(),
- max_det=300):
- """Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes
-
- Returns:
- list of detections, on (n,6) tensor per image [xyxy, conf, cls]
- """
-
- bs = prediction.shape[0] # batch size
- nc = prediction.shape[2] - 5 # number of classes
- xc = prediction[..., 4] > conf_thres # candidates
-
- # Checks
- assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
- assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
-
- # Settings
- # min_wh = 2 # (pixels) minimum box width and height
- max_wh = 7680 # (pixels) maximum box width and height
- max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
- time_limit = 0.3 + 0.03 * bs # seconds to quit after
- redundant = True # require redundant detections
- multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
- merge = False # use merge-NMS
-
- t = time.time()
- output = [torch.zeros((0, 6), device=prediction.device)] * bs
- for xi, x in enumerate(prediction): # image index, image inference
- # Apply constraints
- # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
- x = x[xc[xi]] # confidence
-
- # Cat apriori labels if autolabelling
- if labels and len(labels[xi]):
- lb = labels[xi]
- v = torch.zeros((len(lb), nc + 5), device=x.device)
- v[:, :4] = lb[:, 1:5] # box
- v[:, 4] = 1.0 # conf
- v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
- x = torch.cat((x, v), 0)
-
- # If none remain process next image
- if not x.shape[0]:
- continue
-
- # Compute conf
- x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
-
- # Box (center x, center y, width, height) to (x1, y1, x2, y2)
- box = xywh2xyxy(x[:, :4])
-
- # Detections matrix nx6 (xyxy, conf, cls)
- if multi_label:
- i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
- x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
- else: # best class only
- conf, j = x[:, 5:].max(1, keepdim=True)
- x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
- # Filter by class
- if classes is not None:
- x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
- # Apply finite constraint
- # if not torch.isfinite(x).all():
- # x = x[torch.isfinite(x).all(1)]
-
- # Check shape
- n = x.shape[0] # number of boxes
- if not n: # no boxes
- continue
- elif n > max_nms: # excess boxes
- x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
-
- # Batched NMS
- c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
- boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
- i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
- if i.shape[0] > max_det: # limit detections
- i = i[:max_det]
- if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
- # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
- iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
- weights = iou * scores[None] # box weights
- x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
- if redundant:
- i = i[iou.sum(1) > 1] # require redundancy
-
- output[xi] = x[i]
- if (time.time() - t) > time_limit:
- LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
- break # time limit exceeded
-
- return output
-
-
-def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer()
- # Strip optimizer from 'f' to finalize training, optionally save as 's'
- x = torch.load(f, map_location=torch.device('cpu'))
- if x.get('ema'):
- x['model'] = x['ema'] # replace model with ema
- for k in 'optimizer', 'best_fitness', 'wandb_id', 'ema', 'updates': # keys
- x[k] = None
- x['epoch'] = -1
- x['model'].half() # to FP16
- for p in x['model'].parameters():
- p.requires_grad = False
- torch.save(x, s or f)
- mb = os.path.getsize(s or f) / 1E6 # filesize
- LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
-
-
-def print_mutation(results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')):
- evolve_csv = save_dir / 'evolve.csv'
- evolve_yaml = save_dir / 'hyp_evolve.yaml'
- keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
- 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps]
- keys = tuple(x.strip() for x in keys)
- vals = results + tuple(hyp.values())
- n = len(keys)
-
- # Download (optional)
- if bucket:
- url = f'gs://{bucket}/evolve.csv'
- if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
- os.system(f'gsutil cp {url} {save_dir}') # download evolve.csv if larger than local
-
- # Log to evolve.csv
- s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header
- with open(evolve_csv, 'a') as f:
- f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
-
- # Save yaml
- with open(evolve_yaml, 'w') as f:
- data = pd.read_csv(evolve_csv)
- data = data.rename(columns=lambda x: x.strip()) # strip keys
- i = np.argmax(fitness(data.values[:, :4])) #
- generations = len(data)
- f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' +
- f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) +
- '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
- yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
-
- # Print to screen
- LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix +
- ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}'
- for x in vals) + '\n\n')
-
- if bucket:
- os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}') # upload
-
-
-def apply_classifier(x, model, img, im0):
- # Apply a second stage classifier to YOLO outputs
- # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
- im0 = [im0] if isinstance(im0, np.ndarray) else im0
- for i, d in enumerate(x): # per image
- if d is not None and len(d):
- d = d.clone()
-
- # Reshape and pad cutouts
- b = xyxy2xywh(d[:, :4]) # boxes
- b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
- b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
- d[:, :4] = xywh2xyxy(b).long()
-
- # Rescale boxes from img_size to im0 size
- scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
-
- # Classes
- pred_cls1 = d[:, 5].long()
- ims = []
- for a in d:
- cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
- im = cv2.resize(cutout, (224, 224)) # BGR
-
- im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
- im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
- im /= 255 # 0 - 255 to 0.0 - 1.0
- ims.append(im)
-
- pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
- x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
-
- return x
-
-
-def increment_path(path, exist_ok=False, sep='', mkdir=False):
- # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
- path = Path(path) # os-agnostic
- if path.exists() and not exist_ok:
- path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
-
- # Method 1
- for n in range(2, 9999):
- p = f'{path}{sep}{n}{suffix}' # increment path
- if not os.path.exists(p): #
- break
- path = Path(p)
-
- # Method 2 (deprecated)
- # dirs = glob.glob(f"{path}{sep}*") # similar paths
- # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs]
- # i = [int(m.groups()[0]) for m in matches if m] # indices
- # n = max(i) + 1 if i else 2 # increment number
- # path = Path(f"{path}{sep}{n}{suffix}") # increment path
-
- if mkdir:
- path.mkdir(parents=True, exist_ok=True) # make directory
-
- return path
-
-
-# OpenCV Chinese-friendly functions ------------------------------------------------------------------------------------
-imshow_ = cv2.imshow # copy to avoid recursion errors
-
-
-def imread(path, flags=cv2.IMREAD_COLOR):
- return cv2.imdecode(np.fromfile(path, np.uint8), flags)
-
-
-def imwrite(path, im):
- try:
- cv2.imencode(Path(path).suffix, im)[1].tofile(path)
- return True
- except Exception:
- return False
-
-
-def imshow(path, im):
- imshow_(path.encode('unicode_escape').decode(), im)
-
-
-cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine
-
-# Variables ------------------------------------------------------------------------------------------------------------
-NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns # terminal window size for tqdm
diff --git a/yolov5-6.2/utils/google_app_engine/Dockerfile b/yolov5-6.2/utils/google_app_engine/Dockerfile
deleted file mode 100644
index 0155618f..00000000
--- a/yolov5-6.2/utils/google_app_engine/Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-FROM gcr.io/google-appengine/python
-
-# Create a virtualenv for dependencies. This isolates these packages from
-# system-level packages.
-# Use -p python3 or -p python3.7 to select python version. Default is version 2.
-RUN virtualenv /env -p python3
-
-# Setting these environment variables are the same as running
-# source /env/bin/activate.
-ENV VIRTUAL_ENV /env
-ENV PATH /env/bin:$PATH
-
-RUN apt-get update && apt-get install -y python-opencv
-
-# Copy the application's requirements.txt and run pip to install all
-# dependencies into the virtualenv.
-ADD requirements.txt /app/requirements.txt
-RUN pip install -r /app/requirements.txt
-
-# Add the application source code.
-ADD . /app
-
-# Run a WSGI server to serve the application. gunicorn must be declared as
-# a dependency in requirements.txt.
-CMD gunicorn -b :$PORT main:app
diff --git a/yolov5-6.2/utils/google_app_engine/additional_requirements.txt b/yolov5-6.2/utils/google_app_engine/additional_requirements.txt
deleted file mode 100644
index 42d7ffc0..00000000
--- a/yolov5-6.2/utils/google_app_engine/additional_requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-# add these requirements in your app on top of the existing ones
-pip==21.1
-Flask==1.0.2
-gunicorn==19.9.0
diff --git a/yolov5-6.2/utils/google_app_engine/app.yaml b/yolov5-6.2/utils/google_app_engine/app.yaml
deleted file mode 100644
index 5056b7c1..00000000
--- a/yolov5-6.2/utils/google_app_engine/app.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-runtime: custom
-env: flex
-
-service: yolov5app
-
-liveness_check:
- initial_delay_sec: 600
-
-manual_scaling:
- instances: 1
-resources:
- cpu: 1
- memory_gb: 4
- disk_size_gb: 20
diff --git a/yolov5-6.2/utils/loggers/__init__.py b/yolov5-6.2/utils/loggers/__init__.py
deleted file mode 100644
index 8ec846f8..00000000
--- a/yolov5-6.2/utils/loggers/__init__.py
+++ /dev/null
@@ -1,308 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Logging utils
-"""
-
-import os
-import warnings
-from pathlib import Path
-
-import pkg_resources as pkg
-import torch
-from torch.utils.tensorboard import SummaryWriter
-
-from utils.general import colorstr, cv2
-from utils.loggers.clearml.clearml_utils import ClearmlLogger
-from utils.loggers.wandb.wandb_utils import WandbLogger
-from utils.plots import plot_images, plot_results
-from utils.torch_utils import de_parallel
-
-LOGGERS = ('csv', 'tb', 'wandb', 'clearml') # *.csv, TensorBoard, Weights & Biases, ClearML
-RANK = int(os.getenv('RANK', -1))
-
-try:
- import wandb
-
- assert hasattr(wandb, '__version__') # verify package import not local dir
- if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
- try:
- wandb_login_success = wandb.login(timeout=30)
- except wandb.errors.UsageError: # known non-TTY terminal issue
- wandb_login_success = False
- if not wandb_login_success:
- wandb = None
-except (ImportError, AssertionError):
- wandb = None
-
-try:
- import clearml
-
- assert hasattr(clearml, '__version__') # verify package import not local dir
-except (ImportError, AssertionError):
- clearml = None
-
-
-class Loggers():
- # YOLOv5 Loggers class
- def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
- self.save_dir = save_dir
- self.weights = weights
- self.opt = opt
- self.hyp = hyp
- self.logger = logger # for printing results to console
- self.include = include
- self.keys = [
- 'train/box_loss',
- 'train/obj_loss',
- 'train/cls_loss', # train loss
- 'metrics/precision',
- 'metrics/recall',
- 'metrics/mAP_0.5',
- 'metrics/mAP_0.5:0.95', # metrics
- 'val/box_loss',
- 'val/obj_loss',
- 'val/cls_loss', # val loss
- 'x/lr0',
- 'x/lr1',
- 'x/lr2'] # params
- self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
- for k in LOGGERS:
- setattr(self, k, None) # init empty logger dictionary
- self.csv = True # always log to csv
-
- # Messages
- if not wandb:
- prefix = colorstr('Weights & Biases: ')
- s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
- self.logger.info(s)
- if not clearml:
- prefix = colorstr('ClearML: ')
- s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
- self.logger.info(s)
-
- # TensorBoard
- s = self.save_dir
- if 'tb' in self.include and not self.opt.evolve:
- prefix = colorstr('TensorBoard: ')
- self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
- self.tb = SummaryWriter(str(s))
-
- # W&B
- if wandb and 'wandb' in self.include:
- wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
- run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
- self.opt.hyp = self.hyp # add hyperparameters
- self.wandb = WandbLogger(self.opt, run_id)
- # temp warn. because nested artifacts not supported after 0.12.10
- if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'):
- s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected."
- self.logger.warning(s)
- else:
- self.wandb = None
-
- # ClearML
- if clearml and 'clearml' in self.include:
- self.clearml = ClearmlLogger(self.opt, self.hyp)
- else:
- self.clearml = None
-
- def on_train_start(self):
- # Callback runs on train start
- pass
-
- def on_pretrain_routine_end(self):
- # Callback runs on pre-train routine end
- paths = self.save_dir.glob('*labels*.jpg') # training labels
- if self.wandb:
- self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
- if self.clearml:
- pass # ClearML saves these images automatically using hooks
-
- def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
- # Callback runs on train batch end
- # ni: number integrated batches (since train start)
- if plots:
- if ni == 0 and not self.opt.sync_bn and self.tb:
- log_tensorboard_graph(self.tb, model, imgsz=list(imgs.shape[2:4]))
- if ni < 3:
- f = self.save_dir / f'train_batch{ni}.jpg' # filename
- plot_images(imgs, targets, paths, f)
- if (self.wandb or self.clearml) and ni == 10:
- files = sorted(self.save_dir.glob('train*.jpg'))
- if self.wandb:
- self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
- if self.clearml:
- self.clearml.log_debug_samples(files, title='Mosaics')
-
- def on_train_epoch_end(self, epoch):
- # Callback runs on train epoch end
- if self.wandb:
- self.wandb.current_epoch = epoch + 1
-
- def on_val_image_end(self, pred, predn, path, names, im):
- # Callback runs on val image end
- if self.wandb:
- self.wandb.val_one_image(pred, predn, path, names, im)
- if self.clearml:
- self.clearml.log_image_with_boxes(path, pred, names, im)
-
- def on_val_end(self):
- # Callback runs on val end
- if self.wandb or self.clearml:
- files = sorted(self.save_dir.glob('val*.jpg'))
- if self.wandb:
- self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
- if self.clearml:
- self.clearml.log_debug_samples(files, title='Validation')
-
- def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
- # Callback runs at the end of each fit (train+val) epoch
- x = dict(zip(self.keys, vals))
- if self.csv:
- file = self.save_dir / 'results.csv'
- n = len(x) + 1 # number of cols
- s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header
- with open(file, 'a') as f:
- f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
-
- if self.tb:
- for k, v in x.items():
- self.tb.add_scalar(k, v, epoch)
- elif self.clearml: # log to ClearML if TensorBoard not used
- for k, v in x.items():
- title, series = k.split('/')
- self.clearml.task.get_logger().report_scalar(title, series, v, epoch)
-
- if self.wandb:
- if best_fitness == fi:
- best_results = [epoch] + vals[3:7]
- for i, name in enumerate(self.best_keys):
- self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary
- self.wandb.log(x)
- self.wandb.end_epoch(best_result=best_fitness == fi)
-
- if self.clearml:
- self.clearml.current_epoch_logged_images = set() # reset epoch image limit
- self.clearml.current_epoch += 1
-
- def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
- # Callback runs on model save event
- if self.wandb:
- if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
- self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
-
- if self.clearml:
- if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
- self.clearml.task.update_output_model(model_path=str(last),
- model_name='Latest Model',
- auto_delete_file=False)
-
- def on_train_end(self, last, best, plots, epoch, results):
- # Callback runs on training end
- if plots:
- plot_results(file=self.save_dir / 'results.csv') # save results.png
- files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
- files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
- self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
-
- if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles
- for f in files:
- self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
- if self.wandb:
- self.wandb.log(dict(zip(self.keys[3:10], results)))
- self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
- # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
- if not self.opt.evolve:
- wandb.log_artifact(str(best if best.exists() else last),
- type='model',
- name=f'run_{self.wandb.wandb_run.id}_model',
- aliases=['latest', 'best', 'stripped'])
- self.wandb.finish_run()
-
- if self.clearml:
- # Save the best model here
- if not self.opt.evolve:
- self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
- name='Best Model')
-
- def on_params_update(self, params):
- # Update hyperparams or configs of the experiment
- # params: A dict containing {param: value} pairs
- if self.wandb:
- self.wandb.wandb_run.config.update(params, allow_val_change=True)
-
-
-class GenericLogger:
- """
- YOLOv5 General purpose logger for non-task specific logging
- Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
- Arguments
- opt: Run arguments
- console_logger: Console logger
- include: loggers to include
- """
-
- def __init__(self, opt, console_logger, include=('tb', 'wandb')):
- # init default loggers
- self.save_dir = opt.save_dir
- self.include = include
- self.console_logger = console_logger
- if 'tb' in self.include:
- prefix = colorstr('TensorBoard: ')
- self.console_logger.info(
- f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
- self.tb = SummaryWriter(str(self.save_dir))
-
- if wandb and 'wandb' in self.include:
- self.wandb = wandb.init(project="YOLOv5-Classifier" if opt.project == "runs/train" else opt.project,
- name=None if opt.name == "exp" else opt.name,
- config=opt)
- else:
- self.wandb = None
-
- def log_metrics(self, metrics_dict, epoch):
- # Log metrics dictionary to all loggers
- if self.tb:
- for k, v in metrics_dict.items():
- self.tb.add_scalar(k, v, epoch)
-
- if self.wandb:
- self.wandb.log(metrics_dict, step=epoch)
-
- def log_images(self, files, name='Images', epoch=0):
- # Log images to all loggers
- files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path
- files = [f for f in files if f.exists()] # filter by exists
-
- if self.tb:
- for f in files:
- self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
- if self.wandb:
- self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
-
- def log_graph(self, model, imgsz=(640, 640)):
- # Log model graph to all loggers
- if self.tb:
- log_tensorboard_graph(self.tb, model, imgsz)
-
- def log_model(self, model_path, epoch=0, metadata={}):
- # Log model to all loggers
- if self.wandb:
- art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
- art.add_file(str(model_path))
- wandb.log_artifact(art)
-
-
-def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
- # Log model graph to TensorBoard
- try:
- p = next(model.parameters()) # for device, type
- imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand
- im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress jit trace warning
- tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
- except Exception:
- print('WARNING: TensorBoard graph visualization failure')
diff --git a/yolov5-6.2/utils/loggers/clearml/README.md b/yolov5-6.2/utils/loggers/clearml/README.md
deleted file mode 100644
index 64eef6be..00000000
--- a/yolov5-6.2/utils/loggers/clearml/README.md
+++ /dev/null
@@ -1,222 +0,0 @@
-# ClearML Integration
-
-<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
-
-## About ClearML
-
-[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
-
-🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
-
-🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
-
-🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
-
-🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
-
-🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
-
-<br />
-And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
-<br />
-<br />
-
-
-
-
-<br />
-<br />
-
-## 🦾 Setting Things Up
-
-To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
-
-Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
-
-1. Install the `clearml` python package:
-
- ```bash
- pip install clearml
- ```
-
-1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
-
- ```bash
- clearml-init
- ```
-
-That's it! You're done 😎
-
-<br />
-
-## 🚀 Training YOLOv5 With ClearML
-
-To enable ClearML experiment tracking, simply install the ClearML pip package.
-
-```bash
-pip install clearml
-```
-
-This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. If you want to change the `project_name` or `task_name`, head over to our custom logger, where you can change it: `utils/loggers/clearml/clearml_utils.py`
-
-```bash
-python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
-```
-
-This will capture:
-- Source code + uncommitted changes
-- Installed packages
-- (Hyper)parameters
-- Model files (use `--save-period n` to save a checkpoint every n epochs)
-- Console output
-- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
-- General info such as machine details, runtime, creation date etc.
-- All produced plots such as label correlogram and confusion matrix
-- Images with bounding boxes per epoch
-- Mosaic per epoch
-- Validation images per epoch
-- ...
-
-That's a lot right? 🤯
-Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
-
-There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
-
-<br />
-
-## 🔗 Dataset Version Management
-
-Versioning your data separately from your code is generally a good idea and makes it easy to aqcuire the latest version too. This repository supports supplying a dataset version ID and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
-
-
-
-### Prepare Your Dataset
-
-The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
-
-```
-..
-|_ yolov5
-|_ datasets
- |_ coco128
- |_ images
- |_ labels
- |_ LICENSE
- |_ README.txt
-```
-But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
-
-Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
-
-Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
-
-```
-..
-|_ yolov5
-|_ datasets
- |_ coco128
- |_ images
- |_ labels
- |_ coco128.yaml # <---- HERE!
- |_ LICENSE
- |_ README.txt
-```
-
-### Upload Your Dataset
-
-To get this dataset into ClearML as a versionned dataset, go to the dataset root folder and run the following command:
-```bash
-cd coco128
-clearml-data sync --project YOLOv5 --name coco128 --folder .
-```
-
-The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
-```bash
-# Optionally add --parent <parent_dataset_id> if you want to base
-# this version on another dataset version, so no duplicate files are uploaded!
-clearml-data create --name coco128 --project YOLOv5
-clearml-data add --files .
-clearml-data close
-```
-
-### Run Training Using A ClearML Dataset
-
-Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
-
-```bash
-python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
-```
-
-<br />
-
-## 👀 Hyperparameter Optimization
-
-Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
-
-Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
-
-To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
-
-You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
-
-```bash
-# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
-pip install optuna
-python utils/loggers/clearml/hpo.py
-```
-
-
-
-## 🤯 Remote Execution (advanced)
-
-Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site or you have some budget to use cloud GPUs.
-This is where the ClearML Agent comes into play. Check out what the agent can do here:
-
-- [YouTube video](https://youtu.be/MX3BrXnaULs)
-- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
-
-In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
-
-You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
-```bash
-clearml-agent daemon --queue <queues_to_listen_to> [--docker]
-```
-
-### Cloning, Editing And Enqueuing
-
-With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
-
-🪄 Clone the experiment by right clicking it
-
-🎯 Edit the hyperparameters to what you wish them to be
-
-⏳ Enqueue the task to any of the queues by right clicking it
-
-
-
-### Executing A Task Remotely
-
-Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
-
-To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instatiated:
-```python
-# ...
-# Loggers
-data_dict = None
-if RANK in {-1, 0}:
- loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
- if loggers.clearml:
- loggers.clearml.task.execute_remotely(queue='my_queue') # <------ ADD THIS LINE
- # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
- data_dict = loggers.clearml.data_dict
-# ...
-```
-When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
-
-### Autoscaling workers
-
-ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines and you stop paying!
-
-Check out the autoscalers getting started video below.
-
-[](https://youtu.be/j4XVMAaUt3E)
diff --git a/yolov5-6.2/utils/loggers/clearml/__init__.py b/yolov5-6.2/utils/loggers/clearml/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/yolov5-6.2/utils/loggers/clearml/clearml_utils.py b/yolov5-6.2/utils/loggers/clearml/clearml_utils.py
deleted file mode 100644
index 52320c09..00000000
--- a/yolov5-6.2/utils/loggers/clearml/clearml_utils.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""Main Logger class for ClearML experiment tracking."""
-import glob
-import re
-from pathlib import Path
-
-import numpy as np
-import yaml
-
-from utils.plots import Annotator, colors
-
-try:
- import clearml
- from clearml import Dataset, Task
- assert hasattr(clearml, '__version__') # verify package import not local dir
-except (ImportError, AssertionError):
- clearml = None
-
-
-def construct_dataset(clearml_info_string):
- """Load in a clearml dataset and fill the internal data_dict with its contents.
- """
- dataset_id = clearml_info_string.replace('clearml://', '')
- dataset = Dataset.get(dataset_id=dataset_id)
- dataset_root_path = Path(dataset.get_local_copy())
-
- # We'll search for the yaml file definition in the dataset
- yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml")))
- if len(yaml_filenames) > 1:
- raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains '
- 'the dataset definition this way.')
- elif len(yaml_filenames) == 0:
- raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file '
- 'inside the dataset root path.')
- with open(yaml_filenames[0]) as f:
- dataset_definition = yaml.safe_load(f)
-
- assert set(dataset_definition.keys()).issuperset(
- {'train', 'test', 'val', 'nc', 'names'}
- ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
-
- data_dict = dict()
- data_dict['train'] = str(
- (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None
- data_dict['test'] = str(
- (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None
- data_dict['val'] = str(
- (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None
- data_dict['nc'] = dataset_definition['nc']
- data_dict['names'] = dataset_definition['names']
-
- return data_dict
-
-
-class ClearmlLogger:
- """Log training runs, datasets, models, and predictions to ClearML.
-
- This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default,
- this information includes hyperparameters, system configuration and metrics, model metrics, code information and
- basic data metrics and analyses.
-
- By providing additional command line arguments to train.py, datasets,
- models and predictions can also be logged.
- """
-
- def __init__(self, opt, hyp):
- """
- - Initialize ClearML Task, this object will capture the experiment
- - Upload dataset version to ClearML Data if opt.upload_dataset is True
-
- arguments:
- opt (namespace) -- Commandline arguments for this run
- hyp (dict) -- Hyperparameters for this run
-
- """
- self.current_epoch = 0
- # Keep tracked of amount of logged images to enforce a limit
- self.current_epoch_logged_images = set()
- # Maximum number of images to log to clearML per epoch
- self.max_imgs_to_log_per_epoch = 16
- # Get the interval of epochs when bounding box images should be logged
- self.bbox_interval = opt.bbox_interval
- self.clearml = clearml
- self.task = None
- self.data_dict = None
- if self.clearml:
- self.task = Task.init(
- project_name='YOLOv5',
- task_name='training',
- tags=['YOLOv5'],
- output_uri=True,
- auto_connect_frameworks={'pytorch': False}
- # We disconnect pytorch auto-detection, because we added manual model save points in the code
- )
- # ClearML's hooks will already grab all general parameters
- # Only the hyperparameters coming from the yaml config file
- # will have to be added manually!
- self.task.connect(hyp, name='Hyperparameters')
-
- # Get ClearML Dataset Version if requested
- if opt.data.startswith('clearml://'):
- # data_dict should have the following keys:
- # names, nc (number of classes), test, train, val (all three relative paths to ../datasets)
- self.data_dict = construct_dataset(opt.data)
- # Set data to data_dict because wandb will crash without this information and opt is the best way
- # to give it to them
- opt.data = self.data_dict
-
- def log_debug_samples(self, files, title='Debug Samples'):
- """
- Log files (images) as debug samples in the ClearML task.
-
- arguments:
- files (List(PosixPath)) a list of file paths in PosixPath format
- title (str) A title that groups together images with the same values
- """
- for f in files:
- if f.exists():
- it = re.search(r'_batch(\d+)', f.name)
- iteration = int(it.groups()[0]) if it else 0
- self.task.get_logger().report_image(title=title,
- series=f.name.replace(it.group(), ''),
- local_path=str(f),
- iteration=iteration)
-
- def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25):
- """
- Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
-
- arguments:
- image_path (PosixPath) the path the original image file
- boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
- class_names (dict): dict containing mapping of class int to class name
- image (Tensor): A torch tensor containing the actual image data
- """
- if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0:
- # Log every bbox_interval times and deduplicate for any intermittend extra eval runs
- if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images:
- im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
- annotator = Annotator(im=im, pil=True)
- for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
- color = colors(i)
-
- class_name = class_names[int(class_nr)]
- confidence = round(float(conf) * 100, 2)
- label = f"{class_name}: {confidence}%"
-
- if confidence > conf_threshold:
- annotator.rectangle(box.cpu().numpy(), outline=color)
- annotator.box_label(box.cpu().numpy(), label=label, color=color)
-
- annotated_image = annotator.result()
- self.task.get_logger().report_image(title='Bounding Boxes',
- series=image_path.name,
- iteration=self.current_epoch,
- image=annotated_image)
- self.current_epoch_logged_images.add(image_path)
diff --git a/yolov5-6.2/utils/loggers/clearml/hpo.py b/yolov5-6.2/utils/loggers/clearml/hpo.py
deleted file mode 100644
index 96c2c544..00000000
--- a/yolov5-6.2/utils/loggers/clearml/hpo.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from clearml import Task
-# Connecting ClearML with the current process,
-# from here on everything is logged automatically
-from clearml.automation import HyperParameterOptimizer, UniformParameterRange
-from clearml.automation.optuna import OptimizerOptuna
-
-task = Task.init(project_name='Hyper-Parameter Optimization',
- task_name='YOLOv5',
- task_type=Task.TaskTypes.optimizer,
- reuse_last_task_id=False)
-
-# Example use case:
-optimizer = HyperParameterOptimizer(
- # This is the experiment we want to optimize
- base_task_id='<your_template_task_id>',
- # here we define the hyper-parameters to optimize
- # Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
- # For Example, here we see in the base experiment a section Named: "General"
- # under it a parameter named "batch_size", this becomes "General/batch_size"
- # If you have `argparse` for example, then arguments will appear under the "Args" section,
- # and you should instead pass "Args/batch_size"
- hyper_parameters=[
- UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1),
- UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0),
- UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98),
- UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001),
- UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0),
- UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95),
- UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2),
- UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2),
- UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0),
- UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0),
- UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0),
- UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0),
- UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7),
- UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0),
- UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0),
- UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1),
- UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0),
- UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0),
- UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001),
- UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)],
- # this is the objective metric we want to maximize/minimize
- objective_metric_title='metrics',
- objective_metric_series='mAP_0.5',
- # now we decide if we want to maximize it or minimize it (accuracy we maximize)
- objective_metric_sign='max',
- # let us limit the number of concurrent experiments,
- # this in turn will make sure we do dont bombard the scheduler with experiments.
- # if we have an auto-scaler connected, this, by proxy, will limit the number of machine
- max_number_of_concurrent_tasks=1,
- # this is the optimizer class (actually doing the optimization)
- # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
- optimizer_class=OptimizerOptuna,
- # If specified only the top K performing Tasks will be kept, the others will be automatically archived
- save_top_k_tasks_only=5, # 5,
- compute_time_limit=None,
- total_max_jobs=20,
- min_iteration_per_job=None,
- max_iteration_per_job=None,
-)
-
-# report every 10 seconds, this is way too often, but we are testing here
-optimizer.set_report_period(10)
-# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
-# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
-# set the time limit for the optimization process (2 hours)
-optimizer.set_time_limit(in_minutes=120.0)
-# Start the optimization process in the local environment
-optimizer.start_locally()
-# wait until process is done (notice we are controlling the optimization process in the background)
-optimizer.wait()
-# make sure background optimization stopped
-optimizer.stop()
-
-print('We are done, good bye')
diff --git a/yolov5-6.2/utils/loggers/wandb/README.md b/yolov5-6.2/utils/loggers/wandb/README.md
deleted file mode 100644
index d78324b4..00000000
--- a/yolov5-6.2/utils/loggers/wandb/README.md
+++ /dev/null
@@ -1,162 +0,0 @@
-📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021.
-
-- [About Weights & Biases](#about-weights-&-biases)
-- [First-Time Setup](#first-time-setup)
-- [Viewing runs](#viewing-runs)
-- [Disabling wandb](#disabling-wandb)
-- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
-- [Reports: Share your work with the world!](#reports)
-
-## About Weights & Biases
-
-Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
-
-Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
-
-- [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
-- [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
-- [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
-- [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
-- [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
-- [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
-
-## First-Time Setup
-
-<details open>
- <summary> Toggle Details </summary>
-When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.
-
-W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as:
-
-```shell
-$ python train.py --project ... --name ...
-```
-
-YOLOv5 notebook example: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-<img width="960" alt="Screen Shot 2021-09-29 at 10 23 13 PM" src="https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png">
-
-</details>
-
-## Viewing Runs
-
-<details open>
- <summary> Toggle Details </summary>
-Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in <b>realtime</b> . All important information is logged:
-
-- Training & Validation losses
-- Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95
-- Learning Rate over time
-- A bounding box debugging panel, showing the training progress over time
-- GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**
-- System: Disk I/0, CPU utilization, RAM memory usage
-- Your trained model as W&B Artifact
-- Environment: OS and Python types, Git repository and state, **training command**
-
-<p align="center"><img width="900" alt="Weights & Biases dashboard" src="https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png"></p>
-</details>
-
-## Disabling wandb
-
-- training after running `wandb disabled` inside that directory creates no wandb run
- 
-
-- To enable wandb again, run `wandb online`
- 
-
-## Advanced Usage
-
-You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
-
-<details open>
- <h3> 1: Train and Log Evaluation simultaneousy </h3>
- This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
- Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
- so no images will be uploaded from your system more than once.
- <details open>
- <summary> <b>Usage</b> </summary>
- <b>Code</b> <code> $ python train.py --upload_data val</code>
-
-
-
-</details>
-
-<h3>2. Visualize and Version Datasets</h3>
- Log, visualize, dynamically query, and understand your data with <a href='https://docs.wandb.ai/guides/data-vis/tables'>W&B Tables</a>. You can use the following command to log your dataset as a W&B Table. This will generate a <code>{dataset}_wandb.yaml</code> file which can be used to train from dataset artifact.
- <details>
- <summary> <b>Usage</b> </summary>
- <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
-
-
-
-</details>
-
-<h3> 3: Train using dataset artifact </h3>
- When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
- can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
- <details>
- <summary> <b>Usage</b> </summary>
- <b>Code</b> <code> $ python train.py --data {data}_wandb.yaml </code>
-
-
-
-</details>
-
-<h3> 4: Save model checkpoints as artifacts </h3>
- To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
- You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
-
-<details>
- <summary> <b>Usage</b> </summary>
- <b>Code</b> <code> $ python train.py --save_period 1 </code>
-
-
-
-</details>
-
-</details>
-
-<h3> 5: Resume runs from checkpoint artifacts. </h3>
-Any run can be resumed using artifacts if the <code>--resume</code> argument starts with <code>wandb-artifact://</code> prefix followed by the run path, i.e, <code>wandb-artifact://username/project/runid </code>. This doesn't require the model checkpoint to be present on the local system.
-
-<details>
- <summary> <b>Usage</b> </summary>
- <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-
-
-
-</details>
-
-<h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
- <b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
- The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
- train from <code>_wandb.yaml</code> file and set <code>--save_period</code>
-
-<details>
- <summary> <b>Usage</b> </summary>
- <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-
-
-
-</details>
-
-</details>
-
-<h3> Reports </h3>
-W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).
-
-<img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">
-
-## Environments
-
-YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
-- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
-- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-
-## Status
-
-
-
-If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/yolov5-6.2/utils/loggers/wandb/__init__.py b/yolov5-6.2/utils/loggers/wandb/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/yolov5-6.2/utils/loggers/wandb/log_dataset.py b/yolov5-6.2/utils/loggers/wandb/log_dataset.py
deleted file mode 100644
index 06e81fb6..00000000
--- a/yolov5-6.2/utils/loggers/wandb/log_dataset.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import argparse
-
-from wandb_utils import WandbLogger
-
-from utils.general import LOGGER
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
- logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused
- if not logger.wandb:
- LOGGER.info("install wandb using `pip install wandb` to log the dataset")
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
- parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
- parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
- parser.add_argument('--entity', default=None, help='W&B entity')
- parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
-
- opt = parser.parse_args()
- opt.resume = False # Explicitly disallow resume check for dataset upload job
-
- create_dataset_artifact(opt)
diff --git a/yolov5-6.2/utils/loggers/wandb/sweep.py b/yolov5-6.2/utils/loggers/wandb/sweep.py
deleted file mode 100644
index d49ea6f2..00000000
--- a/yolov5-6.2/utils/loggers/wandb/sweep.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import sys
-from pathlib import Path
-
-import wandb
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-
-from train import parse_opt, train
-from utils.callbacks import Callbacks
-from utils.general import increment_path
-from utils.torch_utils import select_device
-
-
-def sweep():
- wandb.init()
- # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb.
- hyp_dict = vars(wandb.config).get("_items").copy()
-
- # Workaround: get necessary opt args
- opt = parse_opt(known=True)
- opt.batch_size = hyp_dict.get("batch_size")
- opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
- opt.epochs = hyp_dict.get("epochs")
- opt.nosave = True
- opt.data = hyp_dict.get("data")
- opt.weights = str(opt.weights)
- opt.cfg = str(opt.cfg)
- opt.data = str(opt.data)
- opt.hyp = str(opt.hyp)
- opt.project = str(opt.project)
- device = select_device(opt.device, batch_size=opt.batch_size)
-
- # train
- train(hyp_dict, opt, device, callbacks=Callbacks())
-
-
-if __name__ == "__main__":
- sweep()
diff --git a/yolov5-6.2/utils/loggers/wandb/sweep.yaml b/yolov5-6.2/utils/loggers/wandb/sweep.yaml
deleted file mode 100644
index 688b1ea0..00000000
--- a/yolov5-6.2/utils/loggers/wandb/sweep.yaml
+++ /dev/null
@@ -1,143 +0,0 @@
-# Hyperparameters for training
-# To set range-
-# Provide min and max values as:
-# parameter:
-#
-# min: scalar
-# max: scalar
-# OR
-#
-# Set a specific list of search space-
-# parameter:
-# values: [scalar1, scalar2, scalar3...]
-#
-# You can use grid, bayesian and hyperopt search strategy
-# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
-
-program: utils/loggers/wandb/sweep.py
-method: random
-metric:
- name: metrics/mAP_0.5
- goal: maximize
-
-parameters:
- # hyperparameters: set either min, max range or values list
- data:
- value: "data/coco128.yaml"
- batch_size:
- values: [64]
- epochs:
- values: [10]
-
- lr0:
- distribution: uniform
- min: 1e-5
- max: 1e-1
- lrf:
- distribution: uniform
- min: 0.01
- max: 1.0
- momentum:
- distribution: uniform
- min: 0.6
- max: 0.98
- weight_decay:
- distribution: uniform
- min: 0.0
- max: 0.001
- warmup_epochs:
- distribution: uniform
- min: 0.0
- max: 5.0
- warmup_momentum:
- distribution: uniform
- min: 0.0
- max: 0.95
- warmup_bias_lr:
- distribution: uniform
- min: 0.0
- max: 0.2
- box:
- distribution: uniform
- min: 0.02
- max: 0.2
- cls:
- distribution: uniform
- min: 0.2
- max: 4.0
- cls_pw:
- distribution: uniform
- min: 0.5
- max: 2.0
- obj:
- distribution: uniform
- min: 0.2
- max: 4.0
- obj_pw:
- distribution: uniform
- min: 0.5
- max: 2.0
- iou_t:
- distribution: uniform
- min: 0.1
- max: 0.7
- anchor_t:
- distribution: uniform
- min: 2.0
- max: 8.0
- fl_gamma:
- distribution: uniform
- min: 0.0
- max: 4.0
- hsv_h:
- distribution: uniform
- min: 0.0
- max: 0.1
- hsv_s:
- distribution: uniform
- min: 0.0
- max: 0.9
- hsv_v:
- distribution: uniform
- min: 0.0
- max: 0.9
- degrees:
- distribution: uniform
- min: 0.0
- max: 45.0
- translate:
- distribution: uniform
- min: 0.0
- max: 0.9
- scale:
- distribution: uniform
- min: 0.0
- max: 0.9
- shear:
- distribution: uniform
- min: 0.0
- max: 10.0
- perspective:
- distribution: uniform
- min: 0.0
- max: 0.001
- flipud:
- distribution: uniform
- min: 0.0
- max: 1.0
- fliplr:
- distribution: uniform
- min: 0.0
- max: 1.0
- mosaic:
- distribution: uniform
- min: 0.0
- max: 1.0
- mixup:
- distribution: uniform
- min: 0.0
- max: 1.0
- copy_paste:
- distribution: uniform
- min: 0.0
- max: 1.0
diff --git a/yolov5-6.2/utils/loggers/wandb/wandb_utils.py b/yolov5-6.2/utils/loggers/wandb/wandb_utils.py
deleted file mode 100644
index e850d2ac..00000000
--- a/yolov5-6.2/utils/loggers/wandb/wandb_utils.py
+++ /dev/null
@@ -1,584 +0,0 @@
-"""Utilities and tools for tracking runs with Weights & Biases."""
-
-import logging
-import os
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Dict
-
-import yaml
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-
-from utils.dataloaders import LoadImagesAndLabels, img2label_paths
-from utils.general import LOGGER, check_dataset, check_file
-
-try:
- import wandb
-
- assert hasattr(wandb, '__version__') # verify package import not local dir
-except (ImportError, AssertionError):
- wandb = None
-
-RANK = int(os.getenv('RANK', -1))
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
- return from_string[len(prefix):]
-
-
-def check_wandb_config_file(data_config_file):
- wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path
- if Path(wandb_config).is_file():
- return wandb_config
- return data_config_file
-
-
-def check_wandb_dataset(data_file):
- is_trainset_wandb_artifact = False
- is_valset_wandb_artifact = False
- if isinstance(data_file, dict):
- # In that case another dataset manager has already processed it and we don't have to
- return data_file
- if check_file(data_file) and data_file.endswith('.yaml'):
- with open(data_file, errors='ignore') as f:
- data_dict = yaml.safe_load(f)
- is_trainset_wandb_artifact = isinstance(data_dict['train'],
- str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX)
- is_valset_wandb_artifact = isinstance(data_dict['val'],
- str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX)
- if is_trainset_wandb_artifact or is_valset_wandb_artifact:
- return data_dict
- else:
- return check_dataset(data_file)
-
-
-def get_run_info(run_path):
- run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
- run_id = run_path.stem
- project = run_path.parent.stem
- entity = run_path.parent.parent.stem
- model_artifact_name = 'run_' + run_id + '_model'
- return entity, project, run_id, model_artifact_name
-
-
-def check_wandb_resume(opt):
- process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
- if isinstance(opt.resume, str):
- if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
- if RANK not in [-1, 0]: # For resuming DDP runs
- entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
- api = wandb.Api()
- artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
- modeldir = artifact.download()
- opt.weights = str(Path(modeldir) / "last.pt")
- return True
- return None
-
-
-def process_wandb_config_ddp_mode(opt):
- with open(check_file(opt.data), errors='ignore') as f:
- data_dict = yaml.safe_load(f) # data dict
- train_dir, val_dir = None, None
- if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
- api = wandb.Api()
- train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
- train_dir = train_artifact.download()
- train_path = Path(train_dir) / 'data/images/'
- data_dict['train'] = str(train_path)
-
- if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
- api = wandb.Api()
- val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
- val_dir = val_artifact.download()
- val_path = Path(val_dir) / 'data/images/'
- data_dict['val'] = str(val_path)
- if train_dir or val_dir:
- ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
- with open(ddp_data_path, 'w') as f:
- yaml.safe_dump(data_dict, f)
- opt.data = ddp_data_path
-
-
-class WandbLogger():
- """Log training runs, datasets, models, and predictions to Weights & Biases.
-
- This logger sends information to W&B at wandb.ai. By default, this information
- includes hyperparameters, system configuration and metrics, model metrics,
- and basic data metrics and analyses.
-
- By providing additional command line arguments to train.py, datasets,
- models and predictions can also be logged.
-
- For more on how this logger is used, see the Weights & Biases documentation:
- https://docs.wandb.com/guides/integrations/yolov5
- """
-
- def __init__(self, opt, run_id=None, job_type='Training'):
- """
- - Initialize WandbLogger instance
- - Upload dataset if opt.upload_dataset is True
- - Setup training processes if job_type is 'Training'
-
- arguments:
- opt (namespace) -- Commandline arguments for this run
- run_id (str) -- Run ID of W&B run to be resumed
- job_type (str) -- To set the job_type for this run
-
- """
- # Pre-training routine --
- self.job_type = job_type
- self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
- self.val_artifact, self.train_artifact = None, None
- self.train_artifact_path, self.val_artifact_path = None, None
- self.result_artifact = None
- self.val_table, self.result_table = None, None
- self.bbox_media_panel_images = []
- self.val_table_path_map = None
- self.max_imgs_to_log = 16
- self.wandb_artifact_data_dict = None
- self.data_dict = None
- # It's more elegant to stick to 1 wandb.init call,
- # but useful config data is overwritten in the WandbLogger's wandb.init call
- if isinstance(opt.resume, str): # checks resume from artifact
- if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
- entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
- model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
- assert wandb, 'install wandb to resume wandb runs'
- # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
- self.wandb_run = wandb.init(id=run_id,
- project=project,
- entity=entity,
- resume='allow',
- allow_val_change=True)
- opt.resume = model_artifact_name
- elif self.wandb:
- self.wandb_run = wandb.init(config=opt,
- resume="allow",
- project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
- entity=opt.entity,
- name=opt.name if opt.name != 'exp' else None,
- job_type=job_type,
- id=run_id,
- allow_val_change=True) if not wandb.run else wandb.run
- if self.wandb_run:
- if self.job_type == 'Training':
- if opt.upload_dataset:
- if not opt.resume:
- self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
-
- if isinstance(opt.data, dict):
- # This means another dataset manager has already processed the dataset info (e.g. ClearML)
- # and they will have stored the already processed dict in opt.data
- self.data_dict = opt.data
- elif opt.resume:
- # resume from artifact
- if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
- self.data_dict = dict(self.wandb_run.config.data_dict)
- else: # local resume
- self.data_dict = check_wandb_dataset(opt.data)
- else:
- self.data_dict = check_wandb_dataset(opt.data)
- self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
-
- # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
- self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict}, allow_val_change=True)
- self.setup_training(opt)
-
- if self.job_type == 'Dataset Creation':
- self.wandb_run.config.update({"upload_dataset": True})
- self.data_dict = self.check_and_upload_dataset(opt)
-
- def check_and_upload_dataset(self, opt):
- """
- Check if the dataset format is compatible and upload it as W&B artifact
-
- arguments:
- opt (namespace)-- Commandline arguments for current run
-
- returns:
- Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
- """
- assert wandb, 'Install wandb to upload dataset'
- config_path = self.log_dataset_artifact(opt.data, opt.single_cls,
- 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
- with open(config_path, errors='ignore') as f:
- wandb_data_dict = yaml.safe_load(f)
- return wandb_data_dict
-
- def setup_training(self, opt):
- """
- Setup the necessary processes for training YOLO models:
- - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
- - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
- - Setup log_dict, initialize bbox_interval
-
- arguments:
- opt (namespace) -- commandline arguments for this run
-
- """
- self.log_dict, self.current_epoch = {}, 0
- self.bbox_interval = opt.bbox_interval
- if isinstance(opt.resume, str):
- modeldir, _ = self.download_model_artifact(opt)
- if modeldir:
- self.weights = Path(modeldir) / "last.pt"
- config = self.wandb_run.config
- opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
- self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\
- config.hyp, config.imgsz
- data_dict = self.data_dict
- if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download
- self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(
- data_dict.get('train'), opt.artifact_alias)
- self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(
- data_dict.get('val'), opt.artifact_alias)
-
- if self.train_artifact_path is not None:
- train_path = Path(self.train_artifact_path) / 'data/images/'
- data_dict['train'] = str(train_path)
- if self.val_artifact_path is not None:
- val_path = Path(self.val_artifact_path) / 'data/images/'
- data_dict['val'] = str(val_path)
-
- if self.val_artifact is not None:
- self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
- columns = ["epoch", "id", "ground truth", "prediction"]
- columns.extend(self.data_dict['names'])
- self.result_table = wandb.Table(columns)
- self.val_table = self.val_artifact.get("val")
- if self.val_table_path_map is None:
- self.map_val_table_path()
- if opt.bbox_interval == -1:
- self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
- if opt.evolve or opt.noplots:
- self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
- train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
- # Update the the data_dict to point to local artifacts dir
- if train_from_artifact:
- self.data_dict = data_dict
-
- def download_dataset_artifact(self, path, alias):
- """
- download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
-
- arguments:
- path -- path of the dataset to be used for training
- alias (str)-- alias of the artifact to be download/used for training
-
- returns:
- (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
- is found otherwise returns (None, None)
- """
- if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
- artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
- dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
- assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
- datadir = dataset_artifact.download()
- return datadir, dataset_artifact
- return None, None
-
- def download_model_artifact(self, opt):
- """
- download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
-
- arguments:
- opt (namespace) -- Commandline arguments for this run
- """
- if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
- model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
- assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
- modeldir = model_artifact.download()
- # epochs_trained = model_artifact.metadata.get('epochs_trained')
- total_epochs = model_artifact.metadata.get('total_epochs')
- is_finished = total_epochs is None
- assert not is_finished, 'training is finished, can only resume incomplete runs.'
- return modeldir, model_artifact
- return None, None
-
- def log_model(self, path, opt, epoch, fitness_score, best_model=False):
- """
- Log the model checkpoint as W&B artifact
-
- arguments:
- path (Path) -- Path of directory containing the checkpoints
- opt (namespace) -- Command line arguments for this run
- epoch (int) -- Current epoch number
- fitness_score (float) -- fitness score for current epoch
- best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
- """
- model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
- type='model',
- metadata={
- 'original_url': str(path),
- 'epochs_trained': epoch + 1,
- 'save period': opt.save_period,
- 'project': opt.project,
- 'total_epochs': opt.epochs,
- 'fitness_score': fitness_score})
- model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
- wandb.log_artifact(model_artifact,
- aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
- LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
-
- def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
- """
- Log the dataset as W&B artifact and return the new data file with W&B links
-
- arguments:
- data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
- single_class (boolean) -- train multi-class data as single-class
- project (str) -- project name. Used to construct the artifact path
- overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
- file with _wandb postfix. Eg -> data_wandb.yaml
-
- returns:
- the new .yaml file with artifact links. it can be used to start training directly from artifacts
- """
- upload_dataset = self.wandb_run.config.upload_dataset
- log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'
- self.data_dict = check_dataset(data_file) # parse and check
- data = dict(self.data_dict)
- nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
- names = {k: v for k, v in enumerate(names)} # to index dictionary
-
- # log train set
- if not log_val_only:
- self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(data['train'], rect=True, batch_size=1),
- names,
- name='train') if data.get('train') else None
- if data.get('train'):
- data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
-
- self.val_artifact = self.create_dataset_table(
- LoadImagesAndLabels(data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
- if data.get('val'):
- data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
-
- path = Path(data_file)
- # create a _wandb.yaml file with artifacts links if both train and test set are logged
- if not log_val_only:
- path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path
- path = ROOT / 'data' / path
- data.pop('download', None)
- data.pop('path', None)
- with open(path, 'w') as f:
- yaml.safe_dump(data, f)
- LOGGER.info(f"Created dataset config file {path}")
-
- if self.job_type == 'Training': # builds correct artifact pipeline graph
- if not log_val_only:
- self.wandb_run.log_artifact(
- self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED!
- self.wandb_run.use_artifact(self.val_artifact)
- self.val_artifact.wait()
- self.val_table = self.val_artifact.get('val')
- self.map_val_table_path()
- else:
- self.wandb_run.log_artifact(self.train_artifact)
- self.wandb_run.log_artifact(self.val_artifact)
- return path
-
- def map_val_table_path(self):
- """
- Map the validation dataset Table like name of file -> it's id in the W&B Table.
- Useful for - referencing artifacts for evaluation.
- """
- self.val_table_path_map = {}
- LOGGER.info("Mapping dataset")
- for i, data in enumerate(tqdm(self.val_table.data)):
- self.val_table_path_map[data[3]] = data[0]
-
- def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):
- """
- Create and return W&B artifact containing W&B Table of the dataset.
-
- arguments:
- dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
- class_to_id -- hash map that maps class ids to labels
- name -- name of the artifact
-
- returns:
- dataset artifact to be logged or used
- """
- # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
- artifact = wandb.Artifact(name=name, type="dataset")
- img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
- img_files = tqdm(dataset.im_files) if not img_files else img_files
- for img_file in img_files:
- if Path(img_file).is_dir():
- artifact.add_dir(img_file, name='data/images')
- labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
- artifact.add_dir(labels_path, name='data/labels')
- else:
- artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
- label_file = Path(img2label_paths([img_file])[0])
- artifact.add_file(str(label_file), name='data/labels/' +
- label_file.name) if label_file.exists() else None
- table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
- class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
- for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
- box_data, img_classes = [], {}
- for cls, *xywh in labels[:, 1:].tolist():
- cls = int(cls)
- box_data.append({
- "position": {
- "middle": [xywh[0], xywh[1]],
- "width": xywh[2],
- "height": xywh[3]},
- "class_id": cls,
- "box_caption": "%s" % (class_to_id[cls])})
- img_classes[cls] = class_to_id[cls]
- boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space
- table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
- Path(paths).name)
- artifact.add(table, name)
- return artifact
-
- def log_training_progress(self, predn, path, names):
- """
- Build evaluation Table. Uses reference from validation dataset table.
-
- arguments:
- predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
- path (str): local path of the current evaluation image
- names (dict(int, str)): hash map that maps class ids to labels
- """
- class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
- box_data = []
- avg_conf_per_class = [0] * len(self.data_dict['names'])
- pred_class_count = {}
- for *xyxy, conf, cls in predn.tolist():
- if conf >= 0.25:
- cls = int(cls)
- box_data.append({
- "position": {
- "minX": xyxy[0],
- "minY": xyxy[1],
- "maxX": xyxy[2],
- "maxY": xyxy[3]},
- "class_id": cls,
- "box_caption": f"{names[cls]} {conf:.3f}",
- "scores": {
- "class_score": conf},
- "domain": "pixel"})
- avg_conf_per_class[cls] += conf
-
- if cls in pred_class_count:
- pred_class_count[cls] += 1
- else:
- pred_class_count[cls] = 1
-
- for pred_class in pred_class_count.keys():
- avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]
-
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
- id = self.val_table_path_map[Path(path).name]
- self.result_table.add_data(self.current_epoch, id, self.val_table.data[id][1],
- wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
- *avg_conf_per_class)
-
- def val_one_image(self, pred, predn, path, names, im):
- """
- Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
-
- arguments:
- pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
- predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
- path (str): local path of the current evaluation image
- """
- if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
- self.log_training_progress(predn, path, names)
-
- if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
- if self.current_epoch % self.bbox_interval == 0:
- box_data = [{
- "position": {
- "minX": xyxy[0],
- "minY": xyxy[1],
- "maxX": xyxy[2],
- "maxY": xyxy[3]},
- "class_id": int(cls),
- "box_caption": f"{names[int(cls)]} {conf:.3f}",
- "scores": {
- "class_score": conf},
- "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
- self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
-
- def log(self, log_dict):
- """
- save the metrics to the logging dictionary
-
- arguments:
- log_dict (Dict) -- metrics/media to be logged in current step
- """
- if self.wandb_run:
- for key, value in log_dict.items():
- self.log_dict[key] = value
-
- def end_epoch(self, best_result=False):
- """
- commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
-
- arguments:
- best_result (boolean): Boolean representing if the result of this evaluation is best or not
- """
- if self.wandb_run:
- with all_logging_disabled():
- if self.bbox_media_panel_images:
- self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
- try:
- wandb.log(self.log_dict)
- except BaseException as e:
- LOGGER.info(
- f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
- )
- self.wandb_run.finish()
- self.wandb_run = None
-
- self.log_dict = {}
- self.bbox_media_panel_images = []
- if self.result_artifact:
- self.result_artifact.add(self.result_table, 'result')
- wandb.log_artifact(self.result_artifact,
- aliases=[
- 'latest', 'last', 'epoch ' + str(self.current_epoch),
- ('best' if best_result else '')])
-
- wandb.log({"evaluation": self.result_table})
- columns = ["epoch", "id", "ground truth", "prediction"]
- columns.extend(self.data_dict['names'])
- self.result_table = wandb.Table(columns)
- self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-
- def finish_run(self):
- """
- Log metrics if any and finish the current W&B run
- """
- if self.wandb_run:
- if self.log_dict:
- with all_logging_disabled():
- wandb.log(self.log_dict)
- wandb.run.finish()
-
-
-@contextmanager
-def all_logging_disabled(highest_level=logging.CRITICAL):
- """ source - https://gist.github.com/simon-weber/7853144
- A context manager that will prevent any logging messages triggered during the body from being processed.
- :param highest_level: the maximum logging level in use.
- This would only need to be changed if a custom level greater than CRITICAL is defined.
- """
- previous_level = logging.root.manager.disable
- logging.disable(highest_level)
- try:
- yield
- finally:
- logging.disable(previous_level)
diff --git a/yolov5-6.2/utils/loss.py b/yolov5-6.2/utils/loss.py
deleted file mode 100644
index 9b9c3d9f..00000000
--- a/yolov5-6.2/utils/loss.py
+++ /dev/null
@@ -1,234 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Loss functions
-"""
-
-import torch
-import torch.nn as nn
-
-from utils.metrics import bbox_iou
-from utils.torch_utils import de_parallel
-
-
-def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
- # return positive, negative label smoothing BCE targets
- return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
- def __init__(self, alpha=0.05):
- super().__init__()
- self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
- self.alpha = alpha
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- pred = torch.sigmoid(pred) # prob from logits
- dx = pred - true # reduce only missing label effects
- # dx = (pred - true).abs() # reduce missing label and false label effects
- alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
- loss *= alpha_factor
- return loss.mean()
-
-
-class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- # p_t = torch.exp(-loss)
- # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
-
- # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
- pred_prob = torch.sigmoid(pred) # prob from logits
- p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = (1.0 - p_t) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
-
- pred_prob = torch.sigmoid(pred) # prob from logits
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = torch.abs(true - pred_prob) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class ComputeLoss:
- sort_obj_iou = False
-
- # Compute losses
- def __init__(self, model, autobalance=False):
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- m = de_parallel(model).model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
- self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
- self.na = m.na # number of anchors
- self.nc = m.nc # number of classes
- self.nl = m.nl # number of layers
- self.anchors = m.anchors
- self.device = device
-
- def __call__(self, p, targets): # predictions, targets
- lcls = torch.zeros(1, device=self.device) # class loss
- lbox = torch.zeros(1, device=self.device) # box loss
- lobj = torch.zeros(1, device=self.device) # object loss
- tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
- tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
- pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
-
- # Regression
- pxy = pxy.sigmoid() * 2 - 0.5
- pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- iou = iou.detach().clamp(0).type(tobj.dtype)
- if self.sort_obj_iou:
- j = iou.argsort()
- b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
- if self.gr < 1:
- iou = (1.0 - self.gr) + self.gr * iou
- tobj[b, a, gj, gi] = iou # iou ratio
-
- # Classification
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(pcls, self.cn, device=self.device) # targets
- t[range(n), tcls[i]] = self.cp
- lcls += self.BCEcls(pcls, t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
-
- def build_targets(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- tcls, tbox, indices, anch = [], [], [], []
- gain = torch.ones(7, device=self.device) # normalized to gridspace gain
- ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor(
- [
- [0, 0],
- [1, 0],
- [0, 1],
- [-1, 0],
- [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ],
- device=self.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors, shape = self.anchors[i], p[i].shape
- gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain # shape(3,n,7)
- if nt:
- # Matches
- r = t[..., 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1 < g) & (gxy > 1)).T
- l, m = ((gxi % 1 < g) & (gxi > 1)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
- a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid indices
-
- # Append
- indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
- tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- anch.append(anchors[a]) # anchors
- tcls.append(c) # class
-
- return tcls, tbox, indices, anch
diff --git a/yolov5-6.2/utils/metrics.py b/yolov5-6.2/utils/metrics.py
deleted file mode 100644
index 08880cd3..00000000
--- a/yolov5-6.2/utils/metrics.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Model validation metrics
-"""
-
-import math
-import warnings
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-
-def fitness(x):
- # Model fitness as a weighted combination of metrics
- w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
- return (x[:, :4] * w).sum(1)
-
-
-def smooth(y, f=0.05):
- # Box filter of fraction f
- nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
- p = np.ones(nf // 2) # ones padding
- yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
- return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
-
-
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):
- """ Compute the average precision, given the recall and precision curves.
- Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
- # Arguments
- tp: True positives (nparray, nx1 or nx10).
- conf: Objectness value from 0-1 (nparray).
- pred_cls: Predicted object classes (nparray).
- target_cls: True object classes (nparray).
- plot: Plot precision-recall curve at mAP@0.5
- save_dir: Plot save directory
- # Returns
- The average precision as computed in py-faster-rcnn.
- """
-
- # Sort by objectness
- i = np.argsort(-conf)
- tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
-
- # Find unique classes
- unique_classes, nt = np.unique(target_cls, return_counts=True)
- nc = unique_classes.shape[0] # number of classes, number of detections
-
- # Create Precision-Recall curve and compute AP for each class
- px, py = np.linspace(0, 1, 1000), [] # for plotting
- ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
- for ci, c in enumerate(unique_classes):
- i = pred_cls == c
- n_l = nt[ci] # number of labels
- n_p = i.sum() # number of predictions
- if n_p == 0 or n_l == 0:
- continue
-
- # Accumulate FPs and TPs
- fpc = (1 - tp[i]).cumsum(0)
- tpc = tp[i].cumsum(0)
-
- # Recall
- recall = tpc / (n_l + eps) # recall curve
- r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
-
- # Precision
- precision = tpc / (tpc + fpc) # precision curve
- p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
-
- # AP from recall-precision curve
- for j in range(tp.shape[1]):
- ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
- if plot and j == 0:
- py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
-
- # Compute F1 (harmonic mean of precision and recall)
- f1 = 2 * p * r / (p + r + eps)
- names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
- names = dict(enumerate(names)) # to dict
- if plot:
- plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
- plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
- plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
- plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
-
- i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
- p, r, f1 = p[:, i], r[:, i], f1[:, i]
- tp = (r * nt).round() # true positives
- fp = (tp / (p + eps) - tp).round() # false positives
- return tp, fp, p, r, f1, ap, unique_classes.astype(int)
-
-
-def compute_ap(recall, precision):
- """ Compute the average precision, given the recall and precision curves
- # Arguments
- recall: The recall curve (list)
- precision: The precision curve (list)
- # Returns
- Average precision, precision curve, recall curve
- """
-
- # Append sentinel values to beginning and end
- mrec = np.concatenate(([0.0], recall, [1.0]))
- mpre = np.concatenate(([1.0], precision, [0.0]))
-
- # Compute the precision envelope
- mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
-
- # Integrate area under curve
- method = 'interp' # methods: 'continuous', 'interp'
- if method == 'interp':
- x = np.linspace(0, 1, 101) # 101-point interp (COCO)
- ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
- else: # 'continuous'
- i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
- ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
-
- return ap, mpre, mrec
-
-
-class ConfusionMatrix:
- # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
- def __init__(self, nc, conf=0.25, iou_thres=0.45):
- self.matrix = np.zeros((nc + 1, nc + 1))
- self.nc = nc # number of classes
- self.conf = conf
- self.iou_thres = iou_thres
-
- def process_batch(self, detections, labels):
- """
- Return intersection-over-union (Jaccard index) of boxes.
- Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
- Arguments:
- detections (Array[N, 6]), x1, y1, x2, y2, conf, class
- labels (Array[M, 5]), class, x1, y1, x2, y2
- Returns:
- None, updates confusion matrix accordingly
- """
- if detections is None:
- gt_classes = labels.int()
- for i, gc in enumerate(gt_classes):
- self.matrix[self.nc, gc] += 1 # background FN
- return
-
- detections = detections[detections[:, 4] > self.conf]
- gt_classes = labels[:, 0].int()
- detection_classes = detections[:, 5].int()
- iou = box_iou(labels[:, 1:], detections[:, :4])
-
- x = torch.where(iou > self.iou_thres)
- if x[0].shape[0]:
- matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
- if x[0].shape[0] > 1:
- matches = matches[matches[:, 2].argsort()[::-1]]
- matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
- matches = matches[matches[:, 2].argsort()[::-1]]
- matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
- else:
- matches = np.zeros((0, 3))
-
- n = matches.shape[0] > 0
- m0, m1, _ = matches.transpose().astype(int)
- for i, gc in enumerate(gt_classes):
- j = m0 == i
- if n and sum(j) == 1:
- self.matrix[detection_classes[m1[j]], gc] += 1 # correct
- else:
- self.matrix[self.nc, gc] += 1 # background FP
-
- if n:
- for i, dc in enumerate(detection_classes):
- if not any(m1 == i):
- self.matrix[dc, self.nc] += 1 # background FN
-
- def matrix(self):
- return self.matrix
-
- def tp_fp(self):
- tp = self.matrix.diagonal() # true positives
- fp = self.matrix.sum(1) - tp # false positives
- # fn = self.matrix.sum(0) - tp # false negatives (missed detections)
- return tp[:-1], fp[:-1] # remove background class
-
- def plot(self, normalize=True, save_dir='', names=()):
- try:
- import seaborn as sn
-
- array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns
- array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
-
- fig = plt.figure(figsize=(12, 9), tight_layout=True)
- nc, nn = self.nc, len(names) # number of classes, names
- sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
- labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
- sn.heatmap(array,
- annot=nc < 30,
- annot_kws={
- "size": 8},
- cmap='Blues',
- fmt='.2f',
- square=True,
- vmin=0.0,
- xticklabels=names + ['background FP'] if labels else "auto",
- yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
- fig.axes[0].set_xlabel('True')
- fig.axes[0].set_ylabel('Predicted')
- plt.title('Confusion Matrix')
- fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
- plt.close()
- except Exception as e:
- print(f'WARNING: ConfusionMatrix plot failure: {e}')
-
- def print(self):
- for i in range(self.nc + 1):
- print(' '.join(map(str, self.matrix[i])))
-
-
-def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
- # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
-
- # Get the coordinates of bounding boxes
- if xywh: # transform from xywh to xyxy
- (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, 1), box2.chunk(4, 1)
- w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
- b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
- b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
- else: # x1, y1, x2, y2 = box1
- b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, 1)
- b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, 1)
- w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
- w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
-
- # Intersection area
- inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
- (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
-
- # Union Area
- union = w1 * h1 + w2 * h2 - inter + eps
-
- # IoU
- iou = inter / union
- if CIoU or DIoU or GIoU:
- cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
- ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
- if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
- c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
- rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
- if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
- v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2)
- with torch.no_grad():
- alpha = v / (v - iou + (1 + eps))
- return iou - (rho2 / c2 + v * alpha) # CIoU
- return iou - rho2 / c2 # DIoU
- c_area = cw * ch + eps # convex area
- return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
- return iou # IoU
-
-
-def box_area(box):
- # box = xyxy(4,n)
- return (box[2] - box[0]) * (box[3] - box[1])
-
-
-def box_iou(box1, box2, eps=1e-7):
- # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
- """
- Return intersection-over-union (Jaccard index) of boxes.
- Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
- Arguments:
- box1 (Tensor[N, 4])
- box2 (Tensor[M, 4])
- Returns:
- iou (Tensor[N, M]): the NxM matrix containing the pairwise
- IoU values for every element in boxes1 and boxes2
- """
-
- # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
- (a1, a2), (b1, b2) = box1[:, None].chunk(2, 2), box2.chunk(2, 1)
- inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
-
- # IoU = inter / (area1 + area2 - inter)
- return inter / (box_area(box1.T)[:, None] + box_area(box2.T) - inter + eps)
-
-
-def bbox_ioa(box1, box2, eps=1e-7):
- """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
- box1: np.array of shape(4)
- box2: np.array of shape(nx4)
- returns: np.array of shape(n)
- """
-
- # Get the coordinates of bounding boxes
- b1_x1, b1_y1, b1_x2, b1_y2 = box1
- b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
-
- # Intersection area
- inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
- (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
- # box2 area
- box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
-
- # Intersection over box2 area
- return inter_area / box2_area
-
-
-def wh_iou(wh1, wh2, eps=1e-7):
- # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
- wh1 = wh1[:, None] # [N,1,2]
- wh2 = wh2[None] # [1,M,2]
- inter = torch.min(wh1, wh2).prod(2) # [N,M]
- return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter)
-
-
-# Plots ----------------------------------------------------------------------------------------------------------------
-
-
-def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
- # Precision-recall curve
- fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
- py = np.stack(py, axis=1)
-
- if 0 < len(names) < 21: # display per-class legend if < 21 classes
- for i, y in enumerate(py.T):
- ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)
- else:
- ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
-
- ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
- ax.set_xlabel('Recall')
- ax.set_ylabel('Precision')
- ax.set_xlim(0, 1)
- ax.set_ylim(0, 1)
- plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
- plt.title('Precision-Recall Curve')
- fig.savefig(save_dir, dpi=250)
- plt.close()
-
-
-def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
- # Metric-confidence curve
- fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-
- if 0 < len(names) < 21: # display per-class legend if < 21 classes
- for i, y in enumerate(py):
- ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)
- else:
- ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
-
- y = smooth(py.mean(0), 0.05)
- ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
- ax.set_xlabel(xlabel)
- ax.set_ylabel(ylabel)
- ax.set_xlim(0, 1)
- ax.set_ylim(0, 1)
- plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
- plt.title(f'{ylabel}-Confidence Curve')
- fig.savefig(save_dir, dpi=250)
- plt.close()
diff --git a/yolov5-6.2/utils/plots.py b/yolov5-6.2/utils/plots.py
deleted file mode 100644
index 5df27a34..00000000
--- a/yolov5-6.2/utils/plots.py
+++ /dev/null
@@ -1,522 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Plotting utils
-"""
-
-import math
-import os
-from copy import copy
-from pathlib import Path
-from urllib.error import URLError
-
-import cv2
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sn
-import torch
-from PIL import Image, ImageDraw, ImageFont
-
-from utils.general import (CONFIG_DIR, FONT, LOGGER, Timeout, check_font, check_requirements, clip_coords,
- increment_path, is_ascii, threaded, try_except, xywh2xyxy, xyxy2xywh)
-from utils.metrics import fitness
-
-# Settings
-RANK = int(os.getenv('RANK', -1))
-matplotlib.rc('font', **{'size': 11})
-matplotlib.use('Agg') # for writing to files only
-
-
-class Colors:
- # Ultralytics color palette https://ultralytics.com/
- def __init__(self):
- # hex = matplotlib.colors.TABLEAU_COLORS.values()
- hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
- '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
- self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
- self.n = len(self.palette)
-
- def __call__(self, i, bgr=False):
- c = self.palette[int(i) % self.n]
- return (c[2], c[1], c[0]) if bgr else c
-
- @staticmethod
- def hex2rgb(h): # rgb order (PIL)
- return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
-
-
-colors = Colors() # create instance for 'from utils.plots import colors'
-
-
-def check_pil_font(font=FONT, size=10):
- # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
- font = Path(font)
- font = font if font.exists() else (CONFIG_DIR / font.name)
- try:
- return ImageFont.truetype(str(font) if font.exists() else font.name, size)
- except Exception: # download if missing
- try:
- check_font(font)
- return ImageFont.truetype(str(font), size)
- except TypeError:
- check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
- except URLError: # not online
- return ImageFont.load_default()
-
-
-class Annotator:
- # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
- def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
- assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
- non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
- self.pil = pil or non_ascii
- if self.pil: # use PIL
- self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
- self.draw = ImageDraw.Draw(self.im)
- self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
- size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
- else: # use cv2
- self.im = im
- self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
-
- def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
- # Add one xyxy box to image with label
- if self.pil or not is_ascii(label):
- self.draw.rectangle(box, width=self.lw, outline=color) # box
- if label:
- w, h = self.font.getsize(label) # text width, height
- outside = box[1] - h >= 0 # label fits outside box
- self.draw.rectangle(
- (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
- box[1] + 1 if outside else box[1] + h + 1),
- fill=color,
- )
- # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
- self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
- else: # cv2
- p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
- center = (int((box[0]+box[2])/2),int((box[1]+box[3])/2))
- cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
- if label:
- tf = max(self.lw - 1, 1) # font thickness
- w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
- outside = p1[1] - h >= 3
- p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
- cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
- cv2.putText(self.im,
- label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
- 0,
- self.lw / 3,
- txt_color,
- thickness=tf,
- lineType=cv2.LINE_AA)
- cv2.putText(self.im, "("+str(center[0])+","+str(center[1])+")", (p1[0]+5, p1[1] -50 if outside else p1[1] + h + 2), 2, self.lw / 3, txt_color,
- thickness=tf, lineType=cv2.LINE_AA)
-
- def rectangle(self, xy, fill=None, outline=None, width=1):
- # Add rectangle to image (PIL-only)
- self.draw.rectangle(xy, fill, outline, width)
-
- def text(self, xy, text, txt_color=(255, 255, 255)):
- # Add text to image (PIL-only)
- w, h = self.font.getsize(text) # text width, height
- self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
-
- def result(self):
- # Return annotated image as array
- return np.asarray(self.im)
-
-
-def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
- """
- x: Features to be visualized
- module_type: Module type
- stage: Module stage within model
- n: Maximum number of feature maps to plot
- save_dir: Directory to save results
- """
- if 'Detect' not in module_type:
- batch, channels, height, width = x.shape # batch, channels, height, width
- if height > 1 and width > 1:
- f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
-
- blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
- n = min(n, channels) # number of plots
- fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
- ax = ax.ravel()
- plt.subplots_adjust(wspace=0.05, hspace=0.05)
- for i in range(n):
- ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
- ax[i].axis('off')
-
- LOGGER.info(f'Saving {f}... ({n}/{channels})')
- plt.title('Features')
- plt.savefig(f, dpi=300, bbox_inches='tight')
- plt.close()
- np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
-
-
-def hist2d(x, y, n=100):
- # 2d histogram used in labels.png and evolve.png
- xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
- hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
- xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
- yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
- return np.log(hist[xidx, yidx])
-
-
-def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
- from scipy.signal import butter, filtfilt
-
- # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
- def butter_lowpass(cutoff, fs, order):
- nyq = 0.5 * fs
- normal_cutoff = cutoff / nyq
- return butter(order, normal_cutoff, btype='low', analog=False)
-
- b, a = butter_lowpass(cutoff, fs, order=order)
- return filtfilt(b, a, data) # forward-backward filter
-
-
-def output_to_target(output):
- # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
- targets = []
- for i, o in enumerate(output):
- for *box, conf, cls in o.cpu().numpy():
- targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
- return np.array(targets)
-
-
-@threaded
-def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):
- # Plot image grid with labels
- if isinstance(images, torch.Tensor):
- images = images.cpu().float().numpy()
- if isinstance(targets, torch.Tensor):
- targets = targets.cpu().numpy()
- if np.max(images[0]) <= 1:
- images *= 255 # de-normalise (optional)
- bs, _, h, w = images.shape # batch size, _, height, width
- bs = min(bs, max_subplots) # limit plot images
- ns = np.ceil(bs ** 0.5) # number of subplots (square)
-
- # Build Image
- mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
- for i, im in enumerate(images):
- if i == max_subplots: # if last batch has fewer images than we expect
- break
- x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
- im = im.transpose(1, 2, 0)
- mosaic[y:y + h, x:x + w, :] = im
-
- # Resize (optional)
- scale = max_size / ns / max(h, w)
- if scale < 1:
- h = math.ceil(scale * h)
- w = math.ceil(scale * w)
- mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
-
- # Annotate
- fs = int((h + w) * ns * 0.01) # font size
- annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
- for i in range(i + 1):
- x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
- annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
- if paths:
- annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
- if len(targets) > 0:
- ti = targets[targets[:, 0] == i] # image targets
- boxes = xywh2xyxy(ti[:, 2:6]).T
- classes = ti[:, 1].astype('int')
- labels = ti.shape[1] == 6 # labels if no conf column
- conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
-
- if boxes.shape[1]:
- if boxes.max() <= 1.01: # if normalized with tolerance 0.01
- boxes[[0, 2]] *= w # scale to pixels
- boxes[[1, 3]] *= h
- elif scale < 1: # absolute coords need scale if image scales
- boxes *= scale
- boxes[[0, 2]] += x
- boxes[[1, 3]] += y
- for j, box in enumerate(boxes.T.tolist()):
- cls = classes[j]
- color = colors(cls)
- cls = names[cls] if names else cls
- if labels or conf[j] > 0.25: # 0.25 conf thresh
- label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
- annotator.box_label(box, label, color=color)
- annotator.im.save(fname) # save
-
-
-def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
- # Plot LR simulating training for full epochs
- optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
- y = []
- for _ in range(epochs):
- scheduler.step()
- y.append(optimizer.param_groups[0]['lr'])
- plt.plot(y, '.-', label='LR')
- plt.xlabel('epoch')
- plt.ylabel('LR')
- plt.grid()
- plt.xlim(0, epochs)
- plt.ylim(0)
- plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
- plt.close()
-
-
-def plot_val_txt(): # from utils.plots import *; plot_val()
- # Plot val.txt histograms
- x = np.loadtxt('val.txt', dtype=np.float32)
- box = xyxy2xywh(x[:, :4])
- cx, cy = box[:, 0], box[:, 1]
-
- fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
- ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
- ax.set_aspect('equal')
- plt.savefig('hist2d.png', dpi=300)
-
- fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
- ax[0].hist(cx, bins=600)
- ax[1].hist(cy, bins=600)
- plt.savefig('hist1d.png', dpi=200)
-
-
-def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
- # Plot targets.txt histograms
- x = np.loadtxt('targets.txt', dtype=np.float32).T
- s = ['x targets', 'y targets', 'width targets', 'height targets']
- fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
- ax = ax.ravel()
- for i in range(4):
- ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
- ax[i].legend()
- ax[i].set_title(s[i])
- plt.savefig('targets.jpg', dpi=200)
-
-
-def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()
- # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
- save_dir = Path(file).parent if file else Path(dir)
- plot2 = False # plot additional results
- if plot2:
- ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
-
- fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
- # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
- for f in sorted(save_dir.glob('study*.txt')):
- y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
- x = np.arange(y.shape[1]) if x is None else np.array(x)
- if plot2:
- s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
- for i in range(7):
- ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
- ax[i].set_title(s[i])
-
- j = y[3].argmax() + 1
- ax2.plot(y[5, 1:j],
- y[3, 1:j] * 1E2,
- '.-',
- linewidth=2,
- markersize=8,
- label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
-
- ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
- 'k.-',
- linewidth=2,
- markersize=8,
- alpha=.25,
- label='EfficientDet')
-
- ax2.grid(alpha=0.2)
- ax2.set_yticks(np.arange(20, 60, 5))
- ax2.set_xlim(0, 57)
- ax2.set_ylim(25, 55)
- ax2.set_xlabel('GPU Speed (ms/img)')
- ax2.set_ylabel('COCO AP val')
- ax2.legend(loc='lower right')
- f = save_dir / 'study.png'
- print(f'Saving {f}...')
- plt.savefig(f, dpi=300)
-
-
-@try_except # known issue https://github.com/ultralytics/yolov5/issues/5395
-@Timeout(30) # known issue https://github.com/ultralytics/yolov5/issues/5611
-def plot_labels(labels, names=(), save_dir=Path('')):
- # plot dataset labels
- LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
- c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
- nc = int(c.max() + 1) # number of classes
- x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
-
- # seaborn correlogram
- sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
- plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
- plt.close()
-
- # matplotlib labels
- matplotlib.use('svg') # faster
- ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
- y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
- try: # color histogram bars by class
- [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
- except Exception:
- pass
- ax[0].set_ylabel('instances')
- if 0 < len(names) < 30:
- ax[0].set_xticks(range(len(names)))
- ax[0].set_xticklabels(names, rotation=90, fontsize=10)
- else:
- ax[0].set_xlabel('classes')
- sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
- sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
-
- # rectangles
- labels[:, 1:3] = 0.5 # center
- labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
- img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
- for cls, *box in labels[:1000]:
- ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
- ax[1].imshow(img)
- ax[1].axis('off')
-
- for a in [0, 1, 2, 3]:
- for s in ['top', 'right', 'left', 'bottom']:
- ax[a].spines[s].set_visible(False)
-
- plt.savefig(save_dir / 'labels.jpg', dpi=200)
- matplotlib.use('Agg')
- plt.close()
-
-
-def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
- # Show classification image grid with labels (optional) and predictions (optional)
- from utils.augmentations import denormalize
-
- names = names or [f'class{i}' for i in range(1000)]
- blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
- dim=0) # select batch index 0, block by channels
- n = min(len(blocks), nmax) # number of plots
- m = min(8, round(n ** 0.5)) # 8 x 8 default
- fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols
- ax = ax.ravel() if m > 1 else [ax]
- # plt.subplots_adjust(wspace=0.05, hspace=0.05)
- for i in range(n):
- ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
- ax[i].axis('off')
- if labels is not None:
- s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
- ax[i].set_title(s, fontsize=8, verticalalignment='top')
- plt.savefig(f, dpi=300, bbox_inches='tight')
- plt.close()
- if verbose:
- LOGGER.info(f"Saving {f}")
- if labels is not None:
- LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
- if pred is not None:
- LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
- return f
-
-
-def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
- # Plot evolve.csv hyp evolution results
- evolve_csv = Path(evolve_csv)
- data = pd.read_csv(evolve_csv)
- keys = [x.strip() for x in data.columns]
- x = data.values
- f = fitness(x)
- j = np.argmax(f) # max fitness index
- plt.figure(figsize=(10, 12), tight_layout=True)
- matplotlib.rc('font', **{'size': 8})
- print(f'Best results from row {j} of {evolve_csv}:')
- for i, k in enumerate(keys[7:]):
- v = x[:, 7 + i]
- mu = v[j] # best single result
- plt.subplot(6, 5, i + 1)
- plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
- plt.plot(mu, f.max(), 'k+', markersize=15)
- plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
- if i % 5 != 0:
- plt.yticks([])
- print(f'{k:>15}: {mu:.3g}')
- f = evolve_csv.with_suffix('.png') # filename
- plt.savefig(f, dpi=200)
- plt.close()
- print(f'Saved {f}')
-
-
-def plot_results(file='path/to/results.csv', dir=''):
- # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
- save_dir = Path(file).parent if file else Path(dir)
- fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
- ax = ax.ravel()
- files = list(save_dir.glob('results*.csv'))
- assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
- for f in files:
- try:
- data = pd.read_csv(f)
- s = [x.strip() for x in data.columns]
- x = data.values[:, 0]
- for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
- y = data.values[:, j].astype('float')
- # y[y == 0] = np.nan # don't show zero values
- ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
- ax[i].set_title(s[j], fontsize=12)
- # if j in [8, 9, 10]: # share train and val loss y axes
- # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
- except Exception as e:
- LOGGER.info(f'Warning: Plotting error for {f}: {e}')
- ax[1].legend()
- fig.savefig(save_dir / 'results.png', dpi=200)
- plt.close()
-
-
-def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
- # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
- ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
- s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
- files = list(Path(save_dir).glob('frames*.txt'))
- for fi, f in enumerate(files):
- try:
- results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
- n = results.shape[1] # number of rows
- x = np.arange(start, min(stop, n) if stop else n)
- results = results[:, x]
- t = (results[0] - results[0].min()) # set t0=0s
- results[0] = x
- for i, a in enumerate(ax):
- if i < len(results):
- label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
- a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
- a.set_title(s[i])
- a.set_xlabel('time (s)')
- # if fi == len(files) - 1:
- # a.set_ylim(bottom=0)
- for side in ['top', 'right']:
- a.spines[side].set_visible(False)
- else:
- a.remove()
- except Exception as e:
- print(f'Warning: Plotting error for {f}; {e}')
- ax[1].legend()
- plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
-
-
-def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
- # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
- xyxy = torch.tensor(xyxy).view(-1, 4)
- b = xyxy2xywh(xyxy) # boxes
- if square:
- b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
- b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
- xyxy = xywh2xyxy(b).long()
- clip_coords(xyxy, im.shape)
- crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
- if save:
- file.parent.mkdir(parents=True, exist_ok=True) # make directory
- f = str(increment_path(file).with_suffix('.jpg'))
- # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
- Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
- return crop
diff --git a/yolov5-6.2/utils/torch_utils.py b/yolov5-6.2/utils/torch_utils.py
deleted file mode 100644
index 354a802a..00000000
--- a/yolov5-6.2/utils/torch_utils.py
+++ /dev/null
@@ -1,454 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-PyTorch utils
-"""
-
-import math
-import os
-import platform
-import subprocess
-import time
-import warnings
-from contextlib import contextmanager
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.parallel import DistributedDataParallel as DDP
-
-from utils.general import LOGGER, check_version, colorstr, file_date, git_describe
-
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-
-try:
- import thop # for FLOPs computation
-except ImportError:
- thop = None
-
-# Suppress PyTorch warnings
-warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
-
-
-def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
- # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
- def decorate(fn):
- return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
-
- return decorate
-
-
-def smartCrossEntropyLoss(label_smoothing=0.0):
- # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0
- if check_version(torch.__version__, '1.10.0'):
- return nn.CrossEntropyLoss(label_smoothing=label_smoothing) # loss function
- else:
- if label_smoothing > 0:
- LOGGER.warning(f'WARNING: label smoothing {label_smoothing} requires torch>=1.10.0')
- return nn.CrossEntropyLoss() # loss function
-
-
-def smart_DDP(model):
- # Model DDP creation with checks
- assert not check_version(torch.__version__, '1.12.0', pinned=True), \
- 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
- 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
- if check_version(torch.__version__, '1.11.0'):
- return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
- else:
- return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
-
-
-def reshape_classifier_output(model, n=1000):
- # Update a TorchVision classification model to class count 'n' if required
- from models.common import Classify
- name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module
- if isinstance(m, Classify): # YOLOv5 Classify() head
- if m.linear.out_features != n:
- m.linear = nn.Linear(m.linear.in_features, n)
- elif isinstance(m, nn.Linear): # ResNet, EfficientNet
- if m.out_features != n:
- setattr(model, name, nn.Linear(m.in_features, n))
- elif isinstance(m, nn.Sequential):
- types = [type(x) for x in m]
- if nn.Linear in types:
- i = types.index(nn.Linear) # nn.Linear index
- if m[i].out_features != n:
- m[i] = nn.Linear(m[i].in_features, n)
- elif nn.Conv2d in types:
- i = types.index(nn.Conv2d) # nn.Conv2d index
- if m[i].out_channels != n:
- m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias)
-
-
-@contextmanager
-def torch_distributed_zero_first(local_rank: int):
- # Decorator to make all processes in distributed training wait for each local_master to do something
- if local_rank not in [-1, 0]:
- dist.barrier(device_ids=[local_rank])
- yield
- if local_rank == 0:
- dist.barrier(device_ids=[0])
-
-
-def device_count():
- # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows
- assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows'
- try:
- cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows
- return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
- except Exception:
- return 0
-
-
-def select_device(device='', batch_size=0, newline=True):
- # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
- s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
- device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
- cpu = device == 'cpu'
- mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
- if cpu or mps:
- os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
- elif device: # non-cpu device requested
- os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
- assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
- f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
-
- if not (cpu or mps) and torch.cuda.is_available(): # prefer GPU if available
- devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
- n = len(devices) # device count
- if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
- assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
- space = ' ' * (len(s) + 1)
- for i, d in enumerate(devices):
- p = torch.cuda.get_device_properties(i)
- s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
- arg = 'cuda:0'
- elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available
- s += 'MPS\n'
- arg = 'mps'
- else: # revert to CPU
- s += 'CPU\n'
- arg = 'cpu'
-
- if not newline:
- s = s.rstrip()
- LOGGER.info(s)
- return torch.device(arg)
-
-
-def time_sync():
- # PyTorch-accurate time
- if torch.cuda.is_available():
- torch.cuda.synchronize()
- return time.time()
-
-
-def profile(input, ops, n=10, device=None):
- """ YOLOv5 speed/memory/FLOPs profiler
- Usage:
- input = torch.randn(16, 3, 640, 640)
- m1 = lambda x: x * torch.sigmoid(x)
- m2 = nn.SiLU()
- profile(input, [m1, m2], n=100) # profile over 100 iterations
- """
- results = []
- if not isinstance(device, torch.device):
- device = select_device(device)
- print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
- f"{'input':>24s}{'output':>24s}")
-
- for x in input if isinstance(input, list) else [input]:
- x = x.to(device)
- x.requires_grad = True
- for m in ops if isinstance(ops, list) else [ops]:
- m = m.to(device) if hasattr(m, 'to') else m # device
- m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
- tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
- try:
- flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
- except Exception:
- flops = 0
-
- try:
- for _ in range(n):
- t[0] = time_sync()
- y = m(x)
- t[1] = time_sync()
- try:
- _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
- t[2] = time_sync()
- except Exception: # no backward method
- # print(e) # for debug
- t[2] = float('nan')
- tf += (t[1] - t[0]) * 1000 / n # ms per op forward
- tb += (t[2] - t[1]) * 1000 / n # ms per op backward
- mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
- s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes
- p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
- print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
- results.append([p, flops, mem, tf, tb, s_in, s_out])
- except Exception as e:
- print(e)
- results.append(None)
- torch.cuda.empty_cache()
- return results
-
-
-def is_parallel(model):
- # Returns True if model is of type DP or DDP
- return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
-
-
-def de_parallel(model):
- # De-parallelize a model: returns single-GPU model if model is of type DP or DDP
- return model.module if is_parallel(model) else model
-
-
-def initialize_weights(model):
- for m in model.modules():
- t = type(m)
- if t is nn.Conv2d:
- pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif t is nn.BatchNorm2d:
- m.eps = 1e-3
- m.momentum = 0.03
- elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
- m.inplace = True
-
-
-def find_modules(model, mclass=nn.Conv2d):
- # Finds layer indices matching module class 'mclass'
- return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
-
-
-def sparsity(model):
- # Return global model sparsity
- a, b = 0, 0
- for p in model.parameters():
- a += p.numel()
- b += (p == 0).sum()
- return b / a
-
-
-def prune(model, amount=0.3):
- # Prune model to requested global sparsity
- import torch.nn.utils.prune as prune
- for name, m in model.named_modules():
- if isinstance(m, nn.Conv2d):
- prune.l1_unstructured(m, name='weight', amount=amount) # prune
- prune.remove(m, 'weight') # make permanent
- LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity')
-
-
-def fuse_conv_and_bn(conv, bn):
- # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
- fusedconv = nn.Conv2d(conv.in_channels,
- conv.out_channels,
- kernel_size=conv.kernel_size,
- stride=conv.stride,
- padding=conv.padding,
- groups=conv.groups,
- bias=True).requires_grad_(False).to(conv.weight.device)
-
- # Prepare filters
- w_conv = conv.weight.clone().view(conv.out_channels, -1)
- w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
- fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
-
- # Prepare spatial bias
- b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
- b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
- fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
- return fusedconv
-
-
-def model_info(model, verbose=False, imgsz=640):
- # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
- n_p = sum(x.numel() for x in model.parameters()) # number parameters
- n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
- if verbose:
- print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
- for i, (name, p) in enumerate(model.named_parameters()):
- name = name.replace('module_list.', '')
- print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
- (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
-
- try: # FLOPs
- p = next(model.parameters())
- stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
- im = torch.zeros((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
- flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
- imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
- fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs
- except Exception:
- fs = ''
-
- name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model'
- LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
-
-
-def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
- # Scales img(bs,3,y,x) by ratio constrained to gs-multiple
- if ratio == 1.0:
- return img
- h, w = img.shape[2:]
- s = (int(h * ratio), int(w * ratio)) # new size
- img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
- if not same_shape: # pad/crop img
- h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
- return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
-
-
-def copy_attr(a, b, include=(), exclude=()):
- # Copy attributes from b to a, options to only include [...] and to exclude [...]
- for k, v in b.__dict__.items():
- if (len(include) and k not in include) or k.startswith('_') or k in exclude:
- continue
- else:
- setattr(a, k, v)
-
-
-def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
- # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
- g = [], [], [] # optimizer parameter groups
- bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
- for v in model.modules():
- if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias (no decay)
- g[2].append(v.bias)
- if isinstance(v, bn): # weight (no decay)
- g[1].append(v.weight)
- elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
- g[0].append(v.weight)
-
- if name == 'Adam':
- optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
- elif name == 'AdamW':
- optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
- elif name == 'RMSProp':
- optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
- elif name == 'SGD':
- optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
- else:
- raise NotImplementedError(f'Optimizer {name} not implemented.')
-
- optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay
- optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights)
- LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
- f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
- return optimizer
-
-
-def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):
- # YOLOv5 torch.hub.load() wrapper with smart error/issue handling
- if check_version(torch.__version__, '1.9.1'):
- kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors
- if check_version(torch.__version__, '1.12.0'):
- kwargs['trust_repo'] = True # argument required starting in torch 0.12
- try:
- return torch.hub.load(repo, model, **kwargs)
- except Exception:
- return torch.hub.load(repo, model, force_reload=True, **kwargs)
-
-
-def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
- # Resume training from a partially trained checkpoint
- best_fitness = 0.0
- start_epoch = ckpt['epoch'] + 1
- if ckpt['optimizer'] is not None:
- optimizer.load_state_dict(ckpt['optimizer']) # optimizer
- best_fitness = ckpt['best_fitness']
- if ema and ckpt.get('ema'):
- ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA
- ema.updates = ckpt['updates']
- if resume:
- assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \
- f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
- LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs')
- if epochs < start_epoch:
- LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
- epochs += ckpt['epoch'] # finetune additional epochs
- return best_fitness, start_epoch, epochs
-
-
-class EarlyStopping:
- # YOLOv5 simple early stopper
- def __init__(self, patience=30):
- self.best_fitness = 0.0 # i.e. mAP
- self.best_epoch = 0
- self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop
- self.possible_stop = False # possible stop may occur next epoch
-
- def __call__(self, epoch, fitness):
- if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training
- self.best_epoch = epoch
- self.best_fitness = fitness
- delta = epoch - self.best_epoch # epochs without improvement
- self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
- stop = delta >= self.patience # stop training if patience exceeded
- if stop:
- LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '
- f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n'
- f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '
- f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')
- return stop
-
-
-class ModelEMA:
- """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
- Keeps a moving average of everything in the model state_dict (parameters and buffers)
- For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
- """
-
- def __init__(self, model, decay=0.9999, tau=2000, updates=0):
- # Create EMA
- self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
- # if next(model.parameters()).device.type != 'cpu':
- # self.ema.half() # FP16 EMA
- self.updates = updates # number of EMA updates
- self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
- for p in self.ema.parameters():
- p.requires_grad_(False)
-
- @smart_inference_mode()
- def update(self, model):
- # Update EMA parameters
- self.updates += 1
- d = self.decay(self.updates)
-
- msd = de_parallel(model).state_dict() # model state_dict
- for k, v in self.ema.state_dict().items():
- if v.dtype.is_floating_point:
- v *= d
- v += (1 - d) * msd[k].detach()
-
- def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
- # Update EMA attributes
- copy_attr(self.ema, model, include, exclude)
-
-
-import time
-import torch
-
-if torch.cuda.is_available():
- torch.backends.cudnn.benchmark = True
-
-_EPOCHS = {}
-
-def time_synchronized():
- global _EPOCHS
- if not torch.cuda.is_available():
- return time.time()
- else:
- if torch.cuda.current_device() not in _EPOCHS:
- _EPOCHS[torch.cuda.current_device()] = 0
- n = time.time()
- if n - _EPOCHS[torch.cuda.current_device()] > 600:
- torch.cuda.empty_cache()
- _EPOCHS[torch.cuda.current_device()] = n
- return torch.cuda.Event(enable_timing=True)
diff --git a/yolov5-6.2/val.py b/yolov5-6.2/val.py
deleted file mode 100644
index 13049623..00000000
--- a/yolov5-6.2/val.py
+++ /dev/null
@@ -1,396 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Validate a trained YOLOv5 model accuracy on a custom dataset
-
-Usage:
- $ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
-
-Usage - formats:
- $ python path/to/val.py --weights yolov5s.pt # PyTorch
- yolov5s.torchscript # TorchScript
- yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s.xml # OpenVINO
- yolov5s.engine # TensorRT
- yolov5s.mlmodel # CoreML (macOS-only)
- yolov5s_saved_model # TensorFlow SavedModel
- yolov5s.pb # TensorFlow GraphDef
- yolov5s.tflite # TensorFlow Lite
- yolov5s_edgetpu.tflite # TensorFlow Edge TPU
-"""
-
-import argparse
-import json
-import os
-import sys
-from pathlib import Path
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.common import DetectMultiBackend
-from utils.callbacks import Callbacks
-from utils.dataloaders import create_dataloader
-from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_yaml,
- coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
- scale_coords, xywh2xyxy, xyxy2xywh)
-from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
-from utils.plots import output_to_target, plot_images, plot_val_study
-from utils.torch_utils import select_device, smart_inference_mode, time_sync
-
-
-def save_one_txt(predn, save_conf, shape, file):
- # Save one txt result
- gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
- for *xyxy, conf, cls in predn.tolist():
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(file, 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-
-def save_one_json(predn, jdict, path, class_map):
- # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
- image_id = int(path.stem) if path.stem.isnumeric() else path.stem
- box = xyxy2xywh(predn[:, :4]) # xywh
- box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
- for p, b in zip(predn.tolist(), box.tolist()):
- jdict.append({
- 'image_id': image_id,
- 'category_id': class_map[int(p[5])],
- 'bbox': [round(x, 3) for x in b],
- 'score': round(p[4], 5)})
-
-
-def process_batch(detections, labels, iouv):
- """
- Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
- Arguments:
- detections (Array[N, 6]), x1, y1, x2, y2, conf, class
- labels (Array[M, 5]), class, x1, y1, x2, y2
- Returns:
- correct (Array[N, 10]), for 10 IoU levels
- """
- correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
- iou = box_iou(labels[:, 1:], detections[:, :4])
- correct_class = labels[:, 0:1] == detections[:, 5]
- for i in range(len(iouv)):
- x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
- if x[0].shape[0]:
- matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
- if x[0].shape[0] > 1:
- matches = matches[matches[:, 2].argsort()[::-1]]
- matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
- # matches = matches[matches[:, 2].argsort()[::-1]]
- matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
- correct[matches[:, 1].astype(int), i] = True
- return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
-
-
-@smart_inference_mode()
-def run(
- data,
- weights=None, # model.pt path(s)
- batch_size=32, # batch size
- imgsz=640, # inference size (pixels)
- conf_thres=0.001, # confidence threshold
- iou_thres=0.6, # NMS IoU threshold
- task='val', # train, val, test, speed or study
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- workers=8, # max dataloader workers (per RANK in DDP mode)
- single_cls=False, # treat as single-class dataset
- augment=False, # augmented inference
- verbose=False, # verbose output
- save_txt=False, # save results to *.txt
- save_hybrid=False, # save label+prediction hybrid results to *.txt
- save_conf=False, # save confidences in --save-txt labels
- save_json=False, # save a COCO-JSON results file
- project=ROOT / 'runs/val', # save to project/name
- name='exp', # save to project/name
- exist_ok=False, # existing project/name ok, do not increment
- half=True, # use FP16 half-precision inference
- dnn=False, # use OpenCV DNN for ONNX inference
- model=None,
- dataloader=None,
- save_dir=Path(''),
- plots=True,
- callbacks=Callbacks(),
- compute_loss=None,
-):
- # Initialize/load model and set device
- training = model is not None
- if training: # called by train.py
- device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
- half &= device.type != 'cpu' # half precision only supported on CUDA
- model.half() if half else model.float()
- else: # called directly
- device = select_device(device, batch_size=batch_size)
-
- # Directories
- save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
- stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
- imgsz = check_img_size(imgsz, s=stride) # check image size
- half = model.fp16 # FP16 supported on limited backends with CUDA
- if engine:
- batch_size = model.batch_size
- else:
- device = model.device
- if not (pt or jit):
- batch_size = 1 # export.py models default to batch-size 1
- LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
-
- # Data
- data = check_dataset(data) # check
-
- # Configure
- model.eval()
- cuda = device.type != 'cpu'
- is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
- nc = 1 if single_cls else int(data['nc']) # number of classes
- iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
- niou = iouv.numel()
-
- # Dataloader
- if not training:
- if pt and not single_cls: # check --weights are trained on --data
- ncm = model.model.nc
- assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
- f'classes). Pass correct combination of --weights and --data that are trained together.'
- model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
- pad = 0.0 if task in ('speed', 'benchmark') else 0.5
- rect = False if task == 'benchmark' else pt # square inference for benchmarks
- task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
- dataloader = create_dataloader(data[task],
- imgsz,
- batch_size,
- stride,
- single_cls,
- pad=pad,
- rect=rect,
- workers=workers,
- prefix=colorstr(f'{task}: '))[0]
-
- seen = 0
- confusion_matrix = ConfusionMatrix(nc=nc)
- names = dict(enumerate(model.names if hasattr(model, 'names') else model.module.names))
- class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
- s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
- loss = torch.zeros(3, device=device)
- jdict, stats, ap, ap_class = [], [], [], []
- callbacks.run('on_val_start')
- pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
- for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
- callbacks.run('on_val_batch_start')
- t1 = time_sync()
- if cuda:
- im = im.to(device, non_blocking=True)
- targets = targets.to(device)
- im = im.half() if half else im.float() # uint8 to fp16/32
- im /= 255 # 0 - 255 to 0.0 - 1.0
- nb, _, height, width = im.shape # batch size, channels, height, width
- t2 = time_sync()
- dt[0] += t2 - t1
-
- # Inference
- out, train_out = model(im) if training else model(im, augment=augment, val=True) # inference, loss outputs
- dt[1] += time_sync() - t2
-
- # Loss
- if compute_loss:
- loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls
-
- # NMS
- targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
- lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
- t3 = time_sync()
- out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
- dt[2] += time_sync() - t3
-
- # Metrics
- for si, pred in enumerate(out):
- labels = targets[targets[:, 0] == si, 1:]
- nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
- path, shape = Path(paths[si]), shapes[si][0]
- correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
- seen += 1
-
- if npr == 0:
- if nl:
- stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
- if plots:
- confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
- continue
-
- # Predictions
- if single_cls:
- pred[:, 5] = 0
- predn = pred.clone()
- scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
-
- # Evaluate
- if nl:
- tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
- scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
- labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
- correct = process_batch(predn, labelsn, iouv)
- if plots:
- confusion_matrix.process_batch(predn, labelsn)
- stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)
-
- # Save/log
- if save_txt:
- save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
- if save_json:
- save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
- callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
-
- # Plot images
- if plots and batch_i < 3:
- plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
- plot_images(im, output_to_target(out), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
-
- callbacks.run('on_val_batch_end')
-
- # Compute metrics
- stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
- if len(stats) and stats[0].any():
- tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
- ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
- mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
- nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
-
- # Print results
- pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
- LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
- if nt.sum() == 0:
- LOGGER.warning(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️')
-
- # Print results per class
- if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
- for i, c in enumerate(ap_class):
- LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
-
- # Print speeds
- t = tuple(x / seen * 1E3 for x in dt) # speeds per image
- if not training:
- shape = (batch_size, 3, imgsz, imgsz)
- LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
-
- # Plots
- if plots:
- confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
- callbacks.run('on_val_end')
-
- # Save JSON
- if save_json and len(jdict):
- w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
- anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
- pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
- LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
- with open(pred_json, 'w') as f:
- json.dump(jdict, f)
-
- try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
- check_requirements(['pycocotools'])
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
-
- anno = COCO(anno_json) # init annotations api
- pred = anno.loadRes(pred_json) # init predictions api
- eval = COCOeval(anno, pred, 'bbox')
- if is_coco:
- eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
- eval.evaluate()
- eval.accumulate()
- eval.summarize()
- map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
- except Exception as e:
- LOGGER.info(f'pycocotools unable to run: {e}')
-
- # Return results
- model.float() # for training
- if not training:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
- maps = np.zeros(nc) + map
- for i, c in enumerate(ap_class):
- maps[c] = ap[i]
- return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
- parser.add_argument('--batch-size', type=int, default=32, help='batch size')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
- parser.add_argument('--task', default='val', help='train, val, test, speed or study')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
- parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--verbose', action='store_true', help='report mAP by class')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
- parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
- opt = parser.parse_args()
- opt.data = check_yaml(opt.data) # check YAML
- opt.save_json |= opt.data.endswith('coco.yaml')
- opt.save_txt |= opt.save_hybrid
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
-
- if opt.task in ('train', 'val', 'test'): # run normally
- if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
- LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} > 0.001 produces invalid results ⚠️')
- run(**vars(opt))
-
- else:
- weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
- opt.half = True # FP16 for fastest results
- if opt.task == 'speed': # speed benchmarks
- # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
- opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
- for opt.weights in weights:
- run(**vars(opt), plots=False)
-
- elif opt.task == 'study': # speed vs mAP benchmarks
- # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
- for opt.weights in weights:
- f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to
- x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
- for opt.imgsz in x: # img-size
- LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
- r, _, t = run(**vars(opt), plots=False)
- y.append(r + t) # results and times
- np.savetxt(f, y, fmt='%10.4g') # save
- os.system('zip -r study.zip study_*.txt')
- plot_val_study(x=x) # plot
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)