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2b9906a025 | 2 years ago |
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|||||||
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"ExpandedNodes": [
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""
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|
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|
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"SelectedNode": "\\避障",
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"PreviewInSolutionExplorer": false
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}
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@ -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
|
|
||||||
@ -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!
|
|
||||||
@ -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
|
|
||||||
@ -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!
|
|
||||||
@ -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?
|
|
||||||
@ -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.
|
|
||||||
-->
|
|
||||||
@ -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!
|
|
||||||
@ -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
|
|
||||||
@ -1,167 +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:
|
|
||||||
fail-fast: false
|
|
||||||
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: Benchmark DetectionModel
|
|
||||||
run: |
|
|
||||||
python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29
|
|
||||||
- name: Benchmark SegmentationModel
|
|
||||||
run: |
|
|
||||||
python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22
|
|
||||||
- name: Test predictions
|
|
||||||
run: |
|
|
||||||
python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224
|
|
||||||
python detect.py --weights ${{ matrix.model }}.onnx --img 320
|
|
||||||
python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320
|
|
||||||
python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224
|
|
||||||
|
|
||||||
Tests:
|
|
||||||
timeout-minutes: 60
|
|
||||||
runs-on: ${{ matrix.os }}
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
os: [ ubuntu-latest, windows-latest ] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049
|
|
||||||
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 $m.pt --img 64 --include torchscript # export
|
|
||||||
python - <<EOF
|
|
||||||
import torch
|
|
||||||
im = torch.zeros([1, 3, 64, 64])
|
|
||||||
for path in '$m', '$b':
|
|
||||||
model = torch.hub.load('.', 'custom', path=path, source='local')
|
|
||||||
print(model('data/images/bus.jpg'))
|
|
||||||
model(im) # warmup, build grids for trace
|
|
||||||
torch.jit.trace(model, [im])
|
|
||||||
EOF
|
|
||||||
- name: Test segmentation
|
|
||||||
shell: bash # for Windows compatibility
|
|
||||||
run: |
|
|
||||||
m=${{ matrix.model }}-seg # official weights
|
|
||||||
b=runs/train-seg/exp/weights/best # best.pt checkpoint
|
|
||||||
python segment/train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train
|
|
||||||
python segment/train.py --imgsz 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device cpu # train
|
|
||||||
for d in cpu; do # devices
|
|
||||||
for w in $m $b; do # weights
|
|
||||||
python segment/val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val
|
|
||||||
python segment/predict.py --imgsz 64 --weights $w.pt --device $d # predict
|
|
||||||
python export.py --weights $w.pt --img 64 --include torchscript --device $d # export
|
|
||||||
done
|
|
||||||
done
|
|
||||||
- name: Test classification
|
|
||||||
shell: bash # for Windows compatibility
|
|
||||||
run: |
|
|
||||||
m=${{ 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 mnist160 --epochs 1 # train
|
|
||||||
python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist160 # val
|
|
||||||
python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist160/test/7/60.png # predict
|
|
||||||
python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict
|
|
||||||
python export.py --weights $b --img 64 --include torchscript # export
|
|
||||||
python - <<EOF
|
|
||||||
import torch
|
|
||||||
for path in '$m', '$b':
|
|
||||||
model = torch.hub.load('.', 'custom', path=path, source='local')
|
|
||||||
EOF
|
|
||||||
@ -1,57 +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
|
|
||||||
continue-on-error: true
|
|
||||||
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
|
|
||||||
continue-on-error: true
|
|
||||||
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
|
|
||||||
continue-on-error: true
|
|
||||||
with:
|
|
||||||
context: .
|
|
||||||
file: utils/docker/Dockerfile
|
|
||||||
push: true
|
|
||||||
tags: ultralytics/yolov5:latest
|
|
||||||
@ -1,57 +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 `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
|
|
||||||
- ✅ Verify all YOLOv5 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):
|
|
||||||
|
|
||||||
- **Notebooks** with free GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <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="YOLOv5 CI"></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](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
|
|
||||||
|
|
||||||
@ -1,74 +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
|
|
||||||
names:
|
|
||||||
0: person
|
|
||||||
1: bicycle
|
|
||||||
2: car
|
|
||||||
3: motorcycle
|
|
||||||
4: bus
|
|
||||||
5: truck
|
|
||||||
6: traffic_light
|
|
||||||
7: stop_sign
|
|
||||||
|
|
||||||
|
|
||||||
# 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(yaml['path']) # 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
|
|
||||||
@ -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
|
|
||||||
names:
|
|
||||||
0: wheat_head
|
|
||||||
|
|
||||||
|
|
||||||
# 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
|
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,438 +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
|
|
||||||
names:
|
|
||||||
0: Person
|
|
||||||
1: Sneakers
|
|
||||||
2: Chair
|
|
||||||
3: Other Shoes
|
|
||||||
4: Hat
|
|
||||||
5: Car
|
|
||||||
6: Lamp
|
|
||||||
7: Glasses
|
|
||||||
8: Bottle
|
|
||||||
9: Desk
|
|
||||||
10: Cup
|
|
||||||
11: Street Lights
|
|
||||||
12: Cabinet/shelf
|
|
||||||
13: Handbag/Satchel
|
|
||||||
14: Bracelet
|
|
||||||
15: Plate
|
|
||||||
16: Picture/Frame
|
|
||||||
17: Helmet
|
|
||||||
18: Book
|
|
||||||
19: Gloves
|
|
||||||
20: Storage box
|
|
||||||
21: Boat
|
|
||||||
22: Leather Shoes
|
|
||||||
23: Flower
|
|
||||||
24: Bench
|
|
||||||
25: Potted Plant
|
|
||||||
26: Bowl/Basin
|
|
||||||
27: Flag
|
|
||||||
28: Pillow
|
|
||||||
29: Boots
|
|
||||||
30: Vase
|
|
||||||
31: Microphone
|
|
||||||
32: Necklace
|
|
||||||
33: Ring
|
|
||||||
34: SUV
|
|
||||||
35: Wine Glass
|
|
||||||
36: Belt
|
|
||||||
37: Monitor/TV
|
|
||||||
38: Backpack
|
|
||||||
39: Umbrella
|
|
||||||
40: Traffic Light
|
|
||||||
41: Speaker
|
|
||||||
42: Watch
|
|
||||||
43: Tie
|
|
||||||
44: Trash bin Can
|
|
||||||
45: Slippers
|
|
||||||
46: Bicycle
|
|
||||||
47: Stool
|
|
||||||
48: Barrel/bucket
|
|
||||||
49: Van
|
|
||||||
50: Couch
|
|
||||||
51: Sandals
|
|
||||||
52: Basket
|
|
||||||
53: Drum
|
|
||||||
54: Pen/Pencil
|
|
||||||
55: Bus
|
|
||||||
56: Wild Bird
|
|
||||||
57: High Heels
|
|
||||||
58: Motorcycle
|
|
||||||
59: Guitar
|
|
||||||
60: Carpet
|
|
||||||
61: Cell Phone
|
|
||||||
62: Bread
|
|
||||||
63: Camera
|
|
||||||
64: Canned
|
|
||||||
65: Truck
|
|
||||||
66: Traffic cone
|
|
||||||
67: Cymbal
|
|
||||||
68: Lifesaver
|
|
||||||
69: Towel
|
|
||||||
70: Stuffed Toy
|
|
||||||
71: Candle
|
|
||||||
72: Sailboat
|
|
||||||
73: Laptop
|
|
||||||
74: Awning
|
|
||||||
75: Bed
|
|
||||||
76: Faucet
|
|
||||||
77: Tent
|
|
||||||
78: Horse
|
|
||||||
79: Mirror
|
|
||||||
80: Power outlet
|
|
||||||
81: Sink
|
|
||||||
82: Apple
|
|
||||||
83: Air Conditioner
|
|
||||||
84: Knife
|
|
||||||
85: Hockey Stick
|
|
||||||
86: Paddle
|
|
||||||
87: Pickup Truck
|
|
||||||
88: Fork
|
|
||||||
89: Traffic Sign
|
|
||||||
90: Balloon
|
|
||||||
91: Tripod
|
|
||||||
92: Dog
|
|
||||||
93: Spoon
|
|
||||||
94: Clock
|
|
||||||
95: Pot
|
|
||||||
96: Cow
|
|
||||||
97: Cake
|
|
||||||
98: Dinning Table
|
|
||||||
99: Sheep
|
|
||||||
100: Hanger
|
|
||||||
101: Blackboard/Whiteboard
|
|
||||||
102: Napkin
|
|
||||||
103: Other Fish
|
|
||||||
104: Orange/Tangerine
|
|
||||||
105: Toiletry
|
|
||||||
106: Keyboard
|
|
||||||
107: Tomato
|
|
||||||
108: Lantern
|
|
||||||
109: Machinery Vehicle
|
|
||||||
110: Fan
|
|
||||||
111: Green Vegetables
|
|
||||||
112: Banana
|
|
||||||
113: Baseball Glove
|
|
||||||
114: Airplane
|
|
||||||
115: Mouse
|
|
||||||
116: Train
|
|
||||||
117: Pumpkin
|
|
||||||
118: Soccer
|
|
||||||
119: Skiboard
|
|
||||||
120: Luggage
|
|
||||||
121: Nightstand
|
|
||||||
122: Tea pot
|
|
||||||
123: Telephone
|
|
||||||
124: Trolley
|
|
||||||
125: Head Phone
|
|
||||||
126: Sports Car
|
|
||||||
127: Stop Sign
|
|
||||||
128: Dessert
|
|
||||||
129: Scooter
|
|
||||||
130: Stroller
|
|
||||||
131: Crane
|
|
||||||
132: Remote
|
|
||||||
133: Refrigerator
|
|
||||||
134: Oven
|
|
||||||
135: Lemon
|
|
||||||
136: Duck
|
|
||||||
137: Baseball Bat
|
|
||||||
138: Surveillance Camera
|
|
||||||
139: Cat
|
|
||||||
140: Jug
|
|
||||||
141: Broccoli
|
|
||||||
142: Piano
|
|
||||||
143: Pizza
|
|
||||||
144: Elephant
|
|
||||||
145: Skateboard
|
|
||||||
146: Surfboard
|
|
||||||
147: Gun
|
|
||||||
148: Skating and Skiing shoes
|
|
||||||
149: Gas stove
|
|
||||||
150: Donut
|
|
||||||
151: Bow Tie
|
|
||||||
152: Carrot
|
|
||||||
153: Toilet
|
|
||||||
154: Kite
|
|
||||||
155: Strawberry
|
|
||||||
156: Other Balls
|
|
||||||
157: Shovel
|
|
||||||
158: Pepper
|
|
||||||
159: Computer Box
|
|
||||||
160: Toilet Paper
|
|
||||||
161: Cleaning Products
|
|
||||||
162: Chopsticks
|
|
||||||
163: Microwave
|
|
||||||
164: Pigeon
|
|
||||||
165: Baseball
|
|
||||||
166: Cutting/chopping Board
|
|
||||||
167: Coffee Table
|
|
||||||
168: Side Table
|
|
||||||
169: Scissors
|
|
||||||
170: Marker
|
|
||||||
171: Pie
|
|
||||||
172: Ladder
|
|
||||||
173: Snowboard
|
|
||||||
174: Cookies
|
|
||||||
175: Radiator
|
|
||||||
176: Fire Hydrant
|
|
||||||
177: Basketball
|
|
||||||
178: Zebra
|
|
||||||
179: Grape
|
|
||||||
180: Giraffe
|
|
||||||
181: Potato
|
|
||||||
182: Sausage
|
|
||||||
183: Tricycle
|
|
||||||
184: Violin
|
|
||||||
185: Egg
|
|
||||||
186: Fire Extinguisher
|
|
||||||
187: Candy
|
|
||||||
188: Fire Truck
|
|
||||||
189: Billiards
|
|
||||||
190: Converter
|
|
||||||
191: Bathtub
|
|
||||||
192: Wheelchair
|
|
||||||
193: Golf Club
|
|
||||||
194: Briefcase
|
|
||||||
195: Cucumber
|
|
||||||
196: Cigar/Cigarette
|
|
||||||
197: Paint Brush
|
|
||||||
198: Pear
|
|
||||||
199: Heavy Truck
|
|
||||||
200: Hamburger
|
|
||||||
201: Extractor
|
|
||||||
202: Extension Cord
|
|
||||||
203: Tong
|
|
||||||
204: Tennis Racket
|
|
||||||
205: Folder
|
|
||||||
206: American Football
|
|
||||||
207: earphone
|
|
||||||
208: Mask
|
|
||||||
209: Kettle
|
|
||||||
210: Tennis
|
|
||||||
211: Ship
|
|
||||||
212: Swing
|
|
||||||
213: Coffee Machine
|
|
||||||
214: Slide
|
|
||||||
215: Carriage
|
|
||||||
216: Onion
|
|
||||||
217: Green beans
|
|
||||||
218: Projector
|
|
||||||
219: Frisbee
|
|
||||||
220: Washing Machine/Drying Machine
|
|
||||||
221: Chicken
|
|
||||||
222: Printer
|
|
||||||
223: Watermelon
|
|
||||||
224: Saxophone
|
|
||||||
225: Tissue
|
|
||||||
226: Toothbrush
|
|
||||||
227: Ice cream
|
|
||||||
228: Hot-air balloon
|
|
||||||
229: Cello
|
|
||||||
230: French Fries
|
|
||||||
231: Scale
|
|
||||||
232: Trophy
|
|
||||||
233: Cabbage
|
|
||||||
234: Hot dog
|
|
||||||
235: Blender
|
|
||||||
236: Peach
|
|
||||||
237: Rice
|
|
||||||
238: Wallet/Purse
|
|
||||||
239: Volleyball
|
|
||||||
240: Deer
|
|
||||||
241: Goose
|
|
||||||
242: Tape
|
|
||||||
243: Tablet
|
|
||||||
244: Cosmetics
|
|
||||||
245: Trumpet
|
|
||||||
246: Pineapple
|
|
||||||
247: Golf Ball
|
|
||||||
248: Ambulance
|
|
||||||
249: Parking meter
|
|
||||||
250: Mango
|
|
||||||
251: Key
|
|
||||||
252: Hurdle
|
|
||||||
253: Fishing Rod
|
|
||||||
254: Medal
|
|
||||||
255: Flute
|
|
||||||
256: Brush
|
|
||||||
257: Penguin
|
|
||||||
258: Megaphone
|
|
||||||
259: Corn
|
|
||||||
260: Lettuce
|
|
||||||
261: Garlic
|
|
||||||
262: Swan
|
|
||||||
263: Helicopter
|
|
||||||
264: Green Onion
|
|
||||||
265: Sandwich
|
|
||||||
266: Nuts
|
|
||||||
267: Speed Limit Sign
|
|
||||||
268: Induction Cooker
|
|
||||||
269: Broom
|
|
||||||
270: Trombone
|
|
||||||
271: Plum
|
|
||||||
272: Rickshaw
|
|
||||||
273: Goldfish
|
|
||||||
274: Kiwi fruit
|
|
||||||
275: Router/modem
|
|
||||||
276: Poker Card
|
|
||||||
277: Toaster
|
|
||||||
278: Shrimp
|
|
||||||
279: Sushi
|
|
||||||
280: Cheese
|
|
||||||
281: Notepaper
|
|
||||||
282: Cherry
|
|
||||||
283: Pliers
|
|
||||||
284: CD
|
|
||||||
285: Pasta
|
|
||||||
286: Hammer
|
|
||||||
287: Cue
|
|
||||||
288: Avocado
|
|
||||||
289: Hamimelon
|
|
||||||
290: Flask
|
|
||||||
291: Mushroom
|
|
||||||
292: Screwdriver
|
|
||||||
293: Soap
|
|
||||||
294: Recorder
|
|
||||||
295: Bear
|
|
||||||
296: Eggplant
|
|
||||||
297: Board Eraser
|
|
||||||
298: Coconut
|
|
||||||
299: Tape Measure/Ruler
|
|
||||||
300: Pig
|
|
||||||
301: Showerhead
|
|
||||||
302: Globe
|
|
||||||
303: Chips
|
|
||||||
304: Steak
|
|
||||||
305: Crosswalk Sign
|
|
||||||
306: Stapler
|
|
||||||
307: Camel
|
|
||||||
308: Formula 1
|
|
||||||
309: Pomegranate
|
|
||||||
310: Dishwasher
|
|
||||||
311: Crab
|
|
||||||
312: Hoverboard
|
|
||||||
313: Meat ball
|
|
||||||
314: Rice Cooker
|
|
||||||
315: Tuba
|
|
||||||
316: Calculator
|
|
||||||
317: Papaya
|
|
||||||
318: Antelope
|
|
||||||
319: Parrot
|
|
||||||
320: Seal
|
|
||||||
321: Butterfly
|
|
||||||
322: Dumbbell
|
|
||||||
323: Donkey
|
|
||||||
324: Lion
|
|
||||||
325: Urinal
|
|
||||||
326: Dolphin
|
|
||||||
327: Electric Drill
|
|
||||||
328: Hair Dryer
|
|
||||||
329: Egg tart
|
|
||||||
330: Jellyfish
|
|
||||||
331: Treadmill
|
|
||||||
332: Lighter
|
|
||||||
333: Grapefruit
|
|
||||||
334: Game board
|
|
||||||
335: Mop
|
|
||||||
336: Radish
|
|
||||||
337: Baozi
|
|
||||||
338: Target
|
|
||||||
339: French
|
|
||||||
340: Spring Rolls
|
|
||||||
341: Monkey
|
|
||||||
342: Rabbit
|
|
||||||
343: Pencil Case
|
|
||||||
344: Yak
|
|
||||||
345: Red Cabbage
|
|
||||||
346: Binoculars
|
|
||||||
347: Asparagus
|
|
||||||
348: Barbell
|
|
||||||
349: Scallop
|
|
||||||
350: Noddles
|
|
||||||
351: Comb
|
|
||||||
352: Dumpling
|
|
||||||
353: Oyster
|
|
||||||
354: Table Tennis paddle
|
|
||||||
355: Cosmetics Brush/Eyeliner Pencil
|
|
||||||
356: Chainsaw
|
|
||||||
357: Eraser
|
|
||||||
358: Lobster
|
|
||||||
359: Durian
|
|
||||||
360: Okra
|
|
||||||
361: Lipstick
|
|
||||||
362: Cosmetics Mirror
|
|
||||||
363: Curling
|
|
||||||
364: 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)
|
|
||||||
@ -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
|
|
||||||
names:
|
|
||||||
0: object
|
|
||||||
|
|
||||||
|
|
||||||
# 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
|
|
||||||
@ -1,100 +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
|
|
||||||
names:
|
|
||||||
0: aeroplane
|
|
||||||
1: bicycle
|
|
||||||
2: bird
|
|
||||||
3: boat
|
|
||||||
4: bottle
|
|
||||||
5: bus
|
|
||||||
6: car
|
|
||||||
7: cat
|
|
||||||
8: chair
|
|
||||||
9: cow
|
|
||||||
10: diningtable
|
|
||||||
11: dog
|
|
||||||
12: horse
|
|
||||||
13: motorbike
|
|
||||||
14: person
|
|
||||||
15: pottedplant
|
|
||||||
16: sheep
|
|
||||||
17: sofa
|
|
||||||
18: train
|
|
||||||
19: tvmonitor
|
|
||||||
|
|
||||||
|
|
||||||
# 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)
|
|
||||||
|
|
||||||
names = list(yaml['names'].values()) # names list
|
|
||||||
for obj in root.iter('object'):
|
|
||||||
cls = obj.find('name').text
|
|
||||||
if cls in names and 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 = 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
|
|
||||||
@ -1,70 +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
|
|
||||||
names:
|
|
||||||
0: pedestrian
|
|
||||||
1: people
|
|
||||||
2: bicycle
|
|
||||||
3: car
|
|
||||||
4: van
|
|
||||||
5: truck
|
|
||||||
6: tricycle
|
|
||||||
7: awning-tricycle
|
|
||||||
8: bus
|
|
||||||
9: 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
|
|
||||||
@ -1,116 +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
|
|
||||||
names:
|
|
||||||
0: person
|
|
||||||
1: bicycle
|
|
||||||
2: car
|
|
||||||
3: motorcycle
|
|
||||||
4: airplane
|
|
||||||
5: bus
|
|
||||||
6: train
|
|
||||||
7: truck
|
|
||||||
8: boat
|
|
||||||
9: traffic light
|
|
||||||
10: fire hydrant
|
|
||||||
11: stop sign
|
|
||||||
12: parking meter
|
|
||||||
13: bench
|
|
||||||
14: bird
|
|
||||||
15: cat
|
|
||||||
16: dog
|
|
||||||
17: horse
|
|
||||||
18: sheep
|
|
||||||
19: cow
|
|
||||||
20: elephant
|
|
||||||
21: bear
|
|
||||||
22: zebra
|
|
||||||
23: giraffe
|
|
||||||
24: backpack
|
|
||||||
25: umbrella
|
|
||||||
26: handbag
|
|
||||||
27: tie
|
|
||||||
28: suitcase
|
|
||||||
29: frisbee
|
|
||||||
30: skis
|
|
||||||
31: snowboard
|
|
||||||
32: sports ball
|
|
||||||
33: kite
|
|
||||||
34: baseball bat
|
|
||||||
35: baseball glove
|
|
||||||
36: skateboard
|
|
||||||
37: surfboard
|
|
||||||
38: tennis racket
|
|
||||||
39: bottle
|
|
||||||
40: wine glass
|
|
||||||
41: cup
|
|
||||||
42: fork
|
|
||||||
43: knife
|
|
||||||
44: spoon
|
|
||||||
45: bowl
|
|
||||||
46: banana
|
|
||||||
47: apple
|
|
||||||
48: sandwich
|
|
||||||
49: orange
|
|
||||||
50: broccoli
|
|
||||||
51: carrot
|
|
||||||
52: hot dog
|
|
||||||
53: pizza
|
|
||||||
54: donut
|
|
||||||
55: cake
|
|
||||||
56: chair
|
|
||||||
57: couch
|
|
||||||
58: potted plant
|
|
||||||
59: bed
|
|
||||||
60: dining table
|
|
||||||
61: toilet
|
|
||||||
62: tv
|
|
||||||
63: laptop
|
|
||||||
64: mouse
|
|
||||||
65: remote
|
|
||||||
66: keyboard
|
|
||||||
67: cell phone
|
|
||||||
68: microwave
|
|
||||||
69: oven
|
|
||||||
70: toaster
|
|
||||||
71: sink
|
|
||||||
72: refrigerator
|
|
||||||
73: book
|
|
||||||
74: clock
|
|
||||||
75: vase
|
|
||||||
76: scissors
|
|
||||||
77: teddy bear
|
|
||||||
78: hair drier
|
|
||||||
79: toothbrush
|
|
||||||
|
|
||||||
|
|
||||||
# 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)
|
|
||||||
@ -1,101 +0,0 @@
|
|||||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
|
||||||
# COCO128-seg 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-seg ← 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-seg # 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
|
|
||||||
names:
|
|
||||||
0: person
|
|
||||||
1: bicycle
|
|
||||||
2: car
|
|
||||||
3: motorcycle
|
|
||||||
4: airplane
|
|
||||||
5: bus
|
|
||||||
6: train
|
|
||||||
7: truck
|
|
||||||
8: boat
|
|
||||||
9: traffic light
|
|
||||||
10: fire hydrant
|
|
||||||
11: stop sign
|
|
||||||
12: parking meter
|
|
||||||
13: bench
|
|
||||||
14: bird
|
|
||||||
15: cat
|
|
||||||
16: dog
|
|
||||||
17: horse
|
|
||||||
18: sheep
|
|
||||||
19: cow
|
|
||||||
20: elephant
|
|
||||||
21: bear
|
|
||||||
22: zebra
|
|
||||||
23: giraffe
|
|
||||||
24: backpack
|
|
||||||
25: umbrella
|
|
||||||
26: handbag
|
|
||||||
27: tie
|
|
||||||
28: suitcase
|
|
||||||
29: frisbee
|
|
||||||
30: skis
|
|
||||||
31: snowboard
|
|
||||||
32: sports ball
|
|
||||||
33: kite
|
|
||||||
34: baseball bat
|
|
||||||
35: baseball glove
|
|
||||||
36: skateboard
|
|
||||||
37: surfboard
|
|
||||||
38: tennis racket
|
|
||||||
39: bottle
|
|
||||||
40: wine glass
|
|
||||||
41: cup
|
|
||||||
42: fork
|
|
||||||
43: knife
|
|
||||||
44: spoon
|
|
||||||
45: bowl
|
|
||||||
46: banana
|
|
||||||
47: apple
|
|
||||||
48: sandwich
|
|
||||||
49: orange
|
|
||||||
50: broccoli
|
|
||||||
51: carrot
|
|
||||||
52: hot dog
|
|
||||||
53: pizza
|
|
||||||
54: donut
|
|
||||||
55: cake
|
|
||||||
56: chair
|
|
||||||
57: couch
|
|
||||||
58: potted plant
|
|
||||||
59: bed
|
|
||||||
60: dining table
|
|
||||||
61: toilet
|
|
||||||
62: tv
|
|
||||||
63: laptop
|
|
||||||
64: mouse
|
|
||||||
65: remote
|
|
||||||
66: keyboard
|
|
||||||
67: cell phone
|
|
||||||
68: microwave
|
|
||||||
69: oven
|
|
||||||
70: toaster
|
|
||||||
71: sink
|
|
||||||
72: refrigerator
|
|
||||||
73: book
|
|
||||||
74: clock
|
|
||||||
75: vase
|
|
||||||
76: scissors
|
|
||||||
77: teddy bear
|
|
||||||
78: hair drier
|
|
||||||
79: toothbrush
|
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
|
||||||
download: https://ultralytics.com/assets/coco128-seg.zip
|
|
||||||
@ -1,101 +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
|
|
||||||
names:
|
|
||||||
0: person
|
|
||||||
1: bicycle
|
|
||||||
2: car
|
|
||||||
3: motorcycle
|
|
||||||
4: airplane
|
|
||||||
5: bus
|
|
||||||
6: train
|
|
||||||
7: truck
|
|
||||||
8: boat
|
|
||||||
9: traffic light
|
|
||||||
10: fire hydrant
|
|
||||||
11: stop sign
|
|
||||||
12: parking meter
|
|
||||||
13: bench
|
|
||||||
14: bird
|
|
||||||
15: cat
|
|
||||||
16: dog
|
|
||||||
17: horse
|
|
||||||
18: sheep
|
|
||||||
19: cow
|
|
||||||
20: elephant
|
|
||||||
21: bear
|
|
||||||
22: zebra
|
|
||||||
23: giraffe
|
|
||||||
24: backpack
|
|
||||||
25: umbrella
|
|
||||||
26: handbag
|
|
||||||
27: tie
|
|
||||||
28: suitcase
|
|
||||||
29: frisbee
|
|
||||||
30: skis
|
|
||||||
31: snowboard
|
|
||||||
32: sports ball
|
|
||||||
33: kite
|
|
||||||
34: baseball bat
|
|
||||||
35: baseball glove
|
|
||||||
36: skateboard
|
|
||||||
37: surfboard
|
|
||||||
38: tennis racket
|
|
||||||
39: bottle
|
|
||||||
40: wine glass
|
|
||||||
41: cup
|
|
||||||
42: fork
|
|
||||||
43: knife
|
|
||||||
44: spoon
|
|
||||||
45: bowl
|
|
||||||
46: banana
|
|
||||||
47: apple
|
|
||||||
48: sandwich
|
|
||||||
49: orange
|
|
||||||
50: broccoli
|
|
||||||
51: carrot
|
|
||||||
52: hot dog
|
|
||||||
53: pizza
|
|
||||||
54: donut
|
|
||||||
55: cake
|
|
||||||
56: chair
|
|
||||||
57: couch
|
|
||||||
58: potted plant
|
|
||||||
59: bed
|
|
||||||
60: dining table
|
|
||||||
61: toilet
|
|
||||||
62: tv
|
|
||||||
63: laptop
|
|
||||||
64: mouse
|
|
||||||
65: remote
|
|
||||||
66: keyboard
|
|
||||||
67: cell phone
|
|
||||||
68: microwave
|
|
||||||
69: oven
|
|
||||||
70: toaster
|
|
||||||
71: sink
|
|
||||||
72: refrigerator
|
|
||||||
73: book
|
|
||||||
74: clock
|
|
||||||
75: vase
|
|
||||||
76: scissors
|
|
||||||
77: teddy bear
|
|
||||||
78: hair drier
|
|
||||||
79: toothbrush
|
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
|
||||||
download: https://ultralytics.com/assets/coco128.zip
|
|
||||||
@ -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
|
|
||||||
@ -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
|
|
||||||
@ -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)
|
|
||||||
@ -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)
|
|
||||||
@ -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)
|
|
||||||
|
Before Width: | Height: | Size: 476 KiB |
|
Before Width: | Height: | Size: 165 KiB |
@ -1,22 +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 = list('nsmlx') # P5 models
|
|
||||||
p6 = [f'{x}6' for x in p5] # P6 models
|
|
||||||
cls = [f'{x}-cls' for x in p5] # classification models
|
|
||||||
seg = [f'{x}-seg' for x in p5] # classification models
|
|
||||||
|
|
||||||
for x in p5 + p6 + cls + seg:
|
|
||||||
attempt_download(f'weights/yolov5{x}.pt')
|
|
||||||
|
|
||||||
EOF
|
|
||||||
@ -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' # 46 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
|
|
||||||
@ -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 $f &
|
|
||||||
|
|
||||||
wait # finish background tasks
|
|
||||||
@ -1,51 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
|
||||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
|
||||||
# Example usage: bash data/scripts/get_imagenet.sh
|
|
||||||
# parent
|
|
||||||
# ├── yolov5
|
|
||||||
# └── datasets
|
|
||||||
# └── imagenet ← downloads here
|
|
||||||
|
|
||||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
|
||||||
if [ "$#" -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
|
|
||||||
@ -1,153 +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
|
|
||||||
names:
|
|
||||||
0: Fixed-wing Aircraft
|
|
||||||
1: Small Aircraft
|
|
||||||
2: Cargo Plane
|
|
||||||
3: Helicopter
|
|
||||||
4: Passenger Vehicle
|
|
||||||
5: Small Car
|
|
||||||
6: Bus
|
|
||||||
7: Pickup Truck
|
|
||||||
8: Utility Truck
|
|
||||||
9: Truck
|
|
||||||
10: Cargo Truck
|
|
||||||
11: Truck w/Box
|
|
||||||
12: Truck Tractor
|
|
||||||
13: Trailer
|
|
||||||
14: Truck w/Flatbed
|
|
||||||
15: Truck w/Liquid
|
|
||||||
16: Crane Truck
|
|
||||||
17: Railway Vehicle
|
|
||||||
18: Passenger Car
|
|
||||||
19: Cargo Car
|
|
||||||
20: Flat Car
|
|
||||||
21: Tank car
|
|
||||||
22: Locomotive
|
|
||||||
23: Maritime Vessel
|
|
||||||
24: Motorboat
|
|
||||||
25: Sailboat
|
|
||||||
26: Tugboat
|
|
||||||
27: Barge
|
|
||||||
28: Fishing Vessel
|
|
||||||
29: Ferry
|
|
||||||
30: Yacht
|
|
||||||
31: Container Ship
|
|
||||||
32: Oil Tanker
|
|
||||||
33: Engineering Vehicle
|
|
||||||
34: Tower crane
|
|
||||||
35: Container Crane
|
|
||||||
36: Reach Stacker
|
|
||||||
37: Straddle Carrier
|
|
||||||
38: Mobile Crane
|
|
||||||
39: Dump Truck
|
|
||||||
40: Haul Truck
|
|
||||||
41: Scraper/Tractor
|
|
||||||
42: Front loader/Bulldozer
|
|
||||||
43: Excavator
|
|
||||||
44: Cement Mixer
|
|
||||||
45: Ground Grader
|
|
||||||
46: Hut/Tent
|
|
||||||
47: Shed
|
|
||||||
48: Building
|
|
||||||
49: Aircraft Hangar
|
|
||||||
50: Damaged Building
|
|
||||||
51: Facility
|
|
||||||
52: Construction Site
|
|
||||||
53: Vehicle Lot
|
|
||||||
54: Helipad
|
|
||||||
55: Storage Tank
|
|
||||||
56: Shipping container lot
|
|
||||||
57: Shipping Container
|
|
||||||
58: Pylon
|
|
||||||
59: Tower
|
|
||||||
|
|
||||||
|
|
||||||
# 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.dataloaders 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')
|
|
||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -1,857 +0,0 @@
|
|||||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
|
||||||
"""
|
|
||||||
Common modules
|
|
||||||
"""
|
|
||||||
|
|
||||||
import ast
|
|
||||||
import contextlib
|
|
||||||
import json
|
|
||||||
import math
|
|
||||||
import platform
|
|
||||||
import warnings
|
|
||||||
import zipfile
|
|
||||||
from collections import OrderedDict, namedtuple
|
|
||||||
from copy import copy
|
|
||||||
from pathlib import Path
|
|
||||||
from urllib.parse import urlparse
|
|
||||||
|
|
||||||
import cv2
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
import requests
|
|
||||||
import torch
|
|
||||||
import torch.nn as nn
|
|
||||||
from IPython.display import display
|
|
||||||
from PIL import Image
|
|
||||||
from torch.cuda import amp
|
|
||||||
|
|
||||||
from utils import TryExcept
|
|
||||||
from utils.dataloaders import exif_transpose, letterbox
|
|
||||||
from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
|
|
||||||
increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
|
|
||||||
xyxy2xywh, yaml_load)
|
|
||||||
from utils.plots import Annotator, colors, save_one_box
|
|
||||||
from utils.torch_utils import copy_attr, smart_inference_mode
|
|
||||||
|
|
||||||
|
|
||||||
def autopad(k, p=None, d=1): # kernel, padding, dilation
|
|
||||||
# Pad to 'same' shape outputs
|
|
||||||
if d > 1:
|
|
||||||
k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
|
|
||||||
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 with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
|
|
||||||
default_act = nn.SiLU() # default activation
|
|
||||||
|
|
||||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
|
|
||||||
super().__init__()
|
|
||||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
|
|
||||||
self.bn = nn.BatchNorm2d(c2)
|
|
||||||
self.act = self.default_act 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
|
|
||||||
def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation
|
|
||||||
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
|
|
||||||
|
|
||||||
|
|
||||||
class DWConvTranspose2d(nn.ConvTranspose2d):
|
|
||||||
# Depth-wise transpose convolution
|
|
||||||
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=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=act)
|
|
||||||
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=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 --dnn
|
|
||||||
# OpenVINO: *_openvino_model
|
|
||||||
# CoreML: *.mlmodel
|
|
||||||
# TensorRT: *.engine
|
|
||||||
# TensorFlow SavedModel: *_saved_model
|
|
||||||
# TensorFlow GraphDef: *.pb
|
|
||||||
# TensorFlow Lite: *.tflite
|
|
||||||
# TensorFlow Edge TPU: *_edgetpu.tflite
|
|
||||||
# PaddlePaddle: *_paddle_model
|
|
||||||
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, paddle, triton = self._model_type(w)
|
|
||||||
fp16 &= pt or jit or onnx or engine # FP16
|
|
||||||
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
|
|
||||||
stride = 32 # default stride
|
|
||||||
cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
|
|
||||||
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, map_location=device)
|
|
||||||
# model.half() if fp16 else model.float()
|
|
||||||
# if extra_files['config.txt']: # load metadata dict
|
|
||||||
# d = json.loads(extra_files['config.txt'],
|
|
||||||
# object_hook=lambda d: {int(k) if k.isdigit() else k: v
|
|
||||||
# for k, v in d.items()})
|
|
||||||
# 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...')
|
|
||||||
# check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
|
|
||||||
# import onnxruntime
|
|
||||||
# providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
|
|
||||||
# session = onnxruntime.InferenceSession(w, providers=providers)
|
|
||||||
# output_names = [x.name for x in session.get_outputs()]
|
|
||||||
# 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
|
|
||||||
# stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # 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()
|
|
||||||
output_names = []
|
|
||||||
fp16 = False # default updated below
|
|
||||||
dynamic = False
|
|
||||||
for i in range(model.num_bindings):
|
|
||||||
name = model.get_binding_name(i)
|
|
||||||
dtype = trt.nptype(model.get_binding_dtype(i))
|
|
||||||
if model.binding_is_input(i):
|
|
||||||
if -1 in tuple(model.get_binding_shape(i)): # dynamic
|
|
||||||
dynamic = True
|
|
||||||
context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
|
|
||||||
if dtype == np.float16:
|
|
||||||
fp16 = True
|
|
||||||
else: # output
|
|
||||||
output_names.append(name)
|
|
||||||
shape = tuple(context.get_binding_shape(i))
|
|
||||||
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)
|
|
||||||
# elif saved_model: # TF 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))
|
|
||||||
#
|
|
||||||
# def gd_outputs(gd):
|
|
||||||
# name_list, input_list = [], []
|
|
||||||
# for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
|
|
||||||
# name_list.append(node.name)
|
|
||||||
# input_list.extend(node.input)
|
|
||||||
# return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
|
|
||||||
#
|
|
||||||
# gd = tf.Graph().as_graph_def() # TF GraphDef
|
|
||||||
# with open(w, 'rb') as f:
|
|
||||||
# gd.ParseFromString(f.read())
|
|
||||||
# frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
|
|
||||||
# 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: # TF 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: # TFLite
|
|
||||||
# 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
|
|
||||||
# # load metadata
|
|
||||||
# with contextlib.suppress(zipfile.BadZipFile):
|
|
||||||
# with zipfile.ZipFile(w, "r") as model:
|
|
||||||
# meta_file = model.namelist()[0]
|
|
||||||
# meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
|
|
||||||
# stride, names = int(meta['stride']), meta['names']
|
|
||||||
# elif tfjs: # TF.js
|
|
||||||
# raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
|
|
||||||
# elif paddle: # PaddlePaddle
|
|
||||||
# LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
|
|
||||||
# check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
|
|
||||||
# import paddle.inference as pdi
|
|
||||||
# if not Path(w).is_file(): # if not *.pdmodel
|
|
||||||
# w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir
|
|
||||||
# weights = Path(w).with_suffix('.pdiparams')
|
|
||||||
# config = pdi.Config(str(w), str(weights))
|
|
||||||
# if cuda:
|
|
||||||
# config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
|
|
||||||
# predictor = pdi.create_predictor(config)
|
|
||||||
# input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
|
|
||||||
# output_names = predictor.get_output_names()
|
|
||||||
# elif triton: # NVIDIA Triton Inference Server
|
|
||||||
# LOGGER.info(f'Using {w} as Triton Inference Server...')
|
|
||||||
# check_requirements('tritonclient[all]')
|
|
||||||
# from utils.triton import TritonRemoteModel
|
|
||||||
# model = TritonRemoteModel(url=w)
|
|
||||||
# nhwc = model.runtime.startswith("tensorflow")
|
|
||||||
# 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 {i: 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):
|
|
||||||
# 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.nhwc:
|
|
||||||
im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)
|
|
||||||
|
|
||||||
if self.pt: # PyTorch
|
|
||||||
y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
|
|
||||||
# elif self.jit: # TorchScript
|
|
||||||
# y = self.model(im)
|
|
||||||
# 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.output_names, {self.session.get_inputs()[0].name: im})
|
|
||||||
# elif self.xml: # OpenVINO
|
|
||||||
# im = im.cpu().numpy() # FP32
|
|
||||||
# y = list(self.executable_network([im]).values())
|
|
||||||
elif self.engine: # TensorRT
|
|
||||||
if self.dynamic and im.shape != self.bindings['images'].shape:
|
|
||||||
i = self.model.get_binding_index('images')
|
|
||||||
self.context.set_binding_shape(i, im.shape) # reshape if dynamic
|
|
||||||
self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
|
|
||||||
for name in self.output_names:
|
|
||||||
i = self.model.get_binding_index(name)
|
|
||||||
self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
|
|
||||||
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[x].data for x in sorted(self.output_names)]
|
|
||||||
# elif self.coreml: # CoreML
|
|
||||||
# im = im.cpu().numpy()
|
|
||||||
# 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:
|
|
||||||
# y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)
|
|
||||||
# elif self.paddle: # PaddlePaddle
|
|
||||||
# im = im.cpu().numpy().astype(np.float32)
|
|
||||||
# self.input_handle.copy_from_cpu(im)
|
|
||||||
# self.predictor.run()
|
|
||||||
# y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
|
|
||||||
# elif self.triton: # NVIDIA Triton Inference Server
|
|
||||||
# y = self.model(im)
|
|
||||||
# else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
|
|
||||||
# im = im.cpu().numpy()
|
|
||||||
# if self.saved_model: # SavedModel
|
|
||||||
# y = self.model(im, training=False) if self.keras else self.model(im)
|
|
||||||
# elif self.pb: # GraphDef
|
|
||||||
# y = self.frozen_func(x=self.tf.constant(im))
|
|
||||||
# else: # Lite or Edge TPU
|
|
||||||
# input = self.input_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 = []
|
|
||||||
# for output in self.output_details:
|
|
||||||
# x = self.interpreter.get_tensor(output['index'])
|
|
||||||
# if int8:
|
|
||||||
# scale, zero_point = output['quantization']
|
|
||||||
# x = (x.astype(np.float32) - zero_point) * scale # re-scale
|
|
||||||
# y.append(x)
|
|
||||||
# y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
|
|
||||||
# y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels
|
|
||||||
|
|
||||||
if isinstance(y, (list, tuple)):
|
|
||||||
return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
|
|
||||||
else:
|
|
||||||
return self.from_numpy(y)
|
|
||||||
|
|
||||||
def from_numpy(self, x):
|
|
||||||
return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
|
|
||||||
|
|
||||||
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, self.triton
|
|
||||||
if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
|
|
||||||
im = torch.empty(*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
|
|
||||||
# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
|
|
||||||
from export import export_formats
|
|
||||||
from utils.downloads import is_url
|
|
||||||
sf = list(export_formats().Suffix) # export suffixes
|
|
||||||
if not is_url(p, check=False):
|
|
||||||
check_suffix(p, sf) # checks
|
|
||||||
url = urlparse(p) # if url may be Triton inference server
|
|
||||||
types = [s in Path(p).name for s in sf]
|
|
||||||
types[8] &= not types[9] # tflite &= not edgetpu
|
|
||||||
triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
|
|
||||||
return types + [triton]
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _load_metadata(f=Path('path/to/meta.yaml')):
|
|
||||||
# Load metadata from meta.yaml if it exists
|
|
||||||
if f.exists():
|
|
||||||
d = yaml_load(f)
|
|
||||||
return d['stride'], d['names'] # assign stride, names
|
|
||||||
return None, None
|
|
||||||
|
|
||||||
|
|
||||||
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
|
|
||||||
m.export = True # do not output loss values
|
|
||||||
|
|
||||||
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, ims, size=640, augment=False, profile=False):
|
|
||||||
# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
|
|
||||||
# file: ims = '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
|
|
||||||
|
|
||||||
dt = (Profile(), Profile(), Profile())
|
|
||||||
with dt[0]:
|
|
||||||
if isinstance(size, int): # expand
|
|
||||||
size = (size, size)
|
|
||||||
p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param
|
|
||||||
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
|
|
||||||
if isinstance(ims, torch.Tensor): # torch
|
|
||||||
with amp.autocast(autocast):
|
|
||||||
return self.model(ims.to(p.device).type_as(p), augment=augment) # inference
|
|
||||||
|
|
||||||
# Pre-process
|
|
||||||
n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images
|
|
||||||
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
|
||||||
for i, im in enumerate(ims):
|
|
||||||
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 cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input
|
|
||||||
s = im.shape[:2] # HWC
|
|
||||||
shape0.append(s) # image shape
|
|
||||||
g = max(size) / max(s) # gain
|
|
||||||
shape1.append([int(y * g) for y in s])
|
|
||||||
ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
|
||||||
shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape
|
|
||||||
x = [letterbox(im, shape1, auto=False)[0] for im in ims] # 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
|
|
||||||
|
|
||||||
with amp.autocast(autocast):
|
|
||||||
# Inference
|
|
||||||
with dt[1]:
|
|
||||||
y = self.model(x, augment=augment) # forward
|
|
||||||
|
|
||||||
# Post-process
|
|
||||||
with dt[2]:
|
|
||||||
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_boxes(shape1, y[i][:, :4], shape0[i])
|
|
||||||
|
|
||||||
return Detections(ims, y, files, dt, self.names, x.shape)
|
|
||||||
|
|
||||||
|
|
||||||
class Detections:
|
|
||||||
# YOLOv5 detections class for inference results
|
|
||||||
def __init__(self, ims, pred, files, times=(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 ims] # normalizations
|
|
||||||
self.ims = ims # 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(x.t / self.n * 1E3 for x in times) # timestamps (ms)
|
|
||||||
self.s = tuple(shape) # inference BCHW shape
|
|
||||||
|
|
||||||
def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
|
|
||||||
s, crops = '', []
|
|
||||||
for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
|
|
||||||
s += f'\nimage {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
|
|
||||||
s = s.rstrip(', ')
|
|
||||||
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 show:
|
|
||||||
display(im) if is_notebook() else im.show(self.files[i])
|
|
||||||
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.ims[i] = np.asarray(im)
|
|
||||||
if pprint:
|
|
||||||
s = s.lstrip('\n')
|
|
||||||
return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
|
|
||||||
if crop:
|
|
||||||
if save:
|
|
||||||
LOGGER.info(f'Saved results to {save_dir}\n')
|
|
||||||
return crops
|
|
||||||
|
|
||||||
@TryExcept('Showing images is not supported in this environment')
|
|
||||||
def show(self, labels=True):
|
|
||||||
self._run(show=True, labels=labels) # show results
|
|
||||||
|
|
||||||
def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
|
|
||||||
save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir
|
|
||||||
self._run(save=True, labels=labels, save_dir=save_dir) # save results
|
|
||||||
|
|
||||||
def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
|
|
||||||
save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
|
|
||||||
return self._run(crop=True, save=save, save_dir=save_dir) # crop results
|
|
||||||
|
|
||||||
def render(self, labels=True):
|
|
||||||
self._run(render=True, labels=labels) # render results
|
|
||||||
return self.ims
|
|
||||||
|
|
||||||
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.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
|
|
||||||
# for d in x:
|
|
||||||
# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
|
||||||
# setattr(d, k, getattr(d, k)[0]) # pop out of list
|
|
||||||
return x
|
|
||||||
|
|
||||||
def print(self):
|
|
||||||
LOGGER.info(self.__str__())
|
|
||||||
|
|
||||||
def __len__(self): # override len(results)
|
|
||||||
return self.n
|
|
||||||
|
|
||||||
def __str__(self): # override print(results)
|
|
||||||
return self._run(pprint=True) # print results
|
|
||||||
|
|
||||||
def __repr__(self):
|
|
||||||
return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
|
|
||||||
|
|
||||||
|
|
||||||
class Proto(nn.Module):
|
|
||||||
# YOLOv5 mask Proto module for segmentation models
|
|
||||||
def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
|
|
||||||
super().__init__()
|
|
||||||
self.cv1 = Conv(c1, c_, k=3)
|
|
||||||
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
|
|
||||||
self.cv2 = Conv(c_, c_, k=3)
|
|
||||||
self.cv3 = Conv(c_, c2)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
return self.cv3(self.cv2(self.upsample(self.cv1(x))))
|
|
||||||
|
|
||||||
|
|
||||||
class Classify(nn.Module):
|
|
||||||
# YOLOv5 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)))
|
|
||||||
@ -1,111 +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 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(weights), map_location='cpu') # load
|
|
||||||
ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
|
|
||||||
|
|
||||||
# Model compatibility updates
|
|
||||||
if not hasattr(ckpt, 'stride'):
|
|
||||||
ckpt.stride = torch.tensor([32.])
|
|
||||||
if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
|
|
||||||
ckpt.names = dict(enumerate(ckpt.names)) # convert to dict
|
|
||||||
|
|
||||||
model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
|
|
||||||
|
|
||||||
# Module 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
|
|
||||||
@ -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
|
|
||||||
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Loading…
Reference in new issue