From f5da528d280af5d106e80eceddefd427cf9aed32 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Fri, 14 Aug 2020 11:53:44 -0700
Subject: [PATCH] reformat code

---
 .github/workflows/rebase.yml | 16 ++++++++--------
 detect.py                    |  2 +-
 test.py                      |  2 +-
 utils/datasets.py            |  2 +-
 utils/torch_utils.py         |  6 ++++--
 5 files changed, 15 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml
index ae69fef..e86c577 100644
--- a/.github/workflows/rebase.yml
+++ b/.github/workflows/rebase.yml
@@ -11,11 +11,11 @@ jobs:
     if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase')
     runs-on: ubuntu-latest
     steps:
-    - name: Checkout the latest code
-      uses: actions/checkout@v2
-      with:
-        fetch-depth: 0
-    - name: Automatic Rebase
-      uses: cirrus-actions/rebase@1.3.1
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Checkout the latest code
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Automatic Rebase
+        uses: cirrus-actions/rebase@1.3.1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/detect.py b/detect.py
index 61e9a0a..60dd059 100644
--- a/detect.py
+++ b/detect.py
@@ -13,7 +13,7 @@ from numpy import random
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import (
-    check_img_size, non_max_suppression, apply_classifier, scale_coords, 
+    check_img_size, non_max_suppression, apply_classifier, scale_coords,
     xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
diff --git a/test.py b/test.py
index fc0ed62..d72a792 100644
--- a/test.py
+++ b/test.py
@@ -13,7 +13,7 @@ from tqdm import tqdm
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import (
-    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords, 
+    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords,
     xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
 from utils.torch_utils import select_device, time_synchronized
 
diff --git a/utils/datasets.py b/utils/datasets.py
index be21a2e..252d14d 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -423,7 +423,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                 ne += 1  # print('empty labels for image %s' % self.img_files[i])  # file empty
                 # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i]))  # remove
 
-            if rank in [-1,0]:
+            if rank in [-1, 0]:
                 pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
                     cache_path, nf, nm, ne, nd, n)
         if nf == 0:
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index 3489e5c..0906d58 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -12,6 +12,7 @@ import torchvision.models as models
 
 logger = logging.getLogger(__name__)
 
+
 def init_seeds(seed=0):
     torch.manual_seed(seed)
 
@@ -43,7 +44,7 @@ def select_device(device='', batch_size=None):
             if i == 1:
                 s = ' ' * len(s)
             logger.info("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
-                  (s, i, x[i].name, x[i].total_memory / c))
+                        (s, i, x[i].name, x[i].total_memory / c))
     else:
         logger.info('Using CPU')
 
@@ -144,7 +145,8 @@ def model_info(model, verbose=False):
     except:
         fs = ''
 
-    logger.info('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
+    logger.info(
+        'Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
 
 
 def load_classifier(name='resnet101', n=2):