reformat code

.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 }}

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:

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):