VOC names dictionary fix (#9034)

* VOC names dictionary fix

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update dataloaders.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

data/VOC.yaml

@@ -65,12 +65,13 @@ download: |
       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 yaml['names'] and not int(obj.find('difficult').text) == 1:
+          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 = yaml['names'].index(cls)  # class id
+              cls_id = names.index(cls)  # class id
               out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
 
 

utils/dataloaders.py

@@ -35,7 +35,7 @@ from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, che
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
-HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
+HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp'  # include image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
 BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}'  # tqdm bar format
@@ -456,7 +456,7 @@ class LoadImagesAndLabels(Dataset):
             # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
             assert self.im_files, f'{prefix}No images found'
         except Exception as e:
-            raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {HELP_URL}')
+            raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}')
 
         # Check cache
         self.label_files = img2label_paths(self.im_files)  # labels
@@ -475,11 +475,13 @@ class LoadImagesAndLabels(Dataset):
             tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT)  # display cache results
             if cache['msgs']:
                 LOGGER.info('\n'.join(cache['msgs']))  # display warnings
-        assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}'
+        assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'
 
         # Read cache
         [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
         labels, shapes, self.segments = zip(*cache.values())
+        nl = len(np.concatenate(labels, 0))  # number of labels
+        assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'
         self.labels = list(labels)
         self.shapes = np.array(shapes)
         self.im_files = list(cache.keys())  # update
@@ -572,7 +574,7 @@ class LoadImagesAndLabels(Dataset):
         if msgs:
             LOGGER.info('\n'.join(msgs))
         if nf == 0:
-            LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. See {HELP_URL}')
+            LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. {HELP_URL}')
         x['hash'] = get_hash(self.label_files + self.im_files)
         x['results'] = nf, nm, ne, nc, len(self.im_files)
         x['msgs'] = msgs  # warnings