Cache v0.3: improved corrupt image/label reporting (#3676)

utils/datasets.py

@@ -390,7 +390,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')  # cached labels
         if cache_path.is_file():
             cache, exists = torch.load(cache_path), True  # load
-            if cache['hash'] != get_hash(self.label_files + self.img_files):  # changed
+            if cache['hash'] != get_hash(self.label_files + self.img_files) or cache['version'] != 0.3:
                 cache, exists = self.cache_labels(cache_path, prefix), False  # re-cache
         else:
             cache, exists = self.cache_labels(cache_path, prefix), False  # cache
@@ -400,11 +400,12 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         if exists:
             d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
             tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
+            if cache['msgs']:
+                logging.info('\n'.join(cache['msgs']))  # display warnings
         assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
 
         # Read cache
-        cache.pop('hash')  # remove hash
-        cache.pop('version')  # remove version
+        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
         labels, shapes, self.segments = zip(*cache.values())
         self.labels = list(labels)
         self.shapes = np.array(shapes, dtype=np.float64)
@@ -461,26 +462,31 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
     def cache_labels(self, path=Path('./labels.cache'), prefix=''):
         # Cache dataset labels, check images and read shapes
         x = {}  # dict
-        nm, nf, ne, nc = 0, 0, 0, 0  # number missing, found, empty, corrupt
+        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
         desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
         with Pool(num_threads) as pool:
             pbar = tqdm(pool.imap_unordered(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))),
                         desc=desc, total=len(self.img_files))
-            for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f in pbar:
+            for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                 nm += nm_f
                 nf += nf_f
                 ne += ne_f
                 nc += nc_f
                 if im_file:
                     x[im_file] = [l, shape, segments]
+                if msg:
+                    msgs.append(msg)
                 pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
 
         pbar.close()
+        if msgs:
+            logging.info('\n'.join(msgs))
         if nf == 0:
             logging.info(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
         x['hash'] = get_hash(self.label_files + self.img_files)
         x['results'] = nf, nm, ne, nc, len(self.img_files)
-        x['version'] = 0.2  # cache version
+        x['msgs'] = msgs  # warnings
+        x['version'] = 0.3  # cache version
         try:
             torch.save(x, path)  # save cache for next time
             logging.info(f'{prefix}New cache created: {path}')
@@ -1084,11 +1090,11 @@ def verify_image_label(args):
         else:
             nm = 1  # label missing
             l = np.zeros((0, 5), dtype=np.float32)
-        return im_file, l, shape, segments, nm, nf, ne, nc
+        return im_file, l, shape, segments, nm, nf, ne, nc, ''
     except Exception as e:
         nc = 1
-        logging.info(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
-        return [None, None, None, None, nm, nf, ne, nc]
+        msg = f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}'
+        return [None, None, None, None, nm, nf, ne, nc, msg]
 
 
 def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False):