Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly.

results/imagenet_a_indices.txt

@@ -0,0 +1,200 @@
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results/imagenet_a_synsets.txt

@@ -0,0 +1,200 @@
+n01498041
+n01531178
+n01534433
+n01558993
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+n01614925
+n01616318
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results/imagenet_r_indices.txt

@@ -0,0 +1,200 @@
+1
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results/imagenet_r_synsets.txt

@@ -0,0 +1,200 @@
+n01443537
+n01484850
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timm/data/dataset.py

@@ -39,14 +39,13 @@ def find_images_and_targets(folder, types=IMG_EXTENSIONS, class_to_idx=None, lea
         unique_labels = set(labels)
         sorted_labels = list(sorted(unique_labels, key=natural_key))
         class_to_idx = {c: idx for idx, c in enumerate(sorted_labels)}
-    images_and_targets = zip(filenames, [class_to_idx[l] for l in labels])
+    images_and_targets = [(f, class_to_idx[l]) for f, l in zip(filenames, labels) if l in class_to_idx]
     if sort:
         images_and_targets = sorted(images_and_targets, key=lambda k: natural_key(k[0]))
     return images_and_targets, class_to_idx
 
 
 def load_class_map(filename, root=''):
-    class_to_idx = {}
     class_map_path = filename
     if not os.path.exists(class_map_path):
         class_map_path = os.path.join(root, filename)
@@ -74,8 +73,8 @@ class Dataset(data.Dataset):
             class_to_idx = load_class_map(class_map, root)
         images, class_to_idx = find_images_and_targets(root, class_to_idx=class_to_idx)
         if len(images) == 0:
-            raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
-                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
+            raise RuntimeError(f'Found 0 images in subfolders of {root}. '
+                               f'Supported image extensions are {", ".join(IMG_EXTENSIONS)}')
         self.root = root
         self.samples = images
         self.imgs = self.samples  # torchvision ImageFolder compat
@@ -124,7 +123,7 @@ def _extract_tar_info(tarfile, class_to_idx=None, sort=True):
         unique_labels = set(labels)
         sorted_labels = list(sorted(unique_labels, key=natural_key))
         class_to_idx = {c: idx for idx, c in enumerate(sorted_labels)}
-    tarinfo_and_targets = zip(files, [class_to_idx[l] for l in labels])
+    tarinfo_and_targets = [(f, class_to_idx[l]) for f, l in zip(files, labels) if l in class_to_idx]
     if sort:
         tarinfo_and_targets = sorted(tarinfo_and_targets, key=lambda k: natural_key(k[0].path))
     return tarinfo_and_targets, class_to_idx
@@ -141,6 +140,7 @@ class DatasetTar(data.Dataset):
         self.root = root
         with tarfile.open(root) as tf:  # cannot keep this open across processes, reopen later
             self.samples, self.class_to_idx = _extract_tar_info(tf, class_to_idx)
+        self.imgs = self.samples
         self.tarfile = None  # lazy init in __getitem__
         self.load_bytes = load_bytes
         self.transform = transform

validate.py

@@ -83,6 +83,8 @@ parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
                     help='Output csv file for validation results (summary)')
 parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
                     help='Real labels JSON file for imagenet evaluation')
+parser.add_argument('--valid-labels', default='', type=str, metavar='FILENAME',
+                    help='Valid label indices txt file for validation of partial label space')
 
 
 def set_jit_legacy():
@@ -141,6 +143,13 @@ def validate(args):
     else:
         dataset = Dataset(args.data, load_bytes=args.tf_preprocessing, class_map=args.class_map)
 
+    if args.valid_labels:
+        with open(args.valid_labels, 'r') as f:
+            valid_labels = {int(line.rstrip()) for line in f}
+            valid_labels = [i in valid_labels for i in range(args.num_classes)]
+    else:
+        valid_labels = None
+
     if args.real_labels:
         real_labels = RealLabelsImagenet(dataset.filenames(basename=True), real_json=args.real_labels)
     else:
@@ -180,6 +189,8 @@ def validate(args):
 
             # compute output
             output = model(input)
+            if valid_labels is not None:
+                output = output[:, valid_labels]
             loss = criterion(output, target)
 
             if real_labels is not None: