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

4 years ago · c53ec33ae0
parent ec4976fdba
commit c53ec33ae0
8 changed files with 816 additions and 5 deletions
--- a/results/imagenet_a_indices.txt
+++ b/results/imagenet_a_indices.txt
@ -0,0 +1,200 @@
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--- a/results/imagenet_a_synsets.txt
+++ b/results/imagenet_a_synsets.txt
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--- a/results/imagenet_r_indices.txt
+++ b/results/imagenet_r_indices.txt
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--- a/results/imagenet_r_synsets.txt
+++ b/results/imagenet_r_synsets.txt
@ -0,0 +1,200 @@
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--- a/results/imagenet_real_labels.json
+++ b/results/imagenet_real_labels.json
--- a/results/imagenet_lsvrc_2015_synsets.txt
+++ b/results/imagenet_lsvrc_2015_synsets.txt
--- a/timm/data/dataset.py
+++ b/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
--- a/validate.py
+++ b/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: