Move ImageNet metadata (aka info) files to timm/data/_info. Add helper classes to make info available for labelling. Update inference.py for first use.

MANIFEST.in

@@ -1,2 +1,3 @@
-include timm/models/pruned/*.txt
+include timm/models/_pruned/*.txt
-
+include timm/data/_info/*.txt
+include timm/data/_info/*.json

inference.py

@@ -17,7 +17,7 @@ import numpy as np
 import pandas as pd
 import torch
 
-from timm.data import create_dataset, create_loader, resolve_data_config
+from timm.data import create_dataset, create_loader, resolve_data_config, ImageNetInfo, infer_imagenet_subset
 from timm.layers import apply_test_time_pool
 from timm.models import create_model
 from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser, ParseKwargs
@@ -46,6 +46,7 @@ has_compile = hasattr(torch, 'compile')
 
 _FMT_EXT = {
     'json': '.json',
+    'json-record': '.json',
     'json-split': '.json',
     'parquet': '.parquet',
     'csv': '.csv',
@@ -122,7 +123,7 @@ scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                              help="Enable AOT Autograd support.")
 
-parser.add_argument('--results-dir',type=str, default=None,
+parser.add_argument('--results-dir', type=str, default=None,
                     help='folder for output results')
 parser.add_argument('--results-file', type=str, default=None,
                     help='results filename (relative to results-dir)')
@@ -134,14 +135,20 @@ parser.add_argument('--topk', default=1, type=int,
                     metavar='N', help='Top-k to output to CSV')
 parser.add_argument('--fullname', action='store_true', default=False,
                     help='use full sample name in output (not just basename).')
-parser.add_argument('--filename-col', default='filename',
+parser.add_argument('--filename-col', type=str, default='filename',
                     help='name for filename / sample name column')
-parser.add_argument('--index-col', default='index',
+parser.add_argument('--index-col', type=str, default='index',
                     help='name for output indices column(s)')
-parser.add_argument('--output-col', default=None,
+parser.add_argument('--label-col', type=str, default='label',
+                    help='name for output indices column(s)')
+parser.add_argument('--output-col', type=str, default=None,
                     help='name for logit/probs output column(s)')
-parser.add_argument('--output-type', default='prob',
+parser.add_argument('--output-type', type=str, default='prob',
                     help='output type colum ("prob" for probabilities, "logit" for raw logits)')
+parser.add_argument('--label-type', type=str, default='description',
+                    help='type of label to output, one of  "none", "name", "description", "detailed"')
+parser.add_argument('--include-index', action='store_true', default=False,
+                    help='include the class index in results')
 parser.add_argument('--exclude-output', action='store_true', default=False,
                     help='exclude logits/probs from results, just indices. topk must be set !=0.')
 
@@ -237,10 +244,26 @@ def main():
         **data_config,
     )
 
+    to_label = None
+    if args.label_type in ('name', 'description', 'detail'):
+        imagenet_subset = infer_imagenet_subset(model)
+        if imagenet_subset is not None:
+            dataset_info = ImageNetInfo(imagenet_subset)
+            if args.label_type == 'name':
+                to_label = lambda x: dataset_info.index_to_label_name(x)
+            elif args.label_type == 'detail':
+                to_label = lambda x: dataset_info.index_to_description(x, detailed=True)
+            else:
+                to_label = lambda x: dataset_info.index_to_description(x)
+            to_label = np.vectorize(to_label)
+        else:
+            _logger.error("Cannot deduce ImageNet subset from model, no labelling will be performed.")
+
     top_k = min(args.topk, args.num_classes)
     batch_time = AverageMeter()
     end = time.time()
     all_indices = []
+    all_labels = []
     all_outputs = []
     use_probs = args.output_type == 'prob'
     with torch.no_grad():
@@ -254,7 +277,12 @@ def main():
 
             if top_k:
                 output, indices = output.topk(top_k)
-                all_indices.append(indices.cpu().numpy())
+                np_indices = indices.cpu().numpy()
+                if args.include_index:
+                    all_indices.append(np_indices)
+                if to_label is not None:
+                    np_labels = to_label(np_indices)
+                    all_labels.append(np_labels)
 
             all_outputs.append(output.cpu().numpy())
 
@@ -267,6 +295,7 @@ def main():
                     batch_idx, len(loader), batch_time=batch_time))
 
     all_indices = np.concatenate(all_indices, axis=0) if all_indices else None
+    all_labels = np.concatenate(all_labels, axis=0) if all_labels else None
     all_outputs = np.concatenate(all_outputs, axis=0).astype(np.float32)
     filenames = loader.dataset.filenames(basename=not args.fullname)
 
@@ -276,6 +305,9 @@ def main():
         if all_indices is not None:
             for i in range(all_indices.shape[-1]):
                 data_dict[f'{args.index_col}_{i}'] = all_indices[:, i]
+        if all_labels is not None:
+            for i in range(all_labels.shape[-1]):
+                data_dict[f'{args.label_col}_{i}'] = all_labels[:, i]
         for i in range(all_outputs.shape[-1]):
             data_dict[f'{output_col}_{i}'] = all_outputs[:, i]
     else:
@@ -283,6 +315,10 @@ def main():
             if all_indices.shape[-1] == 1:
                 all_indices = all_indices.squeeze(-1)
             data_dict[args.index_col] = list(all_indices)
+        if all_labels is not None:
+            if all_labels.shape[-1] == 1:
+                all_labels = all_labels.squeeze(-1)
+            data_dict[args.label_col] = list(all_labels)
         if all_outputs.shape[-1] == 1:
             all_outputs = all_outputs.squeeze(-1)
         data_dict[output_col] = list(all_outputs)
@@ -291,7 +327,7 @@ def main():
 
     results_filename = args.results_file
     if results_filename:
-        filename_no_ext, ext = os.path.splitext(results_filename)[-1]
+        filename_no_ext, ext = os.path.splitext(results_filename)
         if ext and ext in _FMT_EXT.values():
             # if filename provided with one of expected ext,
             # remove it as it will be added back
@@ -308,7 +344,7 @@ def main():
         save_results(df, results_filename, fmt)
 
     print(f'--result')
-    print(json.dumps(dict(filename=results_filename)))
+    print(df.set_index(args.filename_col).to_json(orient='index', indent=4))
 
 
 def save_results(df, results_filename, results_format='csv', filename_col='filename'):
@@ -316,6 +352,8 @@ def save_results(df, results_filename, results_format='csv', filename_col='filen
     if results_format == 'parquet':
         df.set_index(filename_col).to_parquet(results_filename)
     elif results_format == 'json':
+        df.set_index(filename_col).to_json(results_filename, indent=4, orient='index')
+    elif results_format == 'json-records':
         df.to_json(results_filename, lines=True, orient='records')
     elif results_format == 'json-split':
         df.to_json(results_filename, indent=4, orient='split', index=False)

timm/data/__init__.py

@@ -4,6 +4,8 @@ from .config import resolve_data_config, resolve_model_data_config
 from .constants import *
 from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
 from .dataset_factory import create_dataset
+from .dataset_info import DatasetInfo
+from .imagenet_info import ImageNetInfo, infer_imagenet_subset
 from .loader import create_loader
 from .mixup import Mixup, FastCollateMixup
 from .readers import create_reader

timm/data/dataset_info.py

@@ -0,0 +1,32 @@
+from abc import ABC, abstractmethod
+from typing import Dict, List, Union
+
+
+class DatasetInfo(ABC):
+
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def num_classes(self):
+        pass
+
+    @abstractmethod
+    def label_names(self):
+        pass
+
+    @abstractmethod
+    def label_descriptions(self, detailed: bool = False, as_dict: bool = False) -> Union[List[str], Dict[str, str]]:
+        pass
+
+    @abstractmethod
+    def index_to_label_name(self, index) -> str:
+        pass
+
+    @abstractmethod
+    def index_to_description(self, index: int, detailed: bool = False) -> str:
+        pass
+
+    @abstractmethod
+    def label_name_to_description(self, label: str, detailed: bool = False) -> str:
+        pass

timm/data/imagenet_info.py

@@ -0,0 +1,92 @@
+import csv
+import os
+import pkgutil
+import re
+from typing import Dict, List, Optional, Union
+
+from .dataset_info import DatasetInfo
+
+
+_NUM_CLASSES_TO_SUBSET = {
+    1000: 'imagenet-1k',
+    11821: 'imagenet-12k',
+    21841: 'imagenet-22k',
+    21843: 'imagenet-21k-goog',
+    11221: 'imagenet-21k-miil',
+}
+
+_SUBSETS = {
+    'imagenet1k': 'imagenet_synsets.txt',
+    'imagenet12k': 'imagenet12k_synsets.txt',
+    'imagenet22k': 'imagenet22k_synsets.txt',
+    'imagenet21k': 'imagenet21k_goog_synsets.txt',
+    'imagenet21kgoog': 'imagenet21k_goog_synsets.txt',
+    'imagenet21kmiil': 'imagenet21k_miil_synsets.txt',
+}
+_LEMMA_FILE = 'imagenet_synset_to_lemma.txt'
+_DEFINITION_FILE = 'imagenet_synset_to_definition.txt'
+
+
+def infer_imagenet_subset(model_or_cfg) -> Optional[str]:
+    if isinstance(model_or_cfg, dict):
+        num_classes = model_or_cfg.get('num_classes', None)
+    else:
+        num_classes = getattr(model_or_cfg, 'num_classes', None)
+        if not num_classes:
+            pretrained_cfg = getattr(model_or_cfg, 'pretrained_cfg', {})
+            # FIXME at some point pretrained_cfg should include dataset-tag,
+            # which will be more robust than a guess based on num_classes
+            num_classes = pretrained_cfg.get('num_classes', None)
+    if not num_classes or num_classes not in _NUM_CLASSES_TO_SUBSET:
+        return None
+    return _NUM_CLASSES_TO_SUBSET[num_classes]
+
+
+class ImageNetInfo(DatasetInfo):
+
+    def __init__(self, subset: str = 'imagenet-1k'):
+        super().__init__()
+        subset = re.sub(r'[-_\s]', '', subset.lower())
+        assert subset in _SUBSETS, f'Unknown imagenet subset {subset}.'
+
+        # WordNet synsets (part-of-speach + offset) are the unique class label names for ImageNet classifiers
+        synset_file = _SUBSETS[subset]
+        synset_data = pkgutil.get_data(__name__, os.path.join('_info', synset_file))
+        self._synsets = synset_data.decode('utf-8').splitlines()
+
+        # WordNet lemmas (canonical dictionary form of word) and definitions are used to build
+        # the class descriptions. If detailed=True both are used, otherwise just the lemmas.
+        lemma_data = pkgutil.get_data(__name__, os.path.join('_info', _LEMMA_FILE))
+        reader = csv.reader(lemma_data.decode('utf-8').splitlines(), delimiter='\t')
+        self._lemmas = dict(reader)
+        definition_data = pkgutil.get_data(__name__, os.path.join('_info', _DEFINITION_FILE))
+        reader = csv.reader(definition_data.decode('utf-8').splitlines(), delimiter='\t')
+        self._definitions = dict(reader)
+
+    def num_classes(self):
+        return len(self._synsets)
+
+    def label_names(self):
+        return self._synsets
+
+    def label_descriptions(self, detailed: bool = False, as_dict: bool = False) -> Union[List[str], Dict[str, str]]:
+        if as_dict:
+            return {label: self.label_name_to_description(label, detailed=detailed) for label in self._synsets}
+        else:
+            return [self.label_name_to_description(label, detailed=detailed) for label in self._synsets]
+
+    def index_to_label_name(self, index) -> str:
+        assert 0 <= index < len(self._synsets), \
+            f'Index ({index}) out of range for dataset with {len(self._synsets)} classes.'
+        return self._synsets[index]
+
+    def index_to_description(self, index: int, detailed: bool = False) -> str:
+        label = self.index_to_label_name(index)
+        return self.label_name_to_description(label, detailed=detailed)
+
+    def label_name_to_description(self, label: str, detailed: bool = False) -> str:
+        if detailed:
+            description = f'{self._lemmas[label]}: {self._definitions[label]}'
+        else:
+            description = f'{self._lemmas[label]}'
+        return description

timm/data/real_labels.py

@@ -7,14 +7,19 @@ Hacked together by / Copyright 2020 Ross Wightman
 import os
 import json
 import numpy as np
+import pkgutil
 
 
 class RealLabelsImagenet:
 
-    def __init__(self, filenames, real_json='real.json', topk=(1, 5)):
+    def __init__(self, filenames, real_json=None, topk=(1, 5)):
-        with open(real_json) as real_labels:
+        if real_json is not None:
-            real_labels = json.load(real_labels)
+            with open(real_json) as real_labels:
-            real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)}
+                real_labels = json.load(real_labels)
+        else:
+            real_labels = json.loads(
+                pkgutil.get_data(__name__, os.path.join('_info', 'imagenet_real_labels.json')).decode('utf-8'))
+        real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)}
         self.real_labels = real_labels
         self.filenames = filenames
         assert len(self.filenames) == len(self.real_labels)