Move ImageNet metadata (aka info) files to timm/data/_info. Add helpe…

…r 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 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 @@
 
 _FMT_EXT = {
     'json': '.json',
+    'json-record': '.json',
     'json-split': '.json',
     'parquet': '.parquet',
     'csv': '.csv',
@@ -122,7 +123,7 @@
 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 @@
                     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)
 
@@ -276,13 +305,20 @@ 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:
         if all_indices is not None:
             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
@@ -308,14 +344,16 @@ 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'):
     results_filename += _FMT_EXT[results_format]
     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 .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