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Use args.class_map for labels in inference.py #2319

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Use args.class_map for labels in inference.py #2319

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19 changes: 14 additions & 5 deletions inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,8 @@
import torch

from timm.data import create_dataset, create_loader, resolve_data_config, ImageNetInfo, infer_imagenet_subset
from timm.data.readers.class_map import load_class_map
from timm.data.dataset_info import get_dataset_info_from_class_map
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
Expand Down Expand Up @@ -241,18 +243,25 @@ def main():

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)
dataset_info = None
if args.class_map:
class_map = load_class_map(args.class_map)
dataset_info = get_dataset_info_from_class_map(class_map)
else:
imagenet_subset = infer_imagenet_subset(model)
if imagenet_subset is not None:
dataset_info = ImageNetInfo(imagenet_subset)
else:
_logger.error("Cannot deduce ImageNet subset from model, no labelling will be performed.")

if dataset_info:
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()
Expand Down
5 changes: 5 additions & 0 deletions timm/data/dataset_info.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,3 +71,8 @@ def index_to_label_name(self, index) -> str:
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 get_dataset_info_from_class_map(class_map):
label_names = {v: k for k, v in class_map.items()}
return CustomDatasetInfo(label_names)