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1from urllib.request import urlopen
2from PIL import Image
3import timm
4
5img = Image.open(urlopen(
6 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
7))
8
9model = timm.create_model('inception_v3.tv_in1k', pretrained=True)
10model = model.eval()
11
12# get model specific transforms (normalization, resize)
13data_config = timm.data.resolve_model_data_config(model)
14transforms = timm.data.create_transform(**data_config, is_training=False)
15
16output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
17
18top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)1from urllib.request import urlopen
2from PIL import Image
3import timm
4
5img = Image.open(urlopen(
6 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
7))
8
9model = timm.create_model(
10 'inception_v3.tv_in1k',
11 pretrained=True,
12 features_only=True,
13)
14model = model.eval()
15
16# get model specific transforms (normalization, resize)
17data_config = timm.data.resolve_model_data_config(model)
18transforms = timm.data.create_transform(**data_config, is_training=False)
19
20output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
21
22for o in output:
23 # print shape of each feature map in output
24 # e.g.:
25 # torch.Size([1, 64, 147, 147])
26 # torch.Size([1, 192, 71, 71])
27 # torch.Size([1, 288, 35, 35])
28 # torch.Size([1, 768, 17, 17])
29 # torch.Size([1, 2048, 8, 8])
30
31 print(o.shape)1from urllib.request import urlopen
2from PIL import Image
3import timm
4
5img = Image.open(urlopen(
6 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
7))
8
9model = timm.create_model(
10 'inception_v3.tv_in1k',
11 pretrained=True,
12 num_classes=0, # remove classifier nn.Linear
13)
14model = model.eval()
15
16# get model specific transforms (normalization, resize)
17data_config = timm.data.resolve_model_data_config(model)
18transforms = timm.data.create_transform(**data_config, is_training=False)
19
20output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
21
22# or equivalently (without needing to set num_classes=0)
23
24output = model.forward_features(transforms(img).unsqueeze(0))
25# output is unpooled, a (1, 2048, 8, 8) shaped tensor
26
27output = model.forward_head(output, pre_logits=True)
28# output is a (1, num_features) shaped tensor1@article{DBLP:journals/corr/SzegedyVISW15,
2 author = {Christian Szegedy and
3 Vincent Vanhoucke and
4 Sergey Ioffe and
5 Jonathon Shlens and
6 Zbigniew Wojna},
7 title = {Rethinking the Inception Architecture for Computer Vision},
8 journal = {CoRR},
9 volume = {abs/1512.00567},
10 year = {2015},
11 url = {http://arxiv.org/abs/1512.00567},
12 archivePrefix = {arXiv},
13 eprint = {1512.00567},
14 timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
15 biburl = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib},
16 bibsource = {dblp computer science bibliography, https://dblp.org}
17}