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1from urllib.request import urlopen
2from PIL import Image
3import timm
4import torch
5
6img = Image.open(urlopen(
7 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
8))
9
10model = timm.create_model('rdnet_base.nv_in1k', pretrained=True)
11model = model.eval()
12
13# get model specific transforms (normalization, resize)
14data_config = timm.data.resolve_model_data_config(model)
15transforms = timm.data.create_transform(**data_config, is_training=False)
16
17output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
18
19top5_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 'rdnet_base.nv_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, 408, 56, 56])
26 # torch.Size([1, 584, 28, 28])
27 # torch.Size([1, 1000, 14, 14])
28 # torch.Size([1, 1760, 7, 7])
29
30 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 'rdnet_base.nv_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, 1760, 7, 7) shaped tensor
26
27output = model.forward_head(output, pre_logits=True)
28# output is a (1, num_features) shaped tensor@misc{kim2024densenets,
title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs},
author={Donghyun Kim and Byeongho Heo and Dongyoon Han},
year={2024},
eprint={2403.19588},
archivePrefix={arXiv},
}