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https://github.com/open-mmlab/mmclassification.git
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affb39fe07
* add imagenet bs 4096 * add vit_base_patch16_224_finetune * add vit_base_patch16_224_pretrain * add vit_base_patch16_384_finetune * add vit_base_patch16_384_finetune * add vit_b_p16_224_finetune_imagenet * add vit_b_p16_224_pretrain_imagenet * add vit_b_p16_384_finetune_imagenet * add vit * add vit * add vit head * vit unitest * keep up with ClsHead * test vit * add flag to determiine whether to calculate acc during training * Changes related to mmcv1.3.0 * change checkpoint saving interval to 10 * add label smooth * default_runtime.py recovery * docformatter * docformatter * delete 2 lines of comments * delete configs/_base_/schedules/imagenet_bs4096.py * add configs/_base_/schedules/imagenet_bs2048_AdamW.py * rename imagenet_bs4096.py to imagenet_bs2048_AdamW.py * add helpers.py * test vit hybrid backbone * fix HybridEmbed * use to_2tuple instead
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
import pytest
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import torch
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from torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.backbones import VGG, VisionTransformer
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def is_norm(modules):
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"""Check if is one of the norms."""
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if isinstance(modules, (GroupNorm, _BatchNorm)):
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return True
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return False
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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def test_vit_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = VisionTransformer()
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model.init_weights(pretrained=0)
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# Test ViT base model with input size of 224
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# and patch size of 16
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model = VisionTransformer()
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), True)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat.shape == torch.Size((1, 768))
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def test_vit_hybrid_backbone():
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# Test VGG11+ViT-B/16 hybrid model
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backbone = VGG(11, norm_eval=True)
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backbone.init_weights()
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model = VisionTransformer(hybrid_backbone=backbone)
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), True)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat.shape == torch.Size((1, 768))
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