42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.backbones import TIMMBackbone
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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_timm_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = TIMMBackbone()
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model.init_weights(pretrained=0)
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# Test resnet18 from timm
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model = TIMMBackbone(model_name='resnet18')
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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, 512, 7, 7))
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# Test efficientnet_b1 with pretrained weights
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model = TIMMBackbone(model_name='efficientnet_b1', pretrained=True)
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model.init_weights()
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model.train()
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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, 1280, 7, 7))
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