48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
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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 TNT
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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_tnt_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = TNT()
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model.init_weights(pretrained=0)
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# Test tnt_base_patch16_224
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model = TNT()
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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, 640))
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# Test tnt with embed_dims=768
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arch = {
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'embed_dims_outer': 768,
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'embed_dims_inner': 48,
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'num_layers': 12,
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'num_heads_outer': 6,
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'num_heads_inner': 4
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}
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model = TNT(arch=arch)
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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, 768))
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