86 lines
2.1 KiB
Python
86 lines
2.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmcls.models.backbones import ConvMixer
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def test_assertion():
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with pytest.raises(AssertionError):
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ConvMixer(arch='unknown')
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with pytest.raises(AssertionError):
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# ConvMixer arch dict should include essential_keys,
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ConvMixer(arch=dict(channels=[2, 3, 4, 5]))
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with pytest.raises(AssertionError):
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# ConvMixer out_indices should be valid depth.
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ConvMixer(out_indices=-100)
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@torch.no_grad() # To save memory
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def test_convmixer():
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# Test forward
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model = ConvMixer(arch='768/32')
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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 len(feat) == 1
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assert feat[0].shape == torch.Size([1, 768, 32, 32])
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# Test forward with multiple outputs
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model = ConvMixer(arch='768/32', out_indices=range(32))
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert len(feat) == 32
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for f in feat:
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assert f.shape == torch.Size([1, 768, 32, 32])
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# Test with custom arch
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model = ConvMixer(
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arch={
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'embed_dims': 99,
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'depth': 5,
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'patch_size': 5,
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'kernel_size': 9
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},
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out_indices=range(5))
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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 len(feat) == 5
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for f in feat:
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assert f.shape == torch.Size([1, 99, 44, 44])
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# Test with even kernel size arch
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model = ConvMixer(arch={
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'embed_dims': 99,
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'depth': 5,
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'patch_size': 5,
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'kernel_size': 8
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})
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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 len(feat) == 1
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assert feat[0].shape == torch.Size([1, 99, 44, 44])
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# Test frozen_stages
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model = ConvMixer(arch='768/32', frozen_stages=10)
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model.init_weights()
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model.train()
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for i in range(10):
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assert not model.stages[i].training
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for i in range(10, 32):
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assert model.stages[i].training
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