87 lines
2.6 KiB
Python
87 lines
2.6 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 ConvNeXt
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def test_assertion():
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with pytest.raises(AssertionError):
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ConvNeXt(arch='unknown')
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with pytest.raises(AssertionError):
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# ConvNeXt arch dict should include 'embed_dims',
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ConvNeXt(arch=dict(channels=[2, 3, 4, 5]))
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with pytest.raises(AssertionError):
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# ConvNeXt arch dict should include 'embed_dims',
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ConvNeXt(arch=dict(depths=[2, 3, 4], channels=[2, 3, 4, 5]))
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def test_convnext():
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# Test forward
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model = ConvNeXt(arch='tiny', out_indices=-1)
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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])
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# Test forward with multiple outputs
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model = ConvNeXt(arch='small', out_indices=(0, 1, 2, 3))
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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) == 4
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assert feat[0].shape == torch.Size([1, 96])
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assert feat[1].shape == torch.Size([1, 192])
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assert feat[2].shape == torch.Size([1, 384])
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assert feat[3].shape == torch.Size([1, 768])
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# Test with custom arch
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model = ConvNeXt(
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arch={
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'depths': [2, 3, 4, 5, 6],
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'channels': [16, 32, 64, 128, 256]
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},
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out_indices=(0, 1, 2, 3, 4))
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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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assert feat[0].shape == torch.Size([1, 16])
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assert feat[1].shape == torch.Size([1, 32])
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assert feat[2].shape == torch.Size([1, 64])
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assert feat[3].shape == torch.Size([1, 128])
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assert feat[4].shape == torch.Size([1, 256])
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# Test without gap before final norm
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model = ConvNeXt(
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arch='small', out_indices=(0, 1, 2, 3), gap_before_final_norm=False)
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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) == 4
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assert feat[0].shape == torch.Size([1, 96, 56, 56])
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assert feat[1].shape == torch.Size([1, 192, 28, 28])
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assert feat[2].shape == torch.Size([1, 384, 14, 14])
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assert feat[3].shape == torch.Size([1, 768, 7, 7])
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# Test frozen_stages
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model = ConvNeXt(arch='small', out_indices=(0, 1, 2, 3), frozen_stages=2)
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model.init_weights()
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model.train()
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for i in range(2):
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assert not model.downsample_layers[i].training
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assert not model.stages[i].training
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for i in range(2, 4):
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assert model.downsample_layers[i].training
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assert model.stages[i].training
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