85 lines
2.5 KiB
Python
85 lines
2.5 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 mmpretrain.models.backbones import EdgeNeXt
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def test_assertion():
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with pytest.raises(AssertionError):
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EdgeNeXt(arch='unknown')
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with pytest.raises(AssertionError):
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# EdgeNeXt arch dict should include 'embed_dims',
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EdgeNeXt(arch=dict(channels=[24, 48, 88, 168]))
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with pytest.raises(AssertionError):
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# EdgeNeXt arch dict should include 'embed_dims',
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EdgeNeXt(arch=dict(depths=[2, 2, 6, 2], channels=[24, 48, 88, 168]))
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def test_edgenext():
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# Test forward
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model = EdgeNeXt(arch='xxsmall', 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, 168])
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# Test forward with multiple outputs
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model = EdgeNeXt(arch='xxsmall', out_indices=(0, 1, 2, 3))
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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, 24])
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assert feat[1].shape == torch.Size([1, 48])
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assert feat[2].shape == torch.Size([1, 88])
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assert feat[3].shape == torch.Size([1, 168])
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# Test with custom arch
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model = EdgeNeXt(
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arch={
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'depths': [2, 3, 4, 5],
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'channels': [20, 40, 80, 160],
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'num_heads': [4, 4, 4, 4]
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},
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out_indices=(0, 1, 2, 3))
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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) == 4
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assert feat[0].shape == torch.Size([1, 20])
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assert feat[1].shape == torch.Size([1, 40])
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assert feat[2].shape == torch.Size([1, 80])
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assert feat[3].shape == torch.Size([1, 160])
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# Test without gap before final norm
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model = EdgeNeXt(
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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, 48, 56, 56])
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assert feat[1].shape == torch.Size([1, 96, 28, 28])
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assert feat[2].shape == torch.Size([1, 160, 14, 14])
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assert feat[3].shape == torch.Size([1, 304, 7, 7])
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# Test frozen_stages
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model = EdgeNeXt(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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