96 lines
2.7 KiB
Python
96 lines
2.7 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 DenseNet
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def test_assertion():
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with pytest.raises(AssertionError):
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DenseNet(arch='unknown')
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with pytest.raises(AssertionError):
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# DenseNet arch dict should include essential_keys,
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DenseNet(arch=dict(channels=[2, 3, 4, 5]))
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with pytest.raises(AssertionError):
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# DenseNet out_indices should be valid depth.
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DenseNet(out_indices=-100)
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def test_DenseNet():
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# Test forward
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model = DenseNet(arch='121')
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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, 1024, 7, 7])
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# Test memory efficient option
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model = DenseNet(arch='121', memory_efficient=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 len(feat) == 1
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assert feat[0].shape == torch.Size([1, 1024, 7, 7])
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# Test drop rate
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model = DenseNet(arch='121', drop_rate=0.05)
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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, 1024, 7, 7])
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# Test forward with multiple outputs
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model = DenseNet(arch='121', 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, 128, 28, 28])
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assert feat[1].shape == torch.Size([1, 256, 14, 14])
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assert feat[2].shape == torch.Size([1, 512, 7, 7])
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assert feat[3].shape == torch.Size([1, 1024, 7, 7])
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# Test with custom arch
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model = DenseNet(
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arch={
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'growth_rate': 20,
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'depths': [4, 8, 12, 16, 20],
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'init_channels': 40,
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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, 60, 28, 28])
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assert feat[1].shape == torch.Size([1, 110, 14, 14])
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assert feat[2].shape == torch.Size([1, 175, 7, 7])
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assert feat[3].shape == torch.Size([1, 247, 3, 3])
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assert feat[4].shape == torch.Size([1, 647, 3, 3])
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# Test frozen_stages
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model = DenseNet(arch='121', 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.stages[i].training
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assert not model.transitions[i].training
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for i in range(2, 4):
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assert model.stages[i].training
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assert model.transitions[i].training
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