213 lines
6.2 KiB
Python
213 lines
6.2 KiB
Python
import pytest
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import torch
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from torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.backbones import ShuffleNetv2
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from mmcls.models.backbones.shufflenet_v2 import (InvertedResidual,
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channel_shuffle,
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make_divisible)
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def is_block(modules):
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"""Check if is ResNet building block."""
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if isinstance(modules, (InvertedResidual, )):
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return True
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return False
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def is_norm(modules):
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"""Check if is one of the norms."""
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if isinstance(modules, (GroupNorm, _BatchNorm)):
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return True
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return False
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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_channel_shuffle():
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x = torch.randn(1, 24, 56, 56)
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with pytest.raises(AssertionError):
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# num_channels should be divisible by groups
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channel_shuffle(x, 7)
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def test_make_divisible():
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# test min_value is None
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make_divisible(34, 8, None)
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# test new_value < 0.9 * value
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make_divisible(10, 8, None)
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def test_shufflenetv2_invertedresidual():
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with pytest.raises(AssertionError):
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# when stride==1, 16 == branch_features << 1
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InvertedResidual(24, 32, stride=1)
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# Test InvertedResidual forward
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block = InvertedResidual(24, 48, stride=2)
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x = torch.randn(1, 24, 56, 56)
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x_out = block(x)
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assert x_out.shape == torch.Size((1, 48, 28, 28))
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# Test InvertedResidual with checkpoint forward
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block = InvertedResidual(48, 48, stride=1, with_cp=True)
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assert block.with_cp
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x = torch.randn(1, 48, 56, 56)
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x.requires_grad = True
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x_out = block(x)
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assert x_out.shape == torch.Size((1, 48, 56, 56))
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def test_shufflenetv2_backbone():
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with pytest.raises(ValueError):
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# groups must in 0.5, 1.0, 1.5, 2.0]
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ShuffleNetv2(widen_factor=3.0)
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with pytest.raises(AssertionError):
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# frozen_stages must in [0, 1, 2]
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ShuffleNetv2(widen_factor=3.0, frozen_stages=3)
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with pytest.raises(TypeError):
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# pretrained must be str or None
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model = ShuffleNetv2()
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model.init_weights(pretrained=1)
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# Test ShuffleNetv2 norm state
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model = ShuffleNetv2()
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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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# Test ShuffleNetv2 with first stage frozen
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frozen_stages = 1
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model = ShuffleNetv2(frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for layer in [model.conv1]:
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for param in layer.parameters():
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assert param.requires_grad is False
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for i in range(1, frozen_stages + 1):
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layer = getattr(model, f'layer{i}')
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for mod in layer.modules():
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if isinstance(mod, _BatchNorm):
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assert mod.training is False
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for param in layer.parameters():
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assert param.requires_grad is False
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# Test ShuffleNetv2 with norm_eval
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model = ShuffleNetv2(norm_eval=True)
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), False)
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# Test ShuffleNetv2 forward with widen_factor=0.5
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model = ShuffleNetv2(widen_factor=0.5)
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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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) == 3
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assert feat[0].shape == torch.Size((1, 48, 28, 28))
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assert feat[1].shape == torch.Size((1, 96, 14, 14))
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assert feat[2].shape == torch.Size((1, 192, 7, 7))
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# Test ShuffleNetv2 forward with widen_factor=1.0
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model = ShuffleNetv2(widen_factor=1.0)
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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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) == 3
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assert feat[0].shape == torch.Size((1, 116, 28, 28))
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assert feat[1].shape == torch.Size((1, 232, 14, 14))
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assert feat[2].shape == torch.Size((1, 464, 7, 7))
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# Test ShuffleNetv2 forward with widen_factor=1.5
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model = ShuffleNetv2(widen_factor=1.5)
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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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) == 3
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assert feat[0].shape == torch.Size((1, 176, 28, 28))
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assert feat[1].shape == torch.Size((1, 352, 14, 14))
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assert feat[2].shape == torch.Size((1, 704, 7, 7))
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# Test ShuffleNetv2 forward with widen_factor=2.0
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model = ShuffleNetv2(widen_factor=2.0)
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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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) == 3
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assert feat[0].shape == torch.Size((1, 244, 28, 28))
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assert feat[1].shape == torch.Size((1, 488, 14, 14))
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assert feat[2].shape == torch.Size((1, 976, 7, 7))
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# Test ShuffleNetv2 forward with layers 3 forward
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model = ShuffleNetv2(widen_factor=1.0, out_indices=(2, ))
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert isinstance(feat, torch.Tensor)
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assert feat.shape == torch.Size((1, 464, 7, 7))
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# Test ShuffleNetv2 forward with layers 1 2 forward
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model = ShuffleNetv2(widen_factor=1.0, out_indices=(1, 2))
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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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) == 2
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assert feat[0].shape == torch.Size((1, 232, 14, 14))
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assert feat[1].shape == torch.Size((1, 464, 7, 7))
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# Test ShuffleNetv2 forward with checkpoint forward
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model = ShuffleNetv2(widen_factor=1.0, with_cp=True)
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for m in model.modules():
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if is_block(m):
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assert m.with_cp
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