115 lines
3.7 KiB
Python
115 lines
3.7 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 torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmpretrain.models.backbones import MobileNetV1
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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_mobilenetv1_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = MobileNetV1()
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model.init_weights(pretrained=0)
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with pytest.raises(ValueError):
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# frozen_stages must in range(-1, 8)
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MobileNetV1(frozen_stages=8)
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# Test MobileNetV2 with first stage frozen
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frozen_stages = 1
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model = MobileNetV1(frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for mod in model.modules():
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for param in mod.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 MobileNetV2 with norm_eval=True
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model = MobileNetV1(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 MobileNetV2 forward with dict(type='ReLU')
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model = MobileNetV1(act_cfg=dict(type='ReLU'))
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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) == 7
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assert feat[0].shape == torch.Size((1, 16, 112, 112))
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assert feat[1].shape == torch.Size((1, 24, 56, 56))
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assert feat[2].shape == torch.Size((1, 32, 28, 28))
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assert feat[3].shape == torch.Size((1, 64, 14, 14))
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assert feat[4].shape == torch.Size((1, 96, 14, 14))
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assert feat[5].shape == torch.Size((1, 160, 7, 7))
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assert feat[6].shape == torch.Size((1, 320, 7, 7))
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# Test MobileNetV2 with BatchNorm forward
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model = MobileNetV1()
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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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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) == 7
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assert feat[0].shape == torch.Size((1, 16, 112, 112))
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assert feat[1].shape == torch.Size((1, 24, 56, 56))
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assert feat[2].shape == torch.Size((1, 32, 28, 28))
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assert feat[3].shape == torch.Size((1, 64, 14, 14))
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assert feat[4].shape == torch.Size((1, 96, 14, 14))
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assert feat[5].shape == torch.Size((1, 160, 7, 7))
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assert feat[6].shape == torch.Size((1, 320, 7, 7))
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# Test MobileNetV2 with GroupNorm forward
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model = MobileNetV1(
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norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, GroupNorm)
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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) == 7
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assert feat[0].shape == torch.Size((1, 16, 112, 112))
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assert feat[1].shape == torch.Size((1, 24, 56, 56))
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assert feat[2].shape == torch.Size((1, 32, 28, 28))
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assert feat[3].shape == torch.Size((1, 64, 14, 14))
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assert feat[4].shape == torch.Size((1, 96, 14, 14))
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assert feat[5].shape == torch.Size((1, 160, 7, 7))
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assert feat[6].shape == torch.Size((1, 320, 7, 7))
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