176 lines
6.3 KiB
Python
176 lines
6.3 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 mmcls.models.backbones import MobileNetV3
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from mmcls.models.utils import InvertedResidual
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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_mobilenetv3_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = MobileNetV3()
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model.init_weights(pretrained=0)
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with pytest.raises(AssertionError):
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# arch must in [small, large]
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MobileNetV3(arch='others')
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with pytest.raises(ValueError):
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# frozen_stages must less than 13 when arch is small
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MobileNetV3(arch='small', frozen_stages=13)
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with pytest.raises(ValueError):
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# frozen_stages must less than 17 when arch is large
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MobileNetV3(arch='large', frozen_stages=17)
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with pytest.raises(ValueError):
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# max out_indices must less than 13 when arch is small
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MobileNetV3(arch='small', out_indices=(13, ))
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with pytest.raises(ValueError):
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# max out_indices must less than 17 when arch is large
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MobileNetV3(arch='large', out_indices=(17, ))
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# Test MobileNetV3
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model = MobileNetV3()
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model.init_weights()
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model.train()
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# Test MobileNetV3 with first stage frozen
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frozen_stages = 1
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model = MobileNetV3(frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for i in range(0, 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 MobileNetV3 with norm eval
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model = MobileNetV3(norm_eval=True, out_indices=range(0, 12))
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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 MobileNetV3 forward with small arch
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model = MobileNetV3(out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12))
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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) == 13
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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, 16, 56, 56])
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assert feat[2].shape == torch.Size([1, 24, 28, 28])
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assert feat[3].shape == torch.Size([1, 24, 28, 28])
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assert feat[4].shape == torch.Size([1, 40, 14, 14])
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assert feat[5].shape == torch.Size([1, 40, 14, 14])
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assert feat[6].shape == torch.Size([1, 40, 14, 14])
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assert feat[7].shape == torch.Size([1, 48, 14, 14])
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assert feat[8].shape == torch.Size([1, 48, 14, 14])
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assert feat[9].shape == torch.Size([1, 96, 7, 7])
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assert feat[10].shape == torch.Size([1, 96, 7, 7])
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assert feat[11].shape == torch.Size([1, 96, 7, 7])
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assert feat[12].shape == torch.Size([1, 576, 7, 7])
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# Test MobileNetV3 forward with small arch and GroupNorm
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model = MobileNetV3(
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out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12),
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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) == 13
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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, 16, 56, 56])
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assert feat[2].shape == torch.Size([1, 24, 28, 28])
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assert feat[3].shape == torch.Size([1, 24, 28, 28])
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assert feat[4].shape == torch.Size([1, 40, 14, 14])
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assert feat[5].shape == torch.Size([1, 40, 14, 14])
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assert feat[6].shape == torch.Size([1, 40, 14, 14])
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assert feat[7].shape == torch.Size([1, 48, 14, 14])
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assert feat[8].shape == torch.Size([1, 48, 14, 14])
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assert feat[9].shape == torch.Size([1, 96, 7, 7])
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assert feat[10].shape == torch.Size([1, 96, 7, 7])
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assert feat[11].shape == torch.Size([1, 96, 7, 7])
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assert feat[12].shape == torch.Size([1, 576, 7, 7])
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# Test MobileNetV3 forward with large arch
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model = MobileNetV3(
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arch='large',
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out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16))
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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) == 17
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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, 16, 112, 112])
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assert feat[2].shape == torch.Size([1, 24, 56, 56])
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assert feat[3].shape == torch.Size([1, 24, 56, 56])
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assert feat[4].shape == torch.Size([1, 40, 28, 28])
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assert feat[5].shape == torch.Size([1, 40, 28, 28])
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assert feat[6].shape == torch.Size([1, 40, 28, 28])
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assert feat[7].shape == torch.Size([1, 80, 14, 14])
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assert feat[8].shape == torch.Size([1, 80, 14, 14])
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assert feat[9].shape == torch.Size([1, 80, 14, 14])
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assert feat[10].shape == torch.Size([1, 80, 14, 14])
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assert feat[11].shape == torch.Size([1, 112, 14, 14])
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assert feat[12].shape == torch.Size([1, 112, 14, 14])
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assert feat[13].shape == torch.Size([1, 160, 7, 7])
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assert feat[14].shape == torch.Size([1, 160, 7, 7])
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assert feat[15].shape == torch.Size([1, 160, 7, 7])
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assert feat[16].shape == torch.Size([1, 960, 7, 7])
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# Test MobileNetV3 forward with large arch
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model = MobileNetV3(arch='large', out_indices=(0, ))
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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, 16, 112, 112])
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# Test MobileNetV3 with checkpoint forward
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model = MobileNetV3(with_cp=True)
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for m in model.modules():
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if isinstance(m, InvertedResidual):
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assert m.with_cp
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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, 576, 7, 7])
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