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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 mmpose.models.backbones import ViPNAS_MobileNetV3 |
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from mmpose.models.backbones.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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model = ViPNAS_MobileNetV3() |
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model.init_weights(pretrained=0) |
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with pytest.raises(AttributeError): |
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model = ViPNAS_MobileNetV3(frozen_stages=22) |
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model.train() |
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model = ViPNAS_MobileNetV3() |
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model.init_weights() |
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model.train() |
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frozen_stages = 1 |
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model = ViPNAS_MobileNetV3(frozen_stages=frozen_stages) |
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model.init_weights() |
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model.train() |
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for param in model.conv1.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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model = ViPNAS_MobileNetV3(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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model = ViPNAS_MobileNetV3() |
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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 feat.shape == torch.Size([1, 160, 7, 7]) |
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model = ViPNAS_MobileNetV3( |
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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 feat.shape == torch.Size([1, 160, 7, 7]) |
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model = ViPNAS_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 feat.shape == torch.Size([1, 160, 7, 7]) |
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test_mobilenetv3_backbone() |
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