68 lines
2.1 KiB
Python
68 lines
2.1 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 mmcv.utils.parrots_wrapper import _BatchNorm
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from mmcls.models.backbones import ResNet_CIFAR
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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_resnet_cifar():
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# deep_stem must be False
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with pytest.raises(AssertionError):
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ResNet_CIFAR(depth=18, deep_stem=True)
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# test the feature map size when depth is 18
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model = ResNet_CIFAR(depth=18, out_indices=(0, 1, 2, 3))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 32, 32)
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feat = model.conv1(imgs)
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assert feat.shape == (1, 64, 32, 32)
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feat = model(imgs)
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assert len(feat) == 4
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assert feat[0].shape == (1, 64, 32, 32)
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assert feat[1].shape == (1, 128, 16, 16)
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assert feat[2].shape == (1, 256, 8, 8)
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assert feat[3].shape == (1, 512, 4, 4)
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# test the feature map size when depth is 50
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model = ResNet_CIFAR(depth=50, out_indices=(0, 1, 2, 3))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 32, 32)
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feat = model.conv1(imgs)
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assert feat.shape == (1, 64, 32, 32)
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feat = model(imgs)
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assert len(feat) == 4
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assert feat[0].shape == (1, 256, 32, 32)
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assert feat[1].shape == (1, 512, 16, 16)
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assert feat[2].shape == (1, 1024, 8, 8)
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assert feat[3].shape == (1, 2048, 4, 4)
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# Test ResNet_CIFAR with first stage frozen
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frozen_stages = 1
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model = ResNet_CIFAR(depth=50, frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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check_norm_state([model.norm1], False)
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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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