140 lines
4.0 KiB
Python
140 lines
4.0 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 mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
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from mmcls.models.backbones import VGG
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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_vgg():
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"""Test VGG backbone."""
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with pytest.raises(KeyError):
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# VGG depth should be in [11, 13, 16, 19]
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VGG(18)
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with pytest.raises(AssertionError):
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# In VGG: 1 <= num_stages <= 5
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VGG(11, num_stages=0)
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with pytest.raises(AssertionError):
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# In VGG: 1 <= num_stages <= 5
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VGG(11, num_stages=6)
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with pytest.raises(AssertionError):
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# len(dilations) == num_stages
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VGG(11, dilations=(1, 1), num_stages=3)
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = VGG(11)
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model.init_weights(pretrained=0)
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# Test VGG11 norm_eval=True
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model = VGG(11, 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 VGG11 forward without classifiers
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model = VGG(11, out_indices=(0, 1, 2, 3, 4))
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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) == 5
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 128, 56, 56)
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assert feat[2].shape == (1, 256, 28, 28)
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assert feat[3].shape == (1, 512, 14, 14)
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assert feat[4].shape == (1, 512, 7, 7)
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# Test VGG11 forward with classifiers
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model = VGG(11, num_classes=10, out_indices=(0, 1, 2, 3, 4, 5))
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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) == 6
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 128, 56, 56)
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assert feat[2].shape == (1, 256, 28, 28)
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assert feat[3].shape == (1, 512, 14, 14)
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assert feat[4].shape == (1, 512, 7, 7)
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assert feat[5].shape == (1, 10)
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# Test VGG11BN forward
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model = VGG(11, norm_cfg=dict(type='BN'), out_indices=(0, 1, 2, 3, 4))
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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) == 5
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 128, 56, 56)
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assert feat[2].shape == (1, 256, 28, 28)
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assert feat[3].shape == (1, 512, 14, 14)
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assert feat[4].shape == (1, 512, 7, 7)
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# Test VGG11BN forward with classifiers
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model = VGG(
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11,
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num_classes=10,
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norm_cfg=dict(type='BN'),
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out_indices=(0, 1, 2, 3, 4, 5))
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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) == 6
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 128, 56, 56)
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assert feat[2].shape == (1, 256, 28, 28)
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assert feat[3].shape == (1, 512, 14, 14)
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assert feat[4].shape == (1, 512, 7, 7)
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assert feat[5].shape == (1, 10)
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# Test VGG13 with layers 1, 2, 3 out forward
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model = VGG(13, out_indices=(0, 1, 2))
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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) == 3
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assert feat[0].shape == (1, 64, 112, 112)
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assert feat[1].shape == (1, 128, 56, 56)
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assert feat[2].shape == (1, 256, 28, 28)
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# Test VGG16 with top feature maps out forward
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model = VGG(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) == 1
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assert feat[0].shape == (1, 512, 7, 7)
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# Test VGG19 with classification score out forward
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model = VGG(19, num_classes=10)
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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 == (1, 10)
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