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import pytest |
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import torch |
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from mmocr.models.textrecog.backbones import (ResNet, ResNet31OCR, ResNetABI, |
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ShallowCNN, VeryDeepVgg) |
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def test_resnet31_ocr_backbone(): |
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"""Test resnet backbone.""" |
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with pytest.raises(AssertionError): |
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ResNet31OCR(2.5) |
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with pytest.raises(AssertionError): |
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ResNet31OCR(3, layers=5) |
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with pytest.raises(AssertionError): |
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ResNet31OCR(3, channels=5) |
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model = ResNet31OCR() |
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model.init_weights() |
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model.train() |
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imgs = torch.randn(1, 3, 32, 160) |
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feat = model(imgs) |
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assert feat.shape == torch.Size([1, 512, 4, 40]) |
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def test_vgg_deep_vgg_ocr_backbone(): |
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model = VeryDeepVgg() |
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model.init_weights() |
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model.train() |
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imgs = torch.randn(1, 3, 32, 160) |
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feats = model(imgs) |
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assert feats.shape == torch.Size([1, 512, 1, 41]) |
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def test_shallow_cnn_ocr_backbone(): |
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model = ShallowCNN() |
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model.init_weights() |
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model.train() |
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imgs = torch.randn(1, 1, 32, 100) |
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feat = model(imgs) |
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assert feat.shape == torch.Size([1, 512, 8, 25]) |
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def test_resnet_abi(): |
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"""Test resnet backbone.""" |
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with pytest.raises(AssertionError): |
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ResNetABI(2.5) |
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with pytest.raises(AssertionError): |
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ResNetABI(3, arch_settings=5) |
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with pytest.raises(AssertionError): |
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ResNetABI(3, stem_channels=None) |
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with pytest.raises(AssertionError): |
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ResNetABI(arch_settings=[3, 4, 6, 6], strides=[1, 2, 1, 2, 1]) |
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model = ResNetABI() |
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model.train() |
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imgs = torch.randn(1, 3, 32, 160) |
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feat = model(imgs) |
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assert feat.shape == torch.Size([1, 512, 8, 40]) |
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def test_resnet(): |
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"""Test all ResNet backbones.""" |
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resnet45_aster = ResNet( |
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in_channels=3, |
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stem_channels=[64, 128], |
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block_cfgs=dict(type='BasicBlock', use_conv1x1='True'), |
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arch_layers=[3, 4, 6, 6, 3], |
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arch_channels=[32, 64, 128, 256, 512], |
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strides=[(2, 2), (2, 2), (2, 1), (2, 1), (2, 1)]) |
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resnet45_abi = ResNet( |
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in_channels=3, |
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stem_channels=32, |
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block_cfgs=dict(type='BasicBlock', use_conv1x1='True'), |
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arch_layers=[3, 4, 6, 6, 3], |
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arch_channels=[32, 64, 128, 256, 512], |
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strides=[2, 1, 2, 1, 1]) |
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resnet_31 = ResNet( |
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in_channels=3, |
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stem_channels=[64, 128], |
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block_cfgs=dict(type='BasicBlock'), |
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arch_layers=[1, 2, 5, 3], |
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arch_channels=[256, 256, 512, 512], |
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strides=[1, 1, 1, 1], |
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plugins=[ |
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dict( |
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cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)), |
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stages=(True, True, False, False), |
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position='before_stage'), |
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dict( |
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cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)), |
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stages=(False, False, True, False), |
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position='before_stage'), |
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dict( |
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cfg=dict( |
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type='ConvModule', |
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kernel_size=3, |
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stride=1, |
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padding=1, |
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norm_cfg=dict(type='BN'), |
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act_cfg=dict(type='ReLU')), |
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stages=(True, True, True, True), |
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position='after_stage') |
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]) |
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img = torch.rand(1, 3, 32, 100) |
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assert resnet45_aster(img).shape == torch.Size([1, 512, 1, 25]) |
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assert resnet45_abi(img).shape == torch.Size([1, 512, 8, 25]) |
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assert resnet_31(img).shape == torch.Size([1, 512, 4, 25]) |
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