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103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import torch
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from mmocr.models.textrecog.backbones import ResNet
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class TestResNet(TestCase):
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def setUp(self) -> None:
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self.img = torch.rand(1, 3, 32, 100)
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def test_resnet45_aster(self):
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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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self.assertEqual(
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resnet45_aster(self.img).shape, torch.Size([1, 512, 1, 25]))
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def test_resnet45_abi(self):
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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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self.assertEqual(
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resnet45_abi(self.img).shape, torch.Size([1, 512, 8, 25]))
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def test_resnet31_master(self):
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resnet31_master = 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(
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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(type='GCAModule', ratio=0.0625, n_head=1),
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stages=[True, True, True, True],
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position='after_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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self.assertEqual(
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resnet31_master(self.img).shape, torch.Size([1, 512, 4, 25]))
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def test_resnet31(self):
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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(
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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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self.assertEqual(
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resnet_31(self.img).shape, torch.Size([1, 512, 4, 25]))
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