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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmocr.models.textrecog.backbones import (ResNet, ResNet31OCR, ResNetABI,
ShallowCNN, VeryDeepVgg)
def test_resnet31_ocr_backbone():
"""Test resnet backbone."""
with pytest.raises(AssertionError):
ResNet31OCR(2.5)
with pytest.raises(AssertionError):
ResNet31OCR(3, layers=5)
with pytest.raises(AssertionError):
ResNet31OCR(3, channels=5)
# Test ResNet18 forward
model = ResNet31OCR()
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 32, 160)
feat = model(imgs)
assert feat.shape == torch.Size([1, 512, 4, 40])
def test_vgg_deep_vgg_ocr_backbone():
model = VeryDeepVgg()
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 32, 160)
feats = model(imgs)
assert feats.shape == torch.Size([1, 512, 1, 41])
def test_shallow_cnn_ocr_backbone():
model = ShallowCNN()
model.init_weights()
model.train()
imgs = torch.randn(1, 1, 32, 100)
feat = model(imgs)
assert feat.shape == torch.Size([1, 512, 8, 25])
def test_resnet_abi():
"""Test resnet backbone."""
with pytest.raises(AssertionError):
ResNetABI(2.5)
with pytest.raises(AssertionError):
ResNetABI(3, arch_settings=5)
with pytest.raises(AssertionError):
ResNetABI(3, stem_channels=None)
with pytest.raises(AssertionError):
ResNetABI(arch_settings=[3, 4, 6, 6], strides=[1, 2, 1, 2, 1])
# Test forwarding
model = ResNetABI()
model.train()
imgs = torch.randn(1, 3, 32, 160)
feat = model(imgs)
assert feat.shape == torch.Size([1, 512, 8, 40])
def test_resnet():
"""Test all ResNet backbones."""
resnet45_aster = ResNet(
in_channels=3,
stem_channels=[64, 128],
block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
arch_layers=[3, 4, 6, 6, 3],
arch_channels=[32, 64, 128, 256, 512],
strides=[(2, 2), (2, 2), (2, 1), (2, 1), (2, 1)])
resnet45_abi = ResNet(
in_channels=3,
stem_channels=32,
block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
arch_layers=[3, 4, 6, 6, 3],
arch_channels=[32, 64, 128, 256, 512],
strides=[2, 1, 2, 1, 1])
resnet_31 = ResNet(
in_channels=3,
stem_channels=[64, 128],
block_cfgs=dict(type='BasicBlock'),
arch_layers=[1, 2, 5, 3],
arch_channels=[256, 256, 512, 512],
strides=[1, 1, 1, 1],
plugins=[
dict(
cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
stages=(True, True, False, False),
position='before_stage'),
dict(
cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
stages=(False, False, True, False),
position='before_stage'),
dict(
cfg=dict(
type='ConvModule',
kernel_size=3,
stride=1,
padding=1,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU')),
stages=(True, True, True, True),
position='after_stage')
])
img = torch.rand(1, 3, 32, 100)
assert resnet45_aster(img).shape == torch.Size([1, 512, 1, 25])
assert resnet45_abi(img).shape == torch.Size([1, 512, 8, 25])
assert resnet_31(img).shape == torch.Size([1, 512, 4, 25])
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