mirror of https://github.com/open-mmlab/mmocr.git
126 lines
3.5 KiB
Python
126 lines
3.5 KiB
Python
# 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])
|