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# Copyright (c) OpenMMLab. All rights reserved. | |
import torch.nn as nn | |
from mmengine.model import BaseModule, Sequential | |
import mmocr.utils as utils | |
from mmocr.models.textrecog.layers import BasicBlock | |
from mmocr.registry import MODELS | |
class ResNet31OCR(BaseModule): | |
"""Implement ResNet backbone for text recognition, modified from | |
`ResNet <https://arxiv.org/pdf/1512.03385.pdf>`_ | |
Args: | |
base_channels (int): Number of channels of input image tensor. | |
layers (list[int]): List of BasicBlock number for each stage. | |
channels (list[int]): List of out_channels of Conv2d layer. | |
out_indices (None | Sequence[int]): Indices of output stages. | |
stage4_pool_cfg (dict): Dictionary to construct and configure | |
pooling layer in stage 4. | |
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage. | |
""" | |
def __init__(self, | |
base_channels=3, | |
layers=[1, 2, 5, 3], | |
channels=[64, 128, 256, 256, 512, 512, 512], | |
out_indices=None, | |
stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)), | |
last_stage_pool=False, | |
init_cfg=[ | |
dict(type='Kaiming', layer='Conv2d'), | |
dict(type='Uniform', layer='BatchNorm2d') | |
]): | |
super().__init__(init_cfg=init_cfg) | |
assert isinstance(base_channels, int) | |
assert utils.is_type_list(layers, int) | |
assert utils.is_type_list(channels, int) | |
assert out_indices is None or isinstance(out_indices, (list, tuple)) | |
assert isinstance(last_stage_pool, bool) | |
self.out_indices = out_indices | |
self.last_stage_pool = last_stage_pool | |
# conv 1 (Conv, Conv) | |
self.conv1_1 = nn.Conv2d( | |
base_channels, channels[0], kernel_size=3, stride=1, padding=1) | |
self.bn1_1 = nn.BatchNorm2d(channels[0]) | |
self.relu1_1 = nn.ReLU(inplace=True) | |
self.conv1_2 = nn.Conv2d( | |
channels[0], channels[1], kernel_size=3, stride=1, padding=1) | |
self.bn1_2 = nn.BatchNorm2d(channels[1]) | |
self.relu1_2 = nn.ReLU(inplace=True) | |
# conv 2 (Max-pooling, Residual block, Conv) | |
self.pool2 = nn.MaxPool2d( | |
kernel_size=2, stride=2, padding=0, ceil_mode=True) | |
self.block2 = self._make_layer(channels[1], channels[2], layers[0]) | |
self.conv2 = nn.Conv2d( | |
channels[2], channels[2], kernel_size=3, stride=1, padding=1) | |
self.bn2 = nn.BatchNorm2d(channels[2]) | |
self.relu2 = nn.ReLU(inplace=True) | |
# conv 3 (Max-pooling, Residual block, Conv) | |
self.pool3 = nn.MaxPool2d( | |
kernel_size=2, stride=2, padding=0, ceil_mode=True) | |
self.block3 = self._make_layer(channels[2], channels[3], layers[1]) | |
self.conv3 = nn.Conv2d( | |
channels[3], channels[3], kernel_size=3, stride=1, padding=1) | |
self.bn3 = nn.BatchNorm2d(channels[3]) | |
self.relu3 = nn.ReLU(inplace=True) | |
# conv 4 (Max-pooling, Residual block, Conv) | |
self.pool4 = nn.MaxPool2d(padding=0, ceil_mode=True, **stage4_pool_cfg) | |
self.block4 = self._make_layer(channels[3], channels[4], layers[2]) | |
self.conv4 = nn.Conv2d( | |
channels[4], channels[4], kernel_size=3, stride=1, padding=1) | |
self.bn4 = nn.BatchNorm2d(channels[4]) | |
self.relu4 = nn.ReLU(inplace=True) | |
# conv 5 ((Max-pooling), Residual block, Conv) | |
self.pool5 = None | |
if self.last_stage_pool: | |
self.pool5 = nn.MaxPool2d( | |
kernel_size=2, stride=2, padding=0, ceil_mode=True) # 1/16 | |
self.block5 = self._make_layer(channels[4], channels[5], layers[3]) | |
self.conv5 = nn.Conv2d( | |
channels[5], channels[5], kernel_size=3, stride=1, padding=1) | |
self.bn5 = nn.BatchNorm2d(channels[5]) | |
self.relu5 = nn.ReLU(inplace=True) | |
def _make_layer(self, input_channels, output_channels, blocks): | |
layers = [] | |
for _ in range(blocks): | |
downsample = None | |
if input_channels != output_channels: | |
downsample = Sequential( | |
nn.Conv2d( | |
input_channels, | |
output_channels, | |
kernel_size=1, | |
stride=1, | |
bias=False), | |
nn.BatchNorm2d(output_channels), | |
) | |
layers.append( | |
BasicBlock( | |
input_channels, output_channels, downsample=downsample)) | |
input_channels = output_channels | |
return Sequential(*layers) | |
def forward(self, x): | |
x = self.conv1_1(x) | |
x = self.bn1_1(x) | |
x = self.relu1_1(x) | |
x = self.conv1_2(x) | |
x = self.bn1_2(x) | |
x = self.relu1_2(x) | |
outs = [] | |
for i in range(4): | |
layer_index = i + 2 | |
pool_layer = getattr(self, f'pool{layer_index}') | |
block_layer = getattr(self, f'block{layer_index}') | |
conv_layer = getattr(self, f'conv{layer_index}') | |
bn_layer = getattr(self, f'bn{layer_index}') | |
relu_layer = getattr(self, f'relu{layer_index}') | |
if pool_layer is not None: | |
x = pool_layer(x) | |
x = block_layer(x) | |
x = conv_layer(x) | |
x = bn_layer(x) | |
x = relu_layer(x) | |
outs.append(x) | |
if self.out_indices is not None: | |
return tuple(outs[i] for i in self.out_indices) | |
return x | |