mmocr.models.textrecog.backbones.resnet31_ocr 源代码

# 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


@MODELS.register_module()
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