OpenOCR/openrec/modeling/encoders/rec_resnet_vd.py

import torch.nn as nn
import torch.nn.functional as F

from openrec.modeling.common import Activation


class ConvBNLayer(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        groups=1,
        is_vd_mode=False,
        act=None,
    ):
        super(ConvBNLayer, self).__init__()
        self.act = act
        self.is_vd_mode = is_vd_mode
        self._pool2d_avg = nn.AvgPool2d(kernel_size=stride,
                                        stride=stride,
                                        padding=0,
                                        ceil_mode=False)

        self._conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=1 if is_vd_mode else stride,
            padding=(kernel_size - 1) // 2,
            groups=groups,
            bias=False,
        )

        self._batch_norm = nn.BatchNorm2d(out_channels, )
        if self.act is not None:
            self._act = Activation(act_type=act, inplace=True)

    def forward(self, inputs):
        if self.is_vd_mode:
            inputs = self._pool2d_avg(inputs)
        y = self._conv(inputs)
        y = self._batch_norm(y)
        if self.act is not None:
            y = self._act(y)
        return y


class BottleneckBlock(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        stride,
        shortcut=True,
        if_first=False,
        name=None,
    ):
        super(BottleneckBlock, self).__init__()
        self.scale = 4
        self.conv0 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            act='relu',
        )
        self.conv1 = ConvBNLayer(
            in_channels=out_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=stride,
            act='relu',
        )
        self.conv2 = ConvBNLayer(
            in_channels=out_channels,
            out_channels=out_channels * self.scale,
            kernel_size=1,
            act=None,
        )

        if not shortcut:
            self.short = ConvBNLayer(
                in_channels=in_channels,
                out_channels=out_channels * self.scale,
                kernel_size=1,
                stride=stride,
                is_vd_mode=not if_first and stride[0] != 1,
            )

        self.shortcut = shortcut
        self.out_channels = out_channels * self.scale

    def forward(self, inputs):
        y = self.conv0(inputs)

        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
        y = short + conv2
        y = F.relu(y)
        return y


class BasicBlock(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        stride,
        shortcut=True,
        if_first=False,
        name=None,
    ):
        super(BasicBlock, self).__init__()
        self.stride = stride
        self.scale = 1
        self.conv0 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=stride,
            act='relu',
        )
        self.conv1 = ConvBNLayer(in_channels=out_channels,
                                 out_channels=out_channels,
                                 kernel_size=3,
                                 act=None)

        if not shortcut:
            self.short = ConvBNLayer(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=1,
                stride=stride,
                is_vd_mode=not if_first and stride[0] != 1,
            )

        self.shortcut = shortcut
        self.out_channels = out_channels * self.scale

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
        y = short + conv1
        y = F.relu(y)
        return y


class ResNet(nn.Module):

    def __init__(self, in_channels=3, layers=50, **kwargs):
        super(ResNet, self).__init__()

        self.layers = layers
        supported_layers = [18, 34, 50, 101, 152, 200]
        assert layers in supported_layers, 'supported layers are {} but input layer is {}'.format(
            supported_layers, layers)

        if layers == 18:
            depth = [2, 2, 2, 2]
        elif layers == 34 or layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        elif layers == 200:
            depth = [3, 12, 48, 3]

        if layers >= 50:
            block_class = BottleneckBlock
        else:
            block_class = BasicBlock
        num_filters = [64, 128, 256, 512]

        self.conv1_1 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=32,
            kernel_size=3,
            stride=1,
            act='relu',
        )
        self.conv1_2 = ConvBNLayer(in_channels=32,
                                   out_channels=32,
                                   kernel_size=3,
                                   stride=1,
                                   act='relu')
        self.conv1_3 = ConvBNLayer(in_channels=32,
                                   out_channels=64,
                                   kernel_size=3,
                                   stride=1,
                                   act='relu')
        self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # self.block_list = list()
        self.block_list = nn.Sequential()
        in_channels = 64
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                if layers in [101, 152, 200] and block == 2:
                    if i == 0:
                        conv_name = 'res' + str(block + 2) + 'a'
                    else:
                        conv_name = 'res' + str(block + 2) + 'b' + str(i)
                else:
                    conv_name = 'res' + str(block + 2) + chr(97 + i)

                if i == 0 and block != 0:
                    stride = (2, 1)
                else:
                    stride = (1, 1)

                block_instance = block_class(
                    in_channels=in_channels,
                    out_channels=num_filters[block],
                    stride=stride,
                    shortcut=shortcut,
                    if_first=block == i == 0,
                    name=conv_name,
                )
                shortcut = True
                in_channels = block_instance.out_channels
                # self.block_list.append(bottleneck_block)
                self.block_list.add_module('bb_%d_%d' % (block, i),
                                           block_instance)
            self.out_channels = num_filters[block]
        self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)

    def forward(self, inputs):
        y = self.conv1_1(inputs)
        y = self.conv1_2(y)
        y = self.conv1_3(y)
        y = self.pool2d_max(y)
        for block in self.block_list:
            y = block(y)
        y = self.out_pool(y)

        return y