text_embedding_module/OCR/ocr_recog/RecMv1_enhance.py

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

from .common import Activation


class ConvBNLayer(nn.Module):
    def __init__(
        self, num_channels, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act="hard_swish"
    ):
        super(ConvBNLayer, self).__init__()
        self.act = act
        self._conv = nn.Conv2d(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=padding,
            groups=num_groups,
            bias=False,
        )

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

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


class DepthwiseSeparable(nn.Module):
    def __init__(
        self, num_channels, num_filters1, num_filters2, num_groups, stride, scale, dw_size=3, padding=1, use_se=False
    ):
        super(DepthwiseSeparable, self).__init__()
        self.use_se = use_se
        self._depthwise_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=int(num_filters1 * scale),
            filter_size=dw_size,
            stride=stride,
            padding=padding,
            num_groups=int(num_groups * scale),
        )
        if use_se:
            self._se = SEModule(int(num_filters1 * scale))
        self._pointwise_conv = ConvBNLayer(
            num_channels=int(num_filters1 * scale),
            filter_size=1,
            num_filters=int(num_filters2 * scale),
            stride=1,
            padding=0,
        )

    def forward(self, inputs):
        y = self._depthwise_conv(inputs)
        if self.use_se:
            y = self._se(y)
        y = self._pointwise_conv(y)
        return y


class MobileNetV1Enhance(nn.Module):
    def __init__(self, in_channels=3, scale=0.5, last_conv_stride=1, last_pool_type="max", **kwargs):
        super().__init__()
        self.scale = scale
        self.block_list = []

        self.conv1 = ConvBNLayer(
            num_channels=in_channels, filter_size=3, channels=3, num_filters=int(32 * scale), stride=2, padding=1
        )

        conv2_1 = DepthwiseSeparable(
            num_channels=int(32 * scale), num_filters1=32, num_filters2=64, num_groups=32, stride=1, scale=scale
        )
        self.block_list.append(conv2_1)

        conv2_2 = DepthwiseSeparable(
            num_channels=int(64 * scale), num_filters1=64, num_filters2=128, num_groups=64, stride=1, scale=scale
        )
        self.block_list.append(conv2_2)

        conv3_1 = DepthwiseSeparable(
            num_channels=int(128 * scale), num_filters1=128, num_filters2=128, num_groups=128, stride=1, scale=scale
        )
        self.block_list.append(conv3_1)

        conv3_2 = DepthwiseSeparable(
            num_channels=int(128 * scale),
            num_filters1=128,
            num_filters2=256,
            num_groups=128,
            stride=(2, 1),
            scale=scale,
        )
        self.block_list.append(conv3_2)

        conv4_1 = DepthwiseSeparable(
            num_channels=int(256 * scale), num_filters1=256, num_filters2=256, num_groups=256, stride=1, scale=scale
        )
        self.block_list.append(conv4_1)

        conv4_2 = DepthwiseSeparable(
            num_channels=int(256 * scale),
            num_filters1=256,
            num_filters2=512,
            num_groups=256,
            stride=(2, 1),
            scale=scale,
        )
        self.block_list.append(conv4_2)

        for _ in range(5):
            conv5 = DepthwiseSeparable(
                num_channels=int(512 * scale),
                num_filters1=512,
                num_filters2=512,
                num_groups=512,
                stride=1,
                dw_size=5,
                padding=2,
                scale=scale,
                use_se=False,
            )
            self.block_list.append(conv5)

        conv5_6 = DepthwiseSeparable(
            num_channels=int(512 * scale),
            num_filters1=512,
            num_filters2=1024,
            num_groups=512,
            stride=(2, 1),
            dw_size=5,
            padding=2,
            scale=scale,
            use_se=True,
        )
        self.block_list.append(conv5_6)

        conv6 = DepthwiseSeparable(
            num_channels=int(1024 * scale),
            num_filters1=1024,
            num_filters2=1024,
            num_groups=1024,
            stride=last_conv_stride,
            dw_size=5,
            padding=2,
            use_se=True,
            scale=scale,
        )
        self.block_list.append(conv6)

        self.block_list = nn.Sequential(*self.block_list)
        if last_pool_type == "avg":
            self.pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
        else:
            self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.out_channels = int(1024 * scale)

    def forward(self, inputs):
        y = self.conv1(inputs)
        y = self.block_list(y)
        y = self.pool(y)
        return y


def hardsigmoid(x):
    return F.relu6(x + 3.0, inplace=True) / 6.0


class SEModule(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(
            in_channels=channel, out_channels=channel // reduction, kernel_size=1, stride=1, padding=0, bias=True
        )
        self.conv2 = nn.Conv2d(
            in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1, padding=0, bias=True
        )

    def forward(self, inputs):
        outputs = self.avg_pool(inputs)
        outputs = self.conv1(outputs)
        outputs = F.relu(outputs)
        outputs = self.conv2(outputs)
        outputs = hardsigmoid(outputs)
        x = torch.mul(inputs, outputs)

        return x