OpenOCR/opendet/modeling/necks/db_fpn.py

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


class SEModule(nn.Module):

    def __init__(self, in_channels, reduction=4):
        super(SEModule, self).__init__()

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=in_channels // reduction,
            kernel_size=1,
            stride=1,
            padding=0,
        )
        self.conv2 = nn.Conv2d(
            in_channels=in_channels // reduction,
            out_channels=in_channels,
            kernel_size=1,
            stride=1,
            padding=0,
        )

    def forward(self, inputs):
        outputs = self.avg_pool(inputs)
        outputs = self.conv1(outputs)
        outputs = F.relu(outputs)
        outputs = self.conv2(outputs)
        outputs = F.hardsigmoid(outputs)
        return inputs * outputs


class IntraCLBlock(nn.Module):

    def __init__(self, in_channels=96, reduce_factor=4):
        super(IntraCLBlock, self).__init__()
        self.channels = in_channels
        self.rf = reduce_factor
        # weight_attr = paddle.nn.initializer.KaimingUniform()
        self.conv1x1_reduce_channel = nn.Conv2d(self.channels,
                                                self.channels // self.rf,
                                                kernel_size=1,
                                                stride=1,
                                                padding=0)
        self.conv1x1_return_channel = nn.Conv2d(self.channels // self.rf,
                                                self.channels,
                                                kernel_size=1,
                                                stride=1,
                                                padding=0)

        self.v_layer_7x1 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(7, 1),
            stride=(1, 1),
            padding=(3, 0),
        )
        self.v_layer_5x1 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(5, 1),
            stride=(1, 1),
            padding=(2, 0),
        )
        self.v_layer_3x1 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(3, 1),
            stride=(1, 1),
            padding=(1, 0),
        )

        self.q_layer_1x7 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(1, 7),
            stride=(1, 1),
            padding=(0, 3),
        )
        self.q_layer_1x5 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(1, 5),
            stride=(1, 1),
            padding=(0, 2),
        )
        self.q_layer_1x3 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(1, 3),
            stride=(1, 1),
            padding=(0, 1),
        )

        # base
        self.c_layer_7x7 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(7, 7),
            stride=(1, 1),
            padding=(3, 3),
        )
        self.c_layer_5x5 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(5, 5),
            stride=(1, 1),
            padding=(2, 2),
        )
        self.c_layer_3x3 = nn.Conv2d(
            self.channels // self.rf,
            self.channels // self.rf,
            kernel_size=(3, 3),
            stride=(1, 1),
            padding=(1, 1),
        )

        self.bn = nn.BatchNorm2d(self.channels)
        self.relu = nn.ReLU()

    def forward(self, x):
        x_new = self.conv1x1_reduce_channel(x)

        x_7_c = self.c_layer_7x7(x_new)
        x_7_v = self.v_layer_7x1(x_new)
        x_7_q = self.q_layer_1x7(x_new)
        x_7 = x_7_c + x_7_v + x_7_q

        x_5_c = self.c_layer_5x5(x_7)
        x_5_v = self.v_layer_5x1(x_7)
        x_5_q = self.q_layer_1x5(x_7)
        x_5 = x_5_c + x_5_v + x_5_q

        x_3_c = self.c_layer_3x3(x_5)
        x_3_v = self.v_layer_3x1(x_5)
        x_3_q = self.q_layer_1x3(x_5)
        x_3 = x_3_c + x_3_v + x_3_q

        x_relation = self.conv1x1_return_channel(x_3)

        x_relation = self.bn(x_relation)
        x_relation = self.relu(x_relation)

        return x + x_relation


class DSConv(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        padding,
        stride=1,
        groups=None,
        if_act=True,
        act='relu',
        **kwargs,
    ):
        super(DSConv, self).__init__()
        if groups is None:
            groups = in_channels
        self.if_act = if_act
        self.act = act
        self.conv1 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            bias=False,
        )

        self.bn1 = nn.BatchNorm2d(num_features=in_channels)

        self.conv2 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=int(in_channels * 4),
            kernel_size=1,
            stride=1,
            bias=False,
        )

        self.bn2 = nn.BatchNorm2d(num_features=int(in_channels * 4))

        self.conv3 = nn.Conv2d(
            in_channels=int(in_channels * 4),
            out_channels=out_channels,
            kernel_size=1,
            stride=1,
            bias=False,
        )
        self._c = [in_channels, out_channels]
        if in_channels != out_channels:
            self.conv_end = nn.Conv2d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=1,
                stride=1,
                bias=False,
            )

    def forward(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        if self.if_act:
            if self.act == 'relu':
                x = F.relu(x)
            elif self.act == 'hardswish':
                x = F.hardswish(x)
            else:
                print('The activation function({}) is selected incorrectly.'.
                      format(self.act))
                exit()

        x = self.conv3(x)
        if self._c[0] != self._c[1]:
            x = x + self.conv_end(inputs)
        return x


class DBFPN(nn.Module):

    def __init__(self, in_channels, out_channels, use_asf=False, **kwargs):
        super(DBFPN, self).__init__()
        self.out_channels = out_channels
        self.use_asf = use_asf
        # weight_attr = paddle.nn.initializer.KaimingUniform()

        self.in2_conv = nn.Conv2d(
            in_channels=in_channels[0],
            out_channels=self.out_channels,
            kernel_size=1,
            bias=False,
        )
        self.in3_conv = nn.Conv2d(
            in_channels=in_channels[1],
            out_channels=self.out_channels,
            kernel_size=1,
            bias=False,
        )
        self.in4_conv = nn.Conv2d(
            in_channels=in_channels[2],
            out_channels=self.out_channels,
            kernel_size=1,
            bias=False,
        )
        self.in5_conv = nn.Conv2d(
            in_channels=in_channels[3],
            out_channels=self.out_channels,
            kernel_size=1,
            bias=False,
        )
        self.p5_conv = nn.Conv2d(
            in_channels=self.out_channels,
            out_channels=self.out_channels // 4,
            kernel_size=3,
            padding=1,
            bias=False,
        )
        self.p4_conv = nn.Conv2d(
            in_channels=self.out_channels,
            out_channels=self.out_channels // 4,
            kernel_size=3,
            padding=1,
            bias=False,
        )
        self.p3_conv = nn.Conv2d(
            in_channels=self.out_channels,
            out_channels=self.out_channels // 4,
            kernel_size=3,
            padding=1,
            bias=False,
        )
        self.p2_conv = nn.Conv2d(
            in_channels=self.out_channels,
            out_channels=self.out_channels // 4,
            kernel_size=3,
            padding=1,
            bias=False,
        )

        if self.use_asf is True:
            self.asf = ASFBlock(self.out_channels, self.out_channels // 4)

    def forward(self, x):
        c2, c3, c4, c5 = x

        in5 = self.in5_conv(c5)
        in4 = self.in4_conv(c4)
        in3 = self.in3_conv(c3)
        in2 = self.in2_conv(c2)

        out4 = in4 + F.interpolate(
            in5, scale_factor=2, mode='nearest', align_corners=None)  # 1/16
        out3 = in3 + F.interpolate(
            out4, scale_factor=2, mode='nearest', align_corners=None)  # 1/8
        out2 = in2 + F.interpolate(
            out3, scale_factor=2, mode='nearest', align_corners=None)  # 1/4

        p5 = self.p5_conv(in5)
        p4 = self.p4_conv(out4)
        p3 = self.p3_conv(out3)
        p2 = self.p2_conv(out2)
        p5 = F.interpolate(p5,
                           scale_factor=8,
                           mode='nearest',
                           align_corners=None)
        p4 = F.interpolate(p4,
                           scale_factor=4,
                           mode='nearest',
                           align_corners=None)
        p3 = F.interpolate(p3,
                           scale_factor=2,
                           mode='nearest',
                           align_corners=None)

        fuse = torch.concat([p5, p4, p3, p2], dim=1)

        if self.use_asf is True:
            fuse = self.asf(fuse, [p5, p4, p3, p2])

        return fuse


class RSELayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, shortcut=True):
        super(RSELayer, self).__init__()
        # weight_attr = paddle.nn.initializer.KaimingUniform()
        self.out_channels = out_channels
        self.in_conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=self.out_channels,
            kernel_size=kernel_size,
            padding=int(kernel_size // 2),
            # weight_attr=ParamAttr(initializer=weight_attr),
            bias=False,
        )
        self.se_block = SEModule(self.out_channels)
        self.shortcut = shortcut

    def forward(self, ins):
        x = self.in_conv(ins)
        if self.shortcut:
            out = x + self.se_block(x)
        else:
            out = self.se_block(x)
        return out


class RSEFPN(nn.Module):

    def __init__(self, in_channels, out_channels, shortcut=True, **kwargs):
        super(RSEFPN, self).__init__()
        self.out_channels = out_channels
        self.ins_conv = nn.ModuleList()
        self.inp_conv = nn.ModuleList()
        self.intracl = False
        if 'intracl' in kwargs.keys() and kwargs['intracl'] is True:
            self.intracl = kwargs['intracl']
            self.incl1 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)
            self.incl2 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)
            self.incl3 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)
            self.incl4 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)

        for i in range(len(in_channels)):
            self.ins_conv.append(
                RSELayer(in_channels[i],
                         out_channels,
                         kernel_size=1,
                         shortcut=shortcut))
            self.inp_conv.append(
                RSELayer(out_channels,
                         out_channels // 4,
                         kernel_size=3,
                         shortcut=shortcut))

    def forward(self, x):
        c2, c3, c4, c5 = x

        in5 = self.ins_conv[3](c5)
        in4 = self.ins_conv[2](c4)
        in3 = self.ins_conv[1](c3)
        in2 = self.ins_conv[0](c2)

        out4 = in4 + F.interpolate(
            in5, scale_factor=2, mode='nearest', align_corners=None)  # 1/16
        out3 = in3 + F.interpolate(
            out4, scale_factor=2, mode='nearest', align_corners=None)  # 1/8
        out2 = in2 + F.interpolate(
            out3, scale_factor=2, mode='nearest', align_corners=None)  # 1/4

        p5 = self.inp_conv[3](in5)
        p4 = self.inp_conv[2](out4)
        p3 = self.inp_conv[1](out3)
        p2 = self.inp_conv[0](out2)

        if self.intracl is True:
            p5 = self.incl4(p5)
            p4 = self.incl3(p4)
            p3 = self.incl2(p3)
            p2 = self.incl1(p2)

        p5 = F.interpolate(p5,
                           scale_factor=8,
                           mode='nearest',
                           align_corners=None)
        p4 = F.interpolate(p4,
                           scale_factor=4,
                           mode='nearest',
                           align_corners=None)
        p3 = F.interpolate(p3,
                           scale_factor=2,
                           mode='nearest',
                           align_corners=None)

        fuse = torch.concat([p5, p4, p3, p2], dim=1)
        return fuse


class LKPAN(nn.Module):

    def __init__(self, in_channels, out_channels, mode='large', **kwargs):
        super(LKPAN, self).__init__()
        self.out_channels = out_channels
        # weight_attr = paddle.nn.initializer.KaimingUniform()

        self.ins_conv = nn.ModuleList()
        self.inp_conv = nn.ModuleList()
        # pan head
        self.pan_head_conv = nn.ModuleList()
        self.pan_lat_conv = nn.ModuleList()

        if mode.lower() == 'lite':
            p_layer = DSConv
        elif mode.lower() == 'large':
            p_layer = nn.Conv2D
        else:
            raise ValueError(
                "mode can only be one of ['lite', 'large'], but received {}".
                format(mode))

        for i in range(len(in_channels)):
            self.ins_conv.append(
                nn.Conv2d(
                    in_channels=in_channels[i],
                    out_channels=self.out_channels,
                    kernel_size=1,
                    bias=False,
                ))

            self.inp_conv.append(
                p_layer(
                    in_channels=self.out_channels,
                    out_channels=self.out_channels // 4,
                    kernel_size=9,
                    padding=4,
                    bias=False,
                ))

            if i > 0:
                self.pan_head_conv.append(
                    nn.Conv2d(
                        in_channels=self.out_channels // 4,
                        out_channels=self.out_channels // 4,
                        kernel_size=3,
                        padding=1,
                        stride=2,
                        bias=False,
                    ))
            self.pan_lat_conv.append(
                p_layer(
                    in_channels=self.out_channels // 4,
                    out_channels=self.out_channels // 4,
                    kernel_size=9,
                    padding=4,
                    bias=False,
                ))

        self.intracl = False
        if 'intracl' in kwargs.keys() and kwargs['intracl'] is True:
            self.intracl = kwargs['intracl']
            self.incl1 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)
            self.incl2 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)
            self.incl3 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)
            self.incl4 = IntraCLBlock(self.out_channels // 4, reduce_factor=2)

    def forward(self, x):
        c2, c3, c4, c5 = x

        in5 = self.ins_conv[3](c5)
        in4 = self.ins_conv[2](c4)
        in3 = self.ins_conv[1](c3)
        in2 = self.ins_conv[0](c2)

        out4 = in4 + F.interpolate(
            in5, scale_factor=2, mode='nearest', align_corners=None)  # 1/16
        out3 = in3 + F.interpolate(
            out4, scale_factor=2, mode='nearest', align_corners=None)  # 1/8
        out2 = in2 + F.interpolate(
            out3, scale_factor=2, mode='nearest', align_corners=None)  # 1/4

        f5 = self.inp_conv[3](in5)
        f4 = self.inp_conv[2](out4)
        f3 = self.inp_conv[1](out3)
        f2 = self.inp_conv[0](out2)

        pan3 = f3 + self.pan_head_conv[0](f2)
        pan4 = f4 + self.pan_head_conv[1](pan3)
        pan5 = f5 + self.pan_head_conv[2](pan4)

        p2 = self.pan_lat_conv[0](f2)
        p3 = self.pan_lat_conv[1](pan3)
        p4 = self.pan_lat_conv[2](pan4)
        p5 = self.pan_lat_conv[3](pan5)

        if self.intracl is True:
            p5 = self.incl4(p5)
            p4 = self.incl3(p4)
            p3 = self.incl2(p3)
            p2 = self.incl1(p2)

        p5 = F.interpolate(p5,
                           scale_factor=8,
                           mode='nearest',
                           align_corners=None)
        p4 = F.interpolate(p4,
                           scale_factor=4,
                           mode='nearest',
                           align_corners=None)
        p3 = F.interpolate(p3,
                           scale_factor=2,
                           mode='nearest',
                           align_corners=None)

        fuse = torch.concat([p5, p4, p3, p2], dim=1)
        return fuse


class ASFBlock(nn.Module):
    """
    This code is refered from:
        https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py
    """

    def __init__(self, in_channels, inter_channels, out_features_num=4):
        """
        Adaptive Scale Fusion (ASF) block of DBNet++
        Args:
            in_channels: the number of channels in the input data
            inter_channels: the number of middle channels
            out_features_num: the number of fused stages
        """
        super(ASFBlock, self).__init__()
        # weight_attr = paddle.nn.initializer.KaimingUniform()
        self.in_channels = in_channels
        self.inter_channels = inter_channels
        self.out_features_num = out_features_num
        self.conv = nn.Conv2d(in_channels, inter_channels, 3, padding=1)

        self.spatial_scale = nn.Sequential(
            # Nx1xHxW
            nn.Conv2d(
                in_channels=1,
                out_channels=1,
                kernel_size=3,
                bias=False,
                padding=1,
            ),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=1,
                out_channels=1,
                kernel_size=1,
                bias=False,
            ),
            nn.Sigmoid(),
        )

        self.channel_scale = nn.Sequential(
            nn.Conv2d(
                in_channels=inter_channels,
                out_channels=out_features_num,
                kernel_size=1,
                bias=False,
            ),
            nn.Sigmoid(),
        )

    def forward(self, fuse_features, features_list):
        fuse_features = self.conv(fuse_features)
        spatial_x = torch.mean(fuse_features, dim=1, keepdim=True)
        attention_scores = self.spatial_scale(spatial_x) + fuse_features
        attention_scores = self.channel_scale(attention_scores)
        assert len(features_list) == self.out_features_num

        out_list = []
        for i in range(self.out_features_num):
            out_list.append(attention_scores[:, i:i + 1] * features_list[i])
        return torch.concat(out_list, dim=1)