MinerU/mineru/model/ocr/paddleocr2pytorch/pytorchocr/modeling/necks/rnn.py

import torch
from torch import nn

from ..backbones.rec_svtrnet import Block, ConvBNLayer


class Im2Seq(nn.Module):
    def __init__(self, in_channels, **kwargs):
        super().__init__()
        self.out_channels = in_channels

    # def forward(self, x):
    #     B, C, H, W = x.shape
    #     # assert H == 1
    #     x = x.squeeze(dim=2)
    #     # x = x.transpose([0, 2, 1])  # paddle (NTC)(batch, width, channels)
    #     x = x.permute(0, 2, 1)
    #     return x

    def forward(self, x):
        B, C, H, W = x.shape
        # 处理四维张量，将空间维度展平为序列
        if H == 1:
            # 原来的处理逻辑，适用于H=1的情况
            x = x.squeeze(dim=2)
            x = x.permute(0, 2, 1)  # (B, W, C)
        else:
            # 处理H不为1的情况
            x = x.permute(0, 2, 3, 1)  # (B, H, W, C)
            x = x.reshape(B, H * W, C)  # (B, H*W, C)

        return x

class EncoderWithRNN_(nn.Module):
    def __init__(self, in_channels, hidden_size):
        super(EncoderWithRNN_, self).__init__()
        self.out_channels = hidden_size * 2
        self.rnn1 = nn.LSTM(
            in_channels,
            hidden_size,
            bidirectional=False,
            batch_first=True,
            num_layers=2,
        )
        self.rnn2 = nn.LSTM(
            in_channels,
            hidden_size,
            bidirectional=False,
            batch_first=True,
            num_layers=2,
        )

    def forward(self, x):
        self.rnn1.flatten_parameters()
        self.rnn2.flatten_parameters()
        out1, h1 = self.rnn1(x)
        out2, h2 = self.rnn2(torch.flip(x, [1]))
        return torch.cat([out1, torch.flip(out2, [1])], 2)


class EncoderWithRNN(nn.Module):
    def __init__(self, in_channels, hidden_size):
        super(EncoderWithRNN, self).__init__()
        self.out_channels = hidden_size * 2
        self.lstm = nn.LSTM(
            in_channels, hidden_size, num_layers=2, batch_first=True, bidirectional=True
        )  # batch_first:=True

    def forward(self, x):
        x, _ = self.lstm(x)
        return x


class EncoderWithFC(nn.Module):
    def __init__(self, in_channels, hidden_size):
        super(EncoderWithFC, self).__init__()
        self.out_channels = hidden_size
        self.fc = nn.Linear(
            in_channels,
            hidden_size,
            bias=True,
        )

    def forward(self, x):
        x = self.fc(x)
        return x


class EncoderWithSVTR(nn.Module):
    def __init__(
        self,
        in_channels,
        dims=64,  # XS
        depth=2,
        hidden_dims=120,
        use_guide=False,
        num_heads=8,
        qkv_bias=True,
        mlp_ratio=2.0,
        drop_rate=0.1,
        kernel_size=[3, 3],
        attn_drop_rate=0.1,
        drop_path=0.0,
        qk_scale=None,
    ):
        super(EncoderWithSVTR, self).__init__()
        self.depth = depth
        self.use_guide = use_guide
        self.conv1 = ConvBNLayer(
            in_channels,
            in_channels // 8,
            kernel_size=kernel_size,
            padding=[kernel_size[0] // 2, kernel_size[1] // 2],
            act="swish",
        )
        self.conv2 = ConvBNLayer(
            in_channels // 8, hidden_dims, kernel_size=1, act="swish"
        )

        self.svtr_block = nn.ModuleList(
            [
                Block(
                    dim=hidden_dims,
                    num_heads=num_heads,
                    mixer="Global",
                    HW=None,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    act_layer="swish",
                    attn_drop=attn_drop_rate,
                    drop_path=drop_path,
                    norm_layer="nn.LayerNorm",
                    epsilon=1e-05,
                    prenorm=False,
                )
                for i in range(depth)
            ]
        )
        self.norm = nn.LayerNorm(hidden_dims, eps=1e-6)
        self.conv3 = ConvBNLayer(hidden_dims, in_channels, kernel_size=1, act="swish")
        # last conv-nxn, the input is concat of input tensor and conv3 output tensor
        self.conv4 = ConvBNLayer(
            2 * in_channels, in_channels // 8, padding=1, act="swish"
        )

        self.conv1x1 = ConvBNLayer(in_channels // 8, dims, kernel_size=1, act="swish")
        self.out_channels = dims
        self.apply(self._init_weights)

    def _init_weights(self, m):
        # weight initialization
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode="fan_out")
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.ones_(m.weight)
            nn.init.zeros_(m.bias)
        elif isinstance(m, nn.Linear):
            nn.init.normal_(m.weight, 0, 0.01)
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        elif isinstance(m, nn.ConvTranspose2d):
            nn.init.kaiming_normal_(m.weight, mode="fan_out")
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        elif isinstance(m, nn.LayerNorm):
            nn.init.ones_(m.weight)
            nn.init.zeros_(m.bias)

    def forward(self, x):
        # for use guide
        if self.use_guide:
            z = x.clone()
            z.stop_gradient = True
        else:
            z = x
        # for short cut
        h = z
        # reduce dim
        z = self.conv1(z)
        z = self.conv2(z)
        # SVTR global block
        B, C, H, W = z.shape
        z = z.flatten(2).permute(0, 2, 1)

        for blk in self.svtr_block:
            z = blk(z)

        z = self.norm(z)
        # last stage
        z = z.reshape([-1, H, W, C]).permute(0, 3, 1, 2)
        z = self.conv3(z)
        z = torch.cat((h, z), dim=1)
        z = self.conv1x1(self.conv4(z))

        return z


class SequenceEncoder(nn.Module):
    def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs):
        super(SequenceEncoder, self).__init__()
        self.encoder_reshape = Im2Seq(in_channels)
        self.out_channels = self.encoder_reshape.out_channels
        self.encoder_type = encoder_type
        if encoder_type == "reshape":
            self.only_reshape = True
        else:
            support_encoder_dict = {
                "reshape": Im2Seq,
                "fc": EncoderWithFC,
                "rnn": EncoderWithRNN,
                "svtr": EncoderWithSVTR,
            }
            assert encoder_type in support_encoder_dict, "{} must in {}".format(
                encoder_type, support_encoder_dict.keys()
            )

            if encoder_type == "svtr":
                self.encoder = support_encoder_dict[encoder_type](
                    self.encoder_reshape.out_channels, **kwargs
                )
            else:
                self.encoder = support_encoder_dict[encoder_type](
                    self.encoder_reshape.out_channels, hidden_size
                )
            self.out_channels = self.encoder.out_channels
            self.only_reshape = False

    def forward(self, x):
        if self.encoder_type != "svtr":
            x = self.encoder_reshape(x)
            if not self.only_reshape:
                x = self.encoder(x)
            return x
        else:
            x = self.encoder(x)
            x = self.encoder_reshape(x)
            return x