iopaint/model/anytext/ocr_recog/RecSVTR.py

import torch
import torch.nn as nn
import numpy as np
from torch.nn.init import trunc_normal_, zeros_, ones_
from torch.nn import functional


def drop_path(x, drop_prob=0., training=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = torch.tensor(1 - drop_prob)
    shape = (x.size()[0], ) + (1, ) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype)
    random_tensor = torch.floor(random_tensor)  # binarize
    output = x.divide(keep_prob) * random_tensor
    return output


class Swish(nn.Module):
    def __int__(self):
        super(Swish, self).__int__()

    def forward(self,x):
        return x*torch.sigmoid(x)


class ConvBNLayer(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=0,
                 bias_attr=False,
                 groups=1,
                 act=nn.GELU):
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            # weight_attr=paddle.ParamAttr(initializer=nn.initializer.KaimingUniform()),
            bias=bias_attr)
        self.norm = nn.BatchNorm2d(out_channels)
        self.act = act()

    def forward(self, inputs):
        out = self.conv(inputs)
        out = self.norm(out)
        out = self.act(out)
        return out


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Identity(nn.Module):
    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, input):
        return input


class Mlp(nn.Module):
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        if isinstance(act_layer, str):
            self.act = Swish()
        else:
            self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class ConvMixer(nn.Module):
    def __init__(
            self,
            dim,
            num_heads=8,
            HW=(8, 25),
            local_k=(3, 3), ):
        super().__init__()
        self.HW = HW
        self.dim = dim
        self.local_mixer = nn.Conv2d(
            dim,
            dim,
            local_k,
            1, (local_k[0] // 2, local_k[1] // 2),
            groups=num_heads,
            # weight_attr=ParamAttr(initializer=KaimingNormal())
        )

    def forward(self, x):
        h = self.HW[0]
        w = self.HW[1]
        x = x.transpose([0, 2, 1]).reshape([0, self.dim, h, w])
        x = self.local_mixer(x)
        x = x.flatten(2).transpose([0, 2, 1])
        return x


class Attention(nn.Module):
    def __init__(self,
                 dim,
                 num_heads=8,
                 mixer='Global',
                 HW=(8, 25),
                 local_k=(7, 11),
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.HW = HW
        if HW is not None:
            H = HW[0]
            W = HW[1]
            self.N = H * W
            self.C = dim
        if mixer == 'Local' and HW is not None:
            hk = local_k[0]
            wk = local_k[1]
            mask = torch.ones([H * W, H + hk - 1, W + wk - 1])
            for h in range(0, H):
                for w in range(0, W):
                    mask[h * W + w, h:h + hk, w:w + wk] = 0.
            mask_paddle = mask[:, hk // 2:H + hk // 2, wk // 2:W + wk //
                               2].flatten(1)
            mask_inf = torch.full([H * W, H * W],fill_value=float('-inf'))
            mask = torch.where(mask_paddle < 1, mask_paddle, mask_inf)
            self.mask = mask[None,None,:]
            # self.mask = mask.unsqueeze([0, 1])
        self.mixer = mixer

    def forward(self, x):
        if self.HW is not None:
            N = self.N
            C = self.C
        else:
            _, N, C = x.shape
        qkv = self.qkv(x).reshape((-1, N, 3, self.num_heads, C //self.num_heads)).permute((2, 0, 3, 1, 4))
        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]

        attn = (q.matmul(k.permute((0, 1, 3, 2))))
        if self.mixer == 'Local':
            attn += self.mask
        attn = functional.softmax(attn, dim=-1)
        attn = self.attn_drop(attn)

        x = (attn.matmul(v)).permute((0, 2, 1, 3)).reshape((-1, N, C))
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(self,
                 dim,
                 num_heads,
                 mixer='Global',
                 local_mixer=(7, 11),
                 HW=(8, 25),
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer='nn.LayerNorm',
                 epsilon=1e-6,
                 prenorm=True):
        super().__init__()
        if isinstance(norm_layer, str):
            self.norm1 = eval(norm_layer)(dim, eps=epsilon)
        else:
            self.norm1 = norm_layer(dim)
        if mixer == 'Global' or mixer == 'Local':

            self.mixer = Attention(
                dim,
                num_heads=num_heads,
                mixer=mixer,
                HW=HW,
                local_k=local_mixer,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                proj_drop=drop)
        elif mixer == 'Conv':
            self.mixer = ConvMixer(
                dim, num_heads=num_heads, HW=HW, local_k=local_mixer)
        else:
            raise TypeError("The mixer must be one of [Global, Local, Conv]")

        self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
        if isinstance(norm_layer, str):
            self.norm2 = eval(norm_layer)(dim, eps=epsilon)
        else:
            self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp_ratio = mlp_ratio
        self.mlp = Mlp(in_features=dim,
                       hidden_features=mlp_hidden_dim,
                       act_layer=act_layer,
                       drop=drop)
        self.prenorm = prenorm

    def forward(self, x):
        if self.prenorm:
            x = self.norm1(x + self.drop_path(self.mixer(x)))
            x = self.norm2(x + self.drop_path(self.mlp(x)))
        else:
            x = x + self.drop_path(self.mixer(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """

    def __init__(self,
                 img_size=(32, 100),
                 in_channels=3,
                 embed_dim=768,
                 sub_num=2):
        super().__init__()
        num_patches = (img_size[1] // (2 ** sub_num)) * \
                      (img_size[0] // (2 ** sub_num))
        self.img_size = img_size
        self.num_patches = num_patches
        self.embed_dim = embed_dim
        self.norm = None
        if sub_num == 2:
            self.proj = nn.Sequential(
                ConvBNLayer(
                    in_channels=in_channels,
                    out_channels=embed_dim // 2,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    act=nn.GELU,
                    bias_attr=False),
                ConvBNLayer(
                    in_channels=embed_dim // 2,
                    out_channels=embed_dim,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    act=nn.GELU,
                    bias_attr=False))
        if sub_num == 3:
            self.proj = nn.Sequential(
                ConvBNLayer(
                    in_channels=in_channels,
                    out_channels=embed_dim // 4,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    act=nn.GELU,
                    bias_attr=False),
                ConvBNLayer(
                    in_channels=embed_dim // 4,
                    out_channels=embed_dim // 2,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    act=nn.GELU,
                    bias_attr=False),
                ConvBNLayer(
                    in_channels=embed_dim // 2,
                    out_channels=embed_dim,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    act=nn.GELU,
                    bias_attr=False))

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).permute(0, 2, 1)
        return x


class SubSample(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 types='Pool',
                 stride=(2, 1),
                 sub_norm='nn.LayerNorm',
                 act=None):
        super().__init__()
        self.types = types
        if types == 'Pool':
            self.avgpool = nn.AvgPool2d(
                kernel_size=(3, 5), stride=stride, padding=(1, 2))
            self.maxpool = nn.MaxPool2d(
                kernel_size=(3, 5), stride=stride, padding=(1, 2))
            self.proj = nn.Linear(in_channels, out_channels)
        else:
            self.conv = nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=3,
                stride=stride,
                padding=1,
                # weight_attr=ParamAttr(initializer=KaimingNormal())
            )
        self.norm = eval(sub_norm)(out_channels)
        if act is not None:
            self.act = act()
        else:
            self.act = None

    def forward(self, x):

        if self.types == 'Pool':
            x1 = self.avgpool(x)
            x2 = self.maxpool(x)
            x = (x1 + x2) * 0.5
            out = self.proj(x.flatten(2).permute((0, 2, 1)))
        else:
            x = self.conv(x)
            out = x.flatten(2).permute((0, 2, 1))
        out = self.norm(out)
        if self.act is not None:
            out = self.act(out)

        return out


class SVTRNet(nn.Module):
    def __init__(
            self,
            img_size=[48, 100],
            in_channels=3,
            embed_dim=[64, 128, 256],
            depth=[3, 6, 3],
            num_heads=[2, 4, 8],
            mixer=['Local'] * 6 + ['Global'] *
            6,  # Local atten, Global atten, Conv
            local_mixer=[[7, 11], [7, 11], [7, 11]],
            patch_merging='Conv',  # Conv, Pool, None
            mlp_ratio=4,
            qkv_bias=True,
            qk_scale=None,
            drop_rate=0.,
            last_drop=0.1,
            attn_drop_rate=0.,
            drop_path_rate=0.1,
            norm_layer='nn.LayerNorm',
            sub_norm='nn.LayerNorm',
            epsilon=1e-6,
            out_channels=192,
            out_char_num=25,
            block_unit='Block',
            act='nn.GELU',
            last_stage=True,
            sub_num=2,
            prenorm=True,
            use_lenhead=False,
            **kwargs):
        super().__init__()
        self.img_size = img_size
        self.embed_dim = embed_dim
        self.out_channels = out_channels
        self.prenorm = prenorm
        patch_merging = None if patch_merging != 'Conv' and patch_merging != 'Pool' else patch_merging
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            in_channels=in_channels,
            embed_dim=embed_dim[0],
            sub_num=sub_num)
        num_patches = self.patch_embed.num_patches
        self.HW = [img_size[0] // (2**sub_num), img_size[1] // (2**sub_num)]
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim[0]))
        # self.pos_embed = self.create_parameter(
        #     shape=[1, num_patches, embed_dim[0]], default_initializer=zeros_)

        # self.add_parameter("pos_embed", self.pos_embed)

        self.pos_drop = nn.Dropout(p=drop_rate)
        Block_unit = eval(block_unit)

        dpr = np.linspace(0, drop_path_rate, sum(depth))
        self.blocks1 = nn.ModuleList(
            [
            Block_unit(
                dim=embed_dim[0],
                num_heads=num_heads[0],
                mixer=mixer[0:depth[0]][i],
                HW=self.HW,
                local_mixer=local_mixer[0],
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                act_layer=eval(act),
                attn_drop=attn_drop_rate,
                drop_path=dpr[0:depth[0]][i],
                norm_layer=norm_layer,
                epsilon=epsilon,
                prenorm=prenorm) for i in range(depth[0])
        ]
        )
        if patch_merging is not None:
            self.sub_sample1 = SubSample(
                embed_dim[0],
                embed_dim[1],
                sub_norm=sub_norm,
                stride=[2, 1],
                types=patch_merging)
            HW = [self.HW[0] // 2, self.HW[1]]
        else:
            HW = self.HW
        self.patch_merging = patch_merging
        self.blocks2 = nn.ModuleList([
            Block_unit(
                dim=embed_dim[1],
                num_heads=num_heads[1],
                mixer=mixer[depth[0]:depth[0] + depth[1]][i],
                HW=HW,
                local_mixer=local_mixer[1],
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                act_layer=eval(act),
                attn_drop=attn_drop_rate,
                drop_path=dpr[depth[0]:depth[0] + depth[1]][i],
                norm_layer=norm_layer,
                epsilon=epsilon,
                prenorm=prenorm) for i in range(depth[1])
        ])
        if patch_merging is not None:
            self.sub_sample2 = SubSample(
                embed_dim[1],
                embed_dim[2],
                sub_norm=sub_norm,
                stride=[2, 1],
                types=patch_merging)
            HW = [self.HW[0] // 4, self.HW[1]]
        else:
            HW = self.HW
        self.blocks3 = nn.ModuleList([
            Block_unit(
                dim=embed_dim[2],
                num_heads=num_heads[2],
                mixer=mixer[depth[0] + depth[1]:][i],
                HW=HW,
                local_mixer=local_mixer[2],
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                act_layer=eval(act),
                attn_drop=attn_drop_rate,
                drop_path=dpr[depth[0] + depth[1]:][i],
                norm_layer=norm_layer,
                epsilon=epsilon,
                prenorm=prenorm) for i in range(depth[2])
        ])
        self.last_stage = last_stage
        if last_stage:
            self.avg_pool = nn.AdaptiveAvgPool2d((1, out_char_num))
            self.last_conv = nn.Conv2d(
                in_channels=embed_dim[2],
                out_channels=self.out_channels,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False)
            self.hardswish = nn.Hardswish()
            self.dropout = nn.Dropout(p=last_drop)
        if not prenorm:
            self.norm = eval(norm_layer)(embed_dim[-1], epsilon=epsilon)
        self.use_lenhead = use_lenhead
        if use_lenhead:
            self.len_conv = nn.Linear(embed_dim[2], self.out_channels)
            self.hardswish_len = nn.Hardswish()
            self.dropout_len = nn.Dropout(
                p=last_drop)

        trunc_normal_(self.pos_embed,std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight,std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                zeros_(m.bias)
        elif isinstance(m, nn.LayerNorm):
            zeros_(m.bias)
            ones_(m.weight)

    def forward_features(self, x):
        x = self.patch_embed(x)
        x = x + self.pos_embed
        x = self.pos_drop(x)
        for blk in self.blocks1:
            x = blk(x)
        if self.patch_merging is not None:
            x = self.sub_sample1(
                x.permute([0, 2, 1]).reshape(
                    [-1, self.embed_dim[0], self.HW[0], self.HW[1]]))
        for blk in self.blocks2:
            x = blk(x)
        if self.patch_merging is not None:
            x = self.sub_sample2(
                x.permute([0, 2, 1]).reshape(
                    [-1, self.embed_dim[1], self.HW[0] // 2, self.HW[1]]))
        for blk in self.blocks3:
            x = blk(x)
        if not self.prenorm:
            x = self.norm(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        if self.use_lenhead:
            len_x = self.len_conv(x.mean(1))
            len_x = self.dropout_len(self.hardswish_len(len_x))
        if self.last_stage:
            if self.patch_merging is not None:
                h = self.HW[0] // 4
            else:
                h = self.HW[0]
            x = self.avg_pool(
                x.permute([0, 2, 1]).reshape(
                    [-1, self.embed_dim[2], h, self.HW[1]]))
            x = self.last_conv(x)
            x = self.hardswish(x)
            x = self.dropout(x)
        if self.use_lenhead:
            return x, len_x
        return x


if __name__=="__main__":
    a = torch.rand(1,3,48,100)
    svtr = SVTRNet()

    out = svtr(a)
    print(svtr)
    print(out.size())