AIStoryBoard/python/PaddleOCR-main/ppocr/modeling/backbones/rec_vit_parseq.py

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
This code is refer from:
https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.5/ppcls/arch/backbone/model_zoo/vision_transformer.py
"""

from collections.abc import Callable

import numpy as np
import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal


trunc_normal_ = TruncatedNormal(std=0.02)
normal_ = Normal
zeros_ = Constant(value=0.0)
ones_ = Constant(value=1.0)


def to_2tuple(x):
    return tuple([x] * 2)


def drop_path(x, drop_prob=0.0, 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 == 0.0 or not training:
        return x
    keep_prob = paddle.to_tensor(1 - drop_prob, dtype=x.dtype)
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + paddle.rand(shape).astype(x.dtype)
    random_tensor = paddle.floor(random_tensor)  # binarize
    output = x.divide(keep_prob) * random_tensor
    return output


class DropPath(nn.Layer):
    """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.Layer):
    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, input):
        return input


class Mlp(nn.Layer):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        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 Attention(nn.Layer):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
    ):
        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_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        # B= x.shape[0]
        N, C = x.shape[1:]
        qkv = (
            self.qkv(x)
            .reshape((-1, N, 3, self.num_heads, C // self.num_heads))
            .transpose((2, 0, 3, 1, 4))
        )
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
        attn = nn.functional.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)

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


class Block(nn.Layer):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer="nn.LayerNorm",
        epsilon=1e-5,
    ):
        super().__init__()
        if isinstance(norm_layer, str):
            self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
        elif isinstance(norm_layer, Callable):
            self.norm1 = norm_layer(dim)
        else:
            raise TypeError("The norm_layer must be str or paddle.nn.layer.Layer class")
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
        if isinstance(norm_layer, str):
            self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
        elif isinstance(norm_layer, Callable):
            self.norm2 = norm_layer(dim)
        else:
            raise TypeError("The norm_layer must be str or paddle.nn.layer.Layer class")
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


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

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        if isinstance(img_size, int):
            img_size = to_2tuple(img_size)
        if isinstance(patch_size, int):
            patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2D(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
        )

    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).transpose((0, 2, 1))
        return x


class VisionTransformer(nn.Layer):
    """Vision Transformer with support for patch input"""

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_channels=3,
        class_num=1000,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        qkv_bias=False,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer="nn.LayerNorm",
        epsilon=1e-5,
        **kwargs,
    ):
        super().__init__()
        self.class_num = class_num

        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_channels,
            embed_dim=embed_dim,
        )
        num_patches = self.patch_embed.num_patches

        self.pos_embed = self.create_parameter(
            shape=(1, num_patches, embed_dim), default_initializer=zeros_
        )
        self.add_parameter("pos_embed", self.pos_embed)
        self.cls_token = self.create_parameter(
            shape=(1, 1, embed_dim), default_initializer=zeros_
        )
        self.add_parameter("cls_token", self.cls_token)
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = np.linspace(0, drop_path_rate, depth)

        self.blocks = nn.LayerList(
            [
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    epsilon=epsilon,
                )
                for i in range(depth)
            ]
        )

        self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)

        # Classifier head
        self.head = nn.Linear(embed_dim, class_num) if class_num > 0 else Identity()

        trunc_normal_(self.pos_embed)
        self.out_channels = embed_dim
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight)
            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):
        B = x.shape[0]
        x = self.patch_embed(x)
        x = x + self.pos_embed
        x = self.pos_drop(x)
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


class ViTParseQ(VisionTransformer):
    def __init__(
        self,
        img_size=[224, 224],
        patch_size=[16, 16],
        in_channels=3,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
    ):
        super().__init__(
            img_size,
            patch_size,
            in_channels,
            embed_dim=embed_dim,
            depth=depth,
            num_heads=num_heads,
            mlp_ratio=mlp_ratio,
            qkv_bias=qkv_bias,
            drop_rate=drop_rate,
            attn_drop_rate=attn_drop_rate,
            drop_path_rate=drop_path_rate,
            class_num=0,
        )

    def forward(self, x):
        return self.forward_features(x)