ppocr/modeling/backbones/rec_vitstr.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/roatienza/deep-text-recognition-benchmark/blob/master/modules/vitstr.py
"""

import numpy as np
import paddle
import paddle.nn as nn
from ppocr.modeling.backbones.rec_svtrnet import Block, PatchEmbed, zeros_, trunc_normal_, ones_

scale_dim_heads = {'tiny': [192, 3], 'small': [384, 6], 'base': [768, 12]}


class ViTSTR(nn.Layer):
    def __init__(self,
                 img_size=[224, 224],
                 in_channels=1,
                 scale='tiny',
                 seqlen=27,
                 patch_size=[16, 16],
                 embed_dim=None,
                 depth=12,
                 num_heads=None,
                 mlp_ratio=4,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_path_rate=0.,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 norm_layer='nn.LayerNorm',
                 act_layer='nn.GELU',
                 epsilon=1e-6,
                 out_channels=None,
                 **kwargs):
        super().__init__()
        self.seqlen = seqlen
        embed_dim = embed_dim if embed_dim is not None else scale_dim_heads[
            scale][0]
        num_heads = num_heads if num_heads is not None else scale_dim_heads[
            scale][1]
        out_channels = out_channels if out_channels is not None else embed_dim
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            in_channels=in_channels,
            embed_dim=embed_dim,
            patch_size=patch_size,
            mode='linear')
        num_patches = self.patch_embed.num_patches

        self.pos_embed = self.create_parameter(
            shape=[1, num_patches + 1, 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,
                act_layer=eval(act_layer),
                epsilon=epsilon,
                prenorm=False) for i in range(depth)
        ])
        self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)

        self.out_channels = out_channels

        trunc_normal_(self.pos_embed)
        trunc_normal_(self.cls_token)
        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)
        cls_tokens = paddle.tile(self.cls_token, repeat_times=[B, 1, 1])
        x = paddle.concat((cls_tokens, x), axis=1)
        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 = x[:, :self.seqlen]
        return x.transpose([0, 2, 1]).unsqueeze(2)