121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/roatienza/deep-text-recognition-benchmark/blob/master/modules/vitstr.py
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"""
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import numpy as np
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import paddle
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import paddle.nn as nn
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from ppocr.modeling.backbones.rec_svtrnet import Block, PatchEmbed, zeros_, trunc_normal_, ones_
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scale_dim_heads = {'tiny': [192, 3], 'small': [384, 6], 'base': [768, 12]}
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class ViTSTR(nn.Layer):
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def __init__(self,
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img_size=[224, 224],
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in_channels=1,
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scale='tiny',
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seqlen=27,
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patch_size=[16, 16],
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embed_dim=None,
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depth=12,
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num_heads=None,
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mlp_ratio=4,
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qkv_bias=True,
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qk_scale=None,
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drop_path_rate=0.,
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drop_rate=0.,
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attn_drop_rate=0.,
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norm_layer='nn.LayerNorm',
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act_layer='nn.GELU',
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epsilon=1e-6,
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out_channels=None,
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**kwargs):
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super().__init__()
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self.seqlen = seqlen
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embed_dim = embed_dim if embed_dim is not None else scale_dim_heads[
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scale][0]
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num_heads = num_heads if num_heads is not None else scale_dim_heads[
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scale][1]
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out_channels = out_channels if out_channels is not None else embed_dim
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self.patch_embed = PatchEmbed(
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img_size=img_size,
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in_channels=in_channels,
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embed_dim=embed_dim,
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patch_size=patch_size,
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mode='linear')
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num_patches = self.patch_embed.num_patches
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self.pos_embed = self.create_parameter(
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shape=[1, num_patches + 1, embed_dim], default_initializer=zeros_)
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self.add_parameter("pos_embed", self.pos_embed)
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self.cls_token = self.create_parameter(
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shape=[1, 1, embed_dim], default_initializer=zeros_)
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self.add_parameter("cls_token", self.cls_token)
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = np.linspace(0, drop_path_rate, depth)
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self.blocks = nn.LayerList([
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Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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act_layer=eval(act_layer),
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epsilon=epsilon,
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prenorm=False) for i in range(depth)
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])
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self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
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self.out_channels = out_channels
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trunc_normal_(self.pos_embed)
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trunc_normal_(self.cls_token)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight)
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if isinstance(m, nn.Linear) and m.bias is not None:
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zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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zeros_(m.bias)
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ones_(m.weight)
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def forward_features(self, x):
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = paddle.tile(self.cls_token, repeat_times=[B, 1, 1])
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x = paddle.concat((cls_tokens, x), axis=1)
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x = x + self.pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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x = blk(x)
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x = self.norm(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = x[:, :self.seqlen]
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return x.transpose([0, 2, 1]).unsqueeze(2)
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