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# 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)
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