264 lines
10 KiB
Python
264 lines
10 KiB
Python
# copyright (c) 2022 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/FudanVI/FudanOCR/blob/main/scene-text-telescope/model/tbsrn.py
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"""
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import math
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import warnings
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import numpy as np
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import paddle
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from paddle import nn
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import string
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warnings.filterwarnings("ignore")
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from .tps_spatial_transformer import TPSSpatialTransformer
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from .stn import STN as STNHead
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from .tsrn import GruBlock, mish, UpsampleBLock
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from ppocr.modeling.heads.sr_rensnet_transformer import Transformer, LayerNorm, \
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PositionwiseFeedForward, MultiHeadedAttention
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def positionalencoding2d(d_model, height, width):
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"""
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:param d_model: dimension of the model
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:param height: height of the positions
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:param width: width of the positions
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:return: d_model*height*width position matrix
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"""
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if d_model % 4 != 0:
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raise ValueError("Cannot use sin/cos positional encoding with "
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"odd dimension (got dim={:d})".format(d_model))
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pe = paddle.zeros([d_model, height, width])
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# Each dimension use half of d_model
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d_model = int(d_model / 2)
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div_term = paddle.exp(paddle.arange(0., d_model, 2) *
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-(math.log(10000.0) / d_model))
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pos_w = paddle.arange(0., width, dtype='float32').unsqueeze(1)
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pos_h = paddle.arange(0., height, dtype='float32').unsqueeze(1)
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pe[0:d_model:2, :, :] = paddle.sin(pos_w * div_term).transpose([1, 0]).unsqueeze(1).tile([1, height, 1])
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pe[1:d_model:2, :, :] = paddle.cos(pos_w * div_term).transpose([1, 0]).unsqueeze(1).tile([1, height, 1])
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pe[d_model::2, :, :] = paddle.sin(pos_h * div_term).transpose([1, 0]).unsqueeze(2).tile([1, 1, width])
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pe[d_model + 1::2, :, :] = paddle.cos(pos_h * div_term).transpose([1, 0]).unsqueeze(2).tile([1, 1, width])
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return pe
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class FeatureEnhancer(nn.Layer):
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def __init__(self):
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super(FeatureEnhancer, self).__init__()
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self.multihead = MultiHeadedAttention(h=4, d_model=128, dropout=0.1)
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self.mul_layernorm1 = LayerNorm(features=128)
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self.pff = PositionwiseFeedForward(128, 128)
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self.mul_layernorm3 = LayerNorm(features=128)
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self.linear = nn.Linear(128, 64)
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def forward(self, conv_feature):
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'''
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text : (batch, seq_len, embedding_size)
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global_info: (batch, embedding_size, 1, 1)
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conv_feature: (batch, channel, H, W)
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'''
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batch = conv_feature.shape[0]
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position2d = positionalencoding2d(64, 16, 64).cast('float32').unsqueeze(0).reshape([1, 64, 1024])
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position2d = position2d.tile([batch, 1, 1])
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conv_feature = paddle.concat([conv_feature, position2d], 1) # batch, 128(64+64), 32, 128
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result = conv_feature.transpose([0, 2, 1])
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origin_result = result
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result = self.mul_layernorm1(origin_result + self.multihead(result, result, result, mask=None)[0])
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origin_result = result
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result = self.mul_layernorm3(origin_result + self.pff(result))
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result = self.linear(result)
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return result.transpose([0, 2, 1])
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def str_filt(str_, voc_type):
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alpha_dict = {
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'digit': string.digits,
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'lower': string.digits + string.ascii_lowercase,
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'upper': string.digits + string.ascii_letters,
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'all': string.digits + string.ascii_letters + string.punctuation
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}
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if voc_type == 'lower':
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str_ = str_.lower()
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for char in str_:
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if char not in alpha_dict[voc_type]:
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str_ = str_.replace(char, '')
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str_ = str_.lower()
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return str_
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class TBSRN(nn.Layer):
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def __init__(self,
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in_channels=3,
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scale_factor=2,
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width=128,
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height=32,
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STN=True,
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srb_nums=5,
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mask=False,
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hidden_units=32,
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infer_mode=False):
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super(TBSRN, self).__init__()
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in_planes = 3
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if mask:
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in_planes = 4
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assert math.log(scale_factor, 2) % 1 == 0
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upsample_block_num = int(math.log(scale_factor, 2))
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self.block1 = nn.Sequential(
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nn.Conv2D(in_planes, 2 * hidden_units, kernel_size=9, padding=4),
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nn.PReLU()
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# nn.ReLU()
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)
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self.srb_nums = srb_nums
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for i in range(srb_nums):
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setattr(self, 'block%d' % (i + 2), RecurrentResidualBlock(2 * hidden_units))
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setattr(self, 'block%d' % (srb_nums + 2),
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nn.Sequential(
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nn.Conv2D(2 * hidden_units, 2 * hidden_units, kernel_size=3, padding=1),
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nn.BatchNorm2D(2 * hidden_units)
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))
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# self.non_local = NonLocalBlock2D(64, 64)
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block_ = [UpsampleBLock(2 * hidden_units, 2) for _ in range(upsample_block_num)]
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block_.append(nn.Conv2D(2 * hidden_units, in_planes, kernel_size=9, padding=4))
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setattr(self, 'block%d' % (srb_nums + 3), nn.Sequential(*block_))
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self.tps_inputsize = [height // scale_factor, width // scale_factor]
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tps_outputsize = [height // scale_factor, width // scale_factor]
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num_control_points = 20
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tps_margins = [0.05, 0.05]
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self.stn = STN
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self.out_channels = in_channels
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if self.stn:
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self.tps = TPSSpatialTransformer(
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output_image_size=tuple(tps_outputsize),
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num_control_points=num_control_points,
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margins=tuple(tps_margins))
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self.stn_head = STNHead(
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in_channels=in_planes,
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num_ctrlpoints=num_control_points,
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activation='none')
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self.infer_mode = infer_mode
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self.english_alphabet = '-0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
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self.english_dict = {}
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for index in range(len(self.english_alphabet)):
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self.english_dict[self.english_alphabet[index]] = index
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transformer = Transformer(alphabet='-0123456789abcdefghijklmnopqrstuvwxyz')
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self.transformer = transformer
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for param in self.transformer.parameters():
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param.trainable = False
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def label_encoder(self, label):
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batch = len(label)
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length = [len(i) for i in label]
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length_tensor = paddle.to_tensor(length, dtype='int64')
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max_length = max(length)
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input_tensor = np.zeros((batch, max_length))
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for i in range(batch):
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for j in range(length[i] - 1):
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input_tensor[i][j + 1] = self.english_dict[label[i][j]]
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text_gt = []
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for i in label:
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for j in i:
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text_gt.append(self.english_dict[j])
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text_gt = paddle.to_tensor(text_gt, dtype='int64')
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input_tensor = paddle.to_tensor(input_tensor, dtype='int64')
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return length_tensor, input_tensor, text_gt
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def forward(self, x):
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output = {}
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if self.infer_mode:
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output["lr_img"] = x
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y = x
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else:
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output["lr_img"] = x[0]
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output["hr_img"] = x[1]
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y = x[0]
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if self.stn and self.training:
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_, ctrl_points_x = self.stn_head(y)
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y, _ = self.tps(y, ctrl_points_x)
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block = {'1': self.block1(y)}
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for i in range(self.srb_nums + 1):
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block[str(i + 2)] = getattr(self,
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'block%d' % (i + 2))(block[str(i + 1)])
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block[str(self.srb_nums + 3)] = getattr(self, 'block%d' % (self.srb_nums + 3)) \
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((block['1'] + block[str(self.srb_nums + 2)]))
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sr_img = paddle.tanh(block[str(self.srb_nums + 3)])
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output["sr_img"] = sr_img
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if self.training:
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hr_img = x[1]
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# add transformer
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label = [str_filt(i, 'lower') + '-' for i in x[2]]
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length_tensor, input_tensor, text_gt = self.label_encoder(label)
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hr_pred, word_attention_map_gt, hr_correct_list = self.transformer(hr_img, length_tensor,
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input_tensor)
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sr_pred, word_attention_map_pred, sr_correct_list = self.transformer(sr_img, length_tensor,
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input_tensor)
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output["hr_img"] = hr_img
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output["hr_pred"] = hr_pred
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output["text_gt"] = text_gt
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output["word_attention_map_gt"] = word_attention_map_gt
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output["sr_pred"] = sr_pred
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output["word_attention_map_pred"] = word_attention_map_pred
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return output
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class RecurrentResidualBlock(nn.Layer):
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def __init__(self, channels):
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super(RecurrentResidualBlock, self).__init__()
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self.conv1 = nn.Conv2D(channels, channels, kernel_size=3, padding=1)
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self.bn1 = nn.BatchNorm2D(channels)
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self.gru1 = GruBlock(channels, channels)
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# self.prelu = nn.ReLU()
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self.prelu = mish()
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self.conv2 = nn.Conv2D(channels, channels, kernel_size=3, padding=1)
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self.bn2 = nn.BatchNorm2D(channels)
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self.gru2 = GruBlock(channels, channels)
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self.feature_enhancer = FeatureEnhancer()
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for p in self.parameters():
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if p.dim() > 1:
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paddle.nn.initializer.XavierUniform(p)
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def forward(self, x):
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residual = self.conv1(x)
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residual = self.bn1(residual)
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residual = self.prelu(residual)
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residual = self.conv2(residual)
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residual = self.bn2(residual)
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size = residual.shape
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residual = residual.reshape([size[0], size[1], -1])
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residual = self.feature_enhancer(residual)
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residual = residual.reshape([size[0], size[1], size[2], size[3]])
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return x + residual |