220 lines
7.1 KiB
Python
220 lines
7.1 KiB
Python
# copyright (c) 2020 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/text-gestalt/model/tsrn.py
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"""
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import math
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from collections import OrderedDict
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import sys
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import numpy as np
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import warnings
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import math, copy
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import cv2
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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 STN_model
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from ppocr.modeling.heads.sr_rensnet_transformer import Transformer
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class TSRN(nn.Layer):
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def __init__(self,
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in_channels,
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scale_factor=2,
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width=128,
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height=32,
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STN=False,
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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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**kwargs):
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super(TSRN, 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(
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in_planes, 2 * hidden_units, kernel_size=9, padding=4),
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nn.PReLU())
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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),
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RecurrentResidualBlock(2 * hidden_units))
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setattr(
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self,
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'block%d' % (srb_nums + 2),
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nn.Sequential(
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nn.Conv2D(
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2 * hidden_units,
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2 * hidden_units,
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kernel_size=3,
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padding=1),
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nn.BatchNorm2D(2 * hidden_units)))
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block_ = [
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UpsampleBLock(2 * hidden_units, 2)
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for _ in range(upsample_block_num)
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]
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block_.append(
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nn.Conv2D(
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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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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 = STN_model(
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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.out_channels = in_channels
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self.r34_transformer = Transformer()
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for param in self.r34_transformer.parameters():
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param.trainable = False
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self.infer_mode = infer_mode
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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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length = x[2]
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input_tensor = x[3]
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# add transformer
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sr_pred, word_attention_map_pred, _ = self.r34_transformer(
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sr_img, length, input_tensor)
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hr_pred, word_attention_map_gt, _ = self.r34_transformer(
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hr_img, length, 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["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 = 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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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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residual = self.gru1(residual.transpose([0, 1, 3, 2])).transpose(
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[0, 1, 3, 2])
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return self.gru2(x + residual)
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class UpsampleBLock(nn.Layer):
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def __init__(self, in_channels, up_scale):
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super(UpsampleBLock, self).__init__()
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self.conv = nn.Conv2D(
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in_channels, in_channels * up_scale**2, kernel_size=3, padding=1)
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self.pixel_shuffle = nn.PixelShuffle(up_scale)
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self.prelu = mish()
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def forward(self, x):
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x = self.conv(x)
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x = self.pixel_shuffle(x)
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x = self.prelu(x)
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return x
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class mish(nn.Layer):
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def __init__(self, ):
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super(mish, self).__init__()
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self.activated = True
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def forward(self, x):
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if self.activated:
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x = x * (paddle.tanh(F.softplus(x)))
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return x
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class GruBlock(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super(GruBlock, self).__init__()
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assert out_channels % 2 == 0
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self.conv1 = nn.Conv2D(
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in_channels, out_channels, kernel_size=1, padding=0)
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self.gru = nn.GRU(out_channels,
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out_channels // 2,
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direction='bidirectional')
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def forward(self, x):
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# x: b, c, w, h
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x = self.conv1(x)
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x = x.transpose([0, 2, 3, 1]) # b, w, h, c
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batch_size, w, h, c = x.shape
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x = x.reshape([-1, h, c]) # b*w, h, c
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x, _ = self.gru(x)
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x = x.reshape([-1, w, h, c])
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x = x.transpose([0, 3, 1, 2])
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return x
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