# copyright (c) 2020 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/FudanVI/FudanOCR/blob/main/text-gestalt/model/tsrn.py
"""
import math
import paddle
import paddle.nn.functional as F
from paddle import nn
from collections import OrderedDict
import sys
import numpy as np
import warnings
import math, copy
import cv2
warnings.filterwarnings("ignore")
from .tps_spatial_transformer import TPSSpatialTransformer
from .stn import STN as STN_model
from ppocr.modeling.heads.sr_rensnet_transformer import Transformer
class TSRN(nn.Layer):
def __init__(self,
in_channels,
scale_factor=2,
width=128,
height=32,
STN=False,
srb_nums=5,
mask=False,
hidden_units=32,
infer_mode=False,
**kwargs):
super(TSRN, self).__init__()
in_planes = 3
if mask:
in_planes = 4
assert math.log(scale_factor, 2) % 1 == 0
upsample_block_num = int(math.log(scale_factor, 2))
self.block1 = nn.Sequential(
nn.Conv2D(
in_planes, 2 * hidden_units, kernel_size=9, padding=4),
nn.PReLU())
self.srb_nums = srb_nums
for i in range(srb_nums):
setattr(self, 'block%d' % (i + 2),
RecurrentResidualBlock(2 * hidden_units))
setattr(
self,
'block%d' % (srb_nums + 2),
nn.Sequential(
nn.Conv2D(
2 * hidden_units,
2 * hidden_units,
kernel_size=3,
padding=1),
nn.BatchNorm2D(2 * hidden_units)))
block_ = [
UpsampleBLock(2 * hidden_units, 2)
for _ in range(upsample_block_num)
]
block_.append(
nn.Conv2D(
2 * hidden_units, in_planes, kernel_size=9, padding=4))
setattr(self, 'block%d' % (srb_nums + 3), nn.Sequential(*block_))
self.tps_inputsize = [height // scale_factor, width // scale_factor]
tps_outputsize = [height // scale_factor, width // scale_factor]
num_control_points = 20
tps_margins = [0.05, 0.05]
self.stn = STN
if self.stn:
self.tps = TPSSpatialTransformer(
output_image_size=tuple(tps_outputsize),
num_control_points=num_control_points,
margins=tuple(tps_margins))
self.stn_head = STN_model(
in_channels=in_planes,
num_ctrlpoints=num_control_points,
activation='none')
self.out_channels = in_channels
self.r34_transformer = Transformer()
for param in self.r34_transformer.parameters():
param.trainable = False
self.infer_mode = infer_mode
def forward(self, x):
output = {}
if self.infer_mode:
output["lr_img"] = x
y = x
else:
output["lr_img"] = x[0]
output["hr_img"] = x[1]
y = x[0]
if self.stn and self.training:
_, ctrl_points_x = self.stn_head(y)
y, _ = self.tps(y, ctrl_points_x)
block = {'1': self.block1(y)}
for i in range(self.srb_nums + 1):
block[str(i + 2)] = getattr(self,
'block%d' % (i + 2))(block[str(i + 1)])
block[str(self.srb_nums + 3)] = getattr(self, 'block%d' % (self.srb_nums + 3)) \
((block['1'] + block[str(self.srb_nums + 2)]))
sr_img = paddle.tanh(block[str(self.srb_nums + 3)])
output["sr_img"] = sr_img
if self.training:
hr_img = x[1]
length = x[2]
input_tensor = x[3]
# add transformer
sr_pred, word_attention_map_pred, _ = self.r34_transformer(
sr_img, length, input_tensor)
hr_pred, word_attention_map_gt, _ = self.r34_transformer(
hr_img, length, input_tensor)
output["hr_img"] = hr_img
output["hr_pred"] = hr_pred
output["word_attention_map_gt"] = word_attention_map_gt
output["sr_pred"] = sr_pred
output["word_attention_map_pred"] = word_attention_map_pred
return output
class RecurrentResidualBlock(nn.Layer):
def __init__(self, channels):
super(RecurrentResidualBlock, self).__init__()
self.conv1 = nn.Conv2D(channels, channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2D(channels)
self.gru1 = GruBlock(channels, channels)
self.prelu = mish()
self.conv2 = nn.Conv2D(channels, channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2D(channels)
self.gru2 = GruBlock(channels, channels)
def forward(self, x):
residual = self.conv1(x)
residual = self.bn1(residual)
residual = self.prelu(residual)
residual = self.conv2(residual)
residual = self.bn2(residual)
residual = self.gru1(residual.transpose([0, 1, 3, 2])).transpose(
[0, 1, 3, 2])
return self.gru2(x + residual)
class UpsampleBLock(nn.Layer):
def __init__(self, in_channels, up_scale):
super(UpsampleBLock, self).__init__()
self.conv = nn.Conv2D(
in_channels, in_channels * up_scale**2, kernel_size=3, padding=1)
self.pixel_shuffle = nn.PixelShuffle(up_scale)
self.prelu = mish()
def forward(self, x):
x = self.conv(x)
x = self.pixel_shuffle(x)
x = self.prelu(x)
return x
class mish(nn.Layer):
def __init__(self, ):
super(mish, self).__init__()
self.activated = True
def forward(self, x):
if self.activated:
x = x * (paddle.tanh(F.softplus(x)))
return x
class GruBlock(nn.Layer):
def __init__(self, in_channels, out_channels):
super(GruBlock, self).__init__()
assert out_channels % 2 == 0
self.conv1 = nn.Conv2D(
in_channels, out_channels, kernel_size=1, padding=0)
self.gru = nn.GRU(out_channels,
out_channels // 2,
direction='bidirectional')
def forward(self, x):
# x: b, c, w, h
x = self.conv1(x)
x = x.transpose([0, 2, 3, 1]) # b, w, h, c
batch_size, w, h, c = x.shape
x = x.reshape([-1, h, c]) # b*w, h, c
x, _ = self.gru(x)
x = x.reshape([-1, w, h, c])
x = x.transpose([0, 3, 1, 2])
return x