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