# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import paddle.fluid as fluid
import numpy as np
from .self_attention import WrapEncoderForFeature
from .self_attention import WrapEncoder
from paddle.static import Program
from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
import paddle.fluid.framework as framework
from collections import OrderedDict
gradient_clip = 10
class PVAM(nn.Layer):
def __init__(self, in_channels, char_num, max_text_length, num_heads,
num_encoder_tus, hidden_dims):
super(PVAM, self).__init__()
self.char_num = char_num
self.max_length = max_text_length
self.num_heads = num_heads
self.num_encoder_TUs = num_encoder_tus
self.hidden_dims = hidden_dims
# Transformer encoder
t = 256
c = 512
self.wrap_encoder_for_feature = WrapEncoderForFeature(
src_vocab_size=1,
max_length=t,
n_layer=self.num_encoder_TUs,
n_head=self.num_heads,
d_key=int(self.hidden_dims / self.num_heads),
d_value=int(self.hidden_dims / self.num_heads),
d_model=self.hidden_dims,
d_inner_hid=self.hidden_dims,
prepostprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
preprocess_cmd="n",
postprocess_cmd="da",
weight_sharing=True)
# PVAM
self.flatten0 = paddle.nn.Flatten(start_axis=0, stop_axis=1)
self.fc0 = paddle.nn.Linear(
in_features=in_channels,
out_features=in_channels, )
self.emb = paddle.nn.Embedding(
num_embeddings=self.max_length, embedding_dim=in_channels)
self.flatten1 = paddle.nn.Flatten(start_axis=0, stop_axis=2)
self.fc1 = paddle.nn.Linear(
in_features=in_channels, out_features=1, bias_attr=False)
def forward(self, inputs, encoder_word_pos, gsrm_word_pos):
b, c, h, w = inputs.shape
conv_features = paddle.reshape(inputs, shape=[-1, c, h * w])
conv_features = paddle.transpose(conv_features, perm=[0, 2, 1])
# transformer encoder
b, t, c = conv_features.shape
enc_inputs = [conv_features, encoder_word_pos, None]
word_features = self.wrap_encoder_for_feature(enc_inputs)
# pvam
b, t, c = word_features.shape
word_features = self.fc0(word_features)
word_features_ = paddle.reshape(word_features, [-1, 1, t, c])
word_features_ = paddle.tile(word_features_, [1, self.max_length, 1, 1])
word_pos_feature = self.emb(gsrm_word_pos)
word_pos_feature_ = paddle.reshape(word_pos_feature,
[-1, self.max_length, 1, c])
word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1])
y = word_pos_feature_ + word_features_
y = F.tanh(y)
attention_weight = self.fc1(y)
attention_weight = paddle.reshape(
attention_weight, shape=[-1, self.max_length, t])
attention_weight = F.softmax(attention_weight, axis=-1)
pvam_features = paddle.matmul(attention_weight,
word_features) #[b, max_length, c]
return pvam_features
class GSRM(nn.Layer):
def __init__(self, in_channels, char_num, max_text_length, num_heads,
num_encoder_tus, num_decoder_tus, hidden_dims):
super(GSRM, self).__init__()
self.char_num = char_num
self.max_length = max_text_length
self.num_heads = num_heads
self.num_encoder_TUs = num_encoder_tus
self.num_decoder_TUs = num_decoder_tus
self.hidden_dims = hidden_dims
self.fc0 = paddle.nn.Linear(
in_features=in_channels, out_features=self.char_num)
self.wrap_encoder0 = WrapEncoder(
src_vocab_size=self.char_num + 1,
max_length=self.max_length,
n_layer=self.num_decoder_TUs,
n_head=self.num_heads,
d_key=int(self.hidden_dims / self.num_heads),
d_value=int(self.hidden_dims / self.num_heads),
d_model=self.hidden_dims,
d_inner_hid=self.hidden_dims,
prepostprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
preprocess_cmd="n",
postprocess_cmd="da",
weight_sharing=True)
self.wrap_encoder1 = WrapEncoder(
src_vocab_size=self.char_num + 1,
max_length=self.max_length,
n_layer=self.num_decoder_TUs,
n_head=self.num_heads,
d_key=int(self.hidden_dims / self.num_heads),
d_value=int(self.hidden_dims / self.num_heads),
d_model=self.hidden_dims,
d_inner_hid=self.hidden_dims,
prepostprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
preprocess_cmd="n",
postprocess_cmd="da",
weight_sharing=True)
self.mul = lambda x: paddle.matmul(x=x,
y=self.wrap_encoder0.prepare_decoder.emb0.weight,
transpose_y=True)
def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2):
# ===== GSRM Visual-to-semantic embedding block =====
b, t, c = inputs.shape
pvam_features = paddle.reshape(inputs, [-1, c])
word_out = self.fc0(pvam_features)
word_ids = paddle.argmax(F.softmax(word_out), axis=1)
word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1])
#===== GSRM Semantic reasoning block =====
"""
This module is achieved through bi-transformers,
ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
"""
pad_idx = self.char_num
word1 = paddle.cast(word_ids, "float32")
word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC")
word1 = paddle.cast(word1, "int64")
word1 = word1[:, :-1, :]
word2 = word_ids
enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1]
enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2]
gsrm_feature1 = self.wrap_encoder0(enc_inputs_1)
gsrm_feature2 = self.wrap_encoder1(enc_inputs_2)
gsrm_feature2 = F.pad(gsrm_feature2, [0, 1],
value=0.,
data_format="NLC")
gsrm_feature2 = gsrm_feature2[:, 1:, ]
gsrm_features = gsrm_feature1 + gsrm_feature2
gsrm_out = self.mul(gsrm_features)
b, t, c = gsrm_out.shape
gsrm_out = paddle.reshape(gsrm_out, [-1, c])
return gsrm_features, word_out, gsrm_out
class VSFD(nn.Layer):
def __init__(self, in_channels=512, pvam_ch=512, char_num=38):
super(VSFD, self).__init__()
self.char_num = char_num
self.fc0 = paddle.nn.Linear(
in_features=in_channels * 2, out_features=pvam_ch)
self.fc1 = paddle.nn.Linear(
in_features=pvam_ch, out_features=self.char_num)
def forward(self, pvam_feature, gsrm_feature):
b, t, c1 = pvam_feature.shape
b, t, c2 = gsrm_feature.shape
combine_feature_ = paddle.concat([pvam_feature, gsrm_feature], axis=2)
img_comb_feature_ = paddle.reshape(
combine_feature_, shape=[-1, c1 + c2])
img_comb_feature_map = self.fc0(img_comb_feature_)
img_comb_feature_map = F.sigmoid(img_comb_feature_map)
img_comb_feature_map = paddle.reshape(
img_comb_feature_map, shape=[-1, t, c1])
combine_feature = img_comb_feature_map * pvam_feature + (
1.0 - img_comb_feature_map) * gsrm_feature
img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1])
out = self.fc1(img_comb_feature)
return out
class SRNHead(nn.Layer):
def __init__(self, in_channels, out_channels, max_text_length, num_heads,
num_encoder_TUs, num_decoder_TUs, hidden_dims, **kwargs):
super(SRNHead, self).__init__()
self.char_num = out_channels
self.max_length = max_text_length
self.num_heads = num_heads
self.num_encoder_TUs = num_encoder_TUs
self.num_decoder_TUs = num_decoder_TUs
self.hidden_dims = hidden_dims
self.pvam = PVAM(
in_channels=in_channels,
char_num=self.char_num,
max_text_length=self.max_length,
num_heads=self.num_heads,
num_encoder_tus=self.num_encoder_TUs,
hidden_dims=self.hidden_dims)
self.gsrm = GSRM(
in_channels=in_channels,
char_num=self.char_num,
max_text_length=self.max_length,
num_heads=self.num_heads,
num_encoder_tus=self.num_encoder_TUs,
num_decoder_tus=self.num_decoder_TUs,
hidden_dims=self.hidden_dims)
self.vsfd = VSFD(in_channels=in_channels, char_num=self.char_num)
self.gsrm.wrap_encoder1.prepare_decoder.emb0 = self.gsrm.wrap_encoder0.prepare_decoder.emb0
def forward(self, inputs, others):
encoder_word_pos = others[0]
gsrm_word_pos = others[1]
gsrm_slf_attn_bias1 = others[2]
gsrm_slf_attn_bias2 = others[3]
pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos)
gsrm_feature, word_out, gsrm_out = self.gsrm(
pvam_feature, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2)
final_out = self.vsfd(pvam_feature, gsrm_feature)
if not self.training:
final_out = F.softmax(final_out, axis=1)
_, decoded_out = paddle.topk(final_out, k=1)
predicts = OrderedDict([
('predict', final_out),
('pvam_feature', pvam_feature),
('decoded_out', decoded_out),
('word_out', word_out),
('gsrm_out', gsrm_out),
])
return predicts