mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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279 lines
11 KiB
Python
279 lines
11 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import nn, ParamAttr
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from paddle.nn import functional as F
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import numpy as np
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from .self_attention import WrapEncoderForFeature
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from .self_attention import WrapEncoder
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from paddle.static import Program
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from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
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from collections import OrderedDict
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gradient_clip = 10
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class PVAM(nn.Layer):
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def __init__(self, in_channels, char_num, max_text_length, num_heads,
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num_encoder_tus, hidden_dims):
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super(PVAM, self).__init__()
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self.char_num = char_num
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self.max_length = max_text_length
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self.num_heads = num_heads
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self.num_encoder_TUs = num_encoder_tus
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self.hidden_dims = hidden_dims
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# Transformer encoder
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t = 256
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c = 512
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self.wrap_encoder_for_feature = WrapEncoderForFeature(
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src_vocab_size=1,
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max_length=t,
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n_layer=self.num_encoder_TUs,
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n_head=self.num_heads,
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d_key=int(self.hidden_dims / self.num_heads),
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d_value=int(self.hidden_dims / self.num_heads),
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d_model=self.hidden_dims,
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d_inner_hid=self.hidden_dims,
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prepostprocess_dropout=0.1,
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attention_dropout=0.1,
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relu_dropout=0.1,
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preprocess_cmd="n",
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postprocess_cmd="da",
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weight_sharing=True)
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# PVAM
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self.flatten0 = paddle.nn.Flatten(start_axis=0, stop_axis=1)
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self.fc0 = paddle.nn.Linear(
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in_features=in_channels,
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out_features=in_channels, )
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self.emb = paddle.nn.Embedding(
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num_embeddings=self.max_length, embedding_dim=in_channels)
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self.flatten1 = paddle.nn.Flatten(start_axis=0, stop_axis=2)
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self.fc1 = paddle.nn.Linear(
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in_features=in_channels, out_features=1, bias_attr=False)
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def forward(self, inputs, encoder_word_pos, gsrm_word_pos):
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b, c, h, w = inputs.shape
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conv_features = paddle.reshape(inputs, shape=[-1, c, h * w])
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conv_features = paddle.transpose(conv_features, perm=[0, 2, 1])
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# transformer encoder
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b, t, c = conv_features.shape
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enc_inputs = [conv_features, encoder_word_pos, None]
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word_features = self.wrap_encoder_for_feature(enc_inputs)
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# pvam
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b, t, c = word_features.shape
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word_features = self.fc0(word_features)
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word_features_ = paddle.reshape(word_features, [-1, 1, t, c])
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word_features_ = paddle.tile(word_features_, [1, self.max_length, 1, 1])
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word_pos_feature = self.emb(gsrm_word_pos)
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word_pos_feature_ = paddle.reshape(word_pos_feature,
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[-1, self.max_length, 1, c])
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word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1])
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y = word_pos_feature_ + word_features_
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y = F.tanh(y)
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attention_weight = self.fc1(y)
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attention_weight = paddle.reshape(
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attention_weight, shape=[-1, self.max_length, t])
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attention_weight = F.softmax(attention_weight, axis=-1)
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pvam_features = paddle.matmul(attention_weight,
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word_features) #[b, max_length, c]
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return pvam_features
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class GSRM(nn.Layer):
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def __init__(self, in_channels, char_num, max_text_length, num_heads,
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num_encoder_tus, num_decoder_tus, hidden_dims):
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super(GSRM, self).__init__()
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self.char_num = char_num
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self.max_length = max_text_length
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self.num_heads = num_heads
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self.num_encoder_TUs = num_encoder_tus
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self.num_decoder_TUs = num_decoder_tus
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self.hidden_dims = hidden_dims
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self.fc0 = paddle.nn.Linear(
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in_features=in_channels, out_features=self.char_num)
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self.wrap_encoder0 = WrapEncoder(
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src_vocab_size=self.char_num + 1,
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max_length=self.max_length,
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n_layer=self.num_decoder_TUs,
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n_head=self.num_heads,
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d_key=int(self.hidden_dims / self.num_heads),
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d_value=int(self.hidden_dims / self.num_heads),
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d_model=self.hidden_dims,
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d_inner_hid=self.hidden_dims,
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prepostprocess_dropout=0.1,
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attention_dropout=0.1,
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relu_dropout=0.1,
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preprocess_cmd="n",
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postprocess_cmd="da",
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weight_sharing=True)
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self.wrap_encoder1 = WrapEncoder(
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src_vocab_size=self.char_num + 1,
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max_length=self.max_length,
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n_layer=self.num_decoder_TUs,
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n_head=self.num_heads,
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d_key=int(self.hidden_dims / self.num_heads),
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d_value=int(self.hidden_dims / self.num_heads),
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d_model=self.hidden_dims,
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d_inner_hid=self.hidden_dims,
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prepostprocess_dropout=0.1,
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attention_dropout=0.1,
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relu_dropout=0.1,
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preprocess_cmd="n",
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postprocess_cmd="da",
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weight_sharing=True)
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self.mul = lambda x: paddle.matmul(x=x,
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y=self.wrap_encoder0.prepare_decoder.emb0.weight,
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transpose_y=True)
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def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2):
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# ===== GSRM Visual-to-semantic embedding block =====
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b, t, c = inputs.shape
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pvam_features = paddle.reshape(inputs, [-1, c])
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word_out = self.fc0(pvam_features)
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word_ids = paddle.argmax(F.softmax(word_out), axis=1)
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word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1])
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#===== GSRM Semantic reasoning block =====
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"""
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This module is achieved through bi-transformers,
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ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
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"""
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pad_idx = self.char_num
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word1 = paddle.cast(word_ids, "float32")
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word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC")
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word1 = paddle.cast(word1, "int64")
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word1 = word1[:, :-1, :]
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word2 = word_ids
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enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1]
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enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2]
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gsrm_feature1 = self.wrap_encoder0(enc_inputs_1)
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gsrm_feature2 = self.wrap_encoder1(enc_inputs_2)
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gsrm_feature2 = F.pad(gsrm_feature2, [0, 1],
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value=0.,
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data_format="NLC")
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gsrm_feature2 = gsrm_feature2[:, 1:, ]
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gsrm_features = gsrm_feature1 + gsrm_feature2
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gsrm_out = self.mul(gsrm_features)
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b, t, c = gsrm_out.shape
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gsrm_out = paddle.reshape(gsrm_out, [-1, c])
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return gsrm_features, word_out, gsrm_out
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class VSFD(nn.Layer):
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def __init__(self, in_channels=512, pvam_ch=512, char_num=38):
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super(VSFD, self).__init__()
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self.char_num = char_num
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self.fc0 = paddle.nn.Linear(
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in_features=in_channels * 2, out_features=pvam_ch)
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self.fc1 = paddle.nn.Linear(
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in_features=pvam_ch, out_features=self.char_num)
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def forward(self, pvam_feature, gsrm_feature):
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b, t, c1 = pvam_feature.shape
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b, t, c2 = gsrm_feature.shape
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combine_feature_ = paddle.concat([pvam_feature, gsrm_feature], axis=2)
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img_comb_feature_ = paddle.reshape(
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combine_feature_, shape=[-1, c1 + c2])
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img_comb_feature_map = self.fc0(img_comb_feature_)
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img_comb_feature_map = F.sigmoid(img_comb_feature_map)
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img_comb_feature_map = paddle.reshape(
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img_comb_feature_map, shape=[-1, t, c1])
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combine_feature = img_comb_feature_map * pvam_feature + (
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1.0 - img_comb_feature_map) * gsrm_feature
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img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1])
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out = self.fc1(img_comb_feature)
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return out
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class SRNHead(nn.Layer):
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def __init__(self, in_channels, out_channels, max_text_length, num_heads,
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num_encoder_TUs, num_decoder_TUs, hidden_dims, **kwargs):
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super(SRNHead, self).__init__()
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self.char_num = out_channels
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self.max_length = max_text_length
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self.num_heads = num_heads
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self.num_encoder_TUs = num_encoder_TUs
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self.num_decoder_TUs = num_decoder_TUs
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self.hidden_dims = hidden_dims
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self.pvam = PVAM(
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in_channels=in_channels,
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char_num=self.char_num,
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max_text_length=self.max_length,
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num_heads=self.num_heads,
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num_encoder_tus=self.num_encoder_TUs,
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hidden_dims=self.hidden_dims)
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self.gsrm = GSRM(
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in_channels=in_channels,
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char_num=self.char_num,
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max_text_length=self.max_length,
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num_heads=self.num_heads,
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num_encoder_tus=self.num_encoder_TUs,
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num_decoder_tus=self.num_decoder_TUs,
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hidden_dims=self.hidden_dims)
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self.vsfd = VSFD(in_channels=in_channels, char_num=self.char_num)
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self.gsrm.wrap_encoder1.prepare_decoder.emb0 = self.gsrm.wrap_encoder0.prepare_decoder.emb0
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def forward(self, inputs, targets=None):
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others = targets[-4:]
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encoder_word_pos = others[0]
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gsrm_word_pos = others[1]
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gsrm_slf_attn_bias1 = others[2]
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gsrm_slf_attn_bias2 = others[3]
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pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos)
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gsrm_feature, word_out, gsrm_out = self.gsrm(
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pvam_feature, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2)
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final_out = self.vsfd(pvam_feature, gsrm_feature)
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if not self.training:
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final_out = F.softmax(final_out, axis=1)
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_, decoded_out = paddle.topk(final_out, k=1)
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predicts = OrderedDict([
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('predict', final_out),
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('pvam_feature', pvam_feature),
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('decoded_out', decoded_out),
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('word_out', word_out),
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('gsrm_out', gsrm_out),
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])
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return predicts
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