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# copyright (c) 2021 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
import paddle.nn as nn
from paddle import ParamAttr
import paddle.nn.functional as F
import numpy as np
from .rec_att_head import AttentionGRUCell
def get_para_bias_attr(l2_decay, k):
if l2_decay > 0:
regularizer = paddle.regularizer.L2Decay(l2_decay)
stdv = 1.0 / math.sqrt(k * 1.0)
initializer = nn.initializer.Uniform(-stdv, stdv)
else:
regularizer = None
initializer = None
weight_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
bias_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
return [weight_attr, bias_attr]
class TableAttentionHead(nn.Layer):
def __init__(self,
in_channels,
hidden_size,
in_max_len=488,
max_text_length=800,
out_channels=30,
loc_reg_num=4,
**kwargs):
super(TableAttentionHead, self).__init__()
self.input_size = in_channels[-1]
self.hidden_size = hidden_size
self.out_channels = out_channels
self.max_text_length = max_text_length
self.structure_attention_cell = AttentionGRUCell(
self.input_size, hidden_size, self.out_channels, use_gru=False)
self.structure_generator = nn.Linear(hidden_size, self.out_channels)
self.in_max_len = in_max_len
if self.in_max_len == 640:
self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1)
elif self.in_max_len == 800:
self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1)
else:
self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1)
self.loc_generator = nn.Linear(self.input_size + hidden_size,
loc_reg_num)
def _char_to_onehot(self, input_char, onehot_dim):
input_ont_hot = F.one_hot(input_char, onehot_dim)
return input_ont_hot
def forward(self, inputs, targets=None):
# if and else branch are both needed when you want to assign a variable
# if you modify the var in just one branch, then the modification will not work.
fea = inputs[-1]
last_shape = int(np.prod(fea.shape[2:])) # gry added
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
batch_size = fea.shape[0]
hidden = paddle.zeros((batch_size, self.hidden_size))
output_hiddens = paddle.zeros(
(batch_size, self.max_text_length + 1, self.hidden_size))
if self.training and targets is not None:
structure = targets[0]
for i in range(self.max_text_length + 1):
elem_onehots = self._char_to_onehot(
structure[:, i], onehot_dim=self.out_channels)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens[:, i, :] = outputs
structure_probs = self.structure_generator(output_hiddens)
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
else:
temp_elem = paddle.zeros(shape=[batch_size], dtype="int32")
structure_probs = None
loc_preds = None
elem_onehots = None
outputs = None
alpha = None
max_text_length = paddle.to_tensor(self.max_text_length)
for i in range(max_text_length + 1):
elem_onehots = self._char_to_onehot(
temp_elem, onehot_dim=self.out_channels)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens[:, i, :] = outputs
structure_probs_step = self.structure_generator(outputs)
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
structure_probs = self.structure_generator(output_hiddens)
structure_probs = F.softmax(structure_probs)
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
class SLAHead(nn.Layer):
def __init__(self,
in_channels,
hidden_size,
out_channels=30,
max_text_length=500,
loc_reg_num=4,
fc_decay=0.0,
**kwargs):
"""
@param in_channels: input shape
@param hidden_size: hidden_size for RNN and Embedding
@param out_channels: num_classes to rec
@param max_text_length: max text pred
"""
super().__init__()
in_channels = in_channels[-1]
self.hidden_size = hidden_size
self.max_text_length = max_text_length
self.emb = self._char_to_onehot
self.num_embeddings = out_channels
self.loc_reg_num = loc_reg_num
# structure
self.structure_attention_cell = AttentionGRUCell(
in_channels, hidden_size, self.num_embeddings)
weight_attr, bias_attr = get_para_bias_attr(
l2_decay=fc_decay, k=hidden_size)
weight_attr1_1, bias_attr1_1 = get_para_bias_attr(
l2_decay=fc_decay, k=hidden_size)
weight_attr1_2, bias_attr1_2 = get_para_bias_attr(
l2_decay=fc_decay, k=hidden_size)
self.structure_generator = nn.Sequential(
nn.Linear(
self.hidden_size,
self.hidden_size,
weight_attr=weight_attr1_2,
bias_attr=bias_attr1_2),
nn.Linear(
hidden_size,
out_channels,
weight_attr=weight_attr,
bias_attr=bias_attr))
# loc
weight_attr1, bias_attr1 = get_para_bias_attr(
l2_decay=fc_decay, k=self.hidden_size)
weight_attr2, bias_attr2 = get_para_bias_attr(
l2_decay=fc_decay, k=self.hidden_size)
self.loc_generator = nn.Sequential(
nn.Linear(
self.hidden_size,
self.hidden_size,
weight_attr=weight_attr1,
bias_attr=bias_attr1),
nn.Linear(
self.hidden_size,
loc_reg_num,
weight_attr=weight_attr2,
bias_attr=bias_attr2),
nn.Sigmoid())
def forward(self, inputs, targets=None):
fea = inputs[-1]
batch_size = fea.shape[0]
# reshape
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], -1])
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
hidden = paddle.zeros((batch_size, self.hidden_size))
structure_preds = paddle.zeros(
(batch_size, self.max_text_length + 1, self.num_embeddings))
loc_preds = paddle.zeros(
(batch_size, self.max_text_length + 1, self.loc_reg_num))
structure_preds.stop_gradient = True
loc_preds.stop_gradient = True
if self.training and targets is not None:
structure = targets[0]
for i in range(self.max_text_length + 1):
hidden, structure_step, loc_step = self._decode(structure[:, i],
fea, hidden)
structure_preds[:, i, :] = structure_step
loc_preds[:, i, :] = loc_step
else:
pre_chars = paddle.zeros(shape=[batch_size], dtype="int32")
max_text_length = paddle.to_tensor(self.max_text_length)
# for export
loc_step, structure_step = None, None
for i in range(max_text_length + 1):
hidden, structure_step, loc_step = self._decode(pre_chars, fea,
hidden)
pre_chars = structure_step.argmax(axis=1, dtype="int32")
structure_preds[:, i, :] = structure_step
loc_preds[:, i, :] = loc_step
if not self.training:
structure_preds = F.softmax(structure_preds)
return {'structure_probs': structure_preds, 'loc_preds': loc_preds}
def _decode(self, pre_chars, features, hidden):
"""
Predict table label and coordinates for each step
@param pre_chars: Table label in previous step
@param features:
@param hidden: hidden status in previous step
@return:
"""
emb_feature = self.emb(pre_chars)
# output shape is b * self.hidden_size
(output, hidden), alpha = self.structure_attention_cell(
hidden, features, emb_feature)
# structure
structure_step = self.structure_generator(output)
# loc
loc_step = self.loc_generator(output)
return hidden, structure_step, loc_step
def _char_to_onehot(self, input_char):
input_ont_hot = F.one_hot(input_char, self.num_embeddings)
return input_ont_hot
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