264 lines
11 KiB
Python
264 lines
11 KiB
Python
# copyright (c) 2021 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 paddle
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import paddle.nn as nn
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from paddle import ParamAttr
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import paddle.nn.functional as F
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import numpy as np
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from .rec_att_head import AttentionGRUCell
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def get_para_bias_attr(l2_decay, k):
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if l2_decay > 0:
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regularizer = paddle.regularizer.L2Decay(l2_decay)
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stdv = 1.0 / math.sqrt(k * 1.0)
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initializer = nn.initializer.Uniform(-stdv, stdv)
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else:
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regularizer = None
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initializer = None
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weight_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
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bias_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
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return [weight_attr, bias_attr]
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class TableAttentionHead(nn.Layer):
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def __init__(self,
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in_channels,
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hidden_size,
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loc_type,
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in_max_len=488,
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max_text_length=800,
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out_channels=30,
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loc_reg_num=4,
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**kwargs):
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super(TableAttentionHead, self).__init__()
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self.input_size = in_channels[-1]
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self.hidden_size = hidden_size
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self.out_channels = out_channels
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self.max_text_length = max_text_length
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self.structure_attention_cell = AttentionGRUCell(
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self.input_size, hidden_size, self.out_channels, use_gru=False)
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self.structure_generator = nn.Linear(hidden_size, self.out_channels)
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self.loc_type = loc_type
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self.in_max_len = in_max_len
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if self.loc_type == 1:
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self.loc_generator = nn.Linear(hidden_size, 4)
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else:
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if self.in_max_len == 640:
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self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1)
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elif self.in_max_len == 800:
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self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1)
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else:
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self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1)
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self.loc_generator = nn.Linear(self.input_size + hidden_size,
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loc_reg_num)
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def _char_to_onehot(self, input_char, onehot_dim):
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input_ont_hot = F.one_hot(input_char, onehot_dim)
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return input_ont_hot
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def forward(self, inputs, targets=None):
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# if and else branch are both needed when you want to assign a variable
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# if you modify the var in just one branch, then the modification will not work.
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fea = inputs[-1]
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if len(fea.shape) == 3:
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pass
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else:
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last_shape = int(np.prod(fea.shape[2:])) # gry added
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fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
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fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
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batch_size = fea.shape[0]
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hidden = paddle.zeros((batch_size, self.hidden_size))
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output_hiddens = []
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if self.training and targets is not None:
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structure = targets[0]
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for i in range(self.max_text_length + 1):
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elem_onehots = self._char_to_onehot(
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structure[:, i], onehot_dim=self.out_channels)
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(outputs, hidden), alpha = self.structure_attention_cell(
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hidden, fea, elem_onehots)
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output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
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output = paddle.concat(output_hiddens, axis=1)
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structure_probs = self.structure_generator(output)
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if self.loc_type == 1:
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loc_preds = self.loc_generator(output)
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loc_preds = F.sigmoid(loc_preds)
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else:
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loc_fea = fea.transpose([0, 2, 1])
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loc_fea = self.loc_fea_trans(loc_fea)
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loc_fea = loc_fea.transpose([0, 2, 1])
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loc_concat = paddle.concat([output, loc_fea], axis=2)
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loc_preds = self.loc_generator(loc_concat)
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loc_preds = F.sigmoid(loc_preds)
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else:
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temp_elem = paddle.zeros(shape=[batch_size], dtype="int32")
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structure_probs = None
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loc_preds = None
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elem_onehots = None
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outputs = None
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alpha = None
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max_text_length = paddle.to_tensor(self.max_text_length)
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i = 0
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while i < max_text_length + 1:
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elem_onehots = self._char_to_onehot(
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temp_elem, onehot_dim=self.out_channels)
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(outputs, hidden), alpha = self.structure_attention_cell(
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hidden, fea, elem_onehots)
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output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
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structure_probs_step = self.structure_generator(outputs)
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temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
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i += 1
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output = paddle.concat(output_hiddens, axis=1)
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structure_probs = self.structure_generator(output)
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structure_probs = F.softmax(structure_probs)
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if self.loc_type == 1:
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loc_preds = self.loc_generator(output)
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loc_preds = F.sigmoid(loc_preds)
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else:
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loc_fea = fea.transpose([0, 2, 1])
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loc_fea = self.loc_fea_trans(loc_fea)
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loc_fea = loc_fea.transpose([0, 2, 1])
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loc_concat = paddle.concat([output, loc_fea], axis=2)
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loc_preds = self.loc_generator(loc_concat)
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loc_preds = F.sigmoid(loc_preds)
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return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
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class SLAHead(nn.Layer):
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def __init__(self,
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in_channels,
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hidden_size,
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out_channels=30,
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max_text_length=500,
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loc_reg_num=4,
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fc_decay=0.0,
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**kwargs):
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"""
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@param in_channels: input shape
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@param hidden_size: hidden_size for RNN and Embedding
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@param out_channels: num_classes to rec
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@param max_text_length: max text pred
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"""
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super().__init__()
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in_channels = in_channels[-1]
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self.hidden_size = hidden_size
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self.max_text_length = max_text_length
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self.emb = self._char_to_onehot
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self.num_embeddings = out_channels
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# structure
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self.structure_attention_cell = AttentionGRUCell(
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in_channels, hidden_size, self.num_embeddings)
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weight_attr, bias_attr = get_para_bias_attr(
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l2_decay=fc_decay, k=hidden_size)
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weight_attr1_1, bias_attr1_1 = get_para_bias_attr(
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l2_decay=fc_decay, k=hidden_size)
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weight_attr1_2, bias_attr1_2 = get_para_bias_attr(
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l2_decay=fc_decay, k=hidden_size)
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self.structure_generator = nn.Sequential(
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nn.Linear(
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self.hidden_size,
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self.hidden_size,
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weight_attr=weight_attr1_2,
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bias_attr=bias_attr1_2),
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nn.Linear(
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hidden_size,
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out_channels,
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weight_attr=weight_attr,
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bias_attr=bias_attr))
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# loc
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weight_attr1, bias_attr1 = get_para_bias_attr(
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l2_decay=fc_decay, k=self.hidden_size)
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weight_attr2, bias_attr2 = get_para_bias_attr(
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l2_decay=fc_decay, k=self.hidden_size)
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self.loc_generator = nn.Sequential(
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nn.Linear(
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self.hidden_size,
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self.hidden_size,
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weight_attr=weight_attr1,
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bias_attr=bias_attr1),
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nn.Linear(
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self.hidden_size,
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loc_reg_num,
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weight_attr=weight_attr2,
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bias_attr=bias_attr2),
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nn.Sigmoid())
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def forward(self, inputs, targets=None):
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fea = inputs[-1]
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batch_size = fea.shape[0]
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# reshape
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fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], -1])
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fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
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hidden = paddle.zeros((batch_size, self.hidden_size))
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structure_preds = []
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loc_preds = []
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if self.training and targets is not None:
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structure = targets[0]
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for i in range(self.max_text_length + 1):
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hidden, structure_step, loc_step = self._decode(structure[:, i],
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fea, hidden)
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structure_preds.append(structure_step)
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loc_preds.append(loc_step)
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else:
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pre_chars = paddle.zeros(shape=[batch_size], dtype="int32")
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max_text_length = paddle.to_tensor(self.max_text_length)
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# for export
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loc_step, structure_step = None, None
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for i in range(max_text_length + 1):
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hidden, structure_step, loc_step = self._decode(pre_chars, fea,
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hidden)
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pre_chars = structure_step.argmax(axis=1, dtype="int32")
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structure_preds.append(structure_step)
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loc_preds.append(loc_step)
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structure_preds = paddle.stack(structure_preds, axis=1)
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loc_preds = paddle.stack(loc_preds, axis=1)
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if not self.training:
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structure_preds = F.softmax(structure_preds)
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return {'structure_probs': structure_preds, 'loc_preds': loc_preds}
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def _decode(self, pre_chars, features, hidden):
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"""
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Predict table label and coordinates for each step
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@param pre_chars: Table label in previous step
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@param features:
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@param hidden: hidden status in previous step
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@return:
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"""
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emb_feature = self.emb(pre_chars)
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# output shape is b * self.hidden_size
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(output, hidden), alpha = self.structure_attention_cell(
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hidden, features, emb_feature)
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# structure
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structure_step = self.structure_generator(output)
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# loc
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loc_step = self.loc_generator(output)
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return hidden, structure_step, loc_step
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def _char_to_onehot(self, input_char):
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input_ont_hot = F.one_hot(input_char, self.num_embeddings)
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return input_ont_hot
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