未验证 提交 096265e4 编写于 作者: Z zhoujun 提交者: GitHub

Merge pull request #7751 from WenmuZhou/test1

add paddle2onnx support for tablerec-rare
......@@ -43,7 +43,6 @@ Architecture:
Head:
name: TableAttentionHead
hidden_size: 256
loc_type: 2
max_text_length: *max_text_length
loc_reg_num: &loc_reg_num 4
......
......@@ -16,6 +16,7 @@ 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
......@@ -42,7 +43,6 @@ class TableAttentionHead(nn.Layer):
def __init__(self,
in_channels,
hidden_size,
loc_type,
in_max_len=488,
max_text_length=800,
out_channels=30,
......@@ -57,12 +57,8 @@ class TableAttentionHead(nn.Layer):
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.loc_type = loc_type
self.in_max_len = in_max_len
if self.loc_type == 1:
self.loc_generator = nn.Linear(hidden_size, 4)
else:
if self.in_max_len == 640:
self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1)
elif self.in_max_len == 800:
......@@ -80,16 +76,13 @@ class TableAttentionHead(nn.Layer):
# 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]
if len(fea.shape) == 3:
pass
else:
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 = []
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):
......@@ -97,7 +90,8 @@ class TableAttentionHead(nn.Layer):
structure[:, i], onehot_dim=self.out_channels)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
output_hiddens[:, i, :] = outputs
# output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
output = paddle.concat(output_hiddens, axis=1)
structure_probs = self.structure_generator(output)
if self.loc_type == 1:
......@@ -118,24 +112,19 @@ class TableAttentionHead(nn.Layer):
outputs = None
alpha = None
max_text_length = paddle.to_tensor(self.max_text_length)
i = 0
while i < max_text_length + 1:
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.append(paddle.unsqueeze(outputs, axis=1))
output_hiddens[:, i, :] = outputs
# output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
structure_probs_step = self.structure_generator(outputs)
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
i += 1
output = paddle.concat(output_hiddens, axis=1)
output = output_hiddens
structure_probs = self.structure_generator(output)
structure_probs = F.softmax(structure_probs)
if self.loc_type == 1:
loc_preds = self.loc_generator(output)
loc_preds = F.sigmoid(loc_preds)
else:
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
......
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