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fix tablerec-rare train error

pull/7792/head
WenmuZhou 2022-09-30 07:01:43 +00:00
parent 48a785f9cd
commit 3e8c78b8c1
1 changed files with 15 additions and 20 deletions
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35
ppocr/modeling/heads/table_att_head.py
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@ -82,7 +82,8 @@ class TableAttentionHead(nn.Layer):
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))
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):
@ -91,19 +92,13 @@ class TableAttentionHead(nn.Layer):
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
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:
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])
loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
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
@ -118,17 +113,15 @@ class TableAttentionHead(nn.Layer):
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
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")
output = output_hiddens
structure_probs = self.structure_generator(output)
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, loc_fea], axis=2)
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}
@ -203,8 +196,10 @@ class SLAHead(nn.Layer):
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 = 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: