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Merge pull request #970 from WenmuZhou/dygraph 

解决crnn训练时对labels进行合并的bug
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dyning 2020-10-20 19:21:25 +08:00 committed by GitHub
parent 52b40f36e5 a88ce7a5b6
commit f1048e296e
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 6 additions and 57 deletions
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60
ppocr/modeling/necks/rnn.py
View File

@ -21,18 +21,6 @@ from paddle import nn
from ppocr.modeling.heads.rec_ctc_head import get_para_bias_attr
class EncoderWithReshape(nn.Layer):
def __init__(self, in_channels, **kwargs):
super().__init__()
self.out_channels = in_channels
def forward(self, x):
B, C, H, W = x.shape
x = x.reshape((B, C, -1))
x = x.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
return x
class Im2Seq(nn.Layer):
def __init__(self, in_channels, **kwargs):
super().__init__()
@ -40,9 +28,8 @@ class Im2Seq(nn.Layer):
def forward(self, x):
B, C, H, W = x.shape
assert H == 1
x = x.transpose((0, 2, 3, 1))
x = x.reshape((-1, C))
x = x.reshape((B, -1, W))
x = x.transpose((0, 2, 1)) # (NTC)(batch, width, channels)
return x
@ -50,49 +37,10 @@ class EncoderWithRNN(nn.Layer):
def __init__(self, in_channels, hidden_size):
super(EncoderWithRNN, self).__init__()
self.out_channels = hidden_size * 2
# self.lstm1_fw = nn.LSTMCell(
# in_channels,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st1_fc1_w'),
# bias_ih_attr=ParamAttr(name='lstm_st1_fc1_b'),
# weight_hh_attr=ParamAttr(name='lstm_st1_out1_w'),
# bias_hh_attr=ParamAttr(name='lstm_st1_out1_b'),
# )
# self.lstm1_bw = nn.LSTMCell(
# in_channels,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st1_fc2_w'),
# bias_ih_attr=ParamAttr(name='lstm_st1_fc2_b'),
# weight_hh_attr=ParamAttr(name='lstm_st1_out2_w'),
# bias_hh_attr=ParamAttr(name='lstm_st1_out2_b'),
# )
# self.lstm2_fw = nn.LSTMCell(
# hidden_size,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st2_fc1_w'),
# bias_ih_attr=ParamAttr(name='lstm_st2_fc1_b'),
# weight_hh_attr=ParamAttr(name='lstm_st2_out1_w'),
# bias_hh_attr=ParamAttr(name='lstm_st2_out1_b'),
# )
# self.lstm2_bw = nn.LSTMCell(
# hidden_size,
# hidden_size,
# weight_ih_attr=ParamAttr(name='lstm_st2_fc2_w'),
# bias_ih_attr=ParamAttr(name='lstm_st2_fc2_b'),
# weight_hh_attr=ParamAttr(name='lstm_st2_out2_w'),
# bias_hh_attr=ParamAttr(name='lstm_st2_out2_b'),
# )
self.lstm = nn.LSTM(
in_channels, hidden_size, direction='bidirectional', num_layers=2)
def forward(self, x):
# fw_x, _ = self.lstm1_fw(x)
# fw_x, _ = self.lstm2_fw(fw_x)
#
# # bw
# bw_x, _ = self.lstm1_bw(x)
# bw_x, _ = self.lstm2_bw(bw_x)
# x = paddle.concat([fw_x, bw_x], axis=2)
x, _ = self.lstm(x)
return x
@ -118,13 +66,13 @@ class EncoderWithFC(nn.Layer):
class SequenceEncoder(nn.Layer):
def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs):
super(SequenceEncoder, self).__init__()
self.encoder_reshape = EncoderWithReshape(in_channels)
self.encoder_reshape = Im2Seq(in_channels)
self.out_channels = self.encoder_reshape.out_channels
if encoder_type == 'reshape':
self.only_reshape = True
else:
support_encoder_dict = {
'reshape': EncoderWithReshape,
'reshape': Im2Seq,
'fc': EncoderWithFC,
'rnn': EncoderWithRNN
}

3
ppocr/postprocess/rec_postprocess.py
View File

@ -70,6 +70,7 @@ class BaseRecLabelDecode(object):
if text_index[batch_idx][idx] in ignored_tokens:
continue
if is_remove_duplicate:
# only for predict
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
batch_idx][idx]:
continue
@ -107,7 +108,7 @@ class CTCLabelDecode(BaseRecLabelDecode):
text = self.decode(preds_idx, preds_prob)
if label is None:
return text
label = self.decode(label)
label = self.decode(label, is_remove_duplicate=False)
return text, label
def add_special_char(self, dict_character):