update text.py

hapi/text/text.py

@@ -19,7 +19,6 @@ from __future__ import print_function
 import os
 import six
 import sys
-
 if six.PY2:
     reload(sys)
     sys.setdefaultencoding('utf8')
@@ -50,8 +49,8 @@ __all__ = [
     'BeamSearchDecoder', 'MultiHeadAttention', 'FFN',
     'TransformerEncoderLayer', 'TransformerEncoder', 'TransformerDecoderLayer',
     'TransformerDecoder', 'TransformerBeamSearchDecoder', 'Linear_chain_crf',
-    'Crf_decoding', 'SequenceTagging', 'GRUEncoderLayer', 'CNNEncoder',
-    'BOWEncoder', 'SimpleConvPoolLayer', 'GRUEncoder', 'DynamicGRU', 'LSTMEncoder'
+    'Crf_decoding', 'SequenceTagging', 'GRUEncoderLayer', 'SimCNNEncoder',
+    'SimBOWEncoder', 'SimpleConvPoolLayer', 'SimGRUEncoder', 'DynamicGRU', 'SimLSTMEncoder'
 ]
 
 
@@ -89,12 +88,12 @@ class RNNCell(Layer):
         batch_ref = flatten(batch_ref)[0]
 
         def _is_shape_sequence(seq):
-            if sys.version_info < (3,):
+            if sys.version_info < (3, ):
                 integer_types = (
                     int,
-                    long,)
+                    long, )
             else:
-                integer_types = (int,)
+                integer_types = (int, )
             """For shape, list/tuple of integer is the finest-grained objection"""
             if (isinstance(seq, list) or isinstance(seq, tuple)):
                 if reduce(
@@ -249,8 +248,8 @@ class BasicLSTMCell(RNNCell):
 
         self.use_customized_weight = False
         for _weights in [
-            forget_gate_weights, input_gate_weights, output_gate_weights,
-            cell_weights
+                forget_gate_weights, input_gate_weights, output_gate_weights,
+                cell_weights
         ]:
             for _key in _weights:
                 if _weights[_key] is not None:
@@ -275,7 +274,7 @@ class BasicLSTMCell(RNNCell):
                 is_bias=True)
         else:
             if "w" in forget_gate_weights and forget_gate_weights[
-                "w"] is not None:
+                    "w"] is not None:
                 self.fg_w = forget_gate_weights["w"]
             else:
                 if self._param_attr is not None and self._param_attr.name is not None:
@@ -289,7 +288,7 @@ class BasicLSTMCell(RNNCell):
                     dtype=self._dtype)
 
             if "h" in forget_gate_weights and forget_gate_weights[
-                "h"] is not None:
+                    "h"] is not None:
                 self.fg_h = forget_gate_weights["h"]
             else:
                 if self._param_attr is not None and self._param_attr.name is not None:
@@ -303,7 +302,7 @@ class BasicLSTMCell(RNNCell):
                     dtype=self._dtype)
 
             if "b" in forget_gate_weights and forget_gate_weights[
-                "b"] is not None:
+                    "b"] is not None:
                 self.fg_b = forget_gate_weights["b"]
             else:
                 if self._bias_attr is not None and self._bias_attr.name is not None:
@@ -318,7 +317,7 @@ class BasicLSTMCell(RNNCell):
                     is_bias=True)
 
             if "w" in input_gate_weights and input_gate_weights[
-                "w"] is not None:
+                    "w"] is not None:
                 self.ig_w = input_gate_weights["w"]
             else:
                 if self._param_attr is not None and self._param_attr.name is not None:
@@ -333,7 +332,7 @@ class BasicLSTMCell(RNNCell):
                     dtype=self._dtype)
 
             if "h" in input_gate_weights and input_gate_weights[
-                "h"] is not None:
+                    "h"] is not None:
                 self.ig_h = input_gate_weights["h"]
             else:
                 if self._param_attr is not None and self._param_attr.name is not None:
@@ -348,7 +347,7 @@ class BasicLSTMCell(RNNCell):
                     dtype=self._dtype)
 
             if "b" in input_gate_weights and input_gate_weights[
-                "b"] is not None:
+                    "b"] is not None:
                 self.ig_b = input_gate_weights["b"]
             else:
                 if self._bias_attr is not None and self._bias_attr.name is not None:
@@ -363,7 +362,7 @@ class BasicLSTMCell(RNNCell):
                     is_bias=True)
 
             if "w" in output_gate_weights and output_gate_weights[
-                "w"] is not None:
+                    "w"] is not None:
                 self.og_w = output_gate_weights["w"]
             else:
                 if self._param_attr is not None and self._param_attr.name is not None:
@@ -377,7 +376,7 @@ class BasicLSTMCell(RNNCell):
                     dtype=self._dtype)
 
             if "h" in output_gate_weights and output_gate_weights[
-                "h"] is not None:
+                    "h"] is not None:
                 self.og_h = output_gate_weights["h"]
             else:
                 if self._param_attr is not None and self._param_attr.name is not None:
@@ -392,7 +391,7 @@ class BasicLSTMCell(RNNCell):
                     dtype=self._dtype)
 
             if "b" in output_gate_weights and output_gate_weights[
-                "b"] is not None:
+                    "b"] is not None:
                 self.og_b = output_gate_weights["b"]
             else:
                 if self._bias_attr is not None and self._bias_attr.name is not None:
@@ -547,7 +546,7 @@ class BasicGRUCell(RNNCell):
 
         self.use_customized_weight = False
         for _weights in [
-            update_gate_weights, reset_gate_weights, cell_weights
+                update_gate_weights, reset_gate_weights, cell_weights
         ]:
             for _key in _weights:
                 if _weights[_key] is not None:
@@ -603,7 +602,7 @@ class BasicGRUCell(RNNCell):
 
             # create the parameters of gates in gru
             if "w" in update_gate_weights and update_gate_weights[
-                "w"] is not None:
+                    "w"] is not None:
                 self.ug_w = update_gate_weights["w"]
             else:
                 if gate_param_attr is not None and gate_param_attr.name is not None:
@@ -617,7 +616,7 @@ class BasicGRUCell(RNNCell):
                     dtype=self._dtype)
 
             if "h" in update_gate_weights and update_gate_weights[
-                "h"] is not None:
+                    "h"] is not None:
                 self.ug_h = update_gate_weights["h"]
             else:
                 if gate_param_attr is not None and gate_param_attr.name is not None:
@@ -631,7 +630,7 @@ class BasicGRUCell(RNNCell):
                     dtype=self._dtype)
 
             if "b" in update_gate_weights and update_gate_weights[
-                "b"] is not None:
+                    "b"] is not None:
                 self.ug_b = update_gate_weights["b"]
             else:
                 if gate_bias_attr is not None and gate_bias_attr.name is not None:
@@ -647,7 +646,7 @@ class BasicGRUCell(RNNCell):
 
             # reset gate parameters
             if "w" in reset_gate_weights and reset_gate_weights[
-                "w"] is not None:
+                    "w"] is not None:
                 self.rg_w = reset_gate_weights["w"]
             else:
                 if gate_param_attr is not None and gate_param_attr.name is not None:
@@ -661,7 +660,7 @@ class BasicGRUCell(RNNCell):
                     dtype=self._dtype)
 
             if "h" in reset_gate_weights and reset_gate_weights[
-                "h"] is not None:
+                    "h"] is not None:
                 self.rg_h = reset_gate_weights["h"]
             else:
                 if gate_param_attr is not None and gate_param_attr.name is not None:
@@ -675,7 +674,7 @@ class BasicGRUCell(RNNCell):
                     dtype=self._dtype)
 
             if "b" in reset_gate_weights and reset_gate_weights[
-                "b"] is not None:
+                    "b"] is not None:
                 self.rg_b = reused_params["b"]
             else:
                 if gate_bias_attr is not None and gate_bias_attr.name is not None:
@@ -803,7 +802,7 @@ class RNN(fluid.dygraph.Layer):
                 new_state = fluid.layers.elementwise_mul(
                     new_state, step_mask,
                     axis=0) - fluid.layers.elementwise_mul(
-                    state, (step_mask - 1), axis=0)
+                        state, (step_mask - 1), axis=0)
                 return new_state
 
             flat_inputs = flatten(inputs)
@@ -849,8 +848,8 @@ class RNN(fluid.dygraph.Layer):
                 outputs = map_structure(
                     lambda x: ArrayWrapper(x),
                     step_outputs) if i == 0 else map_structure(
-                    lambda x, x_array: x_array.append(x), step_outputs,
-                    outputs)
+                        lambda x, x_array: x_array.append(x), step_outputs,
+                        outputs)
 
             final_outputs = map_structure(
                 lambda x: fluid.layers.stack(x.array,
@@ -919,7 +918,7 @@ class DynamicDecode(Layer):
                     step_mask.stop_gradient = True
                 new_state = layers.elementwise_mul(
                     state, step_mask, axis=0) - layers.elementwise_mul(
-                    new_state, (step_mask - 1), axis=0)
+                        new_state, (step_mask - 1), axis=0)
                 if convert_dtype(state_dtype) in ["bool"]:
                     new_state = layers.cast(new_state, dtype=state_dtype)
                 return new_state
@@ -961,8 +960,8 @@ class DynamicDecode(Layer):
                 outputs = map_structure(
                     lambda x: ArrayWrapper(x),
                     step_outputs) if step_idx == 0 else map_structure(
-                    lambda x, x_array: x_array.append(x), step_outputs,
-                    outputs)
+                        lambda x, x_array: x_array.append(x), step_outputs,
+                        outputs)
                 inputs, states, finished, sequence_lengths = (
                     next_inputs, next_states, next_finished,
                     next_sequence_lengths)
@@ -991,7 +990,7 @@ class DynamicDecode(Layer):
 
             return (final_outputs, final_states,
                     sequence_lengths) if self.return_length else (
-                final_outputs, final_states)
+                        final_outputs, final_states)
         else:
             return fluid.layers.dynamic_decode(
                 self.decoder,
@@ -1042,7 +1041,7 @@ class TransformerBeamSearchDecoder(layers.BeamSearchDecoder):
         x = layers.reshape(
             x, [0] * (len(x.shape) - var_dim_in_state
                       ) + [self.batch_size * self.beam_size] +
-               [int(size) for size in x.shape[-var_dim_in_state + 2:]])
+            [int(size) for size in x.shape[-var_dim_in_state + 2:]])
         x = layers.transpose(
             x,
             list(range((len(x.shape) + 1 - var_dim_in_state), len(x.shape))) +
@@ -1053,9 +1052,9 @@ class TransformerBeamSearchDecoder(layers.BeamSearchDecoder):
         var_dim_size = layers.shape(x)[self.var_dim_in_state]
         x = layers.reshape(
             x, [-1, self.beam_size] +
-               [int(size)
-                for size in x.shape[1:self.var_dim_in_state]] + [var_dim_size] +
-               [int(size) for size in x.shape[self.var_dim_in_state + 1:]])
+            [int(size)
+             for size in x.shape[1:self.var_dim_in_state]] + [var_dim_size] +
+            [int(size) for size in x.shape[self.var_dim_in_state + 1:]])
         return x
 
     def step(self, time, inputs, states, **kwargs):
@@ -1118,7 +1117,7 @@ class PrePostProcessLayer(Layer):
             elif cmd == "d":  # add dropout
                 self.functors.append(lambda x: layers.dropout(
                     x, dropout_prob=dropout_rate, is_test=False)
-                if dropout_rate else x)
+                                     if dropout_rate else x)
 
     def forward(self, x, residual=None):
         for i, cmd in enumerate(self.process_cmd):
@@ -1219,7 +1218,7 @@ class MultiHeadAttention(Layer):
 
         # scale dot product attention
         product = layers.matmul(
-            x=q, y=k, transpose_y=True, alpha=self.d_model ** -0.5)
+            x=q, y=k, transpose_y=True, alpha=self.d_model**-0.5)
         if attn_bias:
             product += attn_bias
         weights = layers.softmax(product)
@@ -1309,6 +1308,7 @@ class TransformerEncoderLayer(Layer):
                  reused_ffn_weights={"reused_fc1": None,
                                      "reused_fc2": None},
                  reused_post_ffn_layernorm=None):
+
         super(TransformerEncoderLayer, self).__init__()
 
         self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
@@ -1556,7 +1556,7 @@ class TransformerDecoder(Layer):
         ]
 
 
-# TODO: we should merge GRUCell with BasicGRUCell
+#TODO: we should merge GRUCell with BasicGRUCell
 class GRUCell(RNNCell):
     def __init__(self,
                  input_size,
@@ -1590,7 +1590,7 @@ class GRUCell(RNNCell):
         return [self.hidden_size]
 
 
-# TODO: we should merge GRUCell with BasicGRUCell
+#TODO: we should merge GRUCell with BasicGRUCell
 class GRUEncoderCell(RNNCell):
     def __init__(self,
                  num_layers,
@@ -1606,7 +1606,7 @@ class GRUEncoderCell(RNNCell):
             self.gru_cells.append(
                 self.add_sublayer(
                     "gru_%d" % i,
-                    # BasicGRUCell(
+                    #BasicGRUCell(
                     GRUCell(
                         input_size=input_size if i == 0 else hidden_size,
                         hidden_size=hidden_size,
@@ -1673,6 +1673,7 @@ class Linear_chain_crf(fluid.dygraph.Layer):
         self._transition = value
 
     def forward(self, input, label, length=None):
+
         alpha = self._helper.create_variable_for_type_inference(
             dtype=self._dtype)
         emission_exps = self._helper.create_variable_for_type_inference(
@@ -1723,6 +1724,7 @@ class Crf_decoding(fluid.dygraph.Layer):
         self._transition = value
 
     def forward(self, input, label=None, length=None):
+
         viterbi_path = self._helper.create_variable_for_type_inference(
             dtype=self._dtype)
         this_inputs = {
@@ -1919,7 +1921,7 @@ class SimpleConvPoolLayer(Layer):
         return x
 
 
-class CNNEncoder(Layer):
+class SimCNNEncoder(Layer):
     """
     simple CNNEncoder for simnet
     """
@@ -1933,7 +1935,7 @@ class CNNEncoder(Layer):
                  padding_idx,
                  act
                  ):
-        super(CNNEncoder, self).__init__()
+        super(SimCNNEncoder, self).__init__()
         self.dict_size = dict_size
         self.emb_dim = emb_dim
         self.filter_size = filter_size
@@ -1962,7 +1964,7 @@ class CNNEncoder(Layer):
         emb_out=self.cnn_layer(emb_reshape)
         return emb_out
 
-class BOWEncoder(Layer):
+class SimBOWEncoder(Layer):
     """
     simple BOWEncoder for simnet
     """
@@ -1973,7 +1975,7 @@ class BOWEncoder(Layer):
                  seq_len,
                  padding_idx
                  ):
-        super(BOWEncoder, self).__init__()
+        super(SimBOWEncoder, self).__init__()
         self.dict_size = dict_size
         self.bow_dim = bow_dim
         self.seq_len = seq_len
@@ -2034,7 +2036,7 @@ class DynamicGRU(fluid.dygraph.Layer):
         res = fluid.layers.concat(res, axis=1)
         return res
 
-class GRUEncoder(Layer):
+class SimGRUEncoder(Layer):
     """
     simple GRUEncoder for simnet
     """
@@ -2046,7 +2048,7 @@ class GRUEncoder(Layer):
                  padding_idx,
                  seq_len
                  ):
-        super(GRUEncoder, self).__init__()
+        super(SimGRUEncoder, self).__init__()
         self.dict_size = dict_size
         self.emb_dim = emb_dim
         self.gru_dim = gru_dim
@@ -2071,7 +2073,7 @@ class GRUEncoder(Layer):
         gru = fluid.layers.tanh(gru)
         return gru
 
-class LSTMEncoder(Layer):
+class SimLSTMEncoder(Layer):
     """
     simple LSTMEncoder for simnet
     """
@@ -2087,7 +2089,7 @@ class LSTMEncoder(Layer):
         """
         initialize
         """
-        super(LSTMEncoder, self).__init__()
+        super(SimLSTMEncoder, self).__init__()
         self.dict_size = dict_size
         self.emb_dim = emb_dim
         self.lstm_dim = lstm_dim