Merge branch 'develop' of upstream into kaldi_decoder

fluid/DeepASR/data_utils/async_data_reader.py

@@ -218,8 +218,6 @@ class AsyncDataReader(object):
         self._sample_proc_num = self._proc_num - 2
         self._verbose = verbose
         self._force_exit = ForceExitWrapper(self._manager.Value('b', False))
-        self._pool_manager = SharedMemoryPoolManager(self._batch_buffer_size *
-                                                     3, self._manager)
 
     def generate_bucket_list(self, is_shuffle):
         if self._block_info_list is None:
@@ -424,6 +422,9 @@ class AsyncDataReader(object):
         sample_queue = self._start_async_processing()
         batch_queue = self._manager.Queue(self._batch_buffer_size)
 
+        self._pool_manager = SharedMemoryPoolManager(self._batch_buffer_size *
+                                                     3, self._manager)
+
         assembling_proc = DaemonProcessGroup(
             proc_num=1,
             target=batch_assembling_task,
@@ -439,3 +440,6 @@ class AsyncDataReader(object):
                 if isinstance(batch_data, EpochEndSignal):
                     break
                 yield batch_data
+
+        # clean the shared memory
+        del self._pool_manager

fluid/DeepASR/data_utils/util.py

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-import sys
+import sys, time
 from six import reraise
 from tblib import Traceback
 from multiprocessing import Manager, Process
@@ -161,9 +161,10 @@ class SharedMemoryPoolManager(object):
     def __init__(self, pool_size, manager, name_prefix='/deep_asr'):
         self._names = []
         self._dict = manager.dict()
+        self._time_prefix = time.strftime('%Y%m%d%H%M%S')
 
         for i in xrange(pool_size):
-            name = name_prefix + '_' + str(i)
+            name = name_prefix + '_' + self._time_prefix + '_' + str(i)
             self._dict[name] = SharedNDArray(name)
             self._names.append(name)
 

fluid/neural_machine_translation/transformer/config.py

@@ -3,18 +3,37 @@ class TrainTaskConfig(object):
     # the epoch number to train.
     pass_num = 2
 
-    # number of sequences contained in a mini-batch.
+    # the number of sequences contained in a mini-batch.
     batch_size = 64
 
-    # the hyper params for Adam optimizer.
+    # the hyper parameters for Adam optimizer.
     learning_rate = 0.001
     beta1 = 0.9
     beta2 = 0.98
     eps = 1e-9
 
-    # the params for learning rate scheduling
+    # the parameters for learning rate scheduling.
     warmup_steps = 4000
 
+    # the directory for saving trained models.
+    model_dir = "trained_models"
+
+
+class InferTaskConfig(object):
+    use_gpu = False
+    # the number of examples in one run for sequence generation.
+    # currently the batch size can only be set to 1.
+    batch_size = 1
+
+    # the parameters for beam search.
+    beam_size = 5
+    max_length = 30
+    # the number of decoded sentences to output.
+    n_best = 1
+
+    # the directory for loading the trained model.
+    model_path = "trained_models/pass_1.infer.model"
+
 
 class ModelHyperParams(object):
     # Dictionary size for source and target language. This model directly uses
@@ -33,6 +52,11 @@ class ModelHyperParams(object):
     # index for <pad> token in target language.
     trg_pad_idx = trg_vocab_size
 
+    # index for <bos> token
+    bos_idx = 0
+    # index for <eos> token
+    eos_idx = 1
+
     # position value corresponding to the <pad> token.
     pos_pad_idx = 0
 
@@ -64,14 +88,21 @@ pos_enc_param_names = (
     "src_pos_enc_table",
     "trg_pos_enc_table", )
 
-# Names of all data layers listed in order.
-input_data_names = (
+# Names of all data layers in encoder listed in order.
+encoder_input_data_names = (
     "src_word",
     "src_pos",
+    "src_slf_attn_bias", )
+
+# Names of all data layers in decoder listed in order.
+decoder_input_data_names = (
     "trg_word",
     "trg_pos",
-    "src_slf_attn_bias",
     "trg_slf_attn_bias",
     "trg_src_attn_bias",
+    "enc_output", )
+
+# Names of label related data layers listed in order.
+label_data_names = (
     "lbl_word",
     "lbl_weight", )

fluid/neural_machine_translation/transformer/infer.py

+import numpy as np
+
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+
+import model
+from model import wrap_encoder as encoder
+from model import wrap_decoder as decoder
+from config import InferTaskConfig, ModelHyperParams, \
+        encoder_input_data_names, decoder_input_data_names
+from train import pad_batch_data
+
+
+def translate_batch(exe, src_words, encoder, enc_in_names, enc_out_names,
+                    decoder, dec_in_names, dec_out_names, beam_size, max_length,
+                    n_best, batch_size, n_head, src_pad_idx, trg_pad_idx,
+                    bos_idx, eos_idx):
+    """
+    Run the encoder program once and run the decoder program multiple times to
+    implement beam search externally.
+    """
+    # Prepare data for encoder and run the encoder.
+    enc_in_data = pad_batch_data(
+        src_words,
+        src_pad_idx,
+        n_head,
+        is_target=False,
+        return_pos=True,
+        return_attn_bias=True,
+        return_max_len=True)
+    enc_output = exe.run(encoder,
+                         feed=dict(zip(enc_in_names, enc_in_data)),
+                         fetch_list=enc_out_names)[0]
+
+    # Beam Search.
+    # To store the beam info.
+    scores = np.zeros((batch_size, beam_size), dtype="float32")
+    prev_branchs = [[]] * batch_size
+    next_ids = [[]] * batch_size
+    # Use beam_map to map the instance idx in batch to beam idx, since the
+    # size of feeded batch is changing.
+    beam_map = range(batch_size)
+
+    def beam_backtrace(prev_branchs, next_ids, n_best=beam_size, add_bos=True):
+        """
+        Decode and select n_best sequences for one instance by backtrace.
+        """
+        seqs = []
+        for i in range(n_best):
+            k = i
+            seq = []
+            for j in range(len(prev_branchs) - 1, -1, -1):
+                seq.append(next_ids[j][k])
+                k = prev_branchs[j][k]
+            seq = seq[::-1]
+            seq = [bos_idx] + seq if add_bos else seq
+            seqs.append(seq)
+        return seqs
+
+    def init_dec_in_data(batch_size, beam_size, enc_in_data, enc_output):
+        """
+        Initialize the input data for decoder.
+        """
+        trg_words = np.array(
+            [[bos_idx]] * batch_size * beam_size, dtype="int64")
+        trg_pos = np.array([[1]] * batch_size * beam_size, dtype="int64")
+        src_max_length, src_slf_attn_bias, trg_max_len = enc_in_data[
+            -1], enc_in_data[-2], 1
+        # This is used to remove attention on subsequent words.
+        trg_slf_attn_bias = np.ones((batch_size * beam_size, trg_max_len,
+                                     trg_max_len))
+        trg_slf_attn_bias = np.triu(trg_slf_attn_bias, 1).reshape(
+            [-1, 1, trg_max_len, trg_max_len])
+        trg_slf_attn_bias = (np.tile(trg_slf_attn_bias, [1, n_head, 1, 1]) *
+                             [-1e9]).astype("float32")
+        # This is used to remove attention on the paddings of source sequences.
+        trg_src_attn_bias = np.tile(
+            src_slf_attn_bias[:, :, ::src_max_length, :],
+            [beam_size, 1, trg_max_len, 1])
+        enc_output = np.tile(enc_output, [beam_size, 1, 1])
+        return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output
+
+    def update_dec_in_data(dec_in_data, next_ids, active_beams):
+        """
+        Update the input data of decoder mainly by slicing from the previous
+        input data and dropping the finished instance beams.
+        """
+        trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output = dec_in_data
+        trg_cur_len = len(next_ids[0]) + 1  # include the <bos>
+        trg_words = np.array(
+            [
+                beam_backtrace(
+                    prev_branchs[beam_idx], next_ids[beam_idx], add_bos=True)
+                for beam_idx in active_beams
+            ],
+            dtype="int64")
+        trg_words = trg_words.reshape([-1, 1])
+        trg_pos = np.array(
+            [range(1, trg_cur_len + 1)] * len(active_beams) * beam_size,
+            dtype="int64").reshape([-1, 1])
+        active_beams_indice = (
+            (np.array(active_beams) * beam_size)[:, np.newaxis] +
+            np.array(range(beam_size))[np.newaxis, :]).flatten()
+        # This is used to remove attention on subsequent words.
+        trg_slf_attn_bias = np.ones((len(active_beams) * beam_size, trg_cur_len,
+                                     trg_cur_len))
+        trg_slf_attn_bias = np.triu(trg_slf_attn_bias, 1).reshape(
+            [-1, 1, trg_cur_len, trg_cur_len])
+        trg_slf_attn_bias = (np.tile(trg_slf_attn_bias, [1, n_head, 1, 1]) *
+                             [-1e9]).astype("float32")
+        # This is used to remove attention on the paddings of source sequences.
+        trg_src_attn_bias = np.tile(trg_src_attn_bias[
+            active_beams_indice, :, ::trg_src_attn_bias.shape[2], :],
+                                    [1, 1, trg_cur_len, 1])
+        enc_output = enc_output[active_beams_indice, :, :]
+        return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output
+
+    dec_in_data = init_dec_in_data(batch_size, beam_size, enc_in_data,
+                                   enc_output)
+    for i in range(max_length):
+        predict_all = exe.run(decoder,
+                              feed=dict(zip(dec_in_names, dec_in_data)),
+                              fetch_list=dec_out_names)[0]
+        predict_all = np.log(
+            predict_all.reshape([len(beam_map) * beam_size, i + 1, -1])[:,
+                                                                        -1, :])
+        predict_all = (predict_all + scores[beam_map].reshape(
+            [len(beam_map) * beam_size, -1])).reshape(
+                [len(beam_map), beam_size, -1])
+        active_beams = []
+        for inst_idx, beam_idx in enumerate(beam_map):
+            predict = (predict_all[inst_idx, :, :]
+                       if i != 0 else predict_all[inst_idx, 0, :]).flatten()
+            top_k_indice = np.argpartition(predict, -beam_size)[-beam_size:]
+            top_scores_ids = top_k_indice[np.argsort(predict[top_k_indice])[::
+                                                                            -1]]
+            top_scores = predict[top_scores_ids]
+            scores[beam_idx] = top_scores
+            prev_branchs[beam_idx].append(top_scores_ids /
+                                          predict_all.shape[-1])
+            next_ids[beam_idx].append(top_scores_ids % predict_all.shape[-1])
+            if next_ids[beam_idx][-1][0] != eos_idx:
+                active_beams.append(beam_idx)
+        beam_map = active_beams
+        if len(beam_map) == 0:
+            break
+        dec_in_data = update_dec_in_data(dec_in_data, next_ids, active_beams)
+
+    # Decode beams and select n_best sequences for each instance by backtrace.
+    seqs = [beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx], n_best)]
+
+    return seqs, scores[:, :n_best].tolist()
+
+
+def main():
+    place = fluid.CUDAPlace(0) if InferTaskConfig.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    # The current program desc is coupled with batch_size and the only
+    # supported batch size is 1 currently.
+    encoder_program = fluid.Program()
+    model.batch_size = InferTaskConfig.batch_size
+    with fluid.program_guard(main_program=encoder_program):
+        enc_output = encoder(
+            ModelHyperParams.src_vocab_size + 1,
+            ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
+            ModelHyperParams.n_head, ModelHyperParams.d_key,
+            ModelHyperParams.d_value, ModelHyperParams.d_model,
+            ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
+            ModelHyperParams.src_pad_idx, ModelHyperParams.pos_pad_idx)
+
+    model.batch_size = InferTaskConfig.batch_size * InferTaskConfig.beam_size
+    decoder_program = fluid.Program()
+    with fluid.program_guard(main_program=decoder_program):
+        predict = decoder(
+            ModelHyperParams.trg_vocab_size + 1,
+            ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
+            ModelHyperParams.n_head, ModelHyperParams.d_key,
+            ModelHyperParams.d_value, ModelHyperParams.d_model,
+            ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
+            ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
+
+    # Load model parameters of encoder and decoder separately from the saved
+    # transformer model.
+    encoder_var_names = []
+    for op in encoder_program.block(0).ops:
+        encoder_var_names += op.input_arg_names
+    encoder_param_names = filter(
+        lambda var_name: isinstance(encoder_program.block(0).var(var_name),
+            fluid.framework.Parameter),
+        encoder_var_names)
+    encoder_params = map(encoder_program.block(0).var, encoder_param_names)
+    decoder_var_names = []
+    for op in decoder_program.block(0).ops:
+        decoder_var_names += op.input_arg_names
+    decoder_param_names = filter(
+        lambda var_name: isinstance(decoder_program.block(0).var(var_name),
+            fluid.framework.Parameter),
+        decoder_var_names)
+    decoder_params = map(decoder_program.block(0).var, decoder_param_names)
+    fluid.io.load_vars(exe, InferTaskConfig.model_path, vars=encoder_params)
+    fluid.io.load_vars(exe, InferTaskConfig.model_path, vars=decoder_params)
+
+    # This is used here to set dropout to the test mode.
+    encoder_program = fluid.io.get_inference_program(
+        target_vars=[enc_output], main_program=encoder_program)
+    decoder_program = fluid.io.get_inference_program(
+        target_vars=[predict], main_program=decoder_program)
+
+    test_data = paddle.batch(
+        paddle.dataset.wmt16.test(ModelHyperParams.src_vocab_size,
+                                  ModelHyperParams.trg_vocab_size),
+        batch_size=InferTaskConfig.batch_size)
+
+    trg_idx2word = paddle.dataset.wmt16.get_dict(
+        "de", dict_size=ModelHyperParams.trg_vocab_size, reverse=True)
+
+    for batch_id, data in enumerate(test_data()):
+        batch_seqs, batch_scores = translate_batch(
+            exe, [item[0] for item in data], encoder_program,
+            encoder_input_data_names, [enc_output.name], decoder_program,
+            decoder_input_data_names, [predict.name], InferTaskConfig.beam_size,
+            InferTaskConfig.max_length, InferTaskConfig.n_best,
+            len(data), ModelHyperParams.n_head, ModelHyperParams.src_pad_idx,
+            ModelHyperParams.trg_pad_idx, ModelHyperParams.bos_idx,
+            ModelHyperParams.eos_idx)
+        for i in range(len(batch_seqs)):
+            seqs = batch_seqs[i]
+            scores = batch_scores[i]
+            for seq in seqs:
+                print(" ".join([trg_idx2word[idx] for idx in seq]))
+
+
+if __name__ == "__main__":
+    main()

fluid/neural_machine_translation/transformer/model.py

@@ -4,7 +4,8 @@ import numpy as np
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 
-from config import TrainTaskConfig, input_data_names, pos_enc_param_names
+from config import TrainTaskConfig, pos_enc_param_names, \
+    encoder_input_data_names, decoder_input_data_names, label_data_names
 
 # FIXME(guosheng): Remove out the batch_size from the model.
 batch_size = TrainTaskConfig.batch_size
@@ -127,7 +128,9 @@ def multi_head_attention(queries,
 
         scaled_q = layers.scale(x=q, scale=d_model**-0.5)
         product = layers.matmul(x=scaled_q, y=k, transpose_y=True)
-        weights = __softmax(layers.elementwise_add(x=product, y=attn_bias))
+        weights = __softmax(
+            layers.elementwise_add(
+                x=product, y=attn_bias) if attn_bias else product)
         if dropout_rate:
             weights = layers.dropout(
                 weights, dropout_prob=dropout_rate, is_test=False)
@@ -280,8 +283,15 @@ def encoder(enc_input,
     encoder_layer.
     """
     for i in range(n_layer):
-        enc_output = encoder_layer(enc_input, attn_bias, n_head, d_key, d_value,
-                                   d_model, d_inner_hid, dropout_rate)
+        enc_output = encoder_layer(
+            enc_input,
+            attn_bias,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            dropout_rate, )
         enc_input = enc_output
     return enc_output
 
@@ -373,6 +383,62 @@ def decoder(dec_input,
     return dec_output
 
 
+def make_inputs(input_data_names,
+                n_head,
+                d_model,
+                batch_size,
+                max_length,
+                is_pos,
+                slf_attn_bias_flag,
+                src_attn_bias_flag,
+                enc_output_flag=False):
+    """
+    Define the input data layers for the transformer model.
+    """
+    input_layers = []
+    # The shapes here act as placeholder.
+    # The shapes set here is to pass the infer-shape in compile time.
+    word = layers.data(
+        name=input_data_names[len(input_layers)],
+        shape=[batch_size * max_length, 1],
+        dtype="int64",
+        append_batch_size=False)
+    input_layers += [word]
+    # This is used for position data or label weight.
+    pos = layers.data(
+        name=input_data_names[len(input_layers)],
+        shape=[batch_size * max_length, 1],
+        dtype="int64" if is_pos else "float32",
+        append_batch_size=False)
+    input_layers += [pos]
+    if slf_attn_bias_flag:
+        # This input is used to remove attention weights on paddings for the
+        # encoder and to remove attention weights on subsequent words for the
+        # decoder.
+        slf_attn_bias = layers.data(
+            name=input_data_names[len(input_layers)],
+            shape=[batch_size, n_head, max_length, max_length],
+            dtype="float32",
+            append_batch_size=False)
+        input_layers += [slf_attn_bias]
+    if src_attn_bias_flag:
+        # This input is used to remove attention weights on paddings.
+        src_attn_bias = layers.data(
+            name=input_data_names[len(input_layers)],
+            shape=[batch_size, n_head, max_length, max_length],
+            dtype="float32",
+            append_batch_size=False)
+        input_layers += [src_attn_bias]
+    if enc_output_flag:
+        enc_output = layers.data(
+            name=input_data_names[len(input_layers)],
+            shape=[batch_size, max_length, d_model],
+            dtype="float32",
+            append_batch_size=False)
+        input_layers += [enc_output]
+    return input_layers
+
+
 def transformer(
         src_vocab_size,
         trg_vocab_size,
@@ -387,61 +453,72 @@ def transformer(
         src_pad_idx,
         trg_pad_idx,
         pos_pad_idx, ):
-    # The shapes here act as placeholder.
-    # The shapes set here is to pass the infer-shape in compile time. The actual
-    # shape of src_word in run time is:
-    # [batch_size * max_src_length_in_a_batch, 1].
-    src_word = layers.data(
-        name=input_data_names[0],
-        shape=[batch_size * max_length, 1],
-        dtype="int64",
-        append_batch_size=False)
-    # The actual shape of src_pos in runtime is:
-    # [batch_size * max_src_length_in_a_batch, 1].
-    src_pos = layers.data(
-        name=input_data_names[1],
-        shape=[batch_size * max_length, 1],
-        dtype="int64",
-        append_batch_size=False)
-    # The actual shape of trg_word is in runtime is:
-    # [batch_size * max_trg_length_in_a_batch, 1].
-    trg_word = layers.data(
-        name=input_data_names[2],
-        shape=[batch_size * max_length, 1],
-        dtype="int64",
-        append_batch_size=False)
-    # The actual shape of trg_pos in runtime is:
-    # [batch_size * max_trg_length_in_a_batch, 1].
-    trg_pos = layers.data(
-        name=input_data_names[3],
-        shape=[batch_size * max_length, 1],
-        dtype="int64",
-        append_batch_size=False)
-    # The actual shape of src_slf_attn_bias in runtime is:
-    # [batch_size, n_head, max_src_length_in_a_batch, max_src_length_in_a_batch].
-    # This input is used to remove attention weights on paddings.
-    src_slf_attn_bias = layers.data(
-        name=input_data_names[4],
-        shape=[batch_size, n_head, max_length, max_length],
-        dtype="float32",
-        append_batch_size=False)
-    # The actual shape of trg_slf_attn_bias in runtime is:
-    # [batch_size, n_head, max_trg_length_in_batch, max_trg_length_in_batch].
-    # This is used to remove attention weights on paddings and subsequent words.
-    trg_slf_attn_bias = layers.data(
-        name=input_data_names[5],
-        shape=[batch_size, n_head, max_length, max_length],
-        dtype="float32",
-        append_batch_size=False)
-    # The actual shape of trg_src_attn_bias in runtime is:
-    # [batch_size, n_head, max_trg_length_in_batch, max_src_length_in_batch].
-    # This is used to remove attention weights on paddings.
-    trg_src_attn_bias = layers.data(
-        name=input_data_names[6],
-        shape=[batch_size, n_head, max_length, max_length],
-        dtype="float32",
-        append_batch_size=False)
+    enc_input_layers = make_inputs(encoder_input_data_names, n_head, d_model,
+                                   batch_size, max_length, True, True, False)
+
+    enc_output = wrap_encoder(
+        src_vocab_size,
+        max_length,
+        n_layer,
+        n_head,
+        d_key,
+        d_value,
+        d_model,
+        d_inner_hid,
+        dropout_rate,
+        src_pad_idx,
+        pos_pad_idx,
+        enc_input_layers, )
+
+    dec_input_layers = make_inputs(decoder_input_data_names, n_head, d_model,
+                                   batch_size, max_length, True, True, True)
+
+    predict = wrap_decoder(
+        trg_vocab_size,
+        max_length,
+        n_layer,
+        n_head,
+        d_key,
+        d_value,
+        d_model,
+        d_inner_hid,
+        dropout_rate,
+        trg_pad_idx,
+        pos_pad_idx,
+        dec_input_layers,
+        enc_output, )
 
+    # Padding index do not contribute to the total loss. The weights is used to
+    # cancel padding index in calculating the loss.
+    gold, weights = make_inputs(label_data_names, n_head, d_model, batch_size,
+                                max_length, False, False, False)
+    cost = layers.cross_entropy(input=predict, label=gold)
+    weighted_cost = cost * weights
+    return layers.reduce_sum(weighted_cost), predict
+
+
+def wrap_encoder(src_vocab_size,
+                 max_length,
+                 n_layer,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 dropout_rate,
+                 src_pad_idx,
+                 pos_pad_idx,
+                 enc_input_layers=None):
+    """
+    The wrapper assembles together all needed layers for the encoder.
+    """
+    if enc_input_layers is None:
+        # This is used to implement independent encoder program in inference.
+        src_word, src_pos, src_slf_attn_bias = make_inputs(
+            encoder_input_data_names, n_head, d_model, batch_size, max_length,
+            True, True, False)
+    else:
+        src_word, src_pos, src_slf_attn_bias = enc_input_layers
     enc_input = prepare_encoder(
         src_word,
         src_pos,
@@ -460,6 +537,32 @@ def transformer(
         d_model,
         d_inner_hid,
         dropout_rate, )
+    return enc_output
+
+
+def wrap_decoder(trg_vocab_size,
+                 max_length,
+                 n_layer,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 dropout_rate,
+                 trg_pad_idx,
+                 pos_pad_idx,
+                 dec_input_layers=None,
+                 enc_output=None):
+    """
+    The wrapper assembles together all needed layers for the decoder.
+    """
+    if dec_input_layers is None:
+        # This is used to implement independent decoder program in inference.
+        trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output = make_inputs(
+            decoder_input_data_names, n_head, d_model, batch_size, max_length,
+            True, True, True, True)
+    else:
+        trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_input_layers
 
     dec_input = prepare_decoder(
         trg_word,
@@ -482,32 +585,11 @@ def transformer(
         d_inner_hid,
         dropout_rate, )
 
-    # TODO(guosheng): Share the weight matrix between the embedding layers and
-    # the pre-softmax linear transformation.
     predict = layers.reshape(
         x=layers.fc(input=dec_output,
                     size=trg_vocab_size,
-                    param_attr=fluid.initializer.Xavier(uniform=False),
                     bias_attr=False,
                     num_flatten_dims=2),
         shape=[-1, trg_vocab_size],
         act="softmax")
-    # The actual shape of gold in runtime is:
-    # [batch_size * max_trg_length_in_a_batch, 1].
-    gold = layers.data(
-        name=input_data_names[7],
-        shape=[batch_size * max_length, 1],
-        dtype="int64",
-        append_batch_size=False)
-    cost = layers.cross_entropy(input=predict, label=gold)
-    # The actual shape of weights in runtime is:
-    # [batch_size * max_trg_length_in_a_batch, 1].
-    # Padding index do not contribute to the total loss. This Weight is used to
-    # cancel padding index in calculating the loss.
-    weights = layers.data(
-        name=input_data_names[8],
-        shape=[batch_size * max_length, 1],
-        dtype="float32",
-        append_batch_size=False)
-    weighted_cost = cost * weights
-    return layers.reduce_sum(weighted_cost)
+    return predict

fluid/neural_machine_translation/transformer/train.py

+import os
 import numpy as np
 
 import paddle.v2 as paddle
@@ -5,86 +6,74 @@ import paddle.fluid as fluid
 
 from model import transformer, position_encoding_init
 from optim import LearningRateScheduler
-from config import TrainTaskConfig, ModelHyperParams, \
-        pos_enc_param_names, input_data_names
+from config import TrainTaskConfig, ModelHyperParams, pos_enc_param_names, \
+        encoder_input_data_names, decoder_input_data_names, label_data_names
 
 
-def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
-                        max_length, n_head, place):
+def pad_batch_data(insts,
+                   pad_idx,
+                   n_head,
+                   is_target=False,
+                   return_pos=True,
+                   return_attn_bias=True,
+                   return_max_len=True):
     """
     Pad the instances to the max sequence length in batch, and generate the
-    corresponding position data and attention bias. Then, convert the numpy
-    data to tensors and return a dict mapping names to tensors.
+    corresponding position data and attention bias.
+    """
+    return_list = []
+    max_len = max(len(inst) for inst in insts)
+    inst_data = np.array(
+        [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
+    return_list += [inst_data.astype("int64").reshape([-1, 1])]
+    if return_pos:
+        inst_pos = np.array([[
+            pos_i + 1 if w_i != pad_idx else 0 for pos_i, w_i in enumerate(inst)
+        ] for inst in inst_data])
+
+        return_list += [inst_pos.astype("int64").reshape([-1, 1])]
+    if return_attn_bias:
+        if is_target:
+            # This is used to avoid attention on paddings and subsequent
+            # words.
+            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len))
+            slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape(
+                [-1, 1, max_len, max_len])
+            slf_attn_bias_data = np.tile(slf_attn_bias_data,
+                                         [1, n_head, 1, 1]) * [-1e9]
+        else:
+            # This is used to avoid attention on paddings.
+            slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
+                                           (max_len - len(inst))
+                                           for inst in insts])
+            slf_attn_bias_data = np.tile(
+                slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
+                [1, n_head, max_len, 1])
+        return_list += [slf_attn_bias_data.astype("float32")]
+    if return_max_len:
+        return_list += [max_len]
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
+                        max_length, n_head):
+    """
+    Put all padded data needed by training into a dict.
     """
-    input_dict = {}
-
-    def __pad_batch_data(insts,
-                         pad_idx,
-                         is_target=False,
-                         return_pos=True,
-                         return_attn_bias=True,
-                         return_max_len=True):
-        """
-        Pad the instances to the max sequence length in batch, and generate the
-        corresponding position data and attention bias.
-        """
-        return_list = []
-        max_len = max(len(inst) for inst in insts)
-        inst_data = np.array(
-            [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
-        return_list += [inst_data.astype("int64").reshape([-1, 1])]
-        if return_pos:
-            inst_pos = np.array([[
-                pos_i + 1 if w_i != pad_idx else 0
-                for pos_i, w_i in enumerate(inst)
-            ] for inst in inst_data])
-
-            return_list += [inst_pos.astype("int64").reshape([-1, 1])]
-        if return_attn_bias:
-            if is_target:
-                # This is used to avoid attention on paddings and subsequent
-                # words.
-                slf_attn_bias_data = np.ones((inst_data.shape[0], max_len,
-                                              max_len))
-                slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape(
-                    [-1, 1, max_len, max_len])
-                slf_attn_bias_data = np.tile(slf_attn_bias_data,
-                                             [1, n_head, 1, 1]) * [-1e9]
-            else:
-                # This is used to avoid attention on paddings.
-                slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
-                                               (max_len - len(inst))
-                                               for inst in insts])
-                slf_attn_bias_data = np.tile(
-                    slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
-                    [1, n_head, max_len, 1])
-            return_list += [slf_attn_bias_data.astype("float32")]
-        if return_max_len:
-            return_list += [max_len]
-        return return_list if len(return_list) > 1 else return_list[0]
-
-    def data_to_tensor(data_list, name_list, input_dict, place):
-        assert len(data_list) == len(name_list)
-        for i in range(len(name_list)):
-            tensor = fluid.LoDTensor()
-            tensor.set(data_list[i], place)
-            input_dict[name_list[i]] = tensor
-
-    src_word, src_pos, src_slf_attn_bias, src_max_len = __pad_batch_data(
-        [inst[0] for inst in insts], src_pad_idx, is_target=False)
-    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = __pad_batch_data(
-        [inst[1] for inst in insts], trg_pad_idx, is_target=True)
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
+        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
     trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
                                 [1, 1, trg_max_len, 1]).astype("float32")
-    lbl_word = __pad_batch_data([inst[2] for inst in insts], trg_pad_idx, False,
-                                False, False, False)
+    lbl_word = pad_batch_data([inst[2] for inst in insts], trg_pad_idx, n_head,
+                              False, False, False, False)
     lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1])
-
-    data_to_tensor([
-        src_word, src_pos, trg_word, trg_pos, src_slf_attn_bias,
-        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
-    ], input_data_names, input_dict, place)
-
+    input_dict = dict(
+        zip(input_data_names, [
+            src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+            trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+        ]))
     return input_dict
 
 
@@ -92,7 +81,7 @@ def main():
     place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
-    cost = transformer(
+    cost, predict = transformer(
         ModelHyperParams.src_vocab_size + 1,
         ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
         ModelHyperParams.n_layer, ModelHyperParams.n_head,
@@ -118,6 +107,31 @@ def main():
             buf_size=100000),
         batch_size=TrainTaskConfig.batch_size)
 
+    # Program to do validation.
+    test_program = fluid.default_main_program().clone()
+    with fluid.program_guard(test_program):
+        test_program = fluid.io.get_inference_program([cost])
+    val_data = paddle.batch(
+        paddle.dataset.wmt16.validation(ModelHyperParams.src_vocab_size,
+                                        ModelHyperParams.trg_vocab_size),
+        batch_size=TrainTaskConfig.batch_size)
+
+    def test(exe):
+        test_costs = []
+        for batch_id, data in enumerate(val_data()):
+            if len(data) != TrainTaskConfig.batch_size:
+                continue
+            data_input = prepare_batch_input(
+                data, encoder_input_data_names + decoder_input_data_names[:-1] +
+                label_data_names, ModelHyperParams.src_pad_idx,
+                ModelHyperParams.trg_pad_idx, ModelHyperParams.max_length,
+                ModelHyperParams.n_head)
+            test_cost = exe.run(test_program,
+                                feed=data_input,
+                                fetch_list=[cost])[0]
+            test_costs.append(test_cost)
+        return np.mean(test_costs)
+
     # Initialize the parameters.
     exe.run(fluid.framework.default_startup_program())
     for pos_enc_param_name in pos_enc_param_names:
@@ -134,9 +148,10 @@ def main():
             if len(data) != TrainTaskConfig.batch_size:
                 continue
             data_input = prepare_batch_input(
-                data, input_data_names, ModelHyperParams.src_pad_idx,
+                data, encoder_input_data_names + decoder_input_data_names[:-1] +
+                label_data_names, ModelHyperParams.src_pad_idx,
                 ModelHyperParams.trg_pad_idx, ModelHyperParams.max_length,
-                ModelHyperParams.n_head, place)
+                ModelHyperParams.n_head)
             lr_scheduler.update_learning_rate(data_input)
             outs = exe.run(fluid.framework.default_main_program(),
                            feed=data_input,
@@ -145,6 +160,14 @@ def main():
             cost_val = np.array(outs[0])
             print("pass_id = " + str(pass_id) + " batch = " + str(batch_id) +
                   " cost = " + str(cost_val))
+        # Validate and save the model for inference.
+        val_cost = test(exe)
+        print("pass_id = " + str(pass_id) + " val_cost = " + str(val_cost))
+        fluid.io.save_inference_model(
+            os.path.join(TrainTaskConfig.model_dir,
+                         "pass_" + str(pass_id) + ".infer.model"),
+            encoder_input_data_names + decoder_input_data_names[:-1],
+            [predict], exe)
 
 
 if __name__ == "__main__":

fluid/object_detection/image_util.py

-from PIL import Image
+from PIL import Image, ImageEnhance
 import numpy as np
 import random
 import math
@@ -159,3 +159,77 @@ def crop_image(img, bbox_labels, sample_bbox, image_width, image_height):
     sample_img = img[ymin:ymax, xmin:xmax]
     sample_labels = transform_labels(bbox_labels, sample_bbox)
     return sample_img, sample_labels
+
+
+def random_brightness(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._brightness_prob:
+        delta = random.uniform(-settings._brightness_delta,
+                               settings._brightness_delta) + 1
+        img = ImageEnhance.Brightness(img).enhance(delta)
+    return img
+
+
+def random_contrast(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._contrast_prob:
+        delta = random.uniform(-settings._contrast_delta,
+                               settings._contrast_delta) + 1
+        img = ImageEnhance.Contrast(img).enhance(delta)
+    return img
+
+
+def random_saturation(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._saturation_prob:
+        delta = random.uniform(-settings._saturation_delta,
+                               settings._saturation_delta) + 1
+        img = ImageEnhance.Color(img).enhance(delta)
+    return img
+
+
+def random_hue(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._hue_prob:
+        delta = random.uniform(-settings._hue_delta, settings._hue_delta)
+        img_hsv = np.array(img.convert('HSV'))
+        img_hsv[:, :, 0] = img_hsv[:, :, 0] + delta
+        img = Image.fromarray(img_hsv, mode='HSV').convert('RGB')
+    return img
+
+
+def distort_image(img, settings):
+    prob = random.uniform(0, 1)
+    # Apply different distort order
+    if prob > 0.5:
+        img = random_brightness(img, settings)
+        img = random_contrast(img, settings)
+        img = random_saturation(img, settings)
+        img = random_hue(img, settings)
+    else:
+        img = random_brightness(img, settings)
+        img = random_saturation(img, settings)
+        img = random_hue(img, settings)
+        img = random_contrast(img, settings)
+    return img
+
+
+def expand_image(img, bbox_labels, img_width, img_height, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._hue_prob:
+        expand_ratio = random.uniform(1, settings._expand_max_ratio)
+        if expand_ratio - 1 >= 0.01:
+            height = int(img_height * expand_ratio)
+            width = int(img_width * expand_ratio)
+            h_off = math.floor(random.uniform(0, height - img_height))
+            w_off = math.floor(random.uniform(0, width - img_width))
+            expand_bbox = bbox(-w_off / img_width, -h_off / img_height,
+                               (width - w_off) / img_width,
+                               (height - h_off) / img_height)
+            expand_img = np.ones((height, width, 3))
+            expand_img = np.uint8(expand_img * np.squeeze(settings._img_mean))
+            expand_img = Image.fromarray(expand_img)
+            expand_img.paste(img, (int(w_off), int(h_off)))
+            bbox_labels = transform_labels(bbox_labels, expand_bbox)
+            return expand_img, bbox_labels
+    return img, bbox_labels

fluid/object_detection/reader.py

@@ -22,17 +22,38 @@ import os
 
 
 class Settings(object):
-    def __init__(self, data_dir, label_file, resize_h, resize_w, mean_value):
+    def __init__(self, data_dir, label_file, resize_h, resize_w, mean_value,
+                 apply_distort, apply_expand):
         self._data_dir = data_dir
         self._label_list = []
         label_fpath = os.path.join(data_dir, label_file)
         for line in open(label_fpath):
             self._label_list.append(line.strip())
 
+        self._apply_distort = apply_distort
+        self._apply_expand = apply_expand
         self._resize_height = resize_h
         self._resize_width = resize_w
         self._img_mean = np.array(mean_value)[:, np.newaxis, np.newaxis].astype(
             'float32')
+        self._expand_prob = 0.5
+        self._expand_max_ratio = 4
+        self._hue_prob = 0.5
+        self._hue_delta = 18
+        self._contrast_prob = 0.5
+        self._contrast_delta = 0.5
+        self._saturation_prob = 0.5
+        self._saturation_delta = 0.5
+        self._brightness_prob = 0.5
+        self._brightness_delta = 0.125
+
+    @property
+    def apply_distort(self):
+        return self._apply_expand
+
+    @property
+    def apply_distort(self):
+        return self._apply_distort
 
     @property
     def data_dir(self):
@@ -71,7 +92,6 @@ def _reader_creator(settings, file_list, mode, shuffle):
 
                 img = Image.open(img_path)
                 img_width, img_height = img.size
-                img = np.array(img)
 
                 # layout: label | xmin | ymin | xmax | ymax | difficult
                 if mode == 'train' or mode == 'test':
@@ -99,6 +119,12 @@ def _reader_creator(settings, file_list, mode, shuffle):
 
                     sample_labels = bbox_labels
                     if mode == 'train':
+                        if settings._apply_distort:
+                            img = image_util.distort_image(img, settings)
+                        if settings._apply_expand:
+                            img, bbox_labels = image_util.expand_image(
+                                img, bbox_labels, img_width, img_height,
+                                settings)
                         batch_sampler = []
                         # hard-code here
                         batch_sampler.append(
@@ -126,13 +152,14 @@ def _reader_creator(settings, file_list, mode, shuffle):
                         sampled_bbox = image_util.generate_batch_samples(
                             batch_sampler, bbox_labels, img_width, img_height)
 
+                        img = np.array(img)
                         if len(sampled_bbox) > 0:
                             idx = int(random.uniform(0, len(sampled_bbox)))
                             img, sample_labels = image_util.crop_image(
                                 img, bbox_labels, sampled_bbox[idx], img_width,
                                 img_height)
 
-                img = Image.fromarray(img)
+                        img = Image.fromarray(img)
                 img = img.resize((settings.resize_w, settings.resize_h),
                                  Image.ANTIALIAS)
                 img = np.array(img)

fluid/object_detection/train.py

 import paddle.v2 as paddle
 import paddle.fluid as fluid
-import os
 import reader
-import numpy as np
 import load_model as load_model
 from mobilenet_ssd import mobile_net
+from utility import add_arguments, print_arguments
+import os
+import numpy as np
+import argparse
+import functools
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('parallel',    bool,   True,     "Whether use parallel training.")
+add_arg('use_gpu',     bool,   True,     "Whether use GPU.")
+# yapf: disable
 
 
-def train(train_file_list,
+def train(args,
+          train_file_list,
           val_file_list,
           data_args,
           learning_rate,
@@ -25,16 +36,37 @@ def train(train_file_list,
     difficult = fluid.layers.data(
         name='gt_difficult', shape=[1], dtype='int32', lod_level=1)
 
-    mbox_locs, mbox_confs, box, box_var = mobile_net(image, image_shape)
-    nmsed_out = fluid.layers.detection_output(
-        mbox_locs, mbox_confs, box, box_var, nms_threshold=0.45)
-    loss_vec = fluid.layers.ssd_loss(mbox_locs, mbox_confs, gt_box, gt_label,
+    if args.parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            image_ = pd.read_input(image)
+            gt_box_ = pd.read_input(gt_box)
+            gt_label_ = pd.read_input(gt_label)
+            difficult_ = pd.read_input(difficult)
+            locs, confs, box, box_var = mobile_net(image_, image_shape)
+            loss = fluid.layers.ssd_loss(locs, confs, gt_box_, gt_label_,
+                                         box, box_var)
+            pd.write_output(loss)
+            pd.write_output(locs)
+            pd.write_output(confs)
+            pd.write_output(box)
+            pd.write_output(box_var)
+
+        loss, locs, confs, box, box_var = pd()
+        loss = fluid.layers.reduce_sum(loss)
+    else:
+        locs, confs, box, box_var = mobile_net(image, image_shape)
+        nmsed_out = fluid.layers.detection_output(
+            locs, mbox_confs, box, box_var, nms_threshold=0.45)
+        loss = fluid.layers.ssd_loss(locs, mbox_confs, gt_box, gt_label,
                                      box, box_var)
-    loss = fluid.layers.nn.reduce_sum(loss_vec)
+        loss = fluid.layers.reduce_sum(loss)
 
-    map_eval = None
     test_program = fluid.default_main_program().clone(for_test=True)
     with fluid.program_guard(test_program):
+        nmsed_out = fluid.layers.detection_output(
+            locs, confs, box, box_var, nms_threshold=0.45)
         map_eval = fluid.evaluator.DetectionMAP(
             nmsed_out,
             gt_label,
@@ -45,22 +77,20 @@ def train(train_file_list,
             evaluate_difficult=False,
             ap_version='11point')
 
-    optimizer = fluid.optimizer.Momentum(
-        learning_rate=fluid.layers.exponential_decay(
-            learning_rate=learning_rate,
-            decay_steps=40000,
-            decay_rate=0.1,
-            staircase=True),
-        momentum=0.9,
+    boundaries = [40000, 60000]
+    values = [0.001, 0.0005, 0.00025]
+    optimizer = fluid.optimizer.RMSProp(
+        learning_rate=fluid.layers.piecewise_decay(boundaries, values),
         regularization=fluid.regularizer.L2Decay(0.00005), )
 
     optimizer.minimize(loss)
 
-    place = fluid.CUDAPlace(0)
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    load_model.load_paddlev1_vars(place)
+    load_model.load_and_set_vars(place)
+    #load_model.load_paddlev1_vars(place)
     train_reader = paddle.batch(
         reader.train(data_args, train_file_list), batch_size=batch_size)
     test_reader = paddle.batch(
@@ -85,8 +115,9 @@ def train(train_file_list,
             loss_v = exe.run(fluid.default_main_program(),
                              feed=feeder.feed(data),
                              fetch_list=[loss])
-            print("Pass {0}, batch {1}, loss {2}"
-                  .format(pass_id, batch_id, loss_v[0]))
+            if batch_id % 20 == 0:
+                print("Pass {0}, batch {1}, loss {2}"
+                      .format(pass_id, batch_id, loss_v[0]))
         test(pass_id)
 
         if pass_id % 10 == 0:
@@ -97,16 +128,20 @@ def train(train_file_list,
 
 
 if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
     data_args = reader.Settings(
         data_dir='./data',
         label_file='label_list',
+        apply_distort=True,
+        apply_expand=True,
         resize_h=300,
         resize_w=300,
         mean_value=[127.5, 127.5, 127.5])
-    train(
-        train_file_list='./data/trainval.txt',
-        val_file_list='./data/test.txt',
-        data_args=data_args,
-        learning_rate=0.001,
-        batch_size=32,
-        num_passes=300)
+    train(args,
+          train_file_list='./data/trainval.txt',
+          val_file_list='./data/test.txt',
+          data_args=data_args,
+          learning_rate=0.001,
+          batch_size=32,
+          num_passes=300)

fluid/object_detection/utility.py

+"""Contains common utility functions."""
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import distutils.util
+import numpy as np
+from paddle.fluid import core
+
+
+def print_arguments(args):
+    """Print argparse's arguments.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        parser.add_argument("name", default="Jonh", type=str, help="User name.")
+        args = parser.parse_args()
+        print_arguments(args)
+
+    :param args: Input argparse.Namespace for printing.
+    :type args: argparse.Namespace
+    """
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(vars(args).iteritems()):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
+
+
+def add_arguments(argname, type, default, help, argparser, **kwargs):
+    """Add argparse's argument.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        add_argument("name", str, "Jonh", "User name.", parser)
+        args = parser.parse_args()
+    """
+    type = distutils.util.strtobool if type == bool else type
+    argparser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)

fluid/ocr_recognition/crnn_ctc_model.py

@@ -26,7 +26,12 @@ def conv_bn_pool(input,
             bias_attr=bias,
             is_test=is_test)
     tmp = fluid.layers.pool2d(
-        input=tmp, pool_size=2, pool_type='max', pool_stride=2, use_cudnn=True)
+        input=tmp,
+        pool_size=2,
+        pool_type='max',
+        pool_stride=2,
+        use_cudnn=True,
+        ceil_mode=True)
 
     return tmp
 
@@ -136,26 +141,61 @@ def encoder_net(images,
 def ctc_train_net(images, label, args, num_classes):
     regularizer = fluid.regularizer.L2Decay(args.l2)
     gradient_clip = None
-    fc_out = encoder_net(
-        images,
-        num_classes,
-        regularizer=regularizer,
-        gradient_clip=gradient_clip)
+    if args.parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            images_ = pd.read_input(images)
+            label_ = pd.read_input(label)
+
+            fc_out = encoder_net(
+                images_,
+                num_classes,
+                regularizer=regularizer,
+                gradient_clip=gradient_clip)
+
+            cost = fluid.layers.warpctc(
+                input=fc_out,
+                label=label_,
+                blank=num_classes,
+                norm_by_times=True)
+            sum_cost = fluid.layers.reduce_sum(cost)
+
+            decoded_out = fluid.layers.ctc_greedy_decoder(
+                input=fc_out, blank=num_classes)
+
+            pd.write_output(sum_cost)
+            pd.write_output(decoded_out)
+
+        sum_cost, decoded_out = pd()
+        sum_cost = fluid.layers.reduce_sum(sum_cost)
+
+    else:
+        fc_out = encoder_net(
+            images,
+            num_classes,
+            regularizer=regularizer,
+            gradient_clip=gradient_clip)
+
+        cost = fluid.layers.warpctc(
+            input=fc_out, label=label, blank=num_classes, norm_by_times=True)
+        sum_cost = fluid.layers.reduce_sum(cost)
+        decoded_out = fluid.layers.ctc_greedy_decoder(
+            input=fc_out, blank=num_classes)
 
-    cost = fluid.layers.warpctc(
-        input=fc_out, label=label, blank=num_classes, norm_by_times=True)
-    sum_cost = fluid.layers.reduce_sum(cost)
+    casted_label = fluid.layers.cast(x=label, dtype='int64')
+    error_evaluator = fluid.evaluator.EditDistance(
+        input=decoded_out, label=casted_label)
+
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        inference_program = fluid.io.get_inference_program(error_evaluator)
 
     optimizer = fluid.optimizer.Momentum(
         learning_rate=args.learning_rate, momentum=args.momentum)
-    optimizer.minimize(sum_cost)
+    _, params_grads = optimizer.minimize(sum_cost)
 
-    decoded_out = fluid.layers.ctc_greedy_decoder(
-        input=fc_out, blank=num_classes)
-    casted_label = fluid.layers.cast(x=label, dtype='int64')
-    error_evaluator = fluid.evaluator.EditDistance(
-        input=decoded_out, label=casted_label)
-    return sum_cost, error_evaluator
+    return sum_cost, error_evaluator, inference_program
 
 
 def ctc_infer(images, num_classes):

fluid/ocr_recognition/ctc_train.py

 """Trainer for OCR CTC model."""
-import paddle.v2 as paddle
 import paddle.fluid as fluid
 import dummy_reader
 import ctc_reader
@@ -24,12 +23,12 @@ add_arg('momentum',       float, 0.9,    "Momentum.")
 add_arg('rnn_hidden_size',int,   200,    "Hidden size of rnn layers.")
 add_arg('device',         int,   0,      "Device id.'-1' means running on CPU"
                                          "while '0' means GPU-0.")
+add_arg('parallel',     bool,   True,     "Whether use parallel training.")
 # yapf: disable
 
 def load_parameter(place):
     params = load_param('./name.map', './data/model/results_without_avg_window/pass-00000/')
     for name in params:
-        #        print "param: %s" % name
         t = fluid.global_scope().find_var(name).get_tensor()
         t.set(params[name], place)
 
@@ -41,7 +40,8 @@ def train(args, data_reader=dummy_reader):
     # define network
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int32', lod_level=1)
-    sum_cost, error_evaluator = ctc_train_net(images, label, args, num_classes)
+    sum_cost, error_evaluator, inference_program = ctc_train_net(images, label, args, num_classes)
+
     # data reader
     train_reader = data_reader.train(args.batch_size)
     test_reader = data_reader.test()
@@ -51,11 +51,8 @@ def train(args, data_reader=dummy_reader):
         place = fluid.CUDAPlace(args.device)
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
-
     #load_parameter(place)
 
-    inference_program = fluid.io.get_inference_program(error_evaluator)
-
     for pass_id in range(args.pass_num):
         error_evaluator.reset(exe)
         batch_id = 1
@@ -78,7 +75,6 @@ def train(args, data_reader=dummy_reader):
                 sys.stdout.flush()
             batch_id += 1
 
-        # evaluate model on test data
         error_evaluator.reset(exe)
         for data in test_reader():
             exe.run(inference_program, feed=get_feeder_data(data, place))

fluid/sequence_tagging_for_ner/README.md

+# 命名实体识别
+
+以下是本例的简要目录结构及说明：
+
+```text
+.
+├── data                 # 存储运行本例所依赖的数据，从外部获取
+├── network_conf.py      # 模型定义
+├── reader.py            # 数据读取接口, 从外部获取
+├── README.md            # 文档
+├── train.py             # 训练脚本
+├── infer.py             # 预测脚本
+├── utils.py             # 定义通用的函数, 从外部获取
+└── utils_extend.py      # 对utils.py的拓展
+```
+
+
+## 简介，模型详解
+
+在PaddlePaddle v2版本[命名实体识别](https://github.com/PaddlePaddle/models/blob/develop/sequence_tagging_for_ner/README.md)中对于命名实体识别任务有较详细的介绍，在本例中不再重复介绍。
+在模型上，我们沿用了v2版本的模型结构，唯一区别是我们使用LSTM代替原始的RNN。
+
+## 数据获取
+
+请参考PaddlePaddle v2版本[命名实体识别](https://github.com/PaddlePaddle/models/blob/develop/sequence_tagging_for_ner/README.md) 一节中数据获取方式，将该例中的data文件夹拷贝至本例目录下，运行其中的download.sh脚本获取训练和测试数据。
+
+## 通用脚本获取
+
+请将PaddlePaddle v2版本[命名实体识别](https://github.com/PaddlePaddle/models/blob/develop/sequence_tagging_for_ner/README.md)中提供的用于数据读取的文件[reader.py](https://github.com/PaddlePaddle/models/blob/develop/sequence_tagging_for_ner/reader.py)以及包含字典导入等通用功能的文件[utils.py](https://github.com/PaddlePaddle/models/blob/develop/sequence_tagging_for_ner/utils.py)复制到本目录下。本例将会使用到这两个脚本。
+
+## 训练
+
+1. 运行 `sh data/download.sh`
+2. 修改 `train.py` 的 `main` 函数，指定数据路径
+
+    ```python
+    main(
+        train_data_file="data/train",
+        test_data_file="data/test",
+        vocab_file="data/vocab.txt",
+        target_file="data/target.txt",
+        emb_file="data/wordVectors.txt",
+        model_save_dir="models",
+        num_passes=1000,
+        use_gpu=False,
+        parallel=False)
+    ```
+
+3. 运行命令 `python train.py` ，**需要注意：直接运行使用的是示例数据，请替换真实的标记数据。**
+
+    ```text
+    Pass 127, Batch 9525, Cost 4.0867705, Precision 0.3954984, Recall 0.37846154, F1_score0.38679245
+    Pass 127, Batch 9530, Cost 3.137265, Precision 0.42971888, Recall 0.38351256, F1_score0.405303
+    Pass 127, Batch 9535, Cost 3.6240938, Precision 0.4272152, Recall 0.41795665, F1_score0.4225352
+    Pass 127, Batch 9540, Cost 3.5352352, Precision 0.48464164, Recall 0.4536741, F1_score0.46864685
+    Pass 127, Batch 9545, Cost 4.1130385, Precision 0.40131578, Recall 0.3836478, F1_score0.39228293
+    Pass 127, Batch 9550, Cost 3.6826708, Precision 0.43333334, Recall 0.43730888, F1_score0.43531203
+    Pass 127, Batch 9555, Cost 3.6363933, Precision 0.42424244, Recall 0.3962264, F1_score0.4097561
+    Pass 127, Batch 9560, Cost 3.6101768, Precision 0.51363635, Recall 0.353125, F1_score0.41851854
+    Pass 127, Batch 9565, Cost 3.5935276, Precision 0.5152439, Recall 0.5, F1_score0.5075075
+    Pass 127, Batch 9570, Cost 3.4987144, Precision 0.5, Recall 0.4330218, F1_score0.46410686
+    Pass 127, Batch 9575, Cost 3.4659843, Precision 0.39864865, Recall 0.38064516, F1_score0.38943896
+    Pass 127, Batch 9580, Cost 3.1702557, Precision 0.5, Recall 0.4490446, F1_score0.47315437
+    Pass 127, Batch 9585, Cost 3.1587276, Precision 0.49377593, Recall 0.4089347, F1_score0.4473684
+    Pass 127, Batch 9590, Cost 3.5043538, Precision 0.4556962, Recall 0.4600639, F1_score0.45786962
+    Pass 127, Batch 9595, Cost 2.981989, Precision 0.44981414, Recall 0.45149255, F1_score0.4506518
+    [TrainSet] pass_id:127 pass_precision:[0.46023396] pass_recall:[0.43197003] pass_f1_score:[0.44565433]
+    [TestSet] pass_id:127 pass_precision:[0.4708409] pass_recall:[0.47971722] pass_f1_score:[0.4752376]
+    ```
+## 预测
+1. 修改 [infer.py](./infer.py) 的 `infer` 函数，指定：需要测试的模型的路径、测试数据、字典文件，预测标记文件的路径，默认参数如下：
+
+    ```python
+    infer(
+        model_path="models/params_pass_0",
+        batch_size=6,
+        test_data_file="data/test",
+        vocab_file="data/vocab.txt",
+        target_file="data/target.txt",
+        use_gpu=False
+    )
+    ```
+
+2. 在终端运行 `python infer.py`，开始测试，会看到如下预测结果（以下为训练70个pass所得模型的部分预测结果）：
+
+    ```text
+    leicestershire    B-ORG    B-LOC
+    extended    O    O
+    their    O    O
+    first    O    O
+    innings    O    O
+    by    O    O
+    DGDG    O    O
+    runs    O    O
+    before    O    O
+    being    O    O
+    bowled    O    O
+    out    O    O
+    for    O    O
+    296    O    O
+    with    O    O
+    england    B-LOC    B-LOC
+    discard    O    O
+    andy    B-PER    B-PER
+    caddick    I-PER    I-PER
+    taking    O    O
+    three    O    O
+    for    O    O
+    DGDG    O    O
+    .    O    O
+    ```
+
+    输出分为三列，以“\t” 分隔，第一列是输入的词语，第二列是标准结果，第三列为生成的标记结果。多条输入序列之间以空行分隔。
+
+## 结果示例
+
+<p align="center">
+<img src="imgs/convergence_curve.png" width="80%" align="center"/><br/>
+图1. 学习曲线, 横轴表示训练轮数，纵轴表示F1值
+</p>

fluid/sequence_tagging_for_ner/infer.py

+import numpy as np
+
+import paddle.fluid as fluid
+import paddle.v2 as paddle
+
+from network_conf import ner_net
+import reader
+from utils import load_dict, load_reverse_dict
+from utils_extend import to_lodtensor
+
+
+def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
+          use_gpu):
+    """
+    use the model under model_path to predict the test data, the result will be printed on the screen
+
+    return nothing
+    """
+    word_dict = load_dict(vocab_file)
+    word_reverse_dict = load_reverse_dict(vocab_file)
+
+    label_dict = load_dict(target_file)
+    label_reverse_dict = load_reverse_dict(target_file)
+
+    test_data = paddle.batch(
+        reader.data_reader(test_data_file, word_dict, label_dict),
+        batch_size=batch_size)
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
+        [inference_program, feed_target_names,
+         fetch_targets] = fluid.io.load_inference_model(model_path, exe)
+        for data in test_data():
+            word = to_lodtensor(map(lambda x: x[0], data), place)
+            mark = to_lodtensor(map(lambda x: x[1], data), place)
+            target = to_lodtensor(map(lambda x: x[2], data), place)
+            crf_decode = exe.run(
+                inference_program,
+                feed={"word": word,
+                      "mark": mark,
+                      "target": target},
+                fetch_list=fetch_targets,
+                return_numpy=False)
+            lod_info = (crf_decode[0].lod())[0]
+            np_data = np.array(crf_decode[0])
+            assert len(data) == len(lod_info) - 1
+            for sen_index in xrange(len(data)):
+                assert len(data[sen_index][0]) == lod_info[
+                    sen_index + 1] - lod_info[sen_index]
+                word_index = 0
+                for tag_index in xrange(lod_info[sen_index],
+                                        lod_info[sen_index + 1]):
+                    word = word_reverse_dict[data[sen_index][0][word_index]]
+                    gold_tag = label_reverse_dict[data[sen_index][2][
+                        word_index]]
+                    tag = label_reverse_dict[np_data[tag_index][0]]
+                    print word + "\t" + gold_tag + "\t" + tag
+                    word_index += 1
+                print ""
+
+
+if __name__ == "__main__":
+    infer(
+        model_path="models/params_pass_0",
+        batch_size=6,
+        test_data_file="data/test",
+        vocab_file="data/vocab.txt",
+        target_file="data/target.txt",
+        use_gpu=False)

fluid/sequence_tagging_for_ner/network_conf.py

+import math
+
+import paddle.fluid as fluid
+from paddle.fluid.initializer import NormalInitializer
+
+from utils import logger, load_dict, get_embedding
+
+
+def ner_net(word_dict_len, label_dict_len, parallel, stack_num=2):
+    mark_dict_len = 2
+    word_dim = 50
+    mark_dim = 5
+    hidden_dim = 300
+    IS_SPARSE = True
+    embedding_name = 'emb'
+
+    def _net_conf(word, mark, target):
+        word_embedding = fluid.layers.embedding(
+            input=word,
+            size=[word_dict_len, word_dim],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr=fluid.ParamAttr(
+                name=embedding_name, trainable=False))
+
+        mark_embedding = fluid.layers.embedding(
+            input=mark,
+            size=[mark_dict_len, mark_dim],
+            dtype='float32',
+            is_sparse=IS_SPARSE)
+
+        word_caps_vector = fluid.layers.concat(
+            input=[word_embedding, mark_embedding], axis=1)
+        mix_hidden_lr = 1
+
+        rnn_para_attr = fluid.ParamAttr(
+            initializer=NormalInitializer(
+                loc=0.0, scale=0.0),
+            learning_rate=mix_hidden_lr)
+        hidden_para_attr = fluid.ParamAttr(
+            initializer=NormalInitializer(
+                loc=0.0, scale=(1. / math.sqrt(hidden_dim) / 3)),
+            learning_rate=mix_hidden_lr)
+
+        hidden = fluid.layers.fc(
+            input=word_caps_vector,
+            name="__hidden00__",
+            size=hidden_dim,
+            act="tanh",
+            bias_attr=fluid.ParamAttr(initializer=NormalInitializer(
+                loc=0.0, scale=(1. / math.sqrt(hidden_dim) / 3))),
+            param_attr=fluid.ParamAttr(initializer=NormalInitializer(
+                loc=0.0, scale=(1. / math.sqrt(hidden_dim) / 3))))
+        fea = []
+        for direction in ["fwd", "bwd"]:
+            for i in range(stack_num):
+                if i != 0:
+                    hidden = fluid.layers.fc(
+                        name="__hidden%02d_%s__" % (i, direction),
+                        size=hidden_dim,
+                        act="stanh",
+                        bias_attr=fluid.ParamAttr(initializer=NormalInitializer(
+                            loc=0.0, scale=1.0)),
+                        input=[hidden, rnn[0], rnn[1]],
+                        param_attr=[
+                            hidden_para_attr, rnn_para_attr, rnn_para_attr
+                        ])
+                rnn = fluid.layers.dynamic_lstm(
+                    name="__rnn%02d_%s__" % (i, direction),
+                    input=hidden,
+                    size=hidden_dim,
+                    candidate_activation='relu',
+                    gate_activation='sigmoid',
+                    cell_activation='sigmoid',
+                    bias_attr=fluid.ParamAttr(initializer=NormalInitializer(
+                        loc=0.0, scale=1.0)),
+                    is_reverse=(i % 2) if direction == "fwd" else not i % 2,
+                    param_attr=rnn_para_attr)
+            fea += [hidden, rnn[0], rnn[1]]
+
+        rnn_fea = fluid.layers.fc(
+            size=hidden_dim,
+            bias_attr=fluid.ParamAttr(initializer=NormalInitializer(
+                loc=0.0, scale=(1. / math.sqrt(hidden_dim) / 3))),
+            act="stanh",
+            input=fea,
+            param_attr=[hidden_para_attr, rnn_para_attr, rnn_para_attr] * 2)
+
+        emission = fluid.layers.fc(
+            size=label_dict_len,
+            input=rnn_fea,
+            param_attr=fluid.ParamAttr(initializer=NormalInitializer(
+                loc=0.0, scale=(1. / math.sqrt(hidden_dim) / 3))))
+
+        crf_cost = fluid.layers.linear_chain_crf(
+            input=emission,
+            label=target,
+            param_attr=fluid.ParamAttr(
+                name='crfw',
+                initializer=NormalInitializer(
+                    loc=0.0, scale=(1. / math.sqrt(hidden_dim) / 3)),
+                learning_rate=mix_hidden_lr))
+        avg_cost = fluid.layers.mean(x=crf_cost)
+        return avg_cost, emission
+
+    word = fluid.layers.data(name='word', shape=[1], dtype='int64', lod_level=1)
+    mark = fluid.layers.data(name='mark', shape=[1], dtype='int64', lod_level=1)
+    target = fluid.layers.data(
+        name="target", shape=[1], dtype='int64', lod_level=1)
+
+    if parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            word_ = pd.read_input(word)
+            mark_ = pd.read_input(mark)
+            target_ = pd.read_input(target)
+            avg_cost, emission_base = _net_conf(word_, mark_, target_)
+            pd.write_output(avg_cost)
+            pd.write_output(emission_base)
+        avg_cost_list, emission = pd()
+        avg_cost = fluid.layers.mean(x=avg_cost_list)
+        emission.stop_gradient = True
+    else:
+        avg_cost, emission = _net_conf(word, mark, target)
+
+    return avg_cost, emission, word, mark, target

fluid/sequence_tagging_for_ner/train.py

+import os
+import math
+import numpy as np
+
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+
+import reader
+from network_conf import ner_net
+from utils import logger, load_dict
+from utils_extend import to_lodtensor, get_embedding
+
+
+def test(exe, chunk_evaluator, inference_program, test_data, place):
+    chunk_evaluator.reset(exe)
+    for data in test_data():
+        word = to_lodtensor(map(lambda x: x[0], data), place)
+        mark = to_lodtensor(map(lambda x: x[1], data), place)
+        target = to_lodtensor(map(lambda x: x[2], data), place)
+        acc = exe.run(inference_program,
+                      feed={"word": word,
+                            "mark": mark,
+                            "target": target})
+    return chunk_evaluator.eval(exe)
+
+
+def main(train_data_file, test_data_file, vocab_file, target_file, emb_file,
+         model_save_dir, num_passes, use_gpu, parallel):
+    if not os.path.exists(model_save_dir):
+        os.mkdir(model_save_dir)
+
+    BATCH_SIZE = 200
+    word_dict = load_dict(vocab_file)
+    label_dict = load_dict(target_file)
+
+    word_vector_values = get_embedding(emb_file)
+
+    word_dict_len = len(word_dict)
+    label_dict_len = len(label_dict)
+
+    avg_cost, feature_out, word, mark, target = ner_net(
+        word_dict_len, label_dict_len, parallel)
+
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
+    sgd_optimizer.minimize(avg_cost)
+
+    crf_decode = fluid.layers.crf_decoding(
+        input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
+
+    chunk_evaluator = fluid.evaluator.ChunkEvaluator(
+        input=crf_decode,
+        label=target,
+        chunk_scheme="IOB",
+        num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
+
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        test_target = chunk_evaluator.metrics + chunk_evaluator.states
+        inference_program = fluid.io.get_inference_program(test_target)
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            reader.data_reader(train_data_file, word_dict, label_dict),
+            buf_size=20000),
+        batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        paddle.reader.shuffle(
+            reader.data_reader(test_data_file, word_dict, label_dict),
+            buf_size=20000),
+        batch_size=BATCH_SIZE)
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    feeder = fluid.DataFeeder(feed_list=[word, mark, target], place=place)
+    exe = fluid.Executor(place)
+
+    exe.run(fluid.default_startup_program())
+
+    embedding_name = 'emb'
+    embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
+    embedding_param.set(word_vector_values, place)
+
+    batch_id = 0
+    for pass_id in xrange(num_passes):
+        chunk_evaluator.reset(exe)
+        for data in train_reader():
+            cost, batch_precision, batch_recall, batch_f1_score = exe.run(
+                fluid.default_main_program(),
+                feed=feeder.feed(data),
+                fetch_list=[avg_cost] + chunk_evaluator.metrics)
+            if batch_id % 5 == 0:
+                print("Pass " + str(pass_id) + ", Batch " + str(
+                    batch_id) + ", Cost " + str(cost[0]) + ", Precision " + str(
+                        batch_precision[0]) + ", Recall " + str(batch_recall[0])
+                      + ", F1_score" + str(batch_f1_score[0]))
+            batch_id = batch_id + 1
+
+        pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(exe)
+        print("[TrainSet] pass_id:" + str(pass_id) + " pass_precision:" + str(
+            pass_precision) + " pass_recall:" + str(pass_recall) +
+              " pass_f1_score:" + str(pass_f1_score))
+        pass_precision, pass_recall, pass_f1_score = test(
+            exe, chunk_evaluator, inference_program, test_reader, place)
+        print("[TestSet] pass_id:" + str(pass_id) + " pass_precision:" + str(
+            pass_precision) + " pass_recall:" + str(pass_recall) +
+              " pass_f1_score:" + str(pass_f1_score))
+
+        save_dirname = os.path.join(model_save_dir, "params_pass_%d" % pass_id)
+        fluid.io.save_inference_model(save_dirname, ['word', 'mark', 'target'],
+                                      [crf_decode], exe)
+
+
+if __name__ == "__main__":
+    main(
+        train_data_file="data/train",
+        test_data_file="data/test",
+        vocab_file="data/vocab.txt",
+        target_file="data/target.txt",
+        emb_file="data/wordVectors.txt",
+        model_save_dir="models",
+        num_passes=1000,
+        use_gpu=False,
+        parallel=False)

fluid/sequence_tagging_for_ner/utils_extend.py

+import numpy as np
+
+import paddle.fluid as fluid
+
+
+def get_embedding(emb_file='data/wordVectors.txt'):
+    """
+    Get the trained word vector.
+    """
+    return np.loadtxt(emb_file, dtype='float32')
+
+
+def to_lodtensor(data, place):
+    """
+    convert data to lodtensor
+    """
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = fluid.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res

globally_normalized_reader/README.cn.md

+此目录中代码示例PaddlePaddle所需版本至少为v0.11.0。如果您使用的PaddlePaddle版本早于v0.11.0， [请更新](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
+
+---
+
+# 全球标准化阅读器
+
+该模型实现以下功能：
+
+Jonathan Raiman and John Miller. Globally Normalized Reader. Empirical Methods in Natural Language Processing (EMNLP), 2017
+
+如果您在研究中使用数据集/代码，请引用上述论文：
+
+```text
+@inproceedings{raiman2015gnr,
+    author={Raiman, Jonathan and Miller, John},
+    booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
+    title={Globally Normalized Reader},
+    year={2017},
+}
+```
+
+您也可以访问 https://github.com/baidu-research/GloballyNormalizedReader 以获取更多信息。
+
+
+# 安装
+
+1. 请使用 [docker image](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html) 安装最新的PaddlePaddle，运行方法：
+    ```bash
+    docker pull paddledev/paddle
+    ```
+2. 下载所有必要的数据，运行方法：
+    ```bash
+    cd data && ./download.sh && cd ..
+    ```
+3. 预处理并特征化数据：
+    ```bash
+    python featurize.py --datadir data --outdir data/featurized  --glove-path data/glove.840B.300d.txt
+    ```
+
+# 模型训练
+
+- 根据需要修改config.py来配置模型，然后运行：
+
+    ```bash
+    python train.py 2>&1 | tee train.log
+    ```
+
+# 使用训练过的模型推断
+
+- 运行以下训练模型来推断：
+   ```bash
+   python infer.py \
+     --model_path models/pass_00000.tar.gz \
+     --data_dir data/featurized/ \
+     --batch_size 2 \
+     --use_gpu 0 \
+     --trainer_count 1 \
+     2>&1 | tee infer.log
+   ```

sequence_tagging_for_ner/README.md

-运行本目录下的程序示例需要使用PaddlePaddle v0.10.0 版本。如果您的PaddlePaddle安装版本低于此要求，请按照[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明更新PaddlePaddle安装版本。
+运行本目录下的程序示例需要使用PaddlePaddle v0.10.0 版本。如果您的PaddlePaddle安装版本低于此要求，请按照[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明更新PaddlePaddle安装版本。
 
 ---
 
@@ -25,16 +25,16 @@
 
 命名实体识别（Named Entity Recognition，NER）又称作“专名识别”，是指识别文本中具有特定意义的实体，主要包括人名、地名、机构名、专有名词等，是自然语言处理研究的一个基础问题。NER任务通常包括实体边界识别、确定实体类别两部分，可以将其作为序列标注问题解决。
 
-序列标注可以分为Sequence Classification、Segment Classification和Temporal Classification三类[[1](#参考文献)]，本例只考虑Segment Classification，即对输入序列中的每个元素在输出序列中给出对应的标签。对于NER任务，由于需要标识边界，一般采用[BIO标注方法](http://book.paddlepaddle.org/07.label_semantic_roles/)定义的标签集，如下是一个NER的标注结果示例：
+序列标注可以分为Sequence Classification、Segment Classification和Temporal Classification三类[[1](#参考文献)]，本例只考虑Segment Classification，即对输入序列中的每个元素在输出序列中给出对应的标签。对于NER任务，由于需要标识边界，一般采用[BIO标注方法](http://www.paddlepaddle.org/docs/develop/book/07.label_semantic_roles/index.cn.html)定义的标签集，如下是一个NER的标注结果示例：
 
 <p align="center">
 <img src="images/ner_label_ins.png" width="80%" align="center"/><br/>
 图1. BIO标注方法示例
 </p>
 
-根据序列标注结果可以直接得到实体边界和实体类别。类似的，分词、词性标注、语块识别、[语义角色标注](http://book.paddlepaddle.org/07.label_semantic_roles/index.cn.html)等任务都可通过序列标注来解决。使用神经网络模型解决问题的思路通常是：前层网络学习输入的特征表示，网络的最后一层在特征基础上完成最终的任务；对于序列标注问题，通常：使用基于RNN的网络结构学习特征，将学习到的特征接入CRF完成序列标注。实际上是将传统CRF中的线性模型换成了非线性神经网络。沿用CRF的出发点是：CRF使用句子级别的似然概率，能够更好的解决标记偏置问题[[2](#参考文献)]。本例也将基于此思路建立模型。虽然，这里以NER任务作为示例，但所给出的模型可以应用到其他各种序列标注任务中。
+根据序列标注结果可以直接得到实体边界和实体类别。类似的，分词、词性标注、语块识别、[语义角色标注](http://www.paddlepaddle.org/docs/develop/book/07.label_semantic_roles/index.cn.html)等任务都可通过序列标注来解决。使用神经网络模型解决问题的思路通常是：前层网络学习输入的特征表示，网络的最后一层在特征基础上完成最终的任务；对于序列标注问题，通常：使用基于RNN的网络结构学习特征，将学习到的特征接入CRF完成序列标注。实际上是将传统CRF中的线性模型换成了非线性神经网络。沿用CRF的出发点是：CRF使用句子级别的似然概率，能够更好的解决标记偏置问题[[2](#参考文献)]。本例也将基于此思路建立模型。虽然，这里以NER任务作为示例，但所给出的模型可以应用到其他各种序列标注任务中。
 
-由于序列标注问题的广泛性，产生了[CRF](http://book.paddlepaddle.org/07.label_semantic_roles/index.cn.html)等经典的序列模型，这些模型大多只能使用局部信息或需要人工设计特征。随着深度学习研究的发展，循环神经网络（Recurrent Neural Network，RNN等 序列模型能够处理序列元素之间前后关联问题，能够从原始输入文本中学习特征表示，而更加适合序列标注任务，更多相关知识可参考PaddleBook中[语义角色标注](https://github.com/PaddlePaddle/book/blob/develop/07.label_semantic_roles/README.cn.md)一课。
+由于序列标注问题的广泛性，产生了[CRF](http://www.paddlepaddle.org/docs/develop/book/07.label_semantic_roles/index.cn.html)等经典的序列模型，这些模型大多只能使用局部信息或需要人工设计特征。随着深度学习研究的发展，循环神经网络（Recurrent Neural Network，RNN等 序列模型能够处理序列元素之间前后关联问题，能够从原始输入文本中学习特征表示，而更加适合序列标注任务，更多相关知识可参考PaddleBook中[语义角色标注](https://github.com/PaddlePaddle/book/blob/develop/07.label_semantic_roles/README.cn.md)一课。
 
 ## 模型详解