Merge pull request #636 from kuke/refactor_model

Refactor model conf: add profiling, parallel running, model saving etc

Author: Yibing Liu

fluid/DeepASR/data_utils/data_reader.py

@@ -225,8 +225,8 @@ def ordered_feeding_task(sample_info_queue):
  @suppress_complaints(verbose=self._verbose)
  def ordered_processing_task(sample_info_queue, sample_queue, out_order):
  if self._verbose == 0:
- signal.signal(signal.SIGTERM, suppress_signal())
- signal.signal(signal.SIGINT, suppress_signal())
+ signal.signal(signal.SIGTERM, suppress_signal)
+ signal.signal(signal.SIGINT, suppress_signal)
 
  def read_bytes(fpath, start, size):
  f = open(fpath, 'r')

fluid/DeepASR/data_utils/util.py

@@ -5,6 +5,8 @@
 from six import reraise
 from tblib import Traceback
 
+import numpy as np
+
 
 def to_lodtensor(data, place):
  """convert tensor to lodtensor

fluid/DeepASR/model_utils/model.py

@@ -0,0 +1,105 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+
+def stacked_lstmp_model(hidden_dim,
+ proj_dim,
+ stacked_num,
+ class_num,
+ parallel=False,
+ is_train=True):
+ """ The model for DeepASR. The main structure is composed of stacked 
+ identical LSTMP (LSTM with recurrent projection) layers.
+
+ When running in training and validation phase, the feeding dictionary
+ is {'feature', 'label'}, fed by the LodTensor for feature data and 
+ label data respectively. And in inference, only `feature` is needed.
+
+ Args:
+hidden_dim(int): The hidden state's dimension of the LSTMP layer.
+proj_dim(int): The projection size of the LSTMP layer.
+stacked_num(int): The number of stacked LSTMP layers.
+parallel(bool): Run in parallel or not, default `False`.
+is_train(bool): Run in training phase or not, default `True`.
+class_dim(int): The number of output classes.
+ """
+
+ # network configuration
+ def _net_conf(feature, label):
+ seq_conv1 = fluid.layers.sequence_conv(
+ input=feature,
+ num_filters=1024,
+ filter_size=3,
+ filter_stride=1,
+ bias_attr=True)
+ bn1 = fluid.layers.batch_norm(
+ input=seq_conv1,
+ act="sigmoid",
+ is_test=not is_train,
+ momentum=0.9,
+ epsilon=1e-05,
+ data_layout='NCHW')
+
+ stack_input = bn1
+ for i in range(stacked_num):
+ fc = fluid.layers.fc(input=stack_input,
+ size=hidden_dim * 4,
+ bias_attr=True)
+ proj, cell = fluid.layers.dynamic_lstmp(
+ input=fc,
+ size=hidden_dim * 4,
+ proj_size=proj_dim,
+ bias_attr=True,
+ use_peepholes=True,
+ is_reverse=False,
+ cell_activation="tanh",
+ proj_activation="tanh")
+ bn = fluid.layers.batch_norm(
+ input=proj,
+ act="sigmoid",
+ is_test=not is_train,
+ momentum=0.9,
+ epsilon=1e-05,
+ data_layout='NCHW')
+ stack_input = bn
+
+ prediction = fluid.layers.fc(input=stack_input,
+ size=class_num,
+ act='softmax')
+
+ cost = fluid.layers.cross_entropy(input=prediction, label=label)
+ avg_cost = fluid.layers.mean(x=cost)
+ acc = fluid.layers.accuracy(input=prediction, label=label)
+ return prediction, avg_cost, acc
+
+ # data feeder
+ feature = fluid.layers.data(
+ name="feature", shape=[-1, 120 * 11], dtype="float32", lod_level=1)
+ label = fluid.layers.data(
+ name="label", shape=[-1, 1], dtype="int64", lod_level=1)
+
+ if parallel:
+ # When the execution place is specified to CUDAPlace, the program will
+ # run on all $CUDA_VISIBLE_DEVICES GPUs. Otherwise the program will 
+ # run on all CPU devices.
+ places = fluid.layers.get_places()
+ pd = fluid.layers.ParallelDo(places)
+ with pd.do():
+ feat_ = pd.read_input(feature)
+ label_ = pd.read_input(label)
+ prediction, avg_cost, acc = _net_conf(feat_, label_)
+ for out in [avg_cost, acc]:
+ pd.write_output(out)
+
+ # get mean loss and acc through every devices.
+ avg_cost, acc = pd()
+ avg_cost = fluid.layers.mean(x=avg_cost)
+ acc = fluid.layers.mean(x=acc)
+ else:
+ prediction, avg_cost, acc = _net_conf(feature, label)
+
+ return prediction, avg_cost, acc

fluid/DeepASR/stacked_dynamic_lstm.py

@@ -0,0 +1,19 @@
+"""Add the parent directory to $PYTHONPATH"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path
+import sys
+
+
+def add_path(path):
+ if path not in sys.path:
+ sys.path.insert(0, path)
+
+
+this_dir = os.path.dirname(__file__)
+
+# Add project path to PYTHONPATH
+proj_path = os.path.join(this_dir, '..')
+add_path(proj_path)