Merge pull request #653 from kuke/add_infer

Add the demo script for inference

Author: Yibing Liu

fluid/DeepASR/infer.py

@@ -0,0 +1,112 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import argparse
+import paddle.v2.fluid as fluid
+import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
+import data_utils.augmentor.trans_add_delta as trans_add_delta
+import data_utils.augmentor.trans_splice as trans_splice
+import data_utils.data_reader as reader
+from data_utils.util import lodtensor_to_ndarray
+
+
+def parse_args():
+ parser = argparse.ArgumentParser("Inference for stacked LSTMP model.")
+ parser.add_argument(
+ '--batch_size',
+ type=int,
+ default=32,
+ help='The sequence number of a batch data. (default: %(default)d)')
+ parser.add_argument(
+ '--device',
+ type=str,
+ default='GPU',
+ choices=['CPU', 'GPU'],
+ help='The device type. (default: %(default)s)')
+ parser.add_argument(
+ '--mean_var',
+ type=str,
+ default='data/global_mean_var_search26kHr',
+ help="The path for feature's global mean and variance. "
+ "(default: %(default)s)")
+ parser.add_argument(
+ '--infer_feature_lst',
+ type=str,
+ default='data/infer_feature.lst',
+ help='The feature list path for inference. (default: %(default)s)')
+ parser.add_argument(
+ '--infer_label_lst',
+ type=str,
+ default='data/infer_label.lst',
+ help='The label list path for inference. (default: %(default)s)')
+ parser.add_argument(
+ '--model_save_path',
+ type=str,
+ default='./checkpoints/deep_asr.pass_0.model/',
+ help='The directory for saving model. (default: %(default)s)')
+ args = parser.parse_args()
+ return args
+
+
+def print_arguments(args):
+ print('----------- Configuration Arguments -----------')
+ for arg, value in sorted(vars(args).iteritems()):
+ print('%s: %s' % (arg, value))
+ print('------------------------------------------------')
+
+
+def split_infer_result(infer_seq, lod):
+ infer_batch = []
+ for i in xrange(0, len(lod[0]) - 1):
+ infer_batch.append(infer_seq[lod[0][i]:lod[0][i + 1]])
+ return infer_batch
+
+
+def infer(args):
+ """ Gets one batch of feature data and predicts labels for each sample.
+ """
+
+ if not os.path.exists(args.model_save_path):
+ raise IOError("Invalid model path!")
+
+ place = fluid.CUDAPlace(0) if args.device == 'GPU' else fluid.CPUPlace()
+ exe = fluid.Executor(place)
+
+ # load model
+ [infer_program, feed_dict,
+ fetch_targets] = fluid.io.load_inference_model(args.model_save_path, exe)
+
+ ltrans = [
+ trans_add_delta.TransAddDelta(2, 2),
+ trans_mean_variance_norm.TransMeanVarianceNorm(args.mean_var),
+ trans_splice.TransSplice()
+ ]
+
+ infer_data_reader = reader.DataReader(args.infer_feature_lst,
+ args.infer_label_lst)
+ infer_data_reader.set_transformers(ltrans)
+
+ feature_t = fluid.LoDTensor()
+ one_batch = infer_data_reader.batch_iterator(args.batch_size, 1).next()
+ (features, labels, lod) = one_batch
+ feature_t.set(features, place)
+ feature_t.set_lod([lod])
+
+ results = exe.run(infer_program,
+ feed={feed_dict[0]: feature_t},
+ fetch_list=fetch_targets,
+ return_numpy=False)
+
+ probs, lod = lodtensor_to_ndarray(results[0])
+ preds = probs.argmax(axis=1)
+ infer_batch = split_infer_result(preds, lod)
+ for index, sample in enumerate(infer_batch):
+ print("result %d: " % index, sample, '\n')
+
+
+if __name__ == '__main__':
+ args = parse_args()
+ print_arguments(args)
+ infer(args)

fluid/DeepASR/train.py

@@ -72,33 +72,34 @@ def parse_args():
  '--mean_var',
  type=str,
  default='data/global_mean_var_search26kHr',
- help='mean var path')
+ help="The path for feature's global mean and variance. "
+ "(default: %(default)s)")
  parser.add_argument(
  '--train_feature_lst',
  type=str,
  default='data/feature.lst',
- help='feature list path for training.')
+ help='The feature list path for training. (default: %(default)s)')
  parser.add_argument(
  '--train_label_lst',
  type=str,
  default='data/label.lst',
- help='label list path for training.')
+ help='The label list path for training. (default: %(default)s)')
  parser.add_argument(
  '--val_feature_lst',
  type=str,
  default='data/val_feature.lst',
- help='feature list path for validation.')
+ help='The feature list path for validation. (default: %(default)s)')
  parser.add_argument(
  '--val_label_lst',
  type=str,
  default='data/val_label.lst',
- help='label list path for validation.')
+ help='The label list path for validation. (default: %(default)s)')
  parser.add_argument(
  '--model_save_dir',
  type=str,
  default='./checkpoints',
- help='directory to save model. Do not save model if set to '
- '.')
+ help="The directory for saving model. Do not save model if set to "
+ "''. (default: %(default)s)")
  args = parser.parse_args()
  return args
 
@@ -114,8 +115,6 @@ def train(args):
  """train in loop.
  """
 
- # prediction, avg_cost, accuracy = stacked_lstmp_model(args.hidden_dim, 
- # args.proj_dim, args.stacked_num, class_num=1749, args.parallel)
  prediction, avg_cost, accuracy = stacked_lstmp_model(
  hidden_dim=args.hidden_dim,
  proj_dim=args.proj_dim,
@@ -206,7 +205,7 @@ def test(exe):
  sys.stdout.flush()
  # run test
  val_cost, val_acc = test(exe)
- # save model 
+ # save model
  if args.model_save_dir != '':
  model_path = os.path.join(
  args.model_save_dir, "deep_asr.pass_" + str(pass_id) + ".model")