Merge pull request #653 from kuke/add_infer

Add the demo script for inference

fluid/DeepASR/infer.py

+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 train(args):
                 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")