Add script for inference by using checkpoint

fluid/DeepASR/infer_from_ckpt.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import os
+import numpy as np
+import argparse
+import time
+
+import paddle.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
+from model_utils.model import stacked_lstmp_model
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Run inference by using checkpoint.")
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=32,
+        help='The sequence number of a batch data. (default: %(default)d)')
+    parser.add_argument(
+        '--minimum_batch_size',
+        type=int,
+        default=1,
+        help='The minimum sequence number of a batch data. '
+        '(default: %(default)d)')
+    parser.add_argument(
+        '--stacked_num',
+        type=int,
+        default=5,
+        help='Number of lstmp layers to stack. (default: %(default)d)')
+    parser.add_argument(
+        '--proj_dim',
+        type=int,
+        default=512,
+        help='Project size of lstmp unit. (default: %(default)d)')
+    parser.add_argument(
+        '--hidden_dim',
+        type=int,
+        default=1024,
+        help='Hidden size of lstmp unit. (default: %(default)d)')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        default=0.00016,
+        help='Learning rate used to train. (default: %(default)f)')
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='GPU',
+        choices=['CPU', 'GPU'],
+        help='The device type. (default: %(default)s)')
+    parser.add_argument(
+        '--parallel', action='store_true', help='If set, run in parallel.')
+    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(
+        '--checkpoint',
+        type=str,
+        default='./checkpoint',
+        help="Checkpoints. Do not save checkpoint if set to ''. (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 infer_from_ckpt(args):
+    """inference by using checkpoint.
+    """
+
+    if not os.path.exists(args.checkpoint):
+        raise IOError("Invalid checkpoint!")
+
+    prediction, avg_cost, accuracy = stacked_lstmp_model(
+        hidden_dim=args.hidden_dim,
+        proj_dim=args.proj_dim,
+        stacked_num=args.stacked_num,
+        class_num=1749,
+        parallel=args.parallel)
+
+    infer_program = fluid.default_main_program().clone()
+
+    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+    optimizer.minimize(avg_cost)
+
+    place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    # load checkpoint.
+    fluid.io.load_persistables(exe, args.checkpoint)
+
+    ltrans = [
+        trans_add_delta.TransAddDelta(2, 2),
+        trans_mean_variance_norm.TransMeanVarianceNorm(args.mean_var),
+        trans_splice.TransSplice()
+    ]
+
+    feature_t = fluid.LoDTensor()
+    label_t = fluid.LoDTensor()
+
+    # infer data reader
+    infer_data_reader = reader.DataReader(args.infer_feature_lst,
+                                          args.infer_label_lst)
+    infer_data_reader.set_transformers(ltrans)
+    infer_costs, infer_accs = [], []
+    for batch_id, batch_data in enumerate(
+            infer_data_reader.batch_iterator(args.batch_size,
+                                             args.minimum_batch_size)):
+        # load_data
+        (features, labels, lod) = batch_data
+        feature_t.set(features, place)
+        feature_t.set_lod([lod])
+        label_t.set(labels, place)
+        label_t.set_lod([lod])
+
+        cost, acc = exe.run(infer_program,
+                            feed={"feature": feature_t,
+                                  "label": label_t},
+                            fetch_list=[avg_cost, accuracy],
+                            return_numpy=False)
+        infer_costs.append(lodtensor_to_ndarray(cost)[0])
+        infer_accs.append(lodtensor_to_ndarray(acc)[0])
+    print(np.mean(infer_costs), np.mean(infer_accs))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    print_arguments(args)
+
+    infer_from_ckpt(args)

fluid/DeepASR/train.py

@@ -143,13 +143,11 @@ def train(args):
         class_num=1749,
         parallel=args.parallel)
 
-    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
-    optimizer.minimize(avg_cost)
-
     # program for test
     test_program = fluid.default_main_program().clone()
-    with fluid.program_guard(test_program):
-        test_program = fluid.io.get_inference_program([avg_cost, accuracy])
+
+    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+    optimizer.minimize(avg_cost)
 
     place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
     exe = fluid.Executor(place)