Resolve conficts in decoder

fluid/DeepASR/infer_by_ckpt.py

@@ -32,6 +32,11 @@ def parse_args():
         default=1,
         help='The minimum sequence number of a batch data. '
         '(default: %(default)d)')
+    parser.add_argument(
+        '--frame_dim',
+        type=int,
+        default=120 * 11,
+        help='Frame dimension of feature data. (default: %(default)d)')
     parser.add_argument(
         '--stacked_num',
         type=int,
@@ -47,6 +52,11 @@ def parse_args():
         type=int,
         default=1024,
         help='Hidden size of lstmp unit. (default: %(default)d)')
+    parser.add_argument(
+        '--class_num',
+        type=int,
+        default=1749,
+        help='Number of classes in label. (default: %(default)d)')
     parser.add_argument(
         '--learning_rate',
         type=float,
@@ -114,10 +124,11 @@ def infer_from_ckpt(args):
         raise IOError("Invalid checkpoint!")
 
     prediction, avg_cost, accuracy = stacked_lstmp_model(
+        frame_dim=args.frame_dim,
         hidden_dim=args.hidden_dim,
         proj_dim=args.proj_dim,
         stacked_num=args.stacked_num,
-        class_num=1749,
+        class_num=args.class_num,
         parallel=args.parallel)
 
     infer_program = fluid.default_main_program().clone()

fluid/DeepASR/model_utils/model.py

@@ -6,7 +6,8 @@ import paddle.v2 as paddle
 import paddle.fluid as fluid
 
 
-def stacked_lstmp_model(hidden_dim,
+def stacked_lstmp_model(frame_dim,
+                        hidden_dim,
                         proj_dim,
                         stacked_num,
                         class_num,
@@ -20,12 +21,13 @@ def stacked_lstmp_model(hidden_dim,
         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.
+        frame_dim(int): The frame dimension of feature data.
+        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
@@ -78,7 +80,7 @@ def stacked_lstmp_model(hidden_dim,
 
     # data feeder
     feature = fluid.layers.data(
-        name="feature", shape=[-1, 120 * 11], dtype="float32", lod_level=1)
+        name="feature", shape=[-1, frame_dim], dtype="float32", lod_level=1)
     label = fluid.layers.data(
         name="label", shape=[-1, 1], dtype="int64", lod_level=1)
 
@@ -92,11 +94,12 @@ def stacked_lstmp_model(hidden_dim,
             feat_ = pd.read_input(feature)
             label_ = pd.read_input(label)
             prediction, avg_cost, acc = _net_conf(feat_, label_)
-            for out in [avg_cost, acc]:
+            for out in [prediction, avg_cost, acc]:
                 pd.write_output(out)
 
         # get mean loss and acc through every devices.
-        avg_cost, acc = pd()
+        prediction, avg_cost, acc = pd()
+        prediction.stop_gradient = True
         avg_cost = fluid.layers.mean(x=avg_cost)
         acc = fluid.layers.mean(x=acc)
     else:

fluid/DeepASR/tools/profile.py

@@ -31,6 +31,11 @@ def parse_args():
         default=1,
         help='The minimum sequence number of a batch data. '
         '(default: %(default)d)')
+    parser.add_argument(
+        '--frame_dim',
+        type=int,
+        default=120 * 11,
+        help='Frame dimension of feature data. (default: %(default)d)')
     parser.add_argument(
         '--stacked_num',
         type=int,
@@ -46,6 +51,11 @@ def parse_args():
         type=int,
         default=1024,
         help='Hidden size of lstmp unit. (default: %(default)d)')
+    parser.add_argument(
+        '--class_num',
+        type=int,
+        default=1749,
+        help='Number of classes in label. (default: %(default)d)')
     parser.add_argument(
         '--learning_rate',
         type=float,
@@ -119,10 +129,11 @@ def profile(args):
             "arg 'first_batches_to_skip' must not be smaller than 0.")
 
     _, avg_cost, accuracy = stacked_lstmp_model(
+        frame_dim=args.frame_dim,
         hidden_dim=args.hidden_dim,
         proj_dim=args.proj_dim,
         stacked_num=args.stacked_num,
-        class_num=1749,
+        class_num=args.class_num,
         parallel=args.parallel)
 
     optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)

fluid/DeepASR/train.py

@@ -30,6 +30,11 @@ def parse_args():
         default=1,
         help='The minimum sequence number of a batch data. '
         '(default: %(default)d)')
+    parser.add_argument(
+        '--frame_dim',
+        type=int,
+        default=120 * 11,
+        help='Frame dimension of feature data. (default: %(default)d)')
     parser.add_argument(
         '--stacked_num',
         type=int,
@@ -45,6 +50,11 @@ def parse_args():
         type=int,
         default=1024,
         help='Hidden size of lstmp unit. (default: %(default)d)')
+    parser.add_argument(
+        '--class_num',
+        type=int,
+        default=1749,
+        help='Number of classes in label. (default: %(default)d)')
     parser.add_argument(
         '--pass_num',
         type=int,
@@ -137,10 +147,11 @@ def train(args):
         os.mkdir(args.infer_models)
 
     prediction, avg_cost, accuracy = stacked_lstmp_model(
+        frame_dim=args.frame_dim,
         hidden_dim=args.hidden_dim,
         proj_dim=args.proj_dim,
         stacked_num=args.stacked_num,
-        class_num=1749,
+        class_num=args.class_num,
         parallel=args.parallel)
 
     # program for test