Add more comments in model conf and make it more concise

fluid/DeepASR/model.py

@@ -12,20 +12,82 @@ def stacked_lstmp_model(hidden_dim,
                         parallel=False,
                         is_train=True,
                         class_num=1749):
+    """ The model for DeepASR. The main structure is composed of stacked 
+        identical LSTMP (LSTM with recurrent projection) layers.
+
+    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_, hidden_dim,
-                                                  proj_dim, stacked_num,
-                                                  class_num, is_train)
+            prediction, avg_cost, acc = _net_conf(feat_, label_)
             for out in [avg_cost, acc]:
                 pd.write_output(out)
 
@@ -34,57 +96,6 @@ def stacked_lstmp_model(hidden_dim,
         avg_cost = fluid.layers.mean(x=avg_cost)
         acc = fluid.layers.mean(x=acc)
     else:
-        prediction, avg_cost, acc = _net_conf(feature, label, hidden_dim,
-                                              proj_dim, stacked_num, class_num,
-                                              is_train)
-
-    return prediction, avg_cost, acc
-
-
-def _net_conf(feature, label, hidden_dim, proj_dim, stacked_num, class_num,
-              is_train):
-    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')
+        prediction, avg_cost, acc = _net_conf(feature, label)
 
-    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