fix reviews

models/mobilenetv1.py

@@ -111,15 +111,16 @@ class MobileNetV1(Model):
 
     Args:
         scale (float): scale of channels in each layer. Default: 1.0.
-        num_classes (int): output dim of last fc layer. Default: -1.
-        with_pool (bool): use pool or not. Default: False.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool): use pool before the last fc layer or not. Default: True.
         classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
     """
 
     def __init__(self,
                  scale=1.0,
-                 num_classes=-1,
-                 with_pool=False,
+                 num_classes=1000,
+                 with_pool=True,
                  classifier_activation='softmax'):
         super(MobileNetV1, self).__init__()
         self.scale = scale

models/mobilenetv2.py

@@ -156,15 +156,16 @@ class MobileNetV2(Model):
 
     Args:
         scale (float): scale of channels in each layer. Default: 1.0.
-        num_classes (int): output dim of last fc layer. Default: -1.
-        with_pool (bool): use pool or not. Default: False.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool): use pool before the last fc layer or not. Default: True.
         classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
     """
 
     def __init__(self,
                  scale=1.0,
-                 num_classes=-1,
-                 with_pool=False,
+                 num_classes=1000,
+                 with_pool=True,
                  classifier_activation='softmax'):
         super(MobileNetV2, self).__init__()
         self.scale = scale

models/resnet.py

@@ -163,16 +163,17 @@ class ResNet(Model):
     Args:
         Block (BasicBlock|BottleneckBlock): block module of model.
         depth (int): layers of resnet, default: 50.
-        num_classes (int): output dim of last fc layer, default: 1000.
-        with_pool (bool): use pool or not. Default: False.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool): use pool before the last fc layer or not. Default: True.
         classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
     """
 
     def __init__(self,
                  Block,
                  depth=50,
-                 num_classes=-1,
-                 with_pool=False,
+                 num_classes=1000,
+                 with_pool=True,
                  classifier_activation='softmax'):
         super(ResNet, self).__init__()
 

models/vgg.py

@@ -58,13 +58,14 @@ class VGG(Model):
 
     Args:
         features (fluid.dygraph.Layer): vgg features create by function make_layers.
-        num_classes (int): output dim of last fc layer. Default: -1.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
         classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
     """
 
     def __init__(self,
                  features,
-                 num_classes=-1,
+                 num_classes=1000,
                  classifier_activation='softmax'):
         super(VGG, self).__init__()
         self.features = features

transform/transforms.py

@@ -289,7 +289,8 @@ class Normalize(object):
 class Permute(object):
     """Change input data to a target mode.
     For example, most transforms use HWC mode image,
-    while the Neural Network might use CHW mode input tensor
+    while the Neural Network might use CHW mode input tensor.
+    Input image should be HWC mode and an instance of numpy.ndarray. 
 
     Args:
         mode: Output mode of input. Use "CHW" mode by default.