follow comments of qingqing and code refine

python/paddle/v2/fluid/layers/detection.py

@@ -18,10 +18,9 @@ All layers just related to the detection neural network.
 from ..layer_helper import LayerHelper
 from ..param_attr import ParamAttr
 from ..framework import Variable
-from ..nets import img_conv_with_bn
-from tensor import concat
-from ops import reshape
-from nn import transpose
+import tensor
+import ops
+import nn
 import math
 
 __all__ = [
@@ -184,10 +183,10 @@ def prior_box(inputs,
        name(str, optional, None): Name of the prior box layer.
 
     Returns:
-        boxes(Variable): the output prior boxes of PriorBoxOp.
+        boxes(Variable): the output prior boxes of PriorBox.
              The layout is [num_priors, 4]. num_priors is the total
              box count of each position of inputs.
-        Variances(Variable): the expanded variances of PriorBoxOp.
+        Variances(Variable): the expanded variances of PriorBox.
              The layout is [num_priors, 4]. num_priors is the total
              box count of each position of inputs
 
@@ -250,7 +249,7 @@ def prior_box(inputs,
         new_shape = [
             -1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
         ]
-        out = reshape(x=input, shape=new_shape)
+        out = ops.reshape(x=input, shape=new_shape)
         return out
 
     assert isinstance(inputs, list), 'inputs should be a list.'
@@ -326,8 +325,8 @@ def prior_box(inputs,
             reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
             reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
 
-        box = concat(reshaped_boxes)
-        var = concat(reshaped_vars)
+        box = tensor.concat(reshaped_boxes)
+        var = tensor.concat(reshaped_vars)
 
     return box, var
 
@@ -345,12 +344,14 @@ def multi_box_head(inputs,
                    pad=1,
                    stride=1,
                    use_batchnorm=False,
-                   base_size=None,
-                   name=None):
+                   base_size=None):
     """
     **Multi Box Head**
 
-    input many Variable, and return mbox_loc, mbox_conf
+    Generate prior boxes' location and confidence for SSD(Single
+    Shot MultiBox Detector)algorithm. The details of this algorithm,
+    please refer the section 2.1 of SSD paper (SSD: Single Shot
+    MultiBox Detector)<https://arxiv.org/abs/1512.02325>`_ .
 
     Args:
        inputs(list): The list of input Variables, the format
@@ -376,12 +377,12 @@ def multi_box_head(inputs,
 
     Returns:
 
-        mbox_loc(list): the output prior boxes of PriorBoxOp. The layout is
-             [num_priors, 4]. num_priors is the total box count of each
-              position of inputs.
-        mbox_conf(list): the expanded variances of PriorBoxOp. The layout
-             is [num_priors, 4]. num_priors is the total box count of each
-             position of inputs
+        mbox_loc(list): The predicted boxes' location of the inputs.
+             The layout of each element is [N, H, W, Priors]. Priors
+             is the number of predicted boxof each position of each input.
+        mbox_conf(list): The predicted boxes' confidence of the inputs.
+             The layout of each element is [N, H, W, Priors]. Priors
+             is the number of predicted box of each position of each input.
 
     Examples:
         .. code-block:: python
@@ -396,6 +397,35 @@ def multi_box_head(inputs,
                 flip=True)
     """
 
+    def _conv_with_bn_(input,
+                       conv_num_filter,
+                       conv_padding=1,
+                       conv_filter_size=3,
+                       conv_stride=1,
+                       conv_act=None,
+                       param_attr=None,
+                       conv_with_batchnorm=False,
+                       conv_batchnorm_drop_rate=0.0,
+                       use_cudnn=True):
+
+        conv2d = nn.conv2d(
+            input=input,
+            num_filters=conv_num_filter,
+            filter_size=conv_filter_size,
+            padding=conv_padding,
+            stride=conv_stride,
+            param_attr=param_attr,
+            act=conv_act,
+            use_cudnn=use_cudnn)
+
+        if conv_with_batchnorm:
+            conv2d = nn.batch_norm(input=conv2d)
+            drop_rate = conv_batchnorm_drop_rate
+            if abs(drop_rate) > 1e-5:
+                conv2d = nn.dropout(x=conv2d, dropout_prob=drop_rate)
+
+        return conv2d
+
     if not (isinstance(inputs, list)):
         raise ValueError('inputs should be a list.')
 
@@ -469,26 +499,26 @@ def multi_box_head(inputs,
         if share_location:
             num_loc_output *= num_classes
 
-        mbox_loc = img_conv_with_bn(
+        mbox_loc = _conv_with_bn_(
             input=input,
             conv_num_filter=num_loc_output,
             conv_padding=pad,
             conv_stride=stride,
             conv_filter_size=kernel_size,
             conv_with_batchnorm=use_batchnorm)
-        mbox_loc = transpose(mbox_loc, perm=[0, 2, 3, 1])
+        mbox_loc = nn.transpose(mbox_loc, perm=[0, 2, 3, 1])
         mbox_locs.append(mbox_loc)
 
         # get conf_loc
         num_conf_output = num_priors_per_location * num_classes
-        conf_loc = img_conv_with_bn(
+        conf_loc = _conv_with_bn_(
             input=input,
             conv_num_filter=num_conf_output,
             conv_padding=pad,
             conv_stride=stride,
             conv_filter_size=kernel_size,
             conv_with_batchnorm=use_batchnorm)
-        conf_loc = transpose(conf_loc, perm=[0, 2, 3, 1])
+        conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
         mbox_confs.append(conf_loc)
 
     return mbox_locs, mbox_confs

python/paddle/v2/fluid/tests/test_detection.py

@@ -47,7 +47,7 @@ class TestBook(unittest.TestCase):
             out = layers.detection_output(
                 scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
             self.assertIsNotNone(out)
-        print(str(program))
+        # print(str(program))
 
 
 class TestPriorBox(unittest.TestCase):
@@ -62,36 +62,11 @@ class TestPriorBox(unittest.TestCase):
     def prior_box_output(self, data_shape):
         images = fluid.layers.data(
             name='pixel', shape=data_shape, dtype='float32')
-        conv1 = fluid.layers.conv2d(
-            input=images,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv2 = fluid.layers.conv2d(
-            input=conv1,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv3 = fluid.layers.conv2d(
-            input=conv2,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv4 = fluid.layers.conv2d(
-            input=conv3,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv5 = fluid.layers.conv2d(
-            input=conv4,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
+        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+        conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
+        conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
+        conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
+        conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
 
         box, var = detection.prior_box(
             inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
@@ -112,39 +87,17 @@ class TestMultiBoxHead(unittest.TestCase):
         data_shape = [3, 224, 224]
         mbox_locs, mbox_confs = self.multi_box_output(data_shape)
 
+        for loc, conf in zip(mbox_locs, mbox_confs):
+            assert loc.shape[1:3] == conf.shape[1:3]
+
     def multi_box_output(self, data_shape):
         images = fluid.layers.data(
             name='pixel', shape=data_shape, dtype='float32')
-        conv1 = fluid.layers.conv2d(
-            input=images,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv2 = fluid.layers.conv2d(
-            input=conv1,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv3 = fluid.layers.conv2d(
-            input=conv2,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv4 = fluid.layers.conv2d(
-            input=conv3,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
-        conv5 = fluid.layers.conv2d(
-            input=conv4,
-            num_filters=3,
-            filter_size=3,
-            stride=2,
-            use_cudnn=False)
+        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+        conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
+        conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
+        conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
+        conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
 
         mbox_locs, mbox_confs = detection.multi_box_head(
             inputs=[conv1, conv2, conv3, conv4, conv5, conv5],