follow comments of qingqing

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

@@ -19,30 +19,28 @@ from ..layer_helper import LayerHelper
 from ..framework import Variable
 from tensor import concat
 from ops import reshape
+from operator import mul
 import math
 
-__all__ = [
-    'prior_box',
-    'prior_boxes',
-]
-
-
-def prior_boxes(inputs,
-                image,
-                min_ratio,
-                max_ratio,
-                aspect_ratios,
-                base_size,
-                steps=None,
-                step_w=None,
-                step_h=None,
-                offset=0.5,
-                variance=[0.1, 0.1, 0.1, 0.1],
-                flip=False,
-                clip=False,
-                min_sizes=None,
-                max_sizes=None,
-                name=None):
+__all__ = ['prior_box', ]
+
+
+def prior_box(inputs,
+              image,
+              min_ratio,
+              max_ratio,
+              aspect_ratios,
+              base_size,
+              steps=None,
+              step_w=None,
+              step_h=None,
+              offset=0.5,
+              variance=[0.1, 0.1, 0.1, 0.1],
+              flip=False,
+              clip=False,
+              min_sizes=None,
+              max_sizes=None,
+              name=None):
     """
     **Prior_boxes**
 
@@ -140,9 +138,10 @@ def prior_boxes(inputs,
     def _reshape_with_axis_(input, axis=1):
         if not (axis > 0 and axis < len(input.shape)):
             raise ValueError(
-                "The axis should be smaller than the arity of input's shape.")
+                "The axis should be smaller than the arity of input and bigger than 0."
+            )
         new_shape = [-1, reduce(mul, input.shape[axis:len(input.shape)], 1)]
-        out = reshape([input], shape=new_shape)
+        out = reshape(x=input, shape=new_shape)
         return out
 
     assert isinstance(inputs, list), 'inputs should be a list.'

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

@@ -33,7 +33,7 @@ def prior_box_output(data_shape):
     conv5 = fluid.layers.conv2d(
         input=conv4, num_filters=3, filter_size=3, stride=2, use_cudnn=False)
 
-    box, var = detection.prior_boxes(
+    box, var = detection.prior_box(
         inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
         image=images,
         min_ratio=20,
@@ -57,20 +57,22 @@ def main(use_cuda):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
-    batch = [128]
+    batch = [4]  # batch is not used in the prior_box.
+
+    assert box.shape[1] == 4
+    assert var.shape[1] == 4
+    assert box.shape == var.shape
+    assert len(box.shape) == 2
 
     for _ in range(1):
         x = np.random.random(batch + data_shape).astype("float32")
         tensor_x = core.LoDTensor()
         tensor_x.set(x, place)
-        box, var = exe.run(fluid.default_main_program(),
-                           feed={'pixel': tensor_x},
-                           fetch_list=[box, var])
-        box_arr = np.array(box)
-        var_arr = np.array(var)
-        assert box_arr.shape[1] == 4
-        assert var_arr.shape[1] == 4
-        assert box_arr.shape[0] == var_arr.shape[0]
+        boxes, vars = exe.run(fluid.default_main_program(),
+                              feed={'pixel': tensor_x},
+                              fetch_list=[box, var])
+        assert vars.shape == var.shape
+        assert boxes.shape == box.shape
 
 
 class TestFitALine(unittest.TestCase):