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

@@ -17,11 +17,8 @@ All layers just related to the detection neural network.
 
 from ..layer_helper import LayerHelper
 from ..framework import Variable
-from ..param_attr import ParamAttr
-from ..framework import Variable
-from layer_function_generator import autodoc
 from tensor import concat
-from nn import flatten
+from ops import reshape
 import math
 
 __all__ = [
@@ -30,91 +27,6 @@ __all__ = [
 ]
 
 
-def prior_box(input,
-              image,
-              min_sizes,
-              max_sizes,
-              aspect_ratios,
-              variance,
-              flip=False,
-              clip=False,
-              step_w=0.0,
-              step_h=0.0,
-              offset=0.5,
-              name=None):
-    """
-    **Prior_box**
-
-    Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
-    Each position of the input produce N prior boxes, N is determined by
-    the count of min_sizes, max_sizes and aspect_ratios, The size of the
-    box is in range(min_size, max_size) interval, which is generated in
-    sequence according to the aspect_ratios.
-
-    Args:
-       input(variable): The input feature data of PriorBox,
-             the layout is NCHW.
-       image(variable): The input image data of PriorBox, the
-             layout is NCHW.
-       min_sizes(list): the min sizes of generated prior boxes.
-       max_sizes(list): the max sizes of generated prior boxes.
-       aspect_ratios(list): the aspect ratios of generated prior boxes.
-       variance(list): the variances to be encoded in prior boxes.
-       flip(bool, optional, default=False): Whether to flip aspect ratios.
-       clip(bool, optional, default=False)): Whether to clip
-             out-of-boundary boxes.
-       step_w(int, optional, default=0.0): Prior boxes step across
-             width, 0.0 for auto calculation.
-       step_h(int, optional, default=0.0): Prior boxes step across
-             height, 0.0 for auto calculation.
-       offset(float, optional, default=0.5): Prior boxes center offset.
-       name(str, optional, default=None): Name of the prior box layer.
-
-    Returns:
-        boxes(variable): the output prior boxes of PriorBoxOp. The layout is
-             [H, W, num_priors, 4]. H is the height of input, W is the width
-             of input, num_priors is the box count of each position. Where num_priors =
-             len(aspect_ratios) * len(min_sizes) + len(max_sizes)
-        Variances(variable): the expanded variances of PriorBoxOp. The layout
-             is [H, W, num_priors, 4]. H is the height of input, W is the width
-             of input, num_priors is the box count of each position. Where num_priors =
-             len(aspect_ratios) * len(min_sizes) + len(max_sizes)
-    Examples:
-        .. code-block:: python
-
-          data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
-          conv2d = fluid.layers.conv2d(
-              input=data, num_filters=2, filter_size=3)
-          box, var = fluid.layers.prior_box(conv2d, data,
-              min_size, max_size, aspect_ratio,
-              variance, flip, clip,
-              step_w, step_h, offset)
-    """
-    helper = LayerHelper("prior_box", **locals())
-    dtype = helper.input_dtype()
-
-    box = helper.create_tmp_variable(dtype)
-    var = helper.create_tmp_variable(dtype)
-    helper.append_op(
-        type="prior_box",
-        inputs={"Input": input,
-                "Image": image},
-        outputs={"Boxes": box,
-                 "Variances": var},
-        attrs={
-            'min_sizes': min_sizes,
-            'max_sizes': max_sizes,
-            'aspect_ratios': aspect_ratios,
-            'variances': variance,
-            'flip': flip,
-            'clip': clip,
-            'step_w': step_w,
-            'step_h': step_h,
-            'offset': offset
-        })
-    return box, var
-
-
 def prior_boxes(inputs,
                 image,
                 min_ratio,
@@ -128,20 +40,19 @@ def prior_boxes(inputs,
                 variance=[0.1, 0.1, 0.1, 0.1],
                 flip=False,
                 clip=False,
+                min_sizes=None,
+                max_sizes=None,
                 name=None):
     """
     **Prior_boxes**
 
     Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
-    Each position of the inputs produces many prior boxes respectly, the number
-    of prior boxes which is produced by inputs respectly is determined by
-    the count of min_ratio, max_ratio and aspect_ratios, The size of the
-    box is in range(min_ratio, max_ratio) interval, which is generated in
-    sequence according to the aspect_ratios.
+    The details of this algorithm, please refer the section 2.2 of SSD paper
+    (SSD: Single Shot MultiBox Detector)<https://arxiv.org/abs/1512.02325>`_ .
 
     Args:
-       inputs(list): The list of input variables, the format of all variables is NCHW.
-       image(variable): The input image data of PriorBoxOp, the layout is NCHW.
+       inputs(list): The list of input Variables, the format of all Variables is NCHW.
+       image(Variable): The input image data of PriorBoxOp, the layout is NCHW.
        min_ratio(int): the min ratio of generated prior boxes.
        max_ratio(int): the max ratio of generated prior boxes.
        aspect_ratios(list): the aspect ratios of generated prior boxes.
@@ -159,13 +70,17 @@ def prior_boxes(inputs,
             to be encoded in prior boxes.
        flip(bool, optional, default=False): Whether to flip aspect ratios.
        clip(bool, optional, default=False): Whether to clip out-of-boundary boxes.
+       min_sizes(list, optional, default=None): If `len(inputs) <=2`, min_sizes must
+            be set up, and the length of min_sizes should equal to the length of inputs.
+       max_sizes(list, optional, default=None): If `len(inputs) <=2`, max_sizes must
+            be set up, and the length of min_sizes should equal to the length of inputs.
        name(str, optional, None): Name of the prior box layer.
 
     Returns:
-        boxes(variable): the output prior boxes of PriorBoxOp. The layout is
+        boxes(Variable): the output prior boxes of PriorBoxOp. 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. The layout
+        Variances(Variable): the expanded variances of PriorBoxOp. The layout
              is [num_priors, 4]. num_priors is the total box count of each
              position of inputs
 
@@ -185,13 +100,60 @@ def prior_boxes(inputs,
              flip=True,
              clip=True)
     """
+
+    def _prior_box_(input,
+                    image,
+                    min_sizes,
+                    max_sizes,
+                    aspect_ratios,
+                    variance,
+                    flip=False,
+                    clip=False,
+                    step_w=0.0,
+                    step_h=0.0,
+                    offset=0.5,
+                    name=None):
+        helper = LayerHelper("prior_box", **locals())
+        dtype = helper.input_dtype()
+
+        box = helper.create_tmp_variable(dtype)
+        var = helper.create_tmp_variable(dtype)
+        helper.append_op(
+            type="prior_box",
+            inputs={"Input": input,
+                    "Image": image},
+            outputs={"Boxes": box,
+                     "Variances": var},
+            attrs={
+                'min_sizes': min_sizes,
+                'max_sizes': max_sizes,
+                'aspect_ratios': aspect_ratios,
+                'variances': variance,
+                'flip': flip,
+                'clip': clip,
+                'step_w': step_w,
+                'step_h': step_h,
+                'offset': offset
+            })
+        return box, var
+
+    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.")
+        new_shape = [-1, reduce(mul, input.shape[axis:len(input.shape)], 1)]
+        out = reshape([input], shape=new_shape)
+        return out
+
     assert isinstance(inputs, list), 'inputs should be a list.'
     num_layer = len(inputs)
-    assert num_layer > 2  # TODO(zcd): currently, num_layer must be bigger than two.
 
+    if num_layer <= 2:
+        assert min_sizes is not None and max_sizes is not None
+        assert len(min_sizes) == num_layer and len(max_sizes) == num_layer
+    else:
         min_sizes = []
         max_sizes = []
-    if num_layer > 2:
         step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
         for ratio in xrange(min_ratio, max_ratio + 1, step):
             min_sizes.append(base_size * ratio / 100.)
@@ -199,21 +161,29 @@ def prior_boxes(inputs,
         min_sizes = [base_size * .10] + min_sizes
         max_sizes = [base_size * .20] + max_sizes
 
+    if aspect_ratios:
+        if not (isinstance(aspect_ratios, list) and
+                len(aspect_ratios) == num_layer):
+            raise ValueError(
+                'aspect_ratios should be list and the length of inputs '
+                'and aspect_ratios should be the same.')
     if step_h:
-        assert isinstance(step_h,list) and len(step_h) == num_layer, \
-            'step_h should be list and inputs and step_h should have same length'
+        if not (isinstance(step_h, list) and len(step_h) == num_layer):
+            raise ValueError(
+                'step_h should be list and the length of inputs and '
+                'step_h should be the same.')
     if step_w:
-        assert isinstance(step_w,list) and len(step_w) == num_layer, \
-            'step_w should be list and inputs and step_w should have same length'
+        if not (isinstance(step_w, list) and len(step_w) == num_layer):
+            raise ValueError(
+                'step_w should be list and the length of inputs and '
+                'step_w should be the same.')
     if steps:
-        assert isinstance(steps,list) and len(steps) == num_layer, \
-            'steps should be list and inputs and step_w should have same length'
+        if not (isinstance(steps, list) and len(steps) == num_layer):
+            raise ValueError(
+                'steps should be list and the length of inputs and '
+                'step_w should be the same.')
         step_w = steps
         step_h = steps
-    if aspect_ratios:
-        assert isinstance(aspect_ratios, list) and len(aspect_ratios) == num_layer, \
-            'aspect_ratios should be list and inputs and aspect_ratios should ' \
-            'have same length'
 
     box_results = []
     var_results = []
@@ -230,7 +200,7 @@ def prior_boxes(inputs,
             if not isinstance(aspect_ratio, list):
                 aspect_ratio = [aspect_ratio]
 
-        box, var = prior_box(input, image, min_size, max_size, aspect_ratio,
+        box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
                                variance, flip, clip, step_w[i]
                                if step_w else 0.0, step_h[i]
                                if step_w else 0.0, offset)
@@ -242,17 +212,11 @@ def prior_boxes(inputs,
         box = box_results[0]
         var = var_results[0]
     else:
-        axis = 3
         reshaped_boxes = []
         reshaped_vars = []
         for i in range(len(box_results)):
-            reshaped_boxes += [flatten(box_results[i], axis=3)]
-            reshaped_vars += [flatten(var_results[i], axis=3)]
-
-        helper = LayerHelper("concat", **locals())
-        dtype = helper.input_dtype()
-        box = helper.create_tmp_variable(dtype)
-        var = helper.create_tmp_variable(dtype)
+            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)

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

@@ -21,8 +21,6 @@ from ..framework import Variable
 from ..param_attr import ParamAttr
 from layer_function_generator import autodoc
 from tensor import concat
-import math
-from operator import mul
 
 __all__ = [
     'fc',
@@ -66,8 +64,6 @@ __all__ = [
     'nce',
     'beam_search',
     'row_conv',
-    'reshape_with_axis',
-    'flatten',
     'multiplex',
     'layer_norm',
 ]
@@ -3095,86 +3091,3 @@ def multiplex(inputs, index):
                 'Ids': index},
         outputs={'Out': [out]})
     return out
-
-
-def reshape_with_axis(input, axis):
-    """
-    **ReshapeWithAxis Layer**
-
-    ReshapeWithAxis is used to merge adjacent dimensions according to axis.
-
-    Args:
-       input(variable): The input tensor.
-       axis(list): The axis which is used to merge the adjacent dimensions.
-
-    Returns:
-        Variable: A tensor variable.
-
-    Examples:
-        .. code-block:: python
-
-          x = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
-          reshaped = fluid.layers.reshape_with_axis(input=x, axis=[2])
-          reshaped.shape
-            >> [-1, 1024]
-          reshaped = fluid.layers.reshape_with_axis(input=x, axis=[1,3])
-          reshaped.shape
-            >> [-1, 96, 32]
-    """
-    assert isinstance(axis, list), "axis should be list."
-    assert len(input.shape) > len(
-        axis), "the length of axis should be litter than input.shape's."
-    input_shape = input.shape
-    temp = 0
-    for ax in axis:
-        assert ax < len(input.shape) and ax > 0, \
-            'The data of Axis should be between 1 and len(input.shape)'
-        assert ax > temp, 'Axis should be incremented sequence'
-        temp = ax
-    axis += [len(input.shape)]
-
-    new_shape = []
-    for i in range(len(axis) - 1):
-        new_shape += [reduce(mul, input_shape[axis[i]:axis[i + 1]], 1)]
-    new_shape = [-1] + new_shape
-
-    helper = LayerHelper('reshape', **locals())
-    out = helper.create_tmp_variable(helper.input_dtype())
-    helper.append_op(
-        type='reshape',
-        inputs={'X': [input]},
-        outputs={'Out': [out]},
-        attrs={'shape': new_shape})
-    return out
-
-
-def flatten(input, axis=1):
-    """
-    **Flatten Layer**
-    ReshapeWithAxis is used to merge adjacent dimensions according to axis.
-    Args:
-       input(variable): The input tensor.
-       axis(int):
-    Returns:
-        Variable: A tensor variable.
-    Examples:
-        .. code-block:: python
-          x = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
-          reshaped = fluid.layers.reshape_with_axis(input=x, axis=2)
-          reshaped.shape
-            >> [-1, 1024]
-    """
-    assert len(input.shape) > axis and axis > 0, \
-        "the axis should be litter than input.shape's."
-    input_shape = input.shape
-
-    new_shape = [-1, reduce(mul, input_shape[axis:len(input_shape)], 1)]
-
-    helper = LayerHelper('reshape', **locals())
-    out = helper.create_tmp_variable(helper.input_dtype())
-    helper.append_op(
-        type='reshape',
-        inputs={'X': [input]},
-        outputs={'Out': [out]},
-        attrs={'shape': new_shape})
-    return out

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

@@ -51,15 +51,15 @@ def main(use_cuda):
     if use_cuda:  # prior_box only support CPU.
         return
 
-    box, var = prior_box_output(data_shape=[3, 224, 224])
+    data_shape = [3, 224, 224]
+    box, var = prior_box_output(data_shape)
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
     batch = [128]
 
-    for i in range(1):
-        # print("iteration : %d" % i)
+    for _ in range(1):
         x = np.random.random(batch + data_shape).astype("float32")
         tensor_x = core.LoDTensor()
         tensor_x.set(x, place)
@@ -75,11 +75,9 @@ def main(use_cuda):
 
 class TestFitALine(unittest.TestCase):
     def test_cpu(self):
-        with self.program_scope_guard():
         main(use_cuda=False)
 
     def test_cuda(self):
-        with self.program_scope_guard():
         main(use_cuda=True)