caffe2fluid:add power layer and fix bugs in priorbox and pool layers (#1508)

fluid/PaddleCV/caffe2fluid/kaffe/custom_layers/__init__.py

@@ -15,6 +15,7 @@ import detection_out
 import normalize
 import select
 import crop
+import power
 import reduction
 
 #custom layer import ends

fluid/PaddleCV/caffe2fluid/kaffe/custom_layers/power.py

+""" a custom layer for 'power', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/power.html
+"""
+from .register import register
+
+
+def power_shape(input_shape, shape=None):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    return input_shape
+
+
+def power_layer(input, name, power=1.0, scale=1.0, shift=0.0):
+    """ build a layer of type 'Power' using fluid
+
+    Args:
+        @input (variables): input fluid variable for this layer
+        @name (str): name for this layer
+        @power (float): parameter from caffe's Power layer
+	@scale (float): parameter from caffe's Power layer
+        @shift (float): parameter from caffe's Power layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+    scale_out = fluid.layers.scale(
+        input, scale=scale, bias=shift, bias_after_scale=True)
+    output = fluid.layers.pow(scale_out, factor=power)
+
+    return output
+
+
+register(kind='Power', shape=power_shape, layer=power_layer)

fluid/PaddleCV/caffe2fluid/kaffe/custom_layers/priorbox.py

@@ -31,12 +31,12 @@ def priorbox_shape(input_shapes, min_size, max_size=None, aspect_ratio=None):
 def priorbox_layer(inputs,
                    name,
                    min_size,
-                   step,
                    max_size=None,
                    aspect_ratio=None,
-                   flip=True,
+                   variance=[0.1, 0.1, 0.2, 0.2],
+                   flip=False,
                    clip=False,
-                   variance=[],
+                   step=0.0,
                    offset=0.5):
     """ build a layer of type 'Priorbox' using fluid
 
@@ -52,6 +52,8 @@ def priorbox_layer(inputs,
     assert len(inputs) == 2, "invalid inputs for Priorbox[%s]" % (name)
     input = inputs[0]
     image = inputs[1]
+    steps = tuple(step) if type(step) is list or type(step) is tuple else (step,
+                                                                           step)
     box, variance_ = fluid.layers.prior_box(
         input,
         image,
@@ -60,7 +62,8 @@ def priorbox_layer(inputs,
         aspect_ratio,
         variance,
         flip,
-        clip, (step, step),
+        clip,
+        steps,
         offset,
         min_max_aspect_ratios_order=True)
     """

fluid/PaddleCV/caffe2fluid/kaffe/layers.py

@@ -38,7 +38,7 @@ LAYER_DESCRIPTORS = {
     'MultinomialLogisticLoss': shape_scalar,
     'MVN': shape_not_implemented,
     'Pooling': shape_pool,
-    'Power': shape_identity,
+    'Power': shape_power,
     'ReLU': shape_identity,
     'PReLU': shape_identity,
     'Scale': shape_identity,

fluid/PaddleCV/caffe2fluid/kaffe/paddle/network.py

@@ -280,8 +280,17 @@ class Network(object):
             param_attr=fluid.ParamAttr(name=prefix + 'negslope'))
         return output
 
-    def pool(self, pool_type, input, k_h, k_w, s_h, s_w, ceil_mode, padding,
-             name):
+    def pool(self,
+             pool_type,
+             input,
+             k_h,
+             k_w,
+             s_h,
+             s_w,
+             ceil_mode,
+             padding,
+             name,
+             exclusive=True):
         # Get the number of channels in the input
         in_hw = input.shape[2:]
         k_hw = [k_h, k_w]
@@ -295,7 +304,8 @@ class Network(object):
             pool_stride=s_hw,
             pool_padding=padding,
             ceil_mode=ceil_mode,
-            pool_type=pool_type)
+            pool_type=pool_type,
+            exclusive=exclusive)
         return output
 
     @layer

fluid/PaddleCV/caffe2fluid/kaffe/shapes.py

@@ -67,6 +67,10 @@ def shape_crop(node):
     raise KaffeError('crop function had been defined in customer_layers')
 
 
+def shape_power(node):
+    raise KaffeError('power function had been defined in customer_layers')
+
+
 def shape_data(node):
     if node.output_shape:
         # Old-style input specification