Merge pull request #35 from PaddlePaddle/develop

更新数据

x2paddle/decoder/caffe_decoder.py

@@ -246,9 +246,14 @@ class CaffeDecoder(object):
                 if layer.type == 'PReLU':
                     c_o, c_i, h, w = map(int, [1] + \
                         list(dims) + [1]* (3 - len(dims)))
+                elif layer.type == 'Normalize':
+                    data = np.asarray(list(blob.data), dtype=np.float32)
+                    transformed.append(data)
+                    continue
                 else:
                     c_o, c_i, h, w = map(int, [1] * (4 - len(dims)) \
                         + list(dims))
+
             else:
                 c_o = blob.num
                 c_i = blob.channels

x2paddle/op_mapper/caffe_custom_layer/normalize.py

@@ -12,16 +12,14 @@ def normalize_layer(inputs,
                     input_shape=None,
                     name=None):
     assert across_spatial == False, "Only support across_spatial == False for Normalize"
-
     input = inputs[0]
     l2_norm = fluid.layers.l2_normalize(input, axis=1, name=name + '_l2')
     scale_param = fluid.layers.create_parameter(
-        shape=[1]
-        if channel_shared else [input_shape[0][0], 1, 1, input_shape[0][1]],
+        shape=[1] if channel_shared else [input_shape[0][1]],
         dtype=input.dtype,
         attr=name + '_scale')
     scale_param = fluid.layers.reshape(x=scale_param, \
-                  shape=[1] if channel_shared else [input_shape[0][0], 1, 1, input_shape[0][1]])
+                  shape=[1] if channel_shared else [input_shape[0][1]])
     out = fluid.layers.elementwise_mul(x=l2_norm,
                                        y=scale_param,
                                        axis=-1 if channel_shared else 1)

x2paddle/op_mapper/tf_op_mapper.py

@@ -762,6 +762,29 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
+    def MirrorPad(self, node):
+        input = self.graph.get_node(node.layer.input[0], copy=True)
+        paddings = self.graph.get_node(node.layer.input[1], copy=True)
+        assert paddings.layer_type == "Const", "Padding should be Const"
+        self.add_omit_nodes(paddings.layer_name, node.layer_name)
+        paddings = paddings.value.flatten().tolist()
+        mode = node.get_attr("mode").decode()
+        assert mode == "REFLECT", "Only support 'REFLECT` mode in MirrorPad"
+        if input.tf_data_format == "NHWC" and len(input.out_shapes[0]) == 4:
+            paddings = [paddings[i] for i in [0, 1, 6, 7, 2, 3, 4, 5]]
+
+        pad_op = "pad"
+        if len(input.out_shapes[0]) == 4:
+            if paddings[0] + paddings[1] + paddings[2] + paddings[3] == 0:
+                paddings = paddings[4:]
+                pad_op = "pad2d"
+        attr = {"paddings": paddings, "mode": string("reflect")}
+        node.fluid_code.add_layer(pad_op,
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+
     def Range(self, node):
         start = self.graph.get_node(node.layer.input[0], copy=True)
         limit = self.graph.get_node(node.layer.input[1], copy=True)