add caffe memory data

x2paddle/decoder/caffe_decoder.py

@@ -258,7 +258,7 @@ class CaffeGraph(Graph):
         assert input_node_name in self.node_map, 'The {} isn\'t a valid node'.format(
             name)
         input_node = self.node_map[input_node_name]
-        if len(input_node.layer.top) > 1 and input_node.layer_type != "Input":
+        if len(input_node.layer.top) > 1 and input_node.layer_type not in ["Input", "MemoryData"]:
             need_idx = list(input_node.layer.top).index(node.layer.bottom[idx])
             name = input_node_name + ':' + str(need_idx)
         else:

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_op_mapper.py

@@ -199,6 +199,26 @@ class CaffeOpMapper(OpMapper):
         self.inputs_info["x{}".format(self.input_index)] = [[-1] + shape, "float32"]
         self.input_index += 1
         
+    def MemoryData(self, node):
+        params = node.layer.memory_data_param
+        transform_params = node.layer.transform_param
+        self.paddle_graph.add_layer(
+            "paddle.to_tensor",
+            inputs={},
+            outputs=[node.layer_name],
+            data="x{}".format(self.input_index))
+        shape = list()
+        shape.append(params.batch_size)
+        shape.append(params.channels)
+        if hasattr(transform_params, "crop_size"):
+            shape.append(transform_params.crop_size)
+            shape.append(transform_params.crop_size)
+        else:
+            shape.append(params.width)
+            shape.append(params.height)
+        self.inputs_info["x{}".format(self.input_index)] = [shape, "float32"]
+        self.input_index += 1
+
     def Convolution(self, node):
         conv2d_name = name_generator("conv", self.nn_name2id)
         output_name = node.layer_name

x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py

@@ -124,6 +124,8 @@ class CaffeOpMapper(OpMapper):
     def __init__(self, decoder):
         super(CaffeOpMapper, self).__init__()
         self.graph = decoder.caffe_graph
+        if not self.op_checker():
+            raise Exception("Model is not supported yet.")
         self.params = dict()
         resolver = decoder.resolver
         self.used_custom_layers = {}
@@ -191,6 +193,31 @@ class CaffeOpMapper(OpMapper):
             outputs=[node.name],
             **layer_attrs)
         
+    def MemoryData(self, node):
+        params = node.layer.memory_data_param
+        transform_params = node.layer.transform_param
+        
+        shape = list()
+        shape.append(params.batch_size)
+        shape.append(params.channels)
+        if hasattr(transform_params, "crop_size"):
+            shape.append(transform_params.crop_size)
+            shape.append(transform_params.crop_size)
+        else:
+            shape.append(params.width)
+            shape.append(params.height)
+        dtype = 'float32'
+        layer_attrs = {
+            "dtype": string(dtype),
+            "shape": shape,
+            "name": string(node.name)
+        }
+        self.paddle_graph.add_layer(
+            kernel="paddle.static.data",
+            inputs={},
+            outputs=[node.name],
+            **layer_attrs)
+        
     def Convolution(self, node):
         data = node.data
         params = node.layer.convolution_param