Merge pull request #61 from SunAhong1993/develop

fix the bug

x2paddle/core/graph.py

@@ -49,11 +49,13 @@ class Graph(object):
 
     def _make_input_nodes(self):
         for name, node in self.node_map.items():
+            name = name.replace('/', '_').replace('-', '_')
             if len(node.inputs) == 0:
                 self.input_nodes.append(name)
 
     def _make_output_nodes(self):
         for name, node in self.node_map.items():
+            name = name.replace('/', '_').replace('-', '_')
             if len(node.outputs) == 0:
                 self.output_nodes.append(name)
 

x2paddle/decoder/caffe_decoder.py

@@ -97,7 +97,6 @@ class CaffeGraph(Graph):
         phase_map = {0: 'train', 1: 'test'}
         filtered_layer_names = set()
         filtered_layers = []
-        print('The filter layer:')
         for layer in layers:
             phase = 'test'
             if len(layer.include):
@@ -116,7 +115,7 @@ class CaffeGraph(Graph):
                 assert layer.name not in filtered_layer_names
                 filtered_layer_names.add(layer.name)
             else:
-                print(layer.name)
+                print('The filter layer:' + layer.name)
         return filtered_layers
 
     def build(self):
@@ -184,7 +183,7 @@ class CaffeGraph(Graph):
                 node = self.node_map[layer_name]
                 node.set_params(data)
             else:
-                notice('Ignoring parameters for non-existent layer: %s' % \
+                raise Exception('Ignoring parameters for non-existent layer: %s' % \
                        layer_name)
 
         super(CaffeGraph, self).build()

x2paddle/op_mapper/caffe_custom_layer/__init__.py

@@ -56,7 +56,7 @@ def get_params(layer, layer_type):
             elif ch.isupper() and layer_type[i - 1].islower():
                 tmp_name += (s + '_')
                 s = ''
-            s += ch
+            s += ch.lower()
         tmp_name += s
         param_name = '_'.join((tmp_name, 'param'))
     else:

x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py

@@ -18,19 +18,19 @@ def convolutiondepthwise_shape(input_shape,
     [k_h, k_w] = [1, 1]
     if isinstance(kernel_size, numbers.Number):
         [k_h, k_w] = [kernel_size] * 2
-    elif isinstance(kernel_size, list) and len(kernel_size) > 0:
+    elif len(kernel_size) > 0:
         k_h = kernel_h if kernel_h else kernel_size[0]
         k_w = kernel_w if kernel_w else kernel_size[len(kernel_size) - 1]
     [s_h, s_w] = [1, 1]
     if isinstance(stride, numbers.Number):
         [s_h, s_w] = [stride] * 2
-    elif isinstance(stride, list) and len(stride) > 0:
+    elif len(stride) > 0:
         s_h = stride_h if stride_h else stride[0]
         s_w = stride_w if stride_w else stride[len(stride) - 1]
     [p_h, p_w] = [0, 0]
     if isinstance(pad, numbers.Number):
         [p_h, p_w] = [pad] * 2
-    elif isinstance(pad, list) and len(pad) > 0:
+    elif len(pad) > 0:
         p_h = pad_h if pad_h else pad[0]
         p_w = pad_w if pad_w else pad[len(pad) - 1]
     dila_len = len(dilation)
@@ -69,22 +69,23 @@ def convolutiondepthwise_layer(inputs,
                                stride_w=None,
                                input_shape=None,
                                name=None):
+    import numbers
     [k_h, k_w] = [1, 1]
     if isinstance(kernel_size, numbers.Number):
         [k_h, k_w] = [kernel_size] * 2
-    elif isinstance(kernel_size, list) and len(kernel_size) > 0:
+    elif len(kernel_size) > 0:
         k_h = kernel_h if kernel_h else kernel_size[0]
         k_w = kernel_w if kernel_w else kernel_size[len(kernel_size) - 1]
     [s_h, s_w] = [1, 1]
     if isinstance(stride, numbers.Number):
         [s_h, s_w] = [stride] * 2
-    elif isinstance(stride, list) and len(stride) > 0:
+    elif len(stride) > 0:
         s_h = stride_h if stride_h else stride[0]
         s_w = stride_w if stride_w else stride[len(stride) - 1]
     [p_h, p_w] = [0, 0]
     if isinstance(pad, numbers.Number):
         [p_h, p_w] = [pad] * 2
-    elif isinstance(pad, list) and len(pad) > 0:
+    elif len(pad) > 0:
         p_h = pad_h if pad_h else pad[0]
         p_w = pad_w if pad_w else pad[len(pad) - 1]
     input = inputs[0]

x2paddle/op_mapper/caffe_op_mapper.py

@@ -123,22 +123,21 @@ class CaffeOpMapper(OpMapper):
         [k_h, k_w] = [1, 1]
         if isinstance(params.kernel_size, numbers.Number):
             [k_h, k_w] = [params.kernel_size] * 2
-        elif isinstance(params.kernel_size,
-                        list) and len(params.kernel_size) > 0:
+        elif len(params.kernel_size) > 0:
             k_h = params.kernel_h if params.kernel_h else params.kernel_size[0]
             k_w = params.kernel_w if params.kernel_w else params.kernel_size[
                 len(params.kernel_size) - 1]
         [s_h, s_w] = [1, 1]
         if isinstance(params.stride, numbers.Number):
             [s_h, s_w] = [params.stride] * 2
-        elif isinstance(params.stride, list) and len(params.stride) > 0:
+        elif len(params.stride) > 0:
             s_h = params.stride_h if params.stride_h else params.stride[0]
             s_w = params.stride_w if params.stride_w else params.stride[
                 len(params.stride) - 1]
         [p_h, p_w] = [0, 0]
         if isinstance(params.pad, numbers.Number):
             [p_h, p_w] = [params.pad] * 2
-        elif isinstance(params.pad, list) and len(params.pad) > 0:
+        elif len(params.pad) > 0:
             p_h = params.pad_h if params.pad_h else params.pad[0]
             p_w = params.pad_w if params.pad_w else params.pad[len(params.pad) -
                                                                1]