fix the pad bug and add init

x2paddle/decoder/caffe_decoder.py

@@ -190,7 +190,6 @@ class CaffeGraph(Graph):
                     top_layer[out_name] = [layer.name]
                 else:
                     top_layer[out_name].append(layer.name)
-
         for layer_name, data in self.params:
             if layer_name in self.node_map:
                 node = self.node_map[layer_name]

x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py

@@ -19,20 +19,29 @@ def convolutiondepthwise_shape(input_shape,
     if isinstance(kernel_size, numbers.Number):
         [k_h, k_w] = [kernel_size] * 2
     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]
+        k_h = kernel_h if kernel_h > 0 else kernel_size[0]
+        k_w = kernel_w if kernel_w > 0 else kernel_size[len(kernel_size) - 1]
+    elif kernel_h > 0 or kernel_w > 0:
+        k_h = kernel_h
+        k_w = kernel_w
     [s_h, s_w] = [1, 1]
     if isinstance(stride, numbers.Number):
         [s_h, s_w] = [stride] * 2
     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]
+        s_h = stride_h if stride_h > 0 else stride[0]
+        s_w = stride_w if stride_w > 0 else stride[len(stride) - 1]
+    elif stride_h > 0 or stride_w > 0:
+        s_h = stride_h
+        s_w = stride_w
     [p_h, p_w] = [0, 0]
     if isinstance(pad, numbers.Number):
         [p_h, p_w] = [pad] * 2
     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]
+        p_h = pad_h if pad_h > 0 else pad[0]
+        p_w = pad_w if pad_w > 0 else pad[len(pad) - 1]
+    elif pad_h > 0 or pad_w > 0:
+        p_h = pad_h
+        p_w = pad_w
     dila_len = len(dilation)
     dila_h = 1
     dila_w = 1
@@ -74,20 +83,29 @@ def convolutiondepthwise_layer(inputs,
     if isinstance(kernel_size, numbers.Number):
         [k_h, k_w] = [kernel_size] * 2
     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]
+        k_h = kernel_h if kernel_h > 0 else kernel_size[0]
+        k_w = kernel_w if kernel_w > 0 else kernel_size[len(kernel_size) - 1]
+    elif kernel_h > 0 or kernel_w > 0:
+        k_h = kernel_h
+        k_w = kernel_w
     [s_h, s_w] = [1, 1]
     if isinstance(stride, numbers.Number):
         [s_h, s_w] = [stride] * 2
     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]
+        s_h = stride_h if stride_h > 0 else stride[0]
+        s_w = stride_w if stride_w > 0 else stride[len(stride) - 1]
+    elif stride_h > 0 or stride_w > 0:
+        s_h = stride_h
+        s_w = stride_w
     [p_h, p_w] = [0, 0]
     if isinstance(pad, numbers.Number):
         [p_h, p_w] = [pad] * 2
     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]
+        p_h = pad_h if pad_h > 0 else pad[0]
+        p_w = pad_w if pad_w > 0 else pad[len(pad) - 1]
+    elif pad_h > 0 or pad_w > 0:
+        p_h = pad_h
+        p_w = pad_w
     input = inputs[0]
     dila_len = len(dilation)
     dila_h = 1

x2paddle/op_mapper/caffe_op_mapper.py

@@ -135,23 +135,32 @@ class CaffeOpMapper(OpMapper):
         if isinstance(params.kernel_size, numbers.Number):
             [k_h, k_w] = [params.kernel_size] * 2
         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[
+            k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[0]
+            k_w = params.kernel_w if params.kernel_w > 0 else params.kernel_size[
                 len(params.kernel_size) - 1]
+        elif params.kernel_h > 0 or params.kernel_w > 0:
+            k_h = params.kernel_h
+            k_w = params.kernel_w
         [s_h, s_w] = [1, 1]
         if isinstance(params.stride, numbers.Number):
             [s_h, s_w] = [params.stride] * 2
         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[
+            s_h = params.stride_h if params.stride_h > 0 else params.stride[0]
+            s_w = params.stride_w if params.stride_w > 0 else params.stride[
                 len(params.stride) - 1]
+        elif params.stride_h > 0 or params.stride_w > 0:
+            s_h = params.stride_h
+            s_w = params.stride_w
         [p_h, p_w] = [0, 0]
         if isinstance(params.pad, numbers.Number):
             [p_h, p_w] = [params.pad] * 2
         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) -
+            p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
+            p_w = params.pad_w if params.pad_w > 0 else params.pad[len(params.pad) -
                                                                   1]
+        elif params.pad_h > 0 or params.pad_w > 0:
+            p_h = params.pad_h
+            p_w = params.pad_w
         dila_h = dila_w = 1
         group = 1
         c_o = 1
@@ -211,15 +220,22 @@ class CaffeOpMapper(OpMapper):
 
     def Convolution(self, node):
         data = node.data
-        assert data is not None, 'The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.'.format(
-            node.layer_name, node.layer_type)
+        params = node.layer.convolution_param
+        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        if data is None:
+            data = []
+            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
+            node.layer_name, node.layer_type))
+            input_c = node.input_shape[0][1]
+            output_c = channel
+            data.append(np.zeros([output_c, input_c, kernel[0], kernel[1]]))
+            data.append(np.zeros([output_c,]))
+        else:
             data = self.adjust_parameters(node)
         self.weights[node.layer_name + '_weights'] = data[0]
         if len(data) == 2:
             self.weights[node.layer_name + '_bias'] = data[1]
-        params = node.layer.convolution_param
-        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
-            node.layer_type, params)
         assert len(node.inputs
                    ) == 1, 'The count of Convolution node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
@@ -251,15 +267,22 @@ class CaffeOpMapper(OpMapper):
 
     def Deconvolution(self, node):
         data = node.data
-        assert data is not None, 'The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.'.format(
-            node.layer_name, node.layer_type)
+        params = node.layer.convolution_param
+        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        if data is None:
+            data = []
+            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
+            node.layer_name, node.layer_type))
+            input_c = node.input_shape[0][1]
+            output_c = channel
+            data.append(np.zeros([output_c, input_c, kernel[0], kernel[1]]))
+            data.append(np.zeros([output_c,]))
+        else:
             data = self.adjust_parameters(node)
         self.weights[node.layer_name + '_weights'] = data[0]
         if len(data) == 2:
             self.weights[node.layer_name + '_bias'] = data[1]
-        params = node.layer.convolution_param
-        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
-            node.layer_type, params)
         assert len(node.inputs
                    ) == 1, 'The count of Deconvolution node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
@@ -344,8 +367,16 @@ class CaffeOpMapper(OpMapper):
 
     def InnerProduct(self, node):
         data = node.data
-        assert data is not None, 'The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.'.format(
-            node.layer_name, node.layer_type)
+        params = node.layer.inner_product_param
+        if data is None:
+            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0.'.format(
+            node.layer_name, node.layer_type))
+            input_c = node.input_shape[0][1]
+            output_c = params.num_output
+            data = []
+            data.append(np.zeros([input_c, output_c]))
+            data.append(np.zeros([output_c]))
+        else:
             data = self.adjust_parameters(node)
             # Reshape the parameters to Paddle's ordering
             transpose_order = (1, 0)
@@ -361,7 +392,7 @@ class CaffeOpMapper(OpMapper):
             self.weights[node.layer_name + '_bias'] = data[1]
         assert len(node.inputs
                    ) == 1, 'The count of InnerProduct node\'s input is not 1.'
-        params = node.layer.inner_product_param
+        #params = node.layer.inner_product_param
         assert params.axis == 1
         assert params.bias_term == True
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
@@ -592,7 +623,14 @@ class CaffeOpMapper(OpMapper):
             eps = params.eps
         else:
             eps = 1e-5
-        assert len(node.data) == 3
+        if node.data is None or len(node.data) != 3:
+            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
+            node.layer_name, node.layer_type))
+            input_c = node.input_shape[0][1]
+            mean = np.zeros([input_c,])
+            variance = np.zeros([input_c,])
+            scale = 0
+        else:
             node.data = [np.squeeze(i) for i in node.data]
             mean, variance, scale = node.data
         # Prescale the stats
@@ -616,7 +654,13 @@ class CaffeOpMapper(OpMapper):
                                   param_attr=attr)
 
     def Scale(self, node):
-
+        if node.data is None:
+            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
+            node.layer_name, node.layer_type))
+            input_c = node.input_shape[0][1]
+            self.weights[node.layer_name + '_scale'] = np.zeros([input_c,])
+            self.weights[node.layer_name + '_offset'] = np.zeros([input_c,])
+        else:
             self.weights[node.layer_name + '_scale'] = np.squeeze(node.data[0])
             self.weights[node.layer_name + '_offset'] = np.squeeze(node.data[1])
         params = node.layer.scale_param

x2paddle/op_mapper/caffe_shape.py

@@ -22,22 +22,31 @@ def get_kernel_parameters(params):
     if isinstance(params.kernel_size, numbers.Number):
         [k_h, k_w] = [params.kernel_size] * 2
     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[
+        k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[0]
+        k_w = params.kernel_w if params.kernel_w > 0 else params.kernel_size[
             len(params.kernel_size) - 1]
+    elif params.kernel_h > 0 or params.kernel_w > 0:
+        k_h = params.kernel_h
+        k_w = params.kernel_w
     [s_h, s_w] = [1, 1]
     if isinstance(params.stride, numbers.Number):
         [s_h, s_w] = [params.stride] * 2
     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[
+        s_h = params.stride_h if params.stride_h > 0 else params.stride[0]
+        s_w = params.stride_w if params.stride_w > 0 else params.stride[
             len(params.stride) - 1]
+    elif params.stride_h > 0 or params.stride_w > 0:
+        s_h = params.stride_h
+        s_w = params.stride_w
     [p_h, p_w] = [0, 0]
     if isinstance(params.pad, numbers.Number):
         [p_h, p_w] = [params.pad] * 2
     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]
+        p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
+        p_w = params.pad_w if params.pad_w > 0 else params.pad[len(params.pad) - 1]
+    elif params.pad_h > 0 or params.pad_w > 0:
+        p_h = params.pad_h
+        p_w = params.pad_w
     dila_h = dila_w = 1
     if hasattr(params, 'dilation'):
         dila_len = len(params.dilation)