Fix caffe bug && Add onnx 3d interpolate support (#611)

* fix caffe kernel_size=0 bug

* add onnx 3d interpolate support

x2paddle/op_mapper/caffe2paddle/caffe_op_mapper.py

@@ -62,15 +62,15 @@ def _get_kernel_parameters(kind, params):
         "Convolution", "Pooling", "Deconvolution", "ConvolutionDepthwise"
     ]
     [k_h, k_w] = [1, 1]
-    if isinstance(params.kernel_size, numbers.Number):
+    if params.kernel_h > 0 or params.kernel_w > 0:
+        k_h = params.kernel_h
+        k_w = params.kernel_w
+    elif 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 > 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

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -309,6 +309,51 @@ class OpSet9():
             elif len(node.layer.input) == 4:
                 # opset 11
                 val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
+                val_x_shape = val_x.out_shapes[0]
+                if len(val_x_shape) == 3:
+                    var_n, var_hw = val_sizes.name + '_n', val_sizes.name + '_hw'
+                    self.paddle_graph.add_layer(
+                        'paddle.split',
+                        inputs={"x": val_sizes.name},
+                        outputs=[var_n, var_hw],
+                        num_or_sections=[1, 2],
+                        axis=0)
+                    self.paddle_graph.add_layer(
+                        "paddle.cast",
+                        inputs={"x": var_hw},
+                        outputs=[var_hw],
+                        dtype=string('int32'))
+                    inputs['size'] = var_hw
+                    attrs = {
+                        "align_corners": False,
+                        "mode": string(node.get_attr('mode', 'nearest'))
+                    }
+                    mode = node.get_attr('mode', 'nearest')
+                    if mode == "linear":
+                        attrs["mode"] = string("bilinear")
+                    if node.get_attr('coordinate_transformation_mode',
+                                     'half_pixel') == 'pytorch_half_pixel':
+                        attrs["align_corners"] = False
+                        attrs["align_mode"] = 0
+                    if node.get_attr('coordinate_transformation_mode',
+                                     'half_pixel') == 'align_corners':
+                        attrs["align_corners"] = True
+                    self.paddle_graph.add_layer(
+                        'paddle.unsqueeze',
+                        inputs={"x": val_x.name},
+                        outputs=[val_x.name],
+                        axis=0)
+                    self.paddle_graph.add_layer(
+                        kernel="paddle.nn.functional.interpolate",
+                        inputs=inputs,
+                        outputs=[node.name],
+                        **attrs)
+                    self.paddle_graph.add_layer(
+                        'paddle.squeeze',
+                        inputs={"x": node.name},
+                        outputs=[node.name],
+                        axis=0)
+                else:
                     var_nc, var_hw = val_sizes.name + '_nc', val_sizes.name + '_hw'
                     self.paddle_graph.add_layer(
                         'paddle.split',
@@ -333,6 +378,9 @@ class OpSet9():
                                      'half_pixel') == 'pytorch_half_pixel':
                         attrs["align_corners"] = False
                         attrs["align_mode"] = 0
+                    if node.get_attr('coordinate_transformation_mode',
+                                     'half_pixel') == 'align_corners':
+                        attrs["align_corners"] = True
                     self.paddle_graph.add_layer(
                         kernel="paddle.nn.functional.interpolate",
                         inputs=inputs,