From 9277f0bcb645640d050185f539d37c0d72562ee4 Mon Sep 17 00:00:00 2001
From: WJJ1995 <wjjisloser@163.com>
Date: Thu, 3 Jun 2021 11:16:10 +0800
Subject: [PATCH] Fix caffe bug && Add onnx 3d interpolate support (#611)

* fix caffe kernel_size=0 bug

* add onnx 3d interpolate support
---
 .../op_mapper/caffe2paddle/caffe_op_mapper.py |   8 +-
 .../op_mapper/onnx2paddle/opset9/opset.py     | 106 +++++++++++++-----
 2 files changed, 81 insertions(+), 33 deletions(-)

diff --git a/x2paddle/op_mapper/caffe2paddle/caffe_op_mapper.py b/x2paddle/op_mapper/caffe2paddle/caffe_op_mapper.py
index 364844e..582f90c 100644
--- a/x2paddle/op_mapper/caffe2paddle/caffe_op_mapper.py
+++ b/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
diff --git a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
index 393dfeb..9a61e12 100755
--- a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
+++ b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
@@ -309,35 +309,83 @@ class OpSet9():
             elif len(node.layer.input) == 4:
                 # opset 11
                 val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
-                var_nc, var_hw = val_sizes.name + '_nc', val_sizes.name + '_hw'
-                self.paddle_graph.add_layer(
-                    'paddle.split',
-                    inputs={"x": val_sizes.name},
-                    outputs=[var_nc, var_hw],
-                    num_or_sections=[2, 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
-                self.paddle_graph.add_layer(
-                    kernel="paddle.nn.functional.interpolate",
-                    inputs=inputs,
-                    outputs=[node.name],
-                    **attrs)
+                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',
+                        inputs={"x": val_sizes.name},
+                        outputs=[var_nc, var_hw],
+                        num_or_sections=[2, 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(
+                        kernel="paddle.nn.functional.interpolate",
+                        inputs=inputs,
+                        outputs=[node.name],
+                        **attrs)
                 return
         elif node.layer_type == 'Upsample':
             val_scales = self.graph.get_input_node(node, idx=1, copy=True)
-- 
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