Add support of upsample operator in onnx convertor (#539)

* add support of upsample operator in onnx convertor

* flake8 fixes

* fix cutting of unnecessary inputs

tools/python/transform/onnx_converter.py

@@ -180,7 +180,7 @@ OnnxSupportedOps = [
     # 'TopK',
     'Transpose',
     'Unsqueeze',
-    # 'Upsample',
+    'Upsample',
     # 'Xor',
 ]
 
@@ -392,6 +392,7 @@ class OnnxConverter(base_converter.ConverterInterface):
             OnnxOpType.TargetRMSNorm: self.convert_target_rms_norm,
             OnnxOpType.Transpose.name: self.convert_transpose,
             OnnxOpType.Unsqueeze.name: self.convert_unsqueeze,
+            OnnxOpType.Upsample.name: self.convert_upsample
         }
         self._option = option
         self._mace_net_def = mace_pb2.NetDef()
@@ -454,7 +455,13 @@ class OnnxConverter(base_converter.ConverterInterface):
         tensor.name = name
         tensor.dims.extend(list(shape))
         tensor.data_type = data_type
-        tensor.float_data.extend(value.flat)
+
+        if tensor.data_type == mace_pb2.DT_INT32:
+            tensor.int32_data.extend(value.astype(np.int32).flat)
+        elif tensor.data_type == mace_pb2.DT_FLOAT:
+            tensor.float_data.extend(value.astype(np.float32).flat)
+        else:
+            mace_check(False, "Not supported tensor type: %s" % name)
 
     def run(self):
         graph_def = self._onnx_model.graph
@@ -1533,3 +1540,26 @@ class OnnxConverter(base_converter.ConverterInterface):
         offset_arg = op.arg.add()
         offset_arg.name = 'offset'
         offset_arg.i = offset
+
+    def convert_upsample(self, node):
+        op = self.convert_general_op(node)
+        del op.input[1:]  # cut all unnecessary inputs (onnx>=1.5)
+
+        output_size = self._graph_shapes_dict[op.output[0]]
+        output_size = np.array(output_size[-2:]).astype(np.int32)
+        if node.attrs['mode'] == 'nearest':
+            op.type = MaceOp.ResizeNearestNeighbor.name
+            size_tensor_name = op.name + ":size"
+            self.add_tensor(size_tensor_name, output_size.shape,
+                            mace_pb2.DT_INT32, output_size)
+            op.input.append(size_tensor_name)
+        else:
+            op.type = MaceOp.ResizeBilinear.name
+
+            size_arg = op.arg.add()
+            size_arg.name = MaceKeyword.mace_resize_size_str
+            size_arg.ints.extend(output_size.tolist())
+
+        align_corners_arg = op.arg.add()
+        align_corners_arg.name = MaceKeyword.mace_align_corners_str
+        align_corners_arg.i = node.attrs.get('align_corners', 0)