1 files changed, 124 insertions, 0 deletions
diff --git a/compiler/mir-onnx-importer/Op/Upsample.cpp b/compiler/mir-onnx-importer/Op/Upsample.cpp
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+++ b/compiler/mir-onnx-importer/Op/Upsample.cpp
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+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Upsample.h"
+
+#include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
+
+#include "mir/Tensor.h"
+
+#include "mir/ops/ConstantOp.h"
+#include "mir/ops/ResizeOp.h"
+
+namespace mir_onnx
+{
+
+void convertUpsampleV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
+{
+  std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
+  mir::Graph *graph = context->getGraph();
+
+  // "nearest" is the default mode.
+  std::string mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
+  assert(mode == "nearest" && "Unsupported upscale mode!");
+
+  const float h_scale = getAttributeValue<float>(onnx_node, "height_scale", 0.0f); // required
+  const float w_scale = getAttributeValue<float>(onnx_node, "width_scale", 0.0f);  // required
+  if (h_scale < 1.0f || w_scale < 1.0f)
+    throw std::runtime_error("Wrong scale attributes!");
+
+  assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported");
+  std::vector<float> scales_vector(4);
+  // NCHW
+  scales_vector.at(0) = 1.0f;
+  scales_vector.at(1) = 1.0f;
+  scales_vector.at(2) = h_scale;
+  scales_vector.at(3) = w_scale;
+
+  auto result =
+      createOp<mir::ops::ResizeOp>(graph, inputs[0],
+                                   mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector)
+          ->getOutput(0);
+
+  context->setNodeOutputs(onnx_node, {result});
+}
+
+void convertUpsampleV7(const onnx::NodeProto &onnx_node, ConverterContext *context)
+{
+  std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
+  mir::Graph *graph = context->getGraph();
+
+  // "nearest" is the default mode.
+  std::string mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
+  assert(mode == "nearest" && "Unsupported upscale mode!");
+
+  const auto *scales_attr = findAttribute(onnx_node, "scales");
+  if (!scales_attr)
+    throw std::runtime_error("Not enough required scales attribute!");
+
+  if (scales_attr->floats_size() != inputs[0]->getShape().rank())
+    throw std::runtime_error(
+        "Number of elements of scales should be the same as the rank of input");
+
+  assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported");
+  std::vector<float> scales_vector(4);
+  // NCHW
+  scales_vector.at(0) = scales_attr->floats(0);
+  scales_vector.at(1) = scales_attr->floats(1);
+  scales_vector.at(2) = scales_attr->floats(2);
+  scales_vector.at(3) = scales_attr->floats(3);
+
+  auto result =
+      createOp<mir::ops::ResizeOp>(graph, inputs[0],
+                                   mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector)
+          ->getOutput(0);
+
+  context->setNodeOutputs(onnx_node, {result});
+}
+
+void convertUpsampleV9(const onnx::NodeProto &onnx_node, ConverterContext *context)
+{
+  std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
+  mir::Graph *graph = context->getGraph();
+
+  // "nearest" is the default mode.
+  const auto mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
+  assert(mode == "nearest" && "Unsupported upscale mode!");
+
+  // relies on attributes being lifted to constants (ONNX optimization pass)
+  assert(inputs.size() > 1);
+  auto *scales = dynamic_cast<mir::ops::ConstantOp *>(inputs[1]->getNode());
+  assert(scales && "Weights could be a constant tensor only");
+  auto scales_tensor = mir::Tensor<float>(scales->getValue());
+  int rank = inputs[0]->getShape().rank();
+  assert(scales_tensor.getShape().numElements() == rank &&
+         "The number of elements of 'scales' should be the same as the rank of input 'X'");
+  assert(rank == 4 && "Only rank 4 is supported");
+  std::vector<float> scales_vector(4);
+  assert(scales_tensor.getShape().rank() == 1 && "Scales are a 1d tensor");
+  for (int i = 0; i < scales_tensor.getShape().numElements(); i++)
+    scales_vector[i] = scales_tensor.atOffset(i);
+
+  auto result =
+      createOp<mir::ops::ResizeOp>(graph, inputs[0],
+                                   mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector)
+          ->getOutput(0);
+
+  context->setNodeOutputs(onnx_node, {result});
+}
+
+} // namespace mir_onnx