1 files changed, 112 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/FeatureDecode.cpp b/compiler/locomotiv/src/Node/FeatureDecode.cpp
new file mode 100644
index 000000000..8a56a56b2
--- /dev/null
+++ b/compiler/locomotiv/src/Node/FeatureDecode.cpp
@@ -0,0 +1,112 @@
+/*
+ * 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 "NodeExecution.h"
+
+#include "NodeDataImpl.h"
+#include "NodeDomain.h"
+#include "Validation.h"
+
+#include <nncc/core/ADT/tensor/LexicalLayout.h>
+#include <nncc/core/ADT/tensor/IndexEnumerator.h>
+
+#include <stdexcept>
+#include <cassert>
+
+namespace
+{
+
+using nncc::core::ADT::tensor::Buffer;
+using nncc::core::ADT::tensor::make_buffer;
+using nncc::core::ADT::tensor::LexicalLayout;
+using nncc::core::ADT::tensor::Shape;
+using nncc::core::ADT::tensor::IndexEnumerator;
+using nncc::core::ADT::tensor::Index;
+
+template <typename T>
+std::unique_ptr<locomotiv::NodeData> feature_decode(const loco::FeatureDecode *node,
+                                                    const Buffer<T> *input_buf)
+{
+  auto decoder = node->decoder();
+
+  // Make FeatureShape from input. Note that feature in locomotiv represented as NHWC
+  loco::FeatureShape input_shape;
+  assert(input_buf->shape().rank() == 4);
+  input_shape.count() = input_buf->shape().dim(0);
+  input_shape.height() = input_buf->shape().dim(1);
+  input_shape.width() = input_buf->shape().dim(2);
+  input_shape.depth() = input_buf->shape().dim(3);
+
+  loco::TensorShape node_shape = decoder->shape(input_shape);
+
+  // Make tensor buffer from TensorShape
+  Buffer<T> node_buf =
+      make_buffer<T, LexicalLayout>(Shape{node_shape.dim(0).value(), node_shape.dim(1).value(),
+                                          node_shape.dim(2).value(), node_shape.dim(3).value()});
+
+  // Copy buffer in an order arranged by decoder
+  for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance())
+  {
+    loco::FeatureIndex feature_index = decoder->value(e.current());
+    Index buf_index({feature_index.batch(), feature_index.row(), feature_index.column(),
+                     feature_index.channel()});
+
+    node_buf.at(e.current()) = input_buf->at(buf_index);
+  }
+
+  return locomotiv::make_data(node_buf);
+}
+
+} // namespace
+
+namespace locomotiv
+{
+
+void NodeExecution::execute(loco::FeatureDecode *dec)
+{
+  auto input_data = annot_data(dec->input());
+
+  validate(input_data, "Input of FeatureDecode not ready");
+  validate(annot_domain(dec->input()) == loco::Domain::Feature,
+           "Input of FeatureDecode is not Feature");
+  validate(input_data->shape()->rank() == 4, "Input shape mismatch");
+
+  std::unique_ptr<NodeData> dec_data = nullptr;
+
+  switch (input_data->dtype())
+  {
+    case loco::DataType::S32:
+    {
+      auto input_buf = input_data->as_s32_bufptr();
+      dec_data = feature_decode<int32_t>(dec, input_buf);
+      break;
+    }
+    case loco::DataType::FLOAT32:
+    {
+      auto input_buf = input_data->as_f32_bufptr();
+      dec_data = feature_decode<float>(dec, input_buf);
+      break;
+    }
+    default:
+      throw std::runtime_error("NYI for this DataType");
+  }
+
+  assert(dec_data != nullptr);
+  annot_data(dec, std::move(dec_data));
+  annot_domain(dec, loco::Domain::Tensor);
+}
+
+} // namespace locomotiv