1 files changed, 114 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/FilterEncode.cpp b/compiler/locomotiv/src/Node/FilterEncode.cpp
new file mode 100644
index 000000000..cd9d708dc
--- /dev/null
+++ b/compiler/locomotiv/src/Node/FilterEncode.cpp
@@ -0,0 +1,114 @@
+/*
+ * 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;
+
+template <typename T>
+std::unique_ptr<locomotiv::NodeData> filter_encode(const loco::FilterEncode *node,
+                                                   const Buffer<T> *input_buf)
+{
+  auto encoder = node->encoder();
+
+  // Make TensorShape from input
+  loco::TensorShape input_shape;
+  input_shape.rank(input_buf->shape().rank());
+  assert(input_shape.rank() == 4);
+  for (uint32_t i = 0; i < input_shape.rank(); ++i)
+  {
+    input_shape.dim(i) = input_buf->shape().dim(i);
+  }
+
+  loco::FilterShape node_shape = encoder->shape(input_shape);
+
+  // Make NHWC buffer from FilterShape
+  Buffer<T> node_buf =
+      make_buffer<T, LexicalLayout>(Shape{node_shape.count().value(), node_shape.height().value(),
+                                          node_shape.width().value(), node_shape.depth().value()});
+
+  // Copy buffer in an order arranged by encoder
+  for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance())
+  {
+    loco::FilterIndex index;
+    index.nth() = e.current().at(0);
+    index.row() = e.current().at(1);
+    index.column() = e.current().at(2);
+    index.channel() = e.current().at(3);
+
+    node_buf.at(e.current()) = input_buf->at(encoder->value(index));
+  }
+
+  return locomotiv::make_data(node_buf);
+}
+
+} // namespace
+
+namespace locomotiv
+{
+
+void NodeExecution::execute(loco::FilterEncode *enc)
+{
+  auto input_data = annot_data(enc->input());
+
+  validate(input_data, "Input of FilterEncode not ready");
+  validate(annot_domain(enc->input()) == loco::Domain::Tensor,
+           "Input of FilterEncode is not Tensor");
+  validate(input_data->shape()->rank() == 4, "Input shape mismatch");
+
+  std::unique_ptr<NodeData> enc_data = nullptr;
+
+  switch (input_data->dtype())
+  {
+    case loco::DataType::S32:
+    {
+      auto input_buf = input_data->as_s32_bufptr();
+      enc_data = filter_encode<int32_t>(enc, input_buf);
+      break;
+    }
+    case loco::DataType::FLOAT32:
+    {
+      auto input_buf = input_data->as_f32_bufptr();
+      enc_data = filter_encode<float>(enc, input_buf);
+      break;
+    }
+    default:
+      throw std::runtime_error("NYI for this DataType");
+  }
+
+  assert(enc_data != nullptr);
+  annot_data(enc, std::move(enc_data));
+  annot_domain(enc, loco::Domain::Filter);
+}
+
+} // namespace locomotiv