1 files changed, 113 insertions, 0 deletions
diff --git a/libs/kernel/acl/src/cl/Conv2D.cpp b/libs/kernel/acl/src/cl/Conv2D.cpp
new file mode 100644
index 000000000..4783bdc1d
--- /dev/null
+++ b/libs/kernel/acl/src/cl/Conv2D.cpp
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2018 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 <OperationsUtils.h>
+#include <NeuralNetworks.h>
+
+#include <arm_compute/core/TensorShape.h>
+#include <arm_compute/core/TensorInfo.h>
+
+#include <util/environment.h>
+
+#include "../IO_accessor.h"
+#include "../util.h"
+#include "../shape.h"
+#include "../CLUniqueTensor.h"
+#include "../support.h"
+
+#include "util/feature/TextFormatter.h"
+
+#include "support/nnapi/feature/Reader.h"
+
+namespace nnfw {
+namespace kernel {
+namespace acl {
+
+static int verbose = 0;
+
+bool convFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
+                 const float* filterData, const nnfw::rt::Shape& filterShape,
+                 const float* biasData, const nnfw::rt::Shape& biasShape,
+                 int32_t padding_left, int32_t padding_right,
+                 int32_t padding_top, int32_t padding_bottom,
+                 int32_t stride_width, int32_t stride_height,
+                 int32_t activation,
+                 float* outputData, const nnfw::rt::Shape& outputShape)
+{
+  arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
+  arm_compute::TensorShape filter_shape = util::fromNNShape(filterShape);
+  arm_compute::TensorShape bias_shape = util::fromVectorNNShape(biasShape);
+  arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
+  arm_compute::PadStrideInfo conv_info = arm_compute::PadStrideInfo(stride_width, stride_height,
+                                              padding_left, padding_right,
+                                              padding_top, padding_bottom,
+                                              arm_compute::DimensionRoundingType::FLOOR);
+
+  CLUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
+  CLUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
+  CLUniqueTensor bias(arm_compute::TensorInfo(bias_shape, arm_compute::Format::F32));
+  CLUniqueTensor filter(arm_compute::TensorInfo(filter_shape, arm_compute::Format::F32));
+
+  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
+
+  auto conv_f = std::make_shared<arm_compute::CLConvolutionLayer>();
+
+  conv_f->configure(input.ptr(), filter.ptr(), bias.ptr(), output.ptr(), conv_info);
+
+  fns.emplace_back(conv_f);
+
+  util::insertFusedActivationLayer<CLUniqueTensor, arm_compute::CLActivationLayer>(output, activation, fns);
+
+  input.allocate();
+  output.allocate();
+  bias.allocate();
+  filter.allocate();
+
+  TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
+  TensorAccess<BiasAccessor>(bias.ref(), biasData, biasShape);
+  TensorAccess<WeightAccessor>(filter.ref(), filterData, filterShape);
+
+  nnfw::util::env::IntAccessor("CONV2D_VERBOSE").access(verbose);
+  if (verbose)
+  {
+    input.ref().map();
+    auto ifm_shape = nnfw::support::nnapi::feature::asFeatureShape(inputShape);
+    nnfw::support::nnapi::feature::Reader<float> nnapi_ifm_reader{ifm_shape, inputData};
+    nnfw::support::acl::feature::Reader<float> acl_ifm_reader{input.ptr()};
+
+    std::cout << "NNAPI IFM:" << std::endl;
+    std::cout << nnfw::util::feature::TextFormatter<float>{ifm_shape, nnapi_ifm_reader} << std::endl;
+
+    std::cout << "ARM Compute IFM:" << std::endl;
+    std::cout << nnfw::util::feature::TextFormatter<float>{ifm_shape, acl_ifm_reader} << std::endl;
+    input.ref().unmap();
+  }
+
+  for (const auto &fn : fns)
+  {
+    fn->run();
+  }
+
+  arm_compute::CLScheduler::get().sync();
+
+  TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
+
+  return true;
+}
+
+} // namespace acl
+} // namespace kernel
+} // namespace nnfw