});
}
+void StageGenerator::visit(const model::operation::ResizeBilinearNode &node)
+{
+ const auto ofm_index{node.getOutputs().at(0)};
+
+ const auto ifm_index{node.getInputs().at(model::operation::ResizeBilinearNode::Input::INPUT)};
+ const auto height_index{node.param().height_index};
+ const auto width_index{node.param().width_index};
+
+ struct Param
+ {
+ model::operand::Index ofm_index;
+ model::operand::Index ifm_index;
+
+ int32_t new_height;
+ int32_t new_width;
+ };
+
+ Param param;
+
+ param.ofm_index = ofm_index;
+ param.ifm_index = ifm_index;
+ param.new_height = _ctx.at(height_index).asScalar<int32_t>();
+ param.new_width = _ctx.at(width_index).asScalar<int32_t>();
+
+ auto tensors = _tensor_builder;
+
+ returnStage([tensors, param](IExecutionBuilder &builder) {
+ auto ofm_alloc = tensors->at(param.ofm_index).get();
+ auto ifm_alloc = tensors->at(param.ifm_index).get();
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ auto l = make_layer<::arm_compute::CLScale>();
+
+ l->configure(ifm_alloc->handle(), ofm_alloc->handle(),
+ ::arm_compute::InterpolationPolicy::BILINEAR, ::arm_compute::BorderMode::REPLICATE,
+ ::arm_compute::PixelValue(0.f), ::arm_compute::SamplingPolicy::TOP_LEFT);
+
+ fn = std::move(l);
+
+ auto acl_fn = make_cl_function(std::move(fn));
+
+ builder.append(std::move(acl_fn));
+ });
+}
+
+void StageGenerator::visit(const model::operation::ReLU1Node &node)
+{
+ const auto ofm_index{node.getOutputs().at(0)};
+ const auto ifm_index{node.getInputs().at(model::operation::ReLU1Node::Input::INPUT)};
+
+ struct Param
+ {
+ model::operand::Index ofm_index;
+ model::operand::Index ifm_index;
+ };
+
+ Param param;
+
+ param.ofm_index = ofm_index;
+ param.ifm_index = ifm_index;
+
+ auto tensors = _tensor_builder;
+
+ returnStage([tensors, param](IExecutionBuilder &builder) {
+ auto ofm_alloc = tensors->at(param.ofm_index).get();
+ auto ifm_alloc = tensors->at(param.ifm_index).get();
+
+ const ::arm_compute::ActivationLayerInfo act_info{
+ ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f};
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ auto l = make_layer<::arm_compute::CLActivationLayer>();
+
+ l->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
+
+ fn = std::move(l);
+
+ auto acl_fn = make_cl_function(std::move(fn));
+
+ builder.append(std::move(acl_fn));
+ });
+}
+
+void StageGenerator::visit(const model::operation::ReLU6Node &node)
+{
+ const auto ofm_index{node.getOutputs().at(0)};
+ const auto ifm_index{node.getInputs().at(model::operation::ReLU6Node::Input::INPUT)};
+
+ struct Param
+ {
+ model::operand::Index ofm_index;
+ model::operand::Index ifm_index;
+ };
+
+ Param param;
+
+ param.ofm_index = ofm_index;
+ param.ifm_index = ifm_index;
+
+ auto tensors = _tensor_builder;
+
+ returnStage([tensors, param](IExecutionBuilder &builder) {
+ auto ofm_alloc = tensors->at(param.ofm_index).get();
+ auto ifm_alloc = tensors->at(param.ifm_index).get();
+
+ const ::arm_compute::ActivationLayerInfo act_info{
+ ::arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0f};
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ auto l = make_layer<::arm_compute::CLActivationLayer>();
+
+ l->configure(ifm_alloc->handle(), ofm_alloc->handle(), act_info);
+
+ fn = std::move(l);
+
+ auto acl_fn = make_cl_function(std::move(fn));
+
+ builder.append(std::move(acl_fn));
+ });
+}
+
} // namespace acl_cl
} // namespace backend
} // namespace neurun
virtual void visit(const model::operation::LogicalAndNode &) override;
virtual void visit(const model::operation::RSQRTNode &) override;
virtual void visit(const model::operation::ReLUNode &) override;
+ virtual void visit(const model::operation::ResizeBilinearNode &) override;
+ virtual void visit(const model::operation::ReLU1Node &) override;
+ virtual void visit(const model::operation::ReLU6Node &) override;
private:
const neurun::model::operand::Set &_ctx;
return new operation::ReLUNode{inputs, outputs};
};
+
+ _map[ANEURALNETWORKS_RESIZE_BILINEAR] = [](const OperationFactory::Param &init_param) {
+ assert(init_param.input_count == 3 && init_param.output_count == 1);
+
+ operand::IndexSet outputs{init_param.outputs[0]};
+
+ // Each input should be interpreted as follows:
+ //
+ // 0 -> IFM Index
+ // 1 -> Height Index
+ // 2 -> Width Index
+ operand::IndexSet inputs{init_param.inputs[0]};
+
+ operation::ResizeBilinearNode::Param param;
+ param.height_index = operand::Index{init_param.inputs[1]};
+ param.width_index = operand::Index{init_param.inputs[2]};
+
+ return new operation::ResizeBilinearNode{inputs, outputs, param};
+ };
+
+ _map[ANEURALNETWORKS_RELU1] = [](const OperationFactory::Param &init_param) {
+ assert(init_param.input_count == 1 && init_param.output_count == 1);
+
+ operand::IndexSet outputs{init_param.outputs[0]};
+
+ // Each input should be interpreted as follows:
+ //
+ // 0 -> input Tensor Index
+ operand::IndexSet inputs{init_param.inputs[0]};
+
+ return new operation::ReLU1Node{inputs, outputs};
+ };
+
+ _map[ANEURALNETWORKS_RELU6] = [](const OperationFactory::Param &init_param) {
+ assert(init_param.input_count == 1 && init_param.output_count == 1);
+
+ operand::IndexSet outputs{init_param.outputs[0]};
+
+ // Each input should be interpreted as follows:
+ //
+ // 0 -> input Tensor Index
+ operand::IndexSet inputs{init_param.inputs[0]};
+
+ return new operation::ReLU6Node{inputs, outputs};
+ };
}
neurun::model::operation::Node *OperationFactory::create(ANeuralNetworksOperationType type,
#include "LogicalAndNode.h"
#include "RSQRTNode.h"
#include "ReLUNode.h"
+#include "ResizeBilinearNode.h"
+#include "ReLU1Node.h"
+#include "ReLU6Node.h"
OP(LogicalAndNode , true , LOGICAL_AND_EX)
OP(RSQRTNode , true , RSQRT_EX)
OP(ReLUNode , true , RELU)
+OP(ResizeBilinearNode , true , RESIZE_BILINEAR)
+OP(ReLU1Node , true , RELU1)
+OP(ReLU6Node , true , RELU6)
OP(PermuteNode , false , NOT_AVAILABLE)
--- /dev/null
+/*
+ * 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 "ReLU1Node.h"
+
+#include <cassert>
+
+#include "NodeVisitor.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+void ReLU1Node::accept(NodeVisitor &&v) const { v.visit(*this); }
+
+ReLU1Node::ReLU1Node(const operand::IndexSet &inputs, const operand::IndexSet &outputs)
+ : model::operation::Node{OperandConstraint::createExact(1u), inputs, outputs}
+{
+}
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
--- /dev/null
+/*
+ * 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.
+ */
+
+#ifndef __NEURUN_MODEL_OPERATION_ReLU1_NODE_H__
+#define __NEURUN_MODEL_OPERATION_ReLU1_NODE_H__
+
+#include "model/operation/Node.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+class ReLU1Node : public model::operation::Node
+{
+public:
+ enum Input
+ {
+ INPUT = 0
+ };
+
+public:
+ ReLU1Node(const operand::IndexSet &inputs, const operand::IndexSet &outputs);
+
+public:
+ virtual void accept(NodeVisitor &&) const override;
+ virtual std::string getName() const override { return "ReLU1"; }
+};
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
+
+#endif // __NEURUN_MODEL_OPERATION_ReLU1_NODE_H__
--- /dev/null
+/*
+ * 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 "ReLU6Node.h"
+
+#include <cassert>
+
+#include "NodeVisitor.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+void ReLU6Node::accept(NodeVisitor &&v) const { v.visit(*this); }
+
+ReLU6Node::ReLU6Node(const operand::IndexSet &inputs, const operand::IndexSet &outputs)
+ : model::operation::Node{OperandConstraint::createExact(1u), inputs, outputs}
+{
+}
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
--- /dev/null
+/*
+ * 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.
+ */
+
+#ifndef __NEURUN_MODEL_OPERATION_ReLU6_NODE_H__
+#define __NEURUN_MODEL_OPERATION_ReLU6_NODE_H__
+
+#include "model/operation/Node.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+class ReLU6Node : public model::operation::Node
+{
+public:
+ enum Input
+ {
+ INPUT = 0
+ };
+
+public:
+ ReLU6Node(const operand::IndexSet &inputs, const operand::IndexSet &outputs);
+
+public:
+ virtual void accept(NodeVisitor &&) const override;
+ virtual std::string getName() const override { return "ReLU6"; }
+};
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
+
+#endif // __NEURUN_MODEL_OPERATION_ReLU6_NODE_H__
--- /dev/null
+/*
+ * 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 "ResizeBilinearNode.h"
+
+#include <cassert>
+
+#include "NodeVisitor.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+void ResizeBilinearNode::accept(NodeVisitor &&v) const { v.visit(*this); }
+
+ResizeBilinearNode::ResizeBilinearNode(const operand::IndexSet &inputs,
+ const operand::IndexSet &outputs, const Param ¶m)
+ : model::operation::Node{OperandConstraint::createExact(1u), inputs, outputs}, _param{param}
+{
+}
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
--- /dev/null
+/*
+ * 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.
+ */
+
+#ifndef __NEURUN_MODEL_OPERATION_RESIZE_BILINEAR_NODE_H__
+#define __NEURUN_MODEL_OPERATION_RESIZE_BILINEAR_NODE_H__
+
+#include <memory>
+
+#include "model/operation/Node.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+class ResizeBilinearNode : public model::operation::Node
+{
+public:
+ enum Input
+ {
+ INPUT = 0
+ };
+
+ struct Param
+ {
+ operand::Index height_index;
+ operand::Index width_index;
+ };
+
+public:
+ ResizeBilinearNode(const operand::IndexSet &inputs, const operand::IndexSet &outputs,
+ const Param ¶m);
+
+public:
+ virtual void accept(NodeVisitor &&) const override;
+ virtual std::string getName() const override { return "ResizeBilinear"; }
+
+public:
+ const Param ¶m() const { return _param; }
+
+private:
+ Param _param;
+};
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
+
+#endif // __NEURUN_MODEL_OPERATION_RESIZE_BILINEAR_NODE_H__
GeneratedTests.relu1*
GeneratedTests.relu6*
GeneratedTests.resize_bilinear*
+GeneratedTests.relu*
GeneratedTests.rnn*
GeneratedTests.mean*
GeneratedTests.pad*
relu
reshape
rsqrt
+relu6
+reshape
+resize_bilinear
strided_slice
sub/broadcast
tanh
transpose
MODELS/inception_module
-squeeze
\ No newline at end of file
+squeeze
projects / platform / core / ml / nnfw.git / commitdiff