});
}
+void StageGenerator::visit(const model::operation::EqualNode &node)
+{
+ const auto output_index{node.getOutputs().at(0)};
+ const auto input0_index{node.getInputs().at(model::operation::EqualNode::Input::INPUT0)};
+ const auto input1_index{node.getInputs().at(model::operation::EqualNode::Input::INPUT1)};
+
+ if (!(_ctx.at(input0_index).shape() == _ctx.at(input1_index).shape()))
+ {
+ const auto broadcast_rank =
+ std::max(_ctx.at(input0_index).shape().rank(), _ctx.at(input1_index).shape().rank());
+ const_cast<neurun::model::operand::Shape &>(_ctx.at(input0_index).shape())
+ .extendRank(broadcast_rank);
+ const_cast<neurun::model::operand::Shape &>(_ctx.at(input1_index).shape())
+ .extendRank(broadcast_rank);
+ }
+
+ // Construct operation parameters
+ struct Param
+ {
+ model::operand::Index output_index;
+ model::operand::Index input0_index;
+ model::operand::Index input1_index;
+ };
+
+ Param param;
+
+ param.output_index = output_index;
+ param.input0_index = input0_index;
+ param.input1_index = input1_index;
+
+ auto tensors = _tensor_builder;
+
+ returnStage([tensors, param](IExecutionBuilder &builder) {
+ auto output_alloc = tensors->at(param.output_index).get();
+ auto input0_alloc = tensors->at(param.input0_index).get();
+ auto input1_alloc = tensors->at(param.input1_index).get();
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ auto l = make_layer<::arm_compute::CLComparison>();
+
+ l->configure(input0_alloc->handle(), input1_alloc->handle(), output_alloc->handle(),
+ ::arm_compute::ComparisonOperation::Equal);
+
+ 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::SquaredDifferenceNode &node)
+{
+ const auto ofm_index{node.getOutputs().at(0)};
+ const auto lhs_index{node.getInputs().at(model::operation::SquaredDifferenceNode::Input::LHS)};
+ const auto rhs_index{node.getInputs().at(model::operation::SquaredDifferenceNode::Input::RHS)};
+
+ if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape()))
+ {
+ const auto broadcast_rank =
+ std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank());
+ const_cast<neurun::model::operand::Shape &>(_ctx.at(lhs_index).shape())
+ .extendRank(broadcast_rank);
+ const_cast<neurun::model::operand::Shape &>(_ctx.at(rhs_index).shape())
+ .extendRank(broadcast_rank);
+ }
+
+ // Construct operation parameters
+ struct Param
+ {
+ model::operand::Index ofm_index;
+ model::operand::Index lhs_index;
+ model::operand::Index rhs_index;
+ };
+
+ Param param;
+
+ param.ofm_index = ofm_index;
+ param.lhs_index = lhs_index;
+ param.rhs_index = rhs_index;
+
+ auto tensors = _tensor_builder;
+
+ returnStage([tensors, param](IExecutionBuilder &builder) {
+ auto ofm_alloc = tensors->at(param.ofm_index).get();
+ auto lhs_alloc = tensors->at(param.lhs_index).get();
+ auto rhs_alloc = tensors->at(param.rhs_index).get();
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ auto l = make_layer<::arm_compute::CLSquaredDifference>();
+
+ l->configure(lhs_alloc->handle(), rhs_alloc->handle(), ofm_alloc->handle());
+
+ 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::TopKV2Node &node)
+{
+ const auto outputValues_index{
+ node.getOutputs().at(model::operation::TopKV2Node::Output::OUTPUT_VALUES)};
+ const auto outputIndices_index{
+ node.getOutputs().at(model::operation::TopKV2Node::Output::OUTPUT_INDICES)};
+
+ const auto inputData_index{node.getInputs().at(model::operation::TopKV2Node::Input::INPUT)};
+ const auto k_index{node.param().k_index};
+
+ // Currently, we only support the vector input.
+ assert(_ctx.at(inputData_index).shape().rank() == 1 ||
+ _ctx.at(inputData_index).shape().rank() == 2);
+
+ const int32_t k = _ctx.at(k_index).asScalar<int32_t>();
+
+ // Construct operation parameters
+ struct Param
+ {
+ model::operand::Index outputValues_index;
+ model::operand::Index outputIndices_index;
+
+ model::operand::Index inputData_index;
+ int32_t k;
+ };
+
+ Param param;
+
+ param.outputValues_index = outputValues_index;
+ param.outputIndices_index = outputIndices_index;
+ param.inputData_index = inputData_index;
+ param.k = k;
+
+ auto tensors = _tensor_builder;
+
+ returnStage([tensors, param](IExecutionBuilder &builder) {
+ auto values_alloc = tensors->at(param.outputValues_index).get();
+ auto indices_alloc = tensors->at(param.outputIndices_index).get();
+ auto input_alloc = tensors->at(param.inputData_index).get();
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ auto l = make_layer<::arm_compute::CLTopKV2>();
+
+ l->configure(input_alloc->handle(), param.k, values_alloc->handle(), indices_alloc->handle());
+
+ 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::SQRTNode &) override;
virtual void visit(const model::operation::LogicalOrNode &) override;
virtual void visit(const model::operation::LogicalNotNode &) override;
+ virtual void visit(const model::operation::EqualNode &) override;
+ virtual void visit(const model::operation::SquaredDifferenceNode &) override;
+ virtual void visit(const model::operation::TopKV2Node &) override;
private:
const neurun::model::operand::Set &_ctx;
--- /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_EQUAL_NODE_H__
+#define __NEURUN_MODEL_OPERATION_EQUAL_NODE_H__
+
+#include "model/operation/Node.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+class EqualNode : public model::operation::Node
+{
+public:
+ enum Input
+ {
+ INPUT0 = 0,
+ INPUT1 = 1
+ };
+
+public:
+ EqualNode(const operand::IndexSet &inputs, const operand::IndexSet &outputs);
+
+public:
+ virtual void accept(NodeVisitor &&) const override;
+ virtual std::string getName() const override { return "Equal"; }
+};
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
+
+#endif // __NEURUN_MODEL_OPERATION_EQUAL_NODE_H__
#include "ExpNode.h"
#include "ReduceMaxNode.h"
#include "NotEqualNode.h"
+#include "EqualNode.h"
#include "LogicalAndNode.h"
#include "LogicalOrNode.h"
#include "LogicalNotNode.h"
#include "PReLUNode.h"
#include "TransposeConvNode.h"
#include "SQRTNode.h"
+#include "SquaredDifferenceNode.h"
+#include "TopKV2Node.h"
OP(PReLUNode , true)
OP(TransposeConvNode , true)
OP(SQRTNode , true)
+OP(EqualNode , true)
+OP(SquaredDifferenceNode , true)
+OP(TopKV2Node , true)
OP(PermuteNode , false)
--- /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_SQUARED_DIFFERENCE_NODE_H__
+#define __NEURUN_MODEL_OPERATION_SQUARED_DIFFERENCE_NODE_H__
+
+#include "model/operation/Node.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+class SquaredDifferenceNode : public model::operation::Node
+{
+public:
+ enum Input
+ {
+ LHS = 0,
+ RHS
+ };
+
+public:
+ SquaredDifferenceNode(const operand::IndexSet &inputs, const operand::IndexSet &outputs);
+
+public:
+ virtual void accept(NodeVisitor &&) const override;
+ virtual std::string getName() const override { return "SquaredDifference"; }
+};
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
+
+#endif // __NEURUN_MODEL_OPERATION_SQUARED_DIFFERENCE_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.
+ */
+
+#ifndef __NEURUN_MODEL_OPERATION_TOPK_V2_H__
+#define __NEURUN_MODEL_OPERATION_TOPK_V2_H__
+
+#include <memory>
+
+#include "model/operation/Node.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+class TopKV2Node : public model::operation::Node
+{
+public:
+ enum Input
+ {
+ INPUT
+ };
+
+ enum Output
+ {
+ OUTPUT_VALUES = 0,
+ OUTPUT_INDICES,
+ };
+
+ struct Param
+ {
+ operand::Index k_index;
+ };
+
+public:
+ TopKV2Node(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 "TopKV2"; }
+
+public:
+ const Param ¶m() const { return _param; }
+
+private:
+ Param _param;
+};
+
+} // namespace operation
+} // namespace model
+} // namespace neurun
+
+#endif // __NEURUN_MODEL_OPERATION_TOPK_V2_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 "model/operation/EqualNode.h"
+
+#include <cassert>
+
+#include "model/operation/NodeVisitor.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+void EqualNode::accept(NodeVisitor &&v) const { v.visit(*this); }
+
+EqualNode::EqualNode(const operand::IndexSet &inputs, const operand::IndexSet &outputs)
+ : model::operation::Node{OperandConstraint::createExact(2u), 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.
+ */
+
+#include "model/operation/SquaredDifferenceNode.h"
+
+#include <cassert>
+
+#include "model/operation/NodeVisitor.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+void SquaredDifferenceNode::accept(NodeVisitor &&v) const { v.visit(*this); }
+
+SquaredDifferenceNode::SquaredDifferenceNode(const operand::IndexSet &inputs,
+ const operand::IndexSet &outputs)
+ : model::operation::Node{OperandConstraint::createExact(2u), 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.
+ */
+
+#include "model/operation/TopKV2Node.h"
+
+#include <cassert>
+
+#include "model/operation/NodeVisitor.h"
+
+namespace neurun
+{
+namespace model
+{
+namespace operation
+{
+
+void TopKV2Node::accept(NodeVisitor &&v) const { v.visit(*this); }
+
+TopKV2Node::TopKV2Node(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
return new operation::LogicalNotNode{inputs, outputs};
};
+
+ _map[ANEURALNETWORKS_EQUAL_EX] = [](const OperationFactory::Param &init_param) {
+ assert(init_param.input_count == 2 && init_param.output_count == 1);
+
+ operand::IndexSet outputs{init_param.outputs[0]};
+
+ // Each input should be interpreted as follows:
+ //
+ // 0 -> input0 Tensor Index
+ // 1 -> input1 Tensor Index
+ operand::IndexSet inputs{init_param.inputs[0], init_param.inputs[1]};
+
+ return new operation::EqualNode{inputs, outputs};
+ };
+
+ _map[ANEURALNETWORKS_SQUARED_DIFFERENCE_EX] = [](const OperationFactory::Param &init_param) {
+ assert(init_param.input_count == 2 && init_param.output_count == 1);
+
+ operand::IndexSet outputs{init_param.outputs[0]};
+
+ // Each input should be interpreted as follows:
+ //
+ // 0 -> LHS Tensor Index
+ // 1 -> RHS Tensor Index
+ operand::IndexSet inputs{init_param.inputs[0], init_param.inputs[1]};
+
+ return new operation::SquaredDifferenceNode{inputs, outputs};
+ };
+
+ _map[ANEURALNETWORKS_TOPK_V2_EX] = [](const OperationFactory::Param &init_param) {
+ assert(init_param.input_count == 2 && init_param.output_count == 2);
+
+ // Each output should be interpreted as follows:
+ //
+ // 0 -> Index for Output Values
+ // 1 -> Index for Output Indices
+ operand::IndexSet outputs{init_param.outputs[0], init_param.outputs[1]};
+
+ // Each input should be interpreted as follows:
+ //
+ // 0 -> Index for Input Data
+ // 1 -> Index for K
+ operand::IndexSet inputs{init_param.inputs[0]};
+
+ operation::TopKV2Node::Param param;
+ param.k_index = operand::Index{init_param.inputs[1]};
+
+ return new operation::TopKV2Node{inputs, outputs, param};
+ };
}
neurun::model::operation::Node *OperationFactory::create(ANeuralNetworksOperationType type,
GeneratedTests.argmax*
GeneratedTests.depth_to_space*
GeneratedTests.dequantize
-GeneratedTests.equal_ex*
GeneratedTests.local_response_norm*
GeneratedTests.lsh_projection*
GeneratedTests.lstm*
GeneratedTests.rnn*
GeneratedTests.mean*
GeneratedTests.pad*
-GeneratedTests.squared_difference_ex*
GeneratedTests.svdf*
GeneratedTests.batch_to_space*
GeneratedTests.space_to_batch*
GeneratedTests.gather_ex*
-GeneratedTests.topk_v2*
# Unexpected result
GeneratedTests.logical_or_ex_broadcast_4D_2D
GeneratedTests.split*
depthwise_conv_2d
div/broadcast
embedding_lookup
+equal
exp
floor
fullyconnected/fc1
strided_slice
sub/broadcast
tanh
+topk_v2
transpose
transpose_conv
MODELS/inception_module
projects / platform / core / ml / nnfw.git / commitdiff