ONE - On-device Neural Engine
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DepthwiseConvolutionLayer.h
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1/*
2 * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17#ifndef __ONERT_BACKEND_TRAIN_OPS_DEPTHWISECONVOLUTIONLAYER_H__
18#define __ONERT_BACKEND_TRAIN_OPS_DEPTHWISECONVOLUTIONLAYER_H__
19
20#include <ops/DepthwiseConvolutionLayer.h>
22
23#include "../Tensor.h"
24#include "../ExternalContext.h"
26
27namespace onert
28{
29namespace backend
30{
31namespace train
32{
33namespace ops
34{
35
38{
39public:
41
42 void configureBackward(IPortableTensor *back_prop_input, IPortableTensor *grad_weights,
43 IPortableTensor *grad_bias, const IPortableTensor *back_prop_output,
44 const ir::Activation activation);
45 void forward(bool training) override;
46 void backward() override;
47
48private:
49 void backwardFloat32();
50
51private:
52 IPortableTensor *_grad_weights;
53 IPortableTensor *_grad_bias;
54 IPortableTensor *_back_prop_input;
55 const IPortableTensor *_back_prop_output;
56
57 // TODO Consider if these tensors should be built in TensorBuilder
58 std::unique_ptr<BackPropTensor> _act_back_prop_output;
59 std::unique_ptr<Tensor> _filter_dim_buffers;
60};
61
62} // namespace ops
63} // namespace train
64} // namespace backend
65} // namespace onert
66
67#endif // __ONERT_BACKEND_TRAIN_OPS_DEPTHWISECONVOLUTIONLAYER_H__
A tensor class that is portable for other backends.
void configureBackward(IPortableTensor *back_prop_input, IPortableTensor *grad_weights, IPortableTensor *grad_bias, const IPortableTensor *back_prop_output, const ir::Activation activation)