1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
|
/*
* Copyright (c) 2017-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ACL_ARM_COMPUTE_RUNTIME_NEON_FUNCTIONS_NEWINOGRADCONVOLUTIONLAYER_H
#define ACL_ARM_COMPUTE_RUNTIME_NEON_FUNCTIONS_NEWINOGRADCONVOLUTIONLAYER_H
#include "arm_compute/core/Types.h"
#include "arm_compute/function_info/ActivationLayerInfo.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/Tensor.h"
#include <memory>
namespace arm_compute
{
// Forward declarations
class ITensor;
/** Basic function to simulate a convolution layer. This function calls the following kernels:
*
* -# @ref cpu::CpuWinogradConv2dTransformInputKernel
* -# @ref cpu::CpuWinogradConv2dTransformOutputKernel
* -# @ref cpu::CpuGemmAssemblyDispatch
* -# @ref CPPPermute (three times: weights, input and output)
*
* @note Some Winograd configurations (i.e. F(2x2, 5x5), F(4x4, 5x5)) are supported only with enable_fast_math = true
*/
class NEWinogradConvolutionLayer : public IFunction
{
public:
/** Constructor */
NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradConvolutionLayer(const NEWinogradConvolutionLayer &) = delete;
/** Default move constructor */
NEWinogradConvolutionLayer(NEWinogradConvolutionLayer &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradConvolutionLayer &operator=(const NEWinogradConvolutionLayer &) = delete;
/** Default move assignment operator */
NEWinogradConvolutionLayer &operator=(NEWinogradConvolutionLayer &&) = default;
/** Destructor */
~NEWinogradConvolutionLayer();
/** Set the input and output tensors.
*
* Valid data layouts:
* - NHWC
* - NCHW
*
* Valid data type configurations:
* |src0 |src1 |src2 |dst |
* |:--------------|:--------------|:------|:--------------|
* |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs.
* Data types supported: F16/F32.
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
* Supported kernel sizes: (height, width) -> 3x3, 1x3, 3x1, 5x5, 1x5, 5x1 for Fp32
* -> 3x3 for Fp16
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
*/
void configure(const ITensor *input,
const ITensor *weights,
const ITensor *biases,
ITensor *output,
const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo(),
bool enable_fast_math = false);
// Inherited methods overridden:
void run() override;
void prepare() override;
/** Static function to check if given info will lead to a valid configuration of @ref NEWinogradConvolutionLayer
*
* Similar to @ref NEWinogradConvolutionLayer::configure()
*
* @return a status
*/
static Status validate(const ITensorInfo *input,
const ITensorInfo *weights,
const ITensorInfo *biases,
const ITensorInfo *output,
const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo(),
bool enable_fast_math = false);
private:
struct Impl;
std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif // ACL_ARM_COMPUTE_RUNTIME_NEON_FUNCTIONS_NEWINOGRADCONVOLUTIONLAYER_H
|
