aboutsummaryrefslogtreecommitdiff
path: root/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h
blob: 9c115f8b3d0cc3a3f403ba89131feea1b76963cc (plain)
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
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
/*
 * Copyright (c) 2017-2019 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 __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__
#define __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__

#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLDeconvolutionLayerUpsample.h"

#include "arm_compute/core/CPP/kernels/CPPFlipWeightsKernel.h"

#include "arm_compute/runtime/CL/CLMemoryGroup.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"

#include <memory>

namespace arm_compute
{
class ICLTensor;
/** Function to run the deconvolution layer.
 *
 * Deconvolution Layer is the backward pass of Convolution Layer. First we transform the input depending on the stride and pad info and then perform a 1x1
 * convolution pass. Input stride defines how many zeroes we should put between each element of the input, pad is the amount of padding and finally a is a user
 * specified value where a < stride - 1, that increases the padding top and right of the input image.
 *
 *  The relation between input to output is as follows:
 *  \f[
 *       width\_output = (width\_input - 1) \cdot stride\_x - 2 \cdot padding\_x + kernel\_x
 *  \f]
 *  \f[
 *       height\_output = (height\_input - 1) \cdot stride\_y - 2 \cdot padding\_y + kernel\_y
 *  \f]
 *
 *  where:
 *      width_input is the size of the first input dimension.
 *      height_input is the size of the second input dimension.
 *      width_output is the size of the first output dimension.
 *      height_output is the size of the second output dimension.
 *      kernel_x and kernel_y are the convolution sizes in x and y.
 *      stride_x and stride_y is the input stride of the first and second dimension.
 *
 * The weights used by Deconvolution are supposed to be the same as the ones used for Convolution. Therefore, it will be necessary to use the weights in the
 * reverse order to perform an actual convolution. This is achieved by using the @ref CPPFlipWeightsKernel.
 *
 * This function calls the following OpenCL kernels/functions:
 *
 * -# @ref CLDeconvolutionLayerUpsample
 * -# @ref CLConvolutionLayer
 *
 */
class CLDeconvolutionLayer : public IFunction
{
public:
    /** Constructor */
    CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLDeconvolutionLayer(const CLDeconvolutionLayer &) = delete;
    /** Default move constructor */
    CLDeconvolutionLayer(CLDeconvolutionLayer &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLDeconvolutionLayer &operator=(const CLDeconvolutionLayer &) = delete;
    /** Default move assignment operator */
    CLDeconvolutionLayer &operator=(CLDeconvolutionLayer &&) = default;
    /** Set the input, weights, biases and output tensors.
     *
     * @deprecated This method is deprecated and will be removed in release 19.05
     *
     * @param[in,out] input              Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
     * @param[in]     weights            The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
     * @param[in]     bias               (Optional) The biases have one dimension. Data type supported: Same as @p input.
     * @param[out]    output             Output tensor. The output has the same number of dimensions as the @p input.
     * @param[in]     info               Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
     * @param[in]     inner_border_right The number of zeros added to right edge of the input.
     * @param[in]     inner_border_top   The number of zeros added to top edge of the input.
     * @param[in]     weights_info       (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
     *
     */
    void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
                   unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info = WeightsInfo());
    /** Static function to check if given info will lead to a valid configuration of @ref CLDeconvolutionLayer
     *
     * @deprecated This method is deprecated and will be removed in release 19.05
     *
     * @param[in] input              Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
     * @param[in] weights            The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
     * @param[in] bias               (Optional) The biases have one dimension. Data type supported: Same as @p input.
     * @param[in] output             Output tensor info. The output has the same number of dimensions as the @p input.
     * @param[in] info               Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
     * @param[in] inner_border_right The number of zeros added to right edge of the input.
     * @param[in] inner_border_top   The number of zeros added to top edge of the input.
     * @param[in] weights_info       (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
                           unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info = WeightsInfo());

    /** Set the input, weights, biases and output tensors.
     *
     * @param[in,out] input        Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
     * @param[in]     weights      The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
     * @param[in]     bias         (Optional) The biases have one dimension. Data type supported: Same as @p input.
     * @param[out]    output       Output tensor. The output has the same number of dimensions as the @p input.
     * @param[in]     info         Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
     * @param[in]     weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
     *
     */
    void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo());
    /** Static function to check if given info will lead to a valid configuration of @ref CLDeconvolutionLayer
     *
     * @param[in] input        Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
     * @param[in] weights      The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
     * @param[in] bias         (Optional) The biases have one dimension. Data type supported: Same as @p input.
     * @param[in] output       Output tensor info. The output has the same number of dimensions as the @p input.
     * @param[in] info         Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
     * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo());

    // Inherited methods overridden:
    void run() override;
    void prepare() override;

private:
    CLMemoryGroup                _memory_group;
    CLDeconvolutionLayerUpsample _scale_f;
    CLConvolutionLayer           _conv_f;
    CPPFlipWeightsKernel         _flip_weights;
    CLTensor                     _scaled_output;
    ICLTensor                   *_original_weights;
    CLTensor                     _weights_flipped;
    bool                         _is_prepared;
};
}
#endif /* __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ */