1 /*
2 * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
3 *
4 * SPDX-License-Identifier: Apache-2.0
5 *
6 * Licensed under the Apache License, Version 2.0 (the License); you may
7 * not use this file except in compliance with the License.
8 * You may obtain a copy of the License at
9 *
10 * www.apache.org/licenses/LICENSE-2.0
11 *
12 * Unless required by applicable law or agreed to in writing, software
13 * distributed under the License is distributed on an AS IS BASIS, WITHOUT
14 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 * See the License for the specific language governing permissions and
16 * limitations under the License.
17 */
18
19 /* ----------------------------------------------------------------------
20 * Project: CMSIS NN Library
21 * Title: arm_convolve_wrapper_s8.c
22 * Description: s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in
23 * cmsis-nn to perform the convolution.
24 *
25 * $Date: 09. October 2020
26 * $Revision: V.1.0.1
27 *
28 * Target Processor: Cortex-M cores
29 *
30 * -------------------------------------------------------------------- */
31
32 #include "arm_nnfunctions.h"
33
34 /**
35 * @ingroup groupNN
36 */
37
38 /**
39 * @addtogroup NNConv
40 * @{
41 */
42
43 /*
44 * Convolution layer
45 *
46 * Refer header file for details.
47 *
48 */
49
arm_convolve_wrapper_s8(const cmsis_nn_context * ctx,const cmsis_nn_conv_params * conv_params,const cmsis_nn_per_channel_quant_params * quant_params,const cmsis_nn_dims * input_dims,const q7_t * input_data,const cmsis_nn_dims * filter_dims,const q7_t * filter_data,const cmsis_nn_dims * bias_dims,const int32_t * bias_data,const cmsis_nn_dims * output_dims,q7_t * output_data)50 arm_status arm_convolve_wrapper_s8(const cmsis_nn_context *ctx,
51 const cmsis_nn_conv_params *conv_params,
52 const cmsis_nn_per_channel_quant_params *quant_params,
53 const cmsis_nn_dims *input_dims,
54 const q7_t *input_data,
55 const cmsis_nn_dims *filter_dims,
56 const q7_t *filter_data,
57 const cmsis_nn_dims *bias_dims,
58 const int32_t *bias_data,
59 const cmsis_nn_dims *output_dims,
60 q7_t *output_data)
61 {
62 if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (input_dims->c % 4 == 0) &&
63 (conv_params->stride.w == 1) && (conv_params->stride.h == 1) && (filter_dims->w == 1) && (filter_dims->h == 1))
64 {
65 return arm_convolve_1x1_s8_fast(ctx,
66 conv_params,
67 quant_params,
68 input_dims,
69 input_data,
70 filter_dims,
71 filter_data,
72 bias_dims,
73 bias_data,
74 output_dims,
75 output_data);
76 }
77 else if ((output_dims->h == 1) && (input_dims->h == 1) && (filter_dims->h == 1) && (output_dims->w % 4 == 0) &&
78 (input_dims->n == 1))
79 {
80 return arm_convolve_1_x_n_s8(ctx,
81 conv_params,
82 quant_params,
83 input_dims,
84 input_data,
85 filter_dims,
86 filter_data,
87 bias_dims,
88 bias_data,
89 output_dims,
90 output_data);
91 }
92 else
93 {
94 return arm_convolve_s8(ctx,
95 conv_params,
96 quant_params,
97 input_dims,
98 input_data,
99 filter_dims,
100 filter_data,
101 bias_dims,
102 bias_data,
103 output_dims,
104 output_data);
105 }
106 }
107
arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params * conv_params,const cmsis_nn_dims * input_dims,const cmsis_nn_dims * filter_dims,const cmsis_nn_dims * output_dims)108 int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params *conv_params,
109 const cmsis_nn_dims *input_dims,
110 const cmsis_nn_dims *filter_dims,
111 const cmsis_nn_dims *output_dims)
112 {
113 if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (input_dims->c % 4 == 0) &&
114 (conv_params->stride.w == 1) && (conv_params->stride.h == 1) && (filter_dims->w == 1) && (filter_dims->h == 1))
115 {
116 return arm_convolve_1x1_s8_fast_get_buffer_size(input_dims);
117 }
118 else if ((output_dims->h == 1) && (input_dims->h == 1) && (filter_dims->h == 1) && (output_dims->w % 4 == 0) &&
119 (input_dims->n == 1))
120 {
121 return arm_convolve_1_x_n_s8_get_buffer_size(input_dims, filter_dims);
122 }
123 else
124 {
125 return arm_convolve_s8_get_buffer_size(input_dims, filter_dims);
126 }
127 }
128
129 /**
130 * @} end of NNConv group
131 */
132
