1 /*
2 * Copyright (c) 2017-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "ConvolutionLayer.h"
25
26 #include "tests/validation/Helpers.h"
27
28 namespace arm_compute
29 {
30 namespace test
31 {
32 namespace validation
33 {
34 namespace reference
35 {
36 template <typename T, typename TW, typename TB>
deconvolution_layer(const SimpleTensor<T> & src,const SimpleTensor<TW> & weights,const SimpleTensor<TB> & bias,const TensorShape & output_shape,const PadStrideInfo & info,QuantizationInfo out_qinfo)37 SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape,
38 const PadStrideInfo &info, QuantizationInfo out_qinfo)
39 {
40 // Create reference
41 const unsigned int pad_left = info.pad_left();
42 const unsigned int pad_right = info.pad_right();
43 const unsigned int pad_top = info.pad_top();
44 const unsigned int pad_bottom = info.pad_bottom();
45 const int stride_x = info.stride().first;
46 const int stride_y = info.stride().second;
47 const int weights_width = weights.shape().x();
48 const int weights_height = weights.shape().y();
49 const int weights_upper_dims = weights.shape().total_size() / (weights_width * weights_height);
50
51 ARM_COMPUTE_ERROR_ON(pad_left > (weights.shape().x() - 1));
52 ARM_COMPUTE_ERROR_ON(pad_right > (weights.shape().x() - 1));
53 ARM_COMPUTE_ERROR_ON(pad_top > (weights.shape().y() - 1));
54 ARM_COMPUTE_ERROR_ON(pad_bottom > (weights.shape().y() - 1));
55
56 // Find the upsampled dimensions
57 unsigned int out_x = (src.shape().x() - 1) * stride_x + 1;
58 unsigned int out_y = (src.shape().y() - 1) * stride_y + 1;
59
60 // Find the padding needed for the convolution with stride 1 in order to match output shape
61 unsigned int deconv_pad_x = output_shape.x() - (out_x - weights_width + 1);
62 unsigned int deconv_pad_y = output_shape.y() - (out_y - weights_height + 1);
63 out_x += deconv_pad_x;
64 out_y += deconv_pad_y;
65
66 unsigned int deconv_pad_left = pad_right > pad_left ? pad_right - pad_left : 0;
67 unsigned int deconv_pad_right = pad_left > pad_right ? pad_left - pad_right : 0;
68 deconv_pad_x -= deconv_pad_left + deconv_pad_right;
69 ARM_COMPUTE_ERROR_ON((deconv_pad_x % 2) != 0);
70 deconv_pad_left += deconv_pad_x / 2;
71 deconv_pad_right += deconv_pad_x / 2;
72
73 unsigned int deconv_pad_top = pad_bottom > pad_top ? pad_bottom - pad_top : 0;
74 unsigned int deconv_pad_bottom = pad_top > pad_bottom ? pad_top - pad_bottom : 0;
75 deconv_pad_y -= deconv_pad_top + deconv_pad_bottom;
76 ARM_COMPUTE_ERROR_ON((deconv_pad_y % 2) != 0);
77 deconv_pad_top += deconv_pad_y / 2;
78 deconv_pad_bottom += deconv_pad_y / 2;
79
80 TensorShape scaled_shape = src.shape();
81 scaled_shape.set(0, out_x);
82 scaled_shape.set(1, out_y);
83 SimpleTensor<T> scaled{ scaled_shape, src.data_type(), 1, src.quantization_info() };
84
85 const int width_in = src.shape().x();
86 const int height_in = src.shape().y();
87 const int width_scaled = scaled.shape().x();
88 const int height_scaled = scaled.shape().y();
89 const int num_2d_slices = src.shape().total_size() / (width_in * height_in);
90
91 if(src.data_type() == DataType::QASYMM8 || src.data_type() == DataType::QASYMM8_SIGNED)
92 {
93 const auto quantized_zero = static_cast<T>(src.quantization_info().uniform().offset);
94 std::fill_n(scaled.data(), scaled.num_elements(), quantized_zero);
95 }
96 else
97 {
98 std::fill_n(scaled.data(), scaled.num_elements(), T(0));
99 }
100
101 // Flip weights by 180 degrees
102 SimpleTensor<TW> weights_flipped{ weights.shape(), weights.data_type(), 1, weights.quantization_info(), weights.data_layout() };
103 #if defined(_OPENMP)
104 #pragma omp parallel for
105 #endif /* _OPENMP */
106 for(int ud = 0; ud < weights_upper_dims; ++ud)
107 {
108 const int offset = ud * weights_width * weights_height;
109 for(int y = 0; y < weights_height; ++y)
110 {
111 for(int x = 0; x < weights_width; ++x)
112 {
113 weights_flipped[offset + (weights_height - 1 - y) * weights_width + (weights_width - 1 - x)] = weights[offset + y * weights_width + x];
114 }
115 }
116 }
117 #if defined(_OPENMP)
118 #pragma omp parallel for
119 #endif /* _OPENMP */
120 for(int slice = 0; slice < num_2d_slices; ++slice)
121 {
122 const int offset_slice_in = slice * width_in * height_in;
123 const int offset_slice_out = slice * width_scaled * height_scaled;
124 const int start_x = deconv_pad_left;
125 const int start_y = deconv_pad_top;
126 const int end_x = width_scaled - deconv_pad_right;
127 const int end_y = height_scaled - deconv_pad_bottom;
128
129 for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++)
130 {
131 for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++)
132 {
133 const T *in = src.data() + offset_slice_in + in_y * width_in + in_x;
134 T *out = scaled.data() + offset_slice_out + xi + yi * width_scaled;
135 *out = *in;
136 }
137 }
138 }
139
140 const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
141 return convolution_layer(scaled, weights_flipped, bias, output_shape, conv_info, Size2D(1U, 1U), 1, out_qinfo);
142 }
143
144 template SimpleTensor<uint8_t> deconvolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
145 const PadStrideInfo &info, QuantizationInfo out_quant_info);
146 template SimpleTensor<uint8_t> deconvolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
147 const PadStrideInfo &info, QuantizationInfo out_quant_info);
148 template SimpleTensor<int8_t> deconvolution_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
149 const PadStrideInfo &info, QuantizationInfo out_quant_info);
150 template SimpleTensor<float> deconvolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
151 const PadStrideInfo &info, QuantizationInfo out_quant_info);
152 template SimpleTensor<half> deconvolution_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &output_shape,
153 const PadStrideInfo &info, QuantizationInfo out_quant_info);
154 } // namespace reference
155 } // namespace validation
156 } // namespace test
157 } // namespace arm_compute