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Diffstat (limited to 'tests/validation/reference/Conv3D.cpp')
| -rw-r--r-- | tests/validation/reference/Conv3D.cpp | 260 |
1 files changed, 260 insertions, 0 deletions
diff --git a/tests/validation/reference/Conv3D.cpp b/tests/validation/reference/Conv3D.cpp new file mode 100644 index 0000000000..e4010a507a --- /dev/null +++ b/tests/validation/reference/Conv3D.cpp @@ -0,0 +1,260 @@ +/* + * Copyright (c) 2021, 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. + */ +#include "Conv3D.h" + +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "support/AclRequires.h" +#include "tests/validation/reference/UtilsQuantizedAsymm.h" + +// Source/Destination Tensor shape indices (N D H W C) +constexpr unsigned int batch_dim = 4u; +constexpr unsigned int depth_dim = 3u; +constexpr unsigned int height_dim = 2u; +constexpr unsigned int width_dim = 1u; +constexpr unsigned int channel_dim = 0u; + +// Weight tensor shape indices (D H W Cin Cout) +constexpr unsigned int weights_depth_dim = 4u; +constexpr unsigned int weights_height_dim = 3u; +constexpr unsigned int weights_width_dim = 2u; +constexpr unsigned int weights_CHin_dim = 1u; +constexpr unsigned int weights_CHout_dim = 0u; + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +namespace +{ +inline bool is_valid_pixel(int i, int min, int max) +{ + return (i >= min && i < max); +} + +// Evaluate the weights against an element in a given tensor. +template < typename T, typename TB, typename std::enable_if < validation::is_floating_point<T>::value &&validation::is_floating_point<TB>::value, int >::type = 0 > +T calculate_conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const Size3D &dilation, int batch, + int z_start, int y_start, int x_start, int ch_out, UniformQuantizationInfo oq_info) +{ + ARM_COMPUTE_UNUSED(oq_info); + + const unsigned int weights_width = weights.shape()[weights_width_dim]; + const unsigned int weights_height = weights.shape()[weights_height_dim]; + const unsigned int weights_depth = weights.shape()[weights_depth_dim]; + + const unsigned int src_channels = src.shape()[channel_dim]; + const unsigned int src_width = src.shape()[width_dim]; + const unsigned int src_height = src.shape()[height_dim]; + const unsigned int src_depth = src.shape()[depth_dim]; + + T total(0); + for(unsigned int weight_d = 0; weight_d < weights_depth; ++weight_d) + { + const int idx_z = z_start + dilation.depth * weight_d; + for(unsigned int weight_y = 0; weight_y < weights_height; ++weight_y) + { + const int idx_y = y_start + dilation.height * weight_y; + for(unsigned int weight_x = 0; weight_x < weights_width; ++weight_x) + { + const int idx_x = x_start + dilation.width * weight_x; + + //Check if the point is within padding + const bool is_x_valid = is_valid_pixel(idx_x, 0, src_width); + const bool is_y_valid = is_valid_pixel(idx_y, 0, src_height); + const bool is_z_valid = is_valid_pixel(idx_z, 0, src_depth); + const bool is_invalid_pixel = !(is_x_valid && is_y_valid && is_z_valid); + if(is_invalid_pixel) + { + continue; + } + + for(unsigned int ch_in = 0; ch_in < src_channels; ++ch_in) + { + const T *in_ptr = src.data(); + const T *w_ptr = weights.data(); + + const int in_offset = coord2index(src.shape(), Coordinates{ ch_in, idx_x, idx_y, idx_z, batch }); + const int weight_offset = coord2index(weights.shape(), Coordinates{ ch_out, ch_in, weight_x, weight_y, weight_d }); + T input_value = in_ptr[in_offset]; + T weight_value = w_ptr[weight_offset]; + total += (input_value * weight_value); + } + } + } + } + + const TB *b_ptr = bias.data(); + TB bias_value = b_ptr[ch_out]; + + return total + bias_value; +} + +template < typename T, typename TB, ARM_COMPUTE_REQUIRES_TA(std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) > +T calculate_conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const Size3D &dilation, int batch, + int z_start, int y_start, int x_start, int ch_out, UniformQuantizationInfo oq_info) +{ + const unsigned int weights_width = weights.shape()[weights_width_dim]; + const unsigned int weights_height = weights.shape()[weights_height_dim]; + const unsigned int weights_depth = weights.shape()[weights_depth_dim]; + + const unsigned int src_channels = src.shape()[channel_dim]; + const unsigned int src_width = src.shape()[width_dim]; + const unsigned int src_height = src.shape()[height_dim]; + const unsigned int src_depth = src.shape()[depth_dim]; + + const UniformQuantizationInfo iq_info = src.quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); + + const int input_offset = -iq_info.offset; + const float input_scale = iq_info.scale; + int weights_offset = -wq_info.offset; + float weights_scale = wq_info.scale; + const int output_offset = oq_info.offset; + const float output_scale = oq_info.scale; + + int output_multiplier = 0; + int output_shift = 0; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + + int32_t total(0); + for(unsigned int weight_d = 0; weight_d < weights_depth; ++weight_d) + { + const int idx_z = z_start + dilation.depth * weight_d; + for(unsigned int weight_y = 0; weight_y < weights_height; ++weight_y) + { + const int idx_y = y_start + dilation.height * weight_y; + for(unsigned int weight_x = 0; weight_x < weights_width; ++weight_x) + { + const int idx_x = x_start + dilation.width * weight_x; + + //Check if the point is within padding + const bool is_x_valid = is_valid_pixel(idx_x, 0, src_width); + const bool is_y_valid = is_valid_pixel(idx_y, 0, src_height); + const bool is_z_valid = is_valid_pixel(idx_z, 0, src_depth); + const bool is_invalid_pixel = !(is_x_valid && is_y_valid && is_z_valid); + if(is_invalid_pixel) + { + continue; + } + + for(unsigned int ch_in = 0; ch_in < src_channels; ++ch_in) + { + const T *in_ptr = src.data(); + const T *w_ptr = weights.data(); + + const int in_offset = coord2index(src.shape(), Coordinates{ ch_in, idx_x, idx_y, idx_z, batch }); + const int weight_offset = coord2index(weights.shape(), Coordinates{ ch_out, ch_in, weight_x, weight_y, weight_d }); + T input_value = in_ptr[in_offset]; + T weight_value = w_ptr[weight_offset]; + total += ((input_value + input_offset) * (weight_value + weights_offset)); + } + } + } + } + + const TB *b_ptr = bias.data(); + TB bias_value = b_ptr[ch_out]; + + total += bias_value; + + return validation::quantize_down_scale_by_fixedpoint(total, output_multiplier, output_shift, output_offset, + std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max()); +} +} // namespace + +template <typename T, typename TB> +SimpleTensor<T> conv3d(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, const Conv3dInfo &conv3d_info) +{ + // Compute reference + const unsigned int batch_size = src.shape()[batch_dim]; + const unsigned int dst_width = dst.shape()[width_dim]; + const unsigned int dst_height = dst.shape()[height_dim]; + const unsigned int dst_depth = dst.shape()[depth_dim]; + const unsigned int src_channels = src.shape()[channel_dim]; + const unsigned int weights_out_ch = weights.shape()[weights_CHout_dim]; + const unsigned int dst_channels = dst.shape()[channel_dim]; + const size_t pad_left = conv3d_info.padding.left; + const size_t pad_top = conv3d_info.padding.top; + const size_t pad_front = conv3d_info.padding.front; + const size_t stride_x = conv3d_info.stride.x(); + const size_t stride_y = conv3d_info.stride.y(); + const size_t stride_z = conv3d_info.stride.z(); + + const TensorShape dst_shape = arm_compute::misc::shape_calculator::compute_conv3d_shape(src.shape(), weights.shape(), conv3d_info); + + ARM_COMPUTE_UNUSED(src_channels, weights_out_ch, dst_channels, dst_shape, weights_CHin_dim); + // Number of batches of source and destination tensors must match. + ARM_COMPUTE_ERROR_ON(src.shape()[batch_dim] != dst.shape()[batch_dim]); + // Input channels in the source and weights must match. + ARM_COMPUTE_ERROR_ON(src_channels != weights.shape()[weights_CHin_dim]); + // Weight channels in the destination and weights must match. + ARM_COMPUTE_ERROR_ON(weights_out_ch != dst_channels); + // Bias must match the number of destination channels. + ARM_COMPUTE_ERROR_ON(bias.shape()[0] != dst_channels); + // Compare given dst tensor shape with expected shape. + ARM_COMPUTE_ERROR_ON(dst.shape() != dst_shape); + + for(unsigned int batch = 0; batch < batch_size; ++batch) + { + for(unsigned int z_out = 0; z_out < dst_depth; ++z_out) + { + const int z_start = (z_out * stride_z) - pad_front; + for(unsigned int y_out = 0; y_out < dst_height; ++y_out) + { + const int y_start = (y_out * stride_y) - pad_top; + for(unsigned int x_out = 0; x_out < dst_width; ++x_out) + { + const int x_start = (x_out * stride_x) - pad_left; + for(unsigned int ch_out = 0; ch_out < dst_channels; ++ch_out) + { + T *out_ptr = dst.data(); + + const int out_offset = coord2index(dst.shape(), Coordinates{ ch_out, x_out, y_out, z_out, batch }); + out_ptr[out_offset] = calculate_conv3d<T, TB>(src, weights, bias, conv3d_info.dilation, batch, z_start, y_start, x_start, ch_out, dst.quantization_info().uniform()); + } + } + } + } + } + return dst; +} + +template SimpleTensor<float> conv3d(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, SimpleTensor<float> &dst, + const Conv3dInfo &conv3d_info); +template SimpleTensor<half> conv3d(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, SimpleTensor<half> &dst, + const Conv3dInfo &conv3d_info); +template SimpleTensor<uint8_t> conv3d(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, + const Conv3dInfo &conv3d_info); +template SimpleTensor<int8_t> conv3d(const SimpleTensor<int8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<int8_t> &dst, + const Conv3dInfo &conv3d_info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute |