| /* |
| * 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_TEST_VALIDATION_CONVOLUTION_H |
| #define ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H |
| |
| #include "arm_compute/core/utils/misc/Requires.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/UtilsQuantizedAsymm.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace convolution_3d |
| { |
| namespace detail |
| { |
| inline bool is_valid_pixel(int i, int min, int max) |
| { |
| return (i >= min && i < max); |
| } |
| |
| // 3D convolution for floating point type |
| template < typename T, typename TW, typename TB, typename std::enable_if < validation::is_floating_point<T>::value &&validation::is_floating_point<TW>::value |
| &&validation::is_floating_point<TB>::value, |
| int >::type = 0 > |
| inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out, |
| int i_offset, int w_offset, int b_offset, int o_offset, |
| int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1, int filter_id = 0) |
| { |
| ARM_COMPUTE_UNUSED(filter_id); |
| const T *in_ptr = in.data() + i_offset; |
| const TW *w_ptr = weights.data() + w_offset; |
| const TB *b_ptr = bias.data() + b_offset; |
| T *out_ptr = out.data() + o_offset; |
| |
| const int half_width_weights_start = width_weights / 2; |
| const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start; |
| const int half_height_weights_start = height_weights / 2; |
| const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start; |
| |
| // Reset accumulator |
| T acc(0); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk) |
| { |
| for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights_start; |
| const int idy = yk + half_height_weights_start; |
| |
| const T i_value = in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in]; |
| const TW w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights]; |
| |
| acc += i_value * w_value; |
| } |
| } |
| } |
| } |
| |
| // Accumulate the bias and store the result |
| *out_ptr = acc + (*b_ptr); |
| } |
| |
| // 3D convolution for QASYMM8 type |
| template < typename T, typename TW, typename TB, REQUIRES_TA((std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) &&(std::is_same<TW, uint8_t>::value |
| || std::is_same<TW, int8_t>::value)) > |
| inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out, |
| int i_offset, int w_offset, int b_offset, int o_offset, |
| int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1, int filter_id = 0) |
| { |
| const T *in_ptr = in.data() + i_offset; |
| const TW *w_ptr = weights.data() + w_offset; |
| const TB *b_ptr = bias.data() + b_offset; |
| T *out_ptr = out.data() + o_offset; |
| |
| const UniformQuantizationInfo iq_info = in.quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = out.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; |
| if(is_data_type_quantized_per_channel(weights.data_type())) |
| { |
| if(is_data_type_quantized_asymmetric(weights.data_type())) |
| { |
| weights_offset = weights.quantization_info().offset()[filter_id]; |
| } |
| else |
| { |
| weights_offset = 0; |
| } |
| weights_scale = weights.quantization_info().scale()[filter_id]; |
| } |
| 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); |
| |
| const int half_width_weights_start = width_weights / 2; |
| const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start; |
| const int half_height_weights_start = height_weights / 2; |
| const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start; |
| |
| // Reset accumulator |
| int32_t acc(0); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk) |
| { |
| for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights_start; |
| const int idy = yk + half_height_weights_start; |
| |
| const int32_t i_value = in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in]; |
| const int32_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights]; |
| acc += (i_value + input_offset) * (w_value + weights_offset); |
| } |
| } |
| } |
| } |
| |
| // Accumulate the bias |
| acc += (*b_ptr); |
| |
| // Quantize down |
| acc = validation::quantize_down_scale_by_fixedpoint(acc, output_multiplier, output_shift, output_offset, |
| std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max()); |
| |
| // Store the result |
| *out_ptr = acc; |
| } |
| } // namespace detail |
| } // namespace convolution_3d |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H */ |