Georgios Pinitas | 47d39dc | 2019-03-11 14:03:23 +0000 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2019 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 | |
| 25 | /* |
| 26 | * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |
| 27 | * |
| 28 | * NOTE: Header to be included by implementation files only. |
| 29 | * |
| 30 | * !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |
| 31 | */ |
| 32 | |
| 33 | #include <limits> |
| 34 | |
| 35 | #include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" |
| 36 | #include "arm_compute/core/NEON/kernels/convolution/depthwise/impl_base.hpp" |
| 37 | #include "arm_compute/core/NEON/kernels/convolution/depthwise/depthwise_quantized.hpp" |
| 38 | |
| 39 | #pragma once |
| 40 | |
| 41 | // Comment the following to use floating-point based quantisation, leave |
| 42 | // uncommented to use fixed-point. |
| 43 | #define FIXED_POINT_REQUANTISATION 1 |
| 44 | |
| 45 | using namespace neon_convolution_kernels; |
| 46 | using namespace qasymm8; |
| 47 | |
| 48 | template <typename T> |
| 49 | struct clamp_to_limits |
| 50 | { |
| 51 | template <typename U> |
| 52 | static inline U clamp(const U& v) |
| 53 | { |
| 54 | const std::numeric_limits<T> limits; |
| 55 | const U min = static_cast<U>(limits.min()); |
| 56 | const U max = static_cast<U>(limits.max()); |
| 57 | return std::min(std::max(v, min), max); |
| 58 | } |
| 59 | |
| 60 | template <typename U> |
| 61 | static inline T clamp_and_cast(const U& v) |
| 62 | { |
| 63 | return static_cast<U>(clamp(v)); |
| 64 | } |
| 65 | }; |
| 66 | |
| 67 | template <typename T> |
| 68 | inline T saturating_doubling_high_mul(const T&, const int32_t&); |
| 69 | |
| 70 | template <> |
| 71 | inline int32x4_t saturating_doubling_high_mul(const int32x4_t& a, const int32_t& b) |
| 72 | { |
| 73 | return vqrdmulhq_n_s32(a, b); |
| 74 | } |
| 75 | |
| 76 | template <> |
| 77 | inline int32_t saturating_doubling_high_mul(const int32_t& a, const int32_t& b) |
| 78 | { |
| 79 | return vget_lane_s32(vqrdmulh_n_s32(vdup_n_s32(a), b), 0); |
| 80 | } |
| 81 | |
| 82 | template <typename T> |
| 83 | inline T rounding_divide_by_exp2(const T& x, const int exponent); |
| 84 | |
| 85 | template <> |
| 86 | inline int32x4_t rounding_divide_by_exp2(const int32x4_t& x, const int exponent) |
| 87 | { |
| 88 | const int32x4_t shift = vdupq_n_s32(-exponent); |
| 89 | const int32x4_t fixup = vshrq_n_s32(vandq_s32(x, shift), 31); |
| 90 | const int32x4_t fixed = vqaddq_s32(x, fixup); |
| 91 | return vrshlq_s32(fixed, shift); |
| 92 | } |
| 93 | |
| 94 | template <> |
| 95 | inline int32x2_t rounding_divide_by_exp2(const int32x2_t& x, const int exponent) |
| 96 | { |
| 97 | const int32x2_t shift = vdup_n_s32(-exponent); |
| 98 | const int32x2_t fixup = vshr_n_s32(vand_s32(x, shift), 31); |
| 99 | const int32x2_t fixed = vqadd_s32(x, fixup); |
| 100 | return vrshl_s32(fixed, shift); |
| 101 | } |
| 102 | |
| 103 | template <> |
| 104 | inline int32_t rounding_divide_by_exp2(const int32_t& x, const int exponent) |
| 105 | { |
| 106 | const int32x2_t xs = vdup_n_s32(x); |
| 107 | return vget_lane_s32(rounding_divide_by_exp2(xs, exponent), 0); |
| 108 | } |
| 109 | |
| 110 | namespace depthwise |
| 111 | { |
| 112 | template < |
| 113 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 114 | unsigned int KernelRows, unsigned int KernelCols, |
| 115 | unsigned int StrideRows, unsigned int StrideCols |
| 116 | > |
| 117 | QAsymm8DepthwiseConvolution< |
| 118 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 119 | >::QAsymm8DepthwiseConvolution( |
| 120 | int n_batches, int n_input_rows, int n_input_cols, int n_channels, |
| 121 | const ActivationFunction activation, |
| 122 | const QAsymm8Params& weight_quantisation, |
| 123 | const QAsymm8Params& input_quantisation, |
| 124 | const QAsymm8Params& output_quantisation, |
| 125 | unsigned int padding_top, |
| 126 | unsigned int padding_left, |
| 127 | unsigned int padding_bottom, |
| 128 | unsigned int padding_right |
| 129 | ) : QAsymm8DepthwiseConvolution( |
| 130 | n_batches, n_input_rows, n_input_cols, n_channels, |
| 131 | activation, weight_quantisation, input_quantisation, output_quantisation, |
| 132 | QAsymm8RescaleParams::make_rescale_params(weight_quantisation, input_quantisation, output_quantisation), |
| 133 | padding_top, padding_left, padding_bottom, padding_right |
| 134 | ) |
| 135 | { |
| 136 | } |
| 137 | |
| 138 | template < |
| 139 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 140 | unsigned int KernelRows, unsigned int KernelCols, |
| 141 | unsigned int StrideRows, unsigned int StrideCols |
| 142 | > |
| 143 | QAsymm8DepthwiseConvolution< |
| 144 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 145 | >::QAsymm8DepthwiseConvolution( |
| 146 | int n_batches, int n_input_rows, int n_input_cols, int n_channels, |
| 147 | const ActivationFunction activation, |
| 148 | const QAsymm8Params& weight_quantisation, |
| 149 | const QAsymm8Params& input_quantisation, |
| 150 | const QAsymm8Params& output_quantisation, |
| 151 | const QAsymm8RescaleParams& rescale_params, |
| 152 | unsigned int padding_top, |
| 153 | unsigned int padding_left, |
| 154 | unsigned int padding_bottom, |
| 155 | unsigned int padding_right |
| 156 | ) : Base( |
| 157 | n_batches, n_input_rows, n_input_cols, n_channels, |
| 158 | get_activation_fn(activation, output_quantisation), |
| 159 | padding_top, padding_left, padding_bottom, padding_right |
| 160 | ), |
| 161 | _weights_quant(weight_quantisation), |
| 162 | _inputs_quant(input_quantisation), |
| 163 | _output_quant(output_quantisation), |
| 164 | rescale_parameters(rescale_params) |
| 165 | { |
| 166 | } |
| 167 | |
| 168 | template < |
| 169 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 170 | unsigned int KernelRows, unsigned int KernelCols, |
| 171 | unsigned int StrideRows, unsigned int StrideCols |
| 172 | > |
| 173 | ActivationFunction QAsymm8DepthwiseConvolution< |
| 174 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 175 | >::get_activation_fn( |
| 176 | const ActivationFunction activation, |
| 177 | const QAsymm8Params& output_quant |
| 178 | ) |
| 179 | { |
| 180 | if ( |
| 181 | (activation == ActivationFunction::ReLU && |
| 182 | output_quant.quantize(0) == 0) || |
| 183 | (activation == ActivationFunction::ReLU6 && |
| 184 | output_quant.quantize(0) == 0 && |
| 185 | output_quant.dequantize(255) <= 6.0f) |
| 186 | ) |
| 187 | { |
| 188 | // If the range of values which can be represented by a quantized value are |
| 189 | // within the range that would be produced by the activation function, then |
| 190 | // the activation function is redundant and can be skipped. |
| 191 | return ActivationFunction::None; |
| 192 | } |
| 193 | else if( |
| 194 | activation == ActivationFunction::ReLU6 && |
| 195 | output_quant.dequantize(255) <= 6.0f |
| 196 | ) |
| 197 | { |
| 198 | // If the largest value that can be represented by a quantized value is |
| 199 | // lower than the upper boundary, then the activation function can be |
| 200 | // relaxed to a ReLU. |
| 201 | return ActivationFunction::ReLU; |
| 202 | } |
| 203 | |
| 204 | return activation; |
| 205 | } |
| 206 | |
| 207 | template < |
| 208 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 209 | unsigned int KernelRows, unsigned int KernelCols, |
| 210 | unsigned int StrideRows, unsigned int StrideCols |
| 211 | > |
| 212 | uint8_t QAsymm8DepthwiseConvolution< |
| 213 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 214 | >::_input_padding_value(void) const |
| 215 | { |
| 216 | return _inputs_quant.offset; |
| 217 | } |
| 218 | |
| 219 | template < |
| 220 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 221 | unsigned int KernelRows, unsigned int KernelCols, |
| 222 | unsigned int StrideRows, unsigned int StrideCols |
| 223 | > |
| 224 | void QAsymm8DepthwiseConvolution< |
| 225 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 226 | >::_pack_params( |
| 227 | void * const buffer, |
| 228 | const void * const weights, |
| 229 | const unsigned int weight_row_stride, |
| 230 | const unsigned int weight_col_stride, |
| 231 | const void * const biases |
| 232 | ) const |
| 233 | { |
| 234 | const uint8_t *wptr = static_cast<const uint8_t *>(weights); |
| 235 | const int32_t *bptr = static_cast<const int32_t *>(biases); |
| 236 | uint8_t *outptr = static_cast<uint8_t *>(buffer); |
| 237 | |
| 238 | // We set the vector length to use quad registers on Aarch64 and only doubles |
| 239 | // on Aarch32. NOTE For SVE set this to the actual vector length. |
| 240 | #if defined(__aarch64__) |
| 241 | unsigned int veclen = 16; |
| 242 | #else |
| 243 | #if defined(__arm__) |
| 244 | unsigned int veclen = 8; |
| 245 | #endif |
| 246 | #endif |
| 247 | |
| 248 | // Compute the rank 0 offset arising from the quantisation parameters. |
| 249 | const int32_t rank0_offset = (KernelRows * KernelCols * |
| 250 | static_cast<int32_t>(_weights_quant.offset) * |
| 251 | static_cast<int32_t>(_inputs_quant.offset)); |
| 252 | |
| 253 | // While there are channels left to process, pack a vector length of them at |
| 254 | // a time and reduce the size of vector used as the size of the tensor |
| 255 | // decreases. |
| 256 | for ( |
| 257 | unsigned int n_channels = this->n_channels(); n_channels; |
| 258 | n_channels -= veclen, |
| 259 | outptr += veclen*(sizeof(int32_t) + this->kernel_rows*this->kernel_cols) |
| 260 | ) |
| 261 | { |
| 262 | // NOTE Ignore this section if using SVE, the vector length remains the |
| 263 | // same and we just don't fill a full register for the tail. |
| 264 | while (n_channels < veclen) |
| 265 | { |
| 266 | // Reduce the vector length to either 8 or 1 (scalar) |
| 267 | // TODO Support more vector lengths in `execute_tile`. |
| 268 | veclen = (veclen == 16) ? 8 : 1; |
| 269 | } |
| 270 | |
| 271 | // Get pointers to bias and weight portions of the output structure. |
| 272 | int32_t *out_bptr = reinterpret_cast<int32_t *>(outptr); |
| 273 | uint8_t *out_wptr = outptr + veclen*sizeof(int32_t); |
| 274 | |
| 275 | // Copy a vector length of elements |
| 276 | for (unsigned int n = 0; n < veclen && n < n_channels; n++) |
| 277 | { |
| 278 | int32_t bias = (bptr != nullptr) ? *(bptr++) : 0; |
| 279 | uint32_t weight_sum = 0; |
| 280 | |
| 281 | for (unsigned int i = 0; i < KernelRows; i++) |
| 282 | { |
| 283 | uint8_t *row_outptr = out_wptr + i*KernelCols*veclen; |
| 284 | for (unsigned int j = 0; j < KernelCols; j++) |
| 285 | { |
| 286 | uint8_t w = *(wptr + i*weight_row_stride + j*weight_col_stride); |
| 287 | row_outptr[j*veclen + n] = w; |
| 288 | weight_sum += static_cast<uint32_t>(w); |
| 289 | } |
| 290 | } |
| 291 | wptr++; |
| 292 | |
| 293 | // Include in the bias contributions from the quantisation offset |
| 294 | int32_t rank1_offset = static_cast<int32_t>( |
| 295 | static_cast<uint32_t>(_inputs_quant.offset) * weight_sum |
| 296 | ); |
| 297 | out_bptr[n] = bias + rank0_offset - rank1_offset; |
| 298 | } |
| 299 | } |
| 300 | } |
| 301 | |
| 302 | template < |
| 303 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 304 | unsigned int KernelRows, unsigned int KernelCols, |
| 305 | unsigned int StrideRows, unsigned int StrideCols |
| 306 | > |
| 307 | template<ActivationFunction Activation> |
| 308 | void QAsymm8DepthwiseConvolution< |
| 309 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 310 | >::execute_tile( |
| 311 | int n_channels, |
| 312 | const void* packed_params, |
| 313 | const uint8_t* inptr, |
| 314 | const unsigned int in_row_stride, |
| 315 | const unsigned int in_col_stride, |
| 316 | uint8_t* outptr, |
| 317 | const unsigned int out_row_stride, |
| 318 | const unsigned int out_col_stride |
| 319 | ) |
| 320 | { |
| 321 | // Activation parameters (unused if Activation is None) |
| 322 | const uint8_t aqmin = _output_quant.offset; |
| 323 | const uint8_t aqmax = (Activation == ActivationFunction::ReLU6) ? |
| 324 | std::min<uint8_t>(255u, _output_quant.quantize(6.0f)) : 255u; |
| 325 | |
| 326 | // Byte type pointer to weights and biases |
| 327 | const uint8_t *wbptr = static_cast<const uint8_t *>(packed_params); |
| 328 | |
| 329 | #if defined(__aarch64__) // Under Aarch64 only use quad registers |
| 330 | for (; n_channels >= 16; n_channels -= 16) |
| 331 | { |
| 332 | // Load biases |
| 333 | const int32x4_t biases[4] = { |
| 334 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr)), |
| 335 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 4), |
| 336 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 8), |
| 337 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 12) |
| 338 | }; |
| 339 | wbptr += 16*sizeof(int32_t); |
| 340 | |
| 341 | // Load weights |
| 342 | uint8x16_t weights[KernelRows][KernelCols]; |
| 343 | for (unsigned int i = 0; i < KernelRows; i++) |
| 344 | { |
| 345 | for (unsigned int j = 0; j < KernelCols; j++) |
| 346 | { |
| 347 | weights[i][j] = vld1q_u8(wbptr); |
| 348 | wbptr += 16; |
| 349 | } |
| 350 | } |
| 351 | |
| 352 | // Load the input activations |
| 353 | uint8x16_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; |
| 354 | for (unsigned int i = 0; i < Base::inner_tile_rows; i++) |
| 355 | { |
| 356 | for (unsigned int j = 0; j < Base::inner_tile_cols; j++) |
| 357 | { |
| 358 | inputs[i][j] = vld1q_u8(inptr + i*in_row_stride + j*in_col_stride); |
| 359 | } |
| 360 | } |
| 361 | inptr += 16; |
| 362 | |
| 363 | // Perform the convolution |
| 364 | for (unsigned int oi = 0; oi < OutputTileRows; oi++) |
| 365 | { |
| 366 | for (unsigned int oj = 0; oj < OutputTileCols; oj++) |
| 367 | { |
| 368 | // Two sets of operations are required, we perform the |
| 369 | // multiply-accumulates for the convolution proper but must also sum |
| 370 | // the tile elements to account for the _weight_ offset. |
| 371 | uint32x4_t accs[4]; |
| 372 | for (unsigned int i = 0; i < 4; i++) |
| 373 | { |
| 374 | accs[i] = reinterpret_cast<uint32x4_t>(biases[i]); |
| 375 | } |
| 376 | |
| 377 | for (unsigned int wi = 0; wi < KernelRows; wi++) |
| 378 | { |
| 379 | for (unsigned int wj = 0; wj < KernelCols; wj++) |
| 380 | { |
| 381 | // Get relevant weight and activation pixel |
| 382 | const uint8x16_t w = weights[wi][wj]; |
| 383 | const uint8x16_t x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; |
| 384 | |
| 385 | // Perform multiplication and accumulation |
| 386 | const uint16x8_t muls[2] = { |
| 387 | vmull_u8(vget_low_u8(w), vget_low_u8(x)), |
| 388 | vmull_u8(vget_high_u8(w), vget_high_u8(x)) |
| 389 | }; |
| 390 | |
| 391 | const uint8x8_t woffset = vdup_n_u8(_weights_quant.offset); |
| 392 | const uint16x8_t sum_elems[2] = { |
| 393 | vmull_u8(vget_low_u8(x), woffset), |
| 394 | vmull_u8(vget_high_u8(x), woffset) |
| 395 | }; |
| 396 | |
| 397 | const uint32x4_t tmps[4] = { |
| 398 | vsubl_u16(vget_low_u16(muls[0]), vget_low_u16(sum_elems[0])), |
| 399 | vsubl_u16(vget_high_u16(muls[0]), vget_high_u16(sum_elems[0])), |
| 400 | vsubl_u16(vget_low_u16(muls[1]), vget_low_u16(sum_elems[1])), |
| 401 | vsubl_u16(vget_high_u16(muls[1]), vget_high_u16(sum_elems[1])), |
| 402 | }; |
| 403 | for (unsigned int i = 0; i < 4; i++) |
| 404 | { |
| 405 | accs[i] = vaddq_u32(accs[i], tmps[i]); |
| 406 | } |
| 407 | } |
| 408 | } |
| 409 | |
| 410 | // Rescale the accumulator and add in the new offset. |
| 411 | uint32x4_t final_accs[4]; |
| 412 | for (unsigned int i = 0; i < 4; i++) |
| 413 | { |
| 414 | #ifdef FIXED_POINT_REQUANTISATION |
| 415 | const int32x4_t y = rounding_divide_by_exp2( |
| 416 | saturating_doubling_high_mul( |
| 417 | reinterpret_cast<int32x4_t>(accs[i]), rescale_parameters.multiplier |
| 418 | ), |
| 419 | rescale_parameters.shift |
| 420 | ); |
| 421 | const int32x4_t offset = reinterpret_cast<int32x4_t>(vdupq_n_u32(_output_quant.offset)); |
| 422 | final_accs[i] = reinterpret_cast<uint32x4_t>(vmaxq_s32(vaddq_s32(y, offset), vdupq_n_s32(0))); |
| 423 | #else // floating point requantisation |
| 424 | float32x4_t fp_acc = vcvtq_f32_s32(reinterpret_cast<int32x4_t>(accs[i])); |
| 425 | fp_acc = vmulq_f32(fp_acc, vdupq_n_f32(rescale_parameters.rescale)); |
| 426 | fp_acc = vaddq_f32(fp_acc, vdupq_n_f32(static_cast<float>(_output_quant.offset))); |
| 427 | fp_acc = vmaxq_f32(fp_acc, vdupq_n_f32(0.0f)); |
| 428 | final_accs[i] = vcvtq_u32_f32(fp_acc); |
| 429 | #endif |
| 430 | } |
| 431 | |
| 432 | uint8x16_t output = vcombine_u8( |
| 433 | vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[0]), vqmovn_u32(final_accs[1]))), |
| 434 | vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[2]), vqmovn_u32(final_accs[3]))) |
| 435 | ); |
| 436 | |
| 437 | // Apply the activation function |
| 438 | if (Activation == ActivationFunction::ReLU || |
| 439 | Activation == ActivationFunction::ReLU6) |
| 440 | { |
| 441 | output = vmaxq_u8(output, vdupq_n_u8(aqmin)); |
| 442 | } |
| 443 | if (Activation == ActivationFunction::ReLU6) |
| 444 | { |
| 445 | output = vminq_u8(output, vdupq_n_u8(aqmax)); |
| 446 | } |
| 447 | |
| 448 | vst1q_u8(outptr + oi*out_row_stride + oj*out_col_stride, output); |
| 449 | } |
| 450 | } |
| 451 | outptr += 16; |
| 452 | } |
| 453 | #endif // defined(__aarch64__) |
| 454 | for (; n_channels >= 8; n_channels -= 8) |
| 455 | { |
| 456 | const int32x4_t biases[2] = { |
| 457 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr)), |
| 458 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 4), |
| 459 | }; |
| 460 | wbptr += 8*sizeof(int32_t); |
| 461 | |
| 462 | uint8x8_t weights[KernelRows][KernelCols]; |
| 463 | for (unsigned int i = 0; i < KernelRows; i++) |
| 464 | { |
| 465 | for (unsigned int j = 0; j < KernelCols; j++) |
| 466 | { |
| 467 | weights[i][j] = vld1_u8(wbptr); |
| 468 | wbptr += 8; |
| 469 | } |
| 470 | } |
| 471 | |
| 472 | uint8x8_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; |
| 473 | for (unsigned int i = 0; i < Base::inner_tile_rows; i++) |
| 474 | { |
| 475 | for (unsigned int j = 0; j < Base::inner_tile_cols; j++) |
| 476 | { |
| 477 | inputs[i][j] = vld1_u8(inptr + i*in_row_stride + j*in_col_stride); |
| 478 | } |
| 479 | } |
| 480 | inptr += 8; |
| 481 | |
| 482 | for (unsigned int oi = 0; oi < OutputTileRows; oi++) |
| 483 | { |
| 484 | for (unsigned int oj = 0; oj < OutputTileCols; oj++) |
| 485 | { |
| 486 | uint32x4_t accs[2]; |
| 487 | for (unsigned int i = 0; i < 2; i++) |
| 488 | { |
| 489 | accs[i] = reinterpret_cast<uint32x4_t>(biases[i]); |
| 490 | } |
| 491 | |
| 492 | for (unsigned int wi = 0; wi < KernelRows; wi++) |
| 493 | { |
| 494 | for (unsigned int wj = 0; wj < KernelCols; wj++) |
| 495 | { |
| 496 | const uint8x8_t w = weights[wi][wj]; |
| 497 | const uint8x8_t x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; |
| 498 | |
| 499 | const uint16x8_t muls = vmull_u8(w, x); |
| 500 | const uint8x8_t woffset = vdup_n_u8(_weights_quant.offset); |
| 501 | const uint16x8_t sum_elems = vmull_u8(x, woffset); |
| 502 | |
| 503 | const uint32x4_t tmps[2] = { |
| 504 | vsubl_u16(vget_low_u16(muls), vget_low_u16(sum_elems)), |
| 505 | vsubl_u16(vget_high_u16(muls), vget_high_u16(sum_elems)), |
| 506 | }; |
| 507 | for (unsigned int i = 0; i < 2; i++) |
| 508 | { |
| 509 | accs[i] = vaddq_u32(accs[i], tmps[i]); |
| 510 | } |
| 511 | } |
| 512 | } |
| 513 | |
| 514 | uint32x4_t final_accs[2]; |
| 515 | for (unsigned int i = 0; i < 2; i++) |
| 516 | { |
| 517 | #ifdef FIXED_POINT_REQUANTISATION |
| 518 | const int32x4_t y = rounding_divide_by_exp2( |
| 519 | saturating_doubling_high_mul( |
| 520 | reinterpret_cast<int32x4_t>(accs[i]), rescale_parameters.multiplier |
| 521 | ), |
| 522 | rescale_parameters.shift |
| 523 | ); |
| 524 | const int32x4_t offset = reinterpret_cast<int32x4_t>(vdupq_n_u32(_output_quant.offset)); |
| 525 | final_accs[i] = reinterpret_cast<uint32x4_t>(vmaxq_s32(vaddq_s32(y, offset), vdupq_n_s32(0))); |
| 526 | #else // floating point requantisation |
| 527 | float32x4_t fp_acc = vcvtq_f32_s32(reinterpret_cast<int32x4_t>(accs[i])); |
| 528 | fp_acc = vmulq_f32(fp_acc, vdupq_n_f32(rescale_parameters.rescale)); |
| 529 | fp_acc = vaddq_f32(fp_acc, vdupq_n_f32(static_cast<float>(_output_quant.offset))); |
| 530 | fp_acc = vmaxq_f32(fp_acc, vdupq_n_f32(0.0f)); |
| 531 | final_accs[i] = vcvtq_u32_f32(fp_acc); |
| 532 | #endif |
| 533 | } |
| 534 | |
| 535 | uint8x8_t output = vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[0]), vqmovn_u32(final_accs[1]))); |
| 536 | |
| 537 | // Apply the activation function |
| 538 | if (Activation == ActivationFunction::ReLU || |
| 539 | Activation == ActivationFunction::ReLU6) |
| 540 | { |
| 541 | output = vmax_u8(output, vdup_n_u8(aqmin)); |
| 542 | } |
| 543 | if (Activation == ActivationFunction::ReLU6) |
| 544 | { |
| 545 | output = vmin_u8(output, vdup_n_u8(aqmax)); |
| 546 | } |
| 547 | |
| 548 | vst1_u8(outptr + oi*out_row_stride + oj*out_col_stride, output); |
| 549 | } |
| 550 | } |
| 551 | outptr += 8; |
| 552 | } |
| 553 | for (; n_channels; n_channels--) |
| 554 | { |
| 555 | // Load bias |
| 556 | const int32_t bias = *reinterpret_cast<const int32_t *>(wbptr); |
| 557 | wbptr += sizeof(int32_t); |
| 558 | |
| 559 | // Load weights |
| 560 | uint8_t weights[KernelRows][KernelCols]; |
| 561 | for (unsigned int i = 0; i < KernelRows; i++) |
| 562 | { |
| 563 | for (unsigned int j = 0; j < KernelCols; j++) |
| 564 | { |
| 565 | weights[i][j] = *(wbptr++); |
| 566 | } |
| 567 | } |
| 568 | |
| 569 | // Load the input activations |
| 570 | uint8_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; |
| 571 | for (unsigned int i = 0; i < Base::inner_tile_rows; i++) |
| 572 | { |
| 573 | for (unsigned int j = 0; j < Base::inner_tile_cols; j++) |
| 574 | { |
| 575 | inputs[i][j] = *(inptr + i*in_row_stride + j*in_col_stride); |
| 576 | } |
| 577 | } |
| 578 | inptr++; |
| 579 | |
| 580 | // Perform the convolution |
| 581 | for (unsigned int oi = 0; oi < OutputTileRows; oi++) |
| 582 | { |
| 583 | for (unsigned int oj = 0; oj < OutputTileCols; oj++) |
| 584 | { |
| 585 | int32_t acc = bias; |
| 586 | uint32_t element_sum = 0; |
| 587 | |
| 588 | for (unsigned int wi = 0; wi < KernelRows; wi++) |
| 589 | { |
| 590 | for (unsigned int wj = 0; wj < KernelCols; wj++) |
| 591 | { |
| 592 | const auto w = weights[wi][wj], x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; |
| 593 | acc += static_cast<int32_t>(static_cast<uint32_t>(w) * static_cast<uint32_t>(x)); |
| 594 | element_sum += static_cast<uint32_t>(x); |
| 595 | } |
| 596 | } |
| 597 | |
| 598 | acc -= static_cast<int32_t>(element_sum) * static_cast<int32_t>(_weights_quant.offset); |
| 599 | |
| 600 | // Requantize |
| 601 | #ifdef FIXED_POINT_REQUANTISATION |
| 602 | acc = rounding_divide_by_exp2( |
| 603 | saturating_doubling_high_mul(acc, rescale_parameters.multiplier), |
| 604 | rescale_parameters.shift |
| 605 | ); |
| 606 | acc += _output_quant.offset; |
| 607 | uint8_t output = clamp_to_limits<uint8_t>::clamp_and_cast<int32_t>(acc); |
| 608 | #else // floating point requantization |
| 609 | float fp_acc = static_cast<float>(acc); |
| 610 | fp_acc *= rescale_parameters.rescale; |
| 611 | fp_acc += static_cast<float>(_output_quant.offset); |
| 612 | fp_acc = std::max<float>(fp_acc, 0.0f); |
| 613 | uint8_t output = static_cast<uint8_t>(std::min<int32_t>(static_cast<int32_t>(fp_acc), 255)); |
| 614 | #endif |
| 615 | |
| 616 | // Apply the activation function |
| 617 | if (Activation == ActivationFunction::ReLU || |
| 618 | Activation == ActivationFunction::ReLU6) |
| 619 | { |
| 620 | output = std::max(output, aqmin); |
| 621 | } |
| 622 | if (Activation == ActivationFunction::ReLU6) |
| 623 | { |
| 624 | output = std::min(output, aqmax); |
| 625 | } |
| 626 | |
| 627 | *(outptr + oi*out_row_stride + oj*out_col_stride) = output; |
| 628 | } |
| 629 | } |
| 630 | outptr++; |
| 631 | } |
| 632 | } |
| 633 | |
Georgios Pinitas | a4bba9c | 2019-04-02 15:27:52 +0100 | [diff] [blame^] | 634 | template < |
| 635 | unsigned int OutputTileRows, unsigned int OutputTileCols, |
| 636 | unsigned int KernelRows, unsigned int KernelCols, |
| 637 | unsigned int StrideRows, unsigned int StrideCols |
| 638 | > |
| 639 | template<ActivationFunction Activation> |
| 640 | void QAsymm8DepthwiseConvolution< |
| 641 | OutputTileRows, OutputTileCols, KernelRows, KernelCols, StrideRows, StrideCols |
| 642 | >::execute_tile( |
| 643 | int n_channels, |
| 644 | const void* packed_params, |
| 645 | const uint8_t* inptrs[Base::inner_tile_rows][Base::inner_tile_cols], |
| 646 | uint8_t* outptrs[Base::output_tile_rows][Base::output_tile_cols] |
| 647 | ) |
| 648 | { |
| 649 | // Activation parameters (unused if Activation is None) |
| 650 | const uint8_t aqmin = _output_quant.offset; |
| 651 | const uint8_t aqmax = (Activation == ActivationFunction::ReLU6) ? |
| 652 | std::min<uint8_t>(255u, _output_quant.quantize(6.0f)) : 255u; |
| 653 | |
| 654 | // Byte type pointer to weights and biases |
| 655 | const uint8_t *wbptr = static_cast<const uint8_t *>(packed_params); |
| 656 | |
| 657 | // Offset into input/output tensors |
| 658 | int n = 0; |
| 659 | |
| 660 | #if defined(__aarch64__) // Under Aarch64 only use quad registers |
| 661 | for (; n_channels >= 16; n_channels -= 16, n += 16) |
| 662 | { |
| 663 | // Load biases |
| 664 | const int32x4_t biases[4] = { |
| 665 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr)), |
| 666 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 4), |
| 667 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 8), |
| 668 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 12) |
| 669 | }; |
| 670 | wbptr += 16*sizeof(int32_t); |
| 671 | |
| 672 | // Load weights |
| 673 | uint8x16_t weights[KernelRows][KernelCols]; |
| 674 | for (unsigned int i = 0; i < KernelRows; i++) |
| 675 | { |
| 676 | for (unsigned int j = 0; j < KernelCols; j++) |
| 677 | { |
| 678 | weights[i][j] = vld1q_u8(wbptr); |
| 679 | wbptr += 16; |
| 680 | } |
| 681 | } |
| 682 | |
| 683 | // Load the input activations |
| 684 | uint8x16_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; |
| 685 | for (unsigned int i = 0; i < Base::inner_tile_rows; i++) |
| 686 | { |
| 687 | for (unsigned int j = 0; j < Base::inner_tile_cols; j++) |
| 688 | { |
| 689 | inputs[i][j] = vld1q_u8(inptrs[i][j] + n); |
| 690 | } |
| 691 | } |
| 692 | |
| 693 | // Perform the convolution |
| 694 | for (unsigned int oi = 0; oi < OutputTileRows; oi++) |
| 695 | { |
| 696 | for (unsigned int oj = 0; oj < OutputTileCols; oj++) |
| 697 | { |
| 698 | // Two sets of operations are required, we perform the |
| 699 | // multiply-accumulates for the convolution proper but must also sum |
| 700 | // the tile elements to account for the _weight_ offset. |
| 701 | uint32x4_t accs[4]; |
| 702 | for (unsigned int i = 0; i < 4; i++) |
| 703 | { |
| 704 | accs[i] = reinterpret_cast<uint32x4_t>(biases[i]); |
| 705 | } |
| 706 | |
| 707 | for (unsigned int wi = 0; wi < KernelRows; wi++) |
| 708 | { |
| 709 | for (unsigned int wj = 0; wj < KernelCols; wj++) |
| 710 | { |
| 711 | // Get relevant weight and activation pixel |
| 712 | const uint8x16_t w = weights[wi][wj]; |
| 713 | const uint8x16_t x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; |
| 714 | |
| 715 | // Perform multiplication and accumulation |
| 716 | const uint16x8_t muls[2] = { |
| 717 | vmull_u8(vget_low_u8(w), vget_low_u8(x)), |
| 718 | vmull_u8(vget_high_u8(w), vget_high_u8(x)) |
| 719 | }; |
| 720 | |
| 721 | const uint8x8_t woffset = vdup_n_u8(_weights_quant.offset); |
| 722 | const uint16x8_t sum_elems[2] = { |
| 723 | vmull_u8(vget_low_u8(x), woffset), |
| 724 | vmull_u8(vget_high_u8(x), woffset) |
| 725 | }; |
| 726 | |
| 727 | const uint32x4_t tmps[4] = { |
| 728 | vsubl_u16(vget_low_u16(muls[0]), vget_low_u16(sum_elems[0])), |
| 729 | vsubl_u16(vget_high_u16(muls[0]), vget_high_u16(sum_elems[0])), |
| 730 | vsubl_u16(vget_low_u16(muls[1]), vget_low_u16(sum_elems[1])), |
| 731 | vsubl_u16(vget_high_u16(muls[1]), vget_high_u16(sum_elems[1])), |
| 732 | }; |
| 733 | for (unsigned int i = 0; i < 4; i++) |
| 734 | { |
| 735 | accs[i] = vaddq_u32(accs[i], tmps[i]); |
| 736 | } |
| 737 | } |
| 738 | } |
| 739 | |
| 740 | // Rescale the accumulator and add in the new offset. |
| 741 | uint32x4_t final_accs[4]; |
| 742 | for (unsigned int i = 0; i < 4; i++) |
| 743 | { |
| 744 | #ifdef FIXED_POINT_REQUANTISATION |
| 745 | const int32x4_t y = rounding_divide_by_exp2( |
| 746 | saturating_doubling_high_mul( |
| 747 | reinterpret_cast<int32x4_t>(accs[i]), rescale_parameters.multiplier |
| 748 | ), |
| 749 | rescale_parameters.shift |
| 750 | ); |
| 751 | const int32x4_t offset = reinterpret_cast<int32x4_t>(vdupq_n_u32(_output_quant.offset)); |
| 752 | final_accs[i] = reinterpret_cast<uint32x4_t>(vmaxq_s32(vaddq_s32(y, offset), vdupq_n_s32(0))); |
| 753 | #else // floating point requantisation |
| 754 | float32x4_t fp_acc = vcvtq_f32_s32(reinterpret_cast<int32x4_t>(accs[i])); |
| 755 | fp_acc = vmulq_f32(fp_acc, vdupq_n_f32(rescale_parameters.rescale)); |
| 756 | fp_acc = vaddq_f32(fp_acc, vdupq_n_f32(static_cast<float>(_output_quant.offset))); |
| 757 | fp_acc = vmaxq_f32(fp_acc, vdupq_n_f32(0.0f)); |
| 758 | final_accs[i] = vcvtq_u32_f32(fp_acc); |
| 759 | #endif |
| 760 | } |
| 761 | |
| 762 | uint8x16_t output = vcombine_u8( |
| 763 | vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[0]), vqmovn_u32(final_accs[1]))), |
| 764 | vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[2]), vqmovn_u32(final_accs[3]))) |
| 765 | ); |
| 766 | |
| 767 | // Apply the activation function |
| 768 | if (Activation == ActivationFunction::ReLU || |
| 769 | Activation == ActivationFunction::ReLU6) |
| 770 | { |
| 771 | output = vmaxq_u8(output, vdupq_n_u8(aqmin)); |
| 772 | } |
| 773 | if (Activation == ActivationFunction::ReLU6) |
| 774 | { |
| 775 | output = vminq_u8(output, vdupq_n_u8(aqmax)); |
| 776 | } |
| 777 | |
| 778 | vst1q_u8(outptrs[oi][oj] + n, output); |
| 779 | } |
| 780 | } |
| 781 | } |
| 782 | #endif // defined(__aarch64__) |
| 783 | for (; n_channels >= 8; n_channels -= 8, n += 8) |
| 784 | { |
| 785 | const int32x4_t biases[2] = { |
| 786 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr)), |
| 787 | vld1q_s32(reinterpret_cast<const int32_t *>(wbptr) + 4), |
| 788 | }; |
| 789 | wbptr += 8*sizeof(int32_t); |
| 790 | |
| 791 | uint8x8_t weights[KernelRows][KernelCols]; |
| 792 | for (unsigned int i = 0; i < KernelRows; i++) |
| 793 | { |
| 794 | for (unsigned int j = 0; j < KernelCols; j++) |
| 795 | { |
| 796 | weights[i][j] = vld1_u8(wbptr); |
| 797 | wbptr += 8; |
| 798 | } |
| 799 | } |
| 800 | |
| 801 | uint8x8_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; |
| 802 | for (unsigned int i = 0; i < Base::inner_tile_rows; i++) |
| 803 | { |
| 804 | for (unsigned int j = 0; j < Base::inner_tile_cols; j++) |
| 805 | { |
| 806 | inputs[i][j] = vld1_u8(inptrs[i][j] + n); |
| 807 | } |
| 808 | } |
| 809 | |
| 810 | for (unsigned int oi = 0; oi < OutputTileRows; oi++) |
| 811 | { |
| 812 | for (unsigned int oj = 0; oj < OutputTileCols; oj++) |
| 813 | { |
| 814 | uint32x4_t accs[2]; |
| 815 | for (unsigned int i = 0; i < 2; i++) |
| 816 | { |
| 817 | accs[i] = reinterpret_cast<uint32x4_t>(biases[i]); |
| 818 | } |
| 819 | |
| 820 | for (unsigned int wi = 0; wi < KernelRows; wi++) |
| 821 | { |
| 822 | for (unsigned int wj = 0; wj < KernelCols; wj++) |
| 823 | { |
| 824 | const uint8x8_t w = weights[wi][wj]; |
| 825 | const uint8x8_t x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; |
| 826 | |
| 827 | const uint16x8_t muls = vmull_u8(w, x); |
| 828 | const uint8x8_t woffset = vdup_n_u8(_weights_quant.offset); |
| 829 | const uint16x8_t sum_elems = vmull_u8(x, woffset); |
| 830 | |
| 831 | const uint32x4_t tmps[2] = { |
| 832 | vsubl_u16(vget_low_u16(muls), vget_low_u16(sum_elems)), |
| 833 | vsubl_u16(vget_high_u16(muls), vget_high_u16(sum_elems)), |
| 834 | }; |
| 835 | for (unsigned int i = 0; i < 2; i++) |
| 836 | { |
| 837 | accs[i] = vaddq_u32(accs[i], tmps[i]); |
| 838 | } |
| 839 | } |
| 840 | } |
| 841 | |
| 842 | uint32x4_t final_accs[2]; |
| 843 | for (unsigned int i = 0; i < 2; i++) |
| 844 | { |
| 845 | #ifdef FIXED_POINT_REQUANTISATION |
| 846 | const int32x4_t y = rounding_divide_by_exp2( |
| 847 | saturating_doubling_high_mul( |
| 848 | reinterpret_cast<int32x4_t>(accs[i]), rescale_parameters.multiplier |
| 849 | ), |
| 850 | rescale_parameters.shift |
| 851 | ); |
| 852 | const int32x4_t offset = reinterpret_cast<int32x4_t>(vdupq_n_u32(_output_quant.offset)); |
| 853 | final_accs[i] = reinterpret_cast<uint32x4_t>(vmaxq_s32(vaddq_s32(y, offset), vdupq_n_s32(0))); |
| 854 | #else // floating point requantisation |
| 855 | float32x4_t fp_acc = vcvtq_f32_s32(reinterpret_cast<int32x4_t>(accs[i])); |
| 856 | fp_acc = vmulq_f32(fp_acc, vdupq_n_f32(rescale_parameters.rescale)); |
| 857 | fp_acc = vaddq_f32(fp_acc, vdupq_n_f32(static_cast<float>(_output_quant.offset))); |
| 858 | fp_acc = vmaxq_f32(fp_acc, vdupq_n_f32(0.0f)); |
| 859 | final_accs[i] = vcvtq_u32_f32(fp_acc); |
| 860 | #endif |
| 861 | } |
| 862 | |
| 863 | uint8x8_t output = vqmovn_u16(vcombine_u16(vqmovn_u32(final_accs[0]), vqmovn_u32(final_accs[1]))); |
| 864 | |
| 865 | // Apply the activation function |
| 866 | if (Activation == ActivationFunction::ReLU || |
| 867 | Activation == ActivationFunction::ReLU6) |
| 868 | { |
| 869 | output = vmax_u8(output, vdup_n_u8(aqmin)); |
| 870 | } |
| 871 | if (Activation == ActivationFunction::ReLU6) |
| 872 | { |
| 873 | output = vmin_u8(output, vdup_n_u8(aqmax)); |
| 874 | } |
| 875 | |
| 876 | vst1_u8(outptrs[oi][oj] + n, output); |
| 877 | } |
| 878 | } |
| 879 | } |
| 880 | for (; n_channels; n_channels--, n++) |
| 881 | { |
| 882 | // Load bias |
| 883 | const int32_t bias = *reinterpret_cast<const int32_t *>(wbptr); |
| 884 | wbptr += sizeof(int32_t); |
| 885 | |
| 886 | // Load weights |
| 887 | uint8_t weights[KernelRows][KernelCols]; |
| 888 | for (unsigned int i = 0; i < KernelRows; i++) |
| 889 | { |
| 890 | for (unsigned int j = 0; j < KernelCols; j++) |
| 891 | { |
| 892 | weights[i][j] = *(wbptr++); |
| 893 | } |
| 894 | } |
| 895 | |
| 896 | // Load the input activations |
| 897 | uint8_t inputs[Base::inner_tile_rows][Base::inner_tile_cols]; |
| 898 | for (unsigned int i = 0; i < Base::inner_tile_rows; i++) |
| 899 | { |
| 900 | for (unsigned int j = 0; j < Base::inner_tile_cols; j++) |
| 901 | { |
| 902 | inputs[i][j] = *(inptrs[i][j] + n); |
| 903 | } |
| 904 | } |
| 905 | |
| 906 | // Perform the convolution |
| 907 | for (unsigned int oi = 0; oi < OutputTileRows; oi++) |
| 908 | { |
| 909 | for (unsigned int oj = 0; oj < OutputTileCols; oj++) |
| 910 | { |
| 911 | int32_t acc = bias; |
| 912 | uint32_t element_sum = 0; |
| 913 | |
| 914 | for (unsigned int wi = 0; wi < KernelRows; wi++) |
| 915 | { |
| 916 | for (unsigned int wj = 0; wj < KernelCols; wj++) |
| 917 | { |
| 918 | const auto w = weights[wi][wj], x = inputs[oi*StrideRows + wi][oj*StrideCols + wj]; |
| 919 | acc += static_cast<int32_t>(static_cast<uint32_t>(w) * static_cast<uint32_t>(x)); |
| 920 | element_sum += static_cast<uint32_t>(x); |
| 921 | } |
| 922 | } |
| 923 | |
| 924 | acc -= static_cast<int32_t>(element_sum) * static_cast<int32_t>(_weights_quant.offset); |
| 925 | |
| 926 | // Requantize |
| 927 | #ifdef FIXED_POINT_REQUANTISATION |
| 928 | acc = rounding_divide_by_exp2( |
| 929 | saturating_doubling_high_mul(acc, rescale_parameters.multiplier), |
| 930 | rescale_parameters.shift |
| 931 | ); |
| 932 | acc += _output_quant.offset; |
| 933 | uint8_t output = clamp_to_limits<uint8_t>::clamp_and_cast<int32_t>(acc); |
| 934 | #else // floating point requantization |
| 935 | float fp_acc = static_cast<float>(acc); |
| 936 | fp_acc *= rescale_parameters.rescale; |
| 937 | fp_acc += static_cast<float>(_output_quant.offset); |
| 938 | fp_acc = std::max<float>(fp_acc, 0.0f); |
| 939 | uint8_t output = static_cast<uint8_t>(std::min<int32_t>(static_cast<int32_t>(fp_acc), 255)); |
| 940 | #endif |
| 941 | |
| 942 | // Apply the activation function |
| 943 | if (Activation == ActivationFunction::ReLU || |
| 944 | Activation == ActivationFunction::ReLU6) |
| 945 | { |
| 946 | output = std::max(output, aqmin); |
| 947 | } |
| 948 | if (Activation == ActivationFunction::ReLU6) |
| 949 | { |
| 950 | output = std::min(output, aqmax); |
| 951 | } |
| 952 | |
| 953 | *(outptrs[oi][oj] + n) = output; |
| 954 | } |
| 955 | } |
| 956 | } |
| 957 | } |
| 958 | |
Georgios Pinitas | 47d39dc | 2019-03-11 14:03:23 +0000 | [diff] [blame] | 959 | } // namespace depthwise |