pooling.cu 26.9 KB
Newer Older
C
chengduoZH 已提交
1
/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve.
2 3 4 5 6 7 8 9 10 11 12 13 14 15

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/operators/math/pooling.h"
C
chengduoZH 已提交
16
#include "paddle/platform/cuda_helper.h"
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

namespace paddle {
namespace operators {
namespace math {

template <typename PoolProcess, typename T>
__global__ void KernelPool2dForward(
    const int nthreads, const T* input_data, T* output_data, const int channels,
    const int input_height, const int input_width, const int output_height,
    const int output_width, const int ksize_height, const int ksize_width,
    const int stride_height, const int stride_width, const int padding_height,
    const int padding_width, PoolProcess pool_process) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  if (index < nthreads) {
    int pw = index % output_width;
    int ph = (index / output_width) % output_height;
    int c = (index / output_width / output_height) % channels;
    int batch_idx = index / output_width / output_height / channels;

    int hstart = ph * stride_height - padding_height;
    int hend = min(hstart + ksize_height, input_height);
    hstart = max(hstart, 0);

    int wstart = pw * stride_width - padding_width;
    int wend = min(wstart + ksize_width, input_width);
    wstart = max(wstart, 0);

    input_data += (batch_idx * channels + c) * input_height * input_width;
    T ele = pool_process.initial();
    for (int h = hstart; h < hend; ++h) {
      for (int w = wstart; w < wend; ++w) {
        pool_process.process(ele, input_data[h * input_width + w]);
      }
    }
    int pool_size = (hend - hstart) * (wend - wstart);
    pool_process.finalize(ele, (static_cast<T>(pool_size)));
    output_data[index] = ele;
  }
}

template <typename PoolProcess, typename T>
__global__ void KernelPool2dBackward(
    const int nthreads, const T* input_data, const T* output_data,
    const T* output_grad, T* input_grad, const int channels,
    const int input_height, const int input_width, const int output_height,
    const int output_width, const int ksize_height, const int ksize_width,
    const int stride_height, const int stride_width, const int padding_height,
    const int padding_width, PoolProcess pool_process) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  if (index < nthreads) {
    int offsetW = index % input_width + padding_width;
    int offsetH = (index / input_width) % input_height + padding_height;
    int offsetC = (index / input_width / input_height) % channels;
    int batch_idx = index / input_width / input_height / channels;

    int phstart = (offsetH < ksize_height)
                      ? 0
                      : (offsetH - ksize_height) / stride_height + 1;
    int pwstart = (offsetW < ksize_width)
                      ? 0
                      : (offsetW - ksize_width) / stride_width + 1;
    int phend = min(offsetH / stride_height + 1, output_height);
    int pwend = min(offsetW / stride_width + 1, output_width);
    T gradient = 0;
    T input = input_data[index];
    int output_idx =
        (batch_idx * channels + offsetC) * output_height * output_width;
    output_data += output_idx;
    output_grad += output_idx;
    for (int ph = phstart; ph < phend; ++ph) {
      for (int pw = pwstart; pw < pwend; ++pw) {
        int hstart = ph * stride_height - padding_height;
        int wstart = pw * stride_width - padding_width;
        int hend = min(hstart + ksize_height, input_height);
        int wend = min(wstart + ksize_width, input_width);
        hstart = max(hstart, 0);
        wstart = max(wstart, 0);
        int pool_size = (hend - hstart) * (wend - wstart);
        int output_sub_idx = ph * output_width + pw;
        pool_process.gradProcess(input, output_data[output_sub_idx],
                                 output_grad[output_sub_idx], gradient,
98
                                 static_cast<T>(1.0 / pool_size));
99 100 101 102 103 104
      }
    }
    input_grad[index] = gradient;
  }
}

105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
template <typename T>
__global__ void KernelMaxPool2dBackward(
    const int nthreads, const T* input_data, const T* output_data,
    const T* output_grad, T* input_grad, const int channels,
    const int input_height, const int input_width, const int output_height,
    const int output_width, const int ksize_height, const int ksize_width,
    const int stride_height, const int stride_width, const int padding_height,
    const int padding_width) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  if (index < nthreads) {
    int pw = index % output_width;
    int ph = (index / output_width) % output_height;
    int c = (index / output_width / output_height) % channels;
    int batch_idx = index / output_width / output_height / channels;

    int hstart = ph * stride_height - padding_height;
    int hend = min(hstart + ksize_height, input_height);
    hstart = max(hstart, 0);

    int wstart = pw * stride_width - padding_width;
    int wend = min(wstart + ksize_width, input_width);
    wstart = max(wstart, 0);

    input_data += (batch_idx * channels + c) * input_height * input_width;
    input_grad += (batch_idx * channels + c) * input_height * input_width;

    T ele = output_data[index];
    int maxIndex = -1;
    bool stop = false;
    for (int h = hstart; h < hend && !stop; ++h) {
      for (int w = wstart; w < wend && !stop; ++w) {
        if (ele == input_data[h * input_width + w]) {
          maxIndex = h * input_width + w;
          stop = true;
        }
      }
    }

    if (maxIndex != -1) {
      // atomic add
      atomicAdd(input_grad + maxIndex, output_grad[index]);
    }
  }
}

150 151 152
template <typename PoolProcess, typename T>
class Pool2dForwardFunctor<platform::GPUPlace, PoolProcess, T> {
 public:
153 154
  void operator()(const platform::DeviceContext& context,
                  const framework::Tensor& input, framework::Tensor& output,
155
                  std::vector<int>& ksize, std::vector<int>& strides,
156
                  std::vector<int>& paddings, PoolProcess pool_process) {
157 158 159 160 161 162 163 164 165 166 167 168 169 170 171
    const int batch_size = input.dims()[0];
    const int input_channels = input.dims()[1];
    const int input_height = input.dims()[2];
    const int input_width = input.dims()[3];
    const int output_channels = output.dims()[1];
    const int output_height = output.dims()[2];
    const int output_width = output.dims()[3];
    const int ksize_height = ksize[0];
    const int ksize_width = ksize[1];
    const int stride_height = strides[0];
    const int stride_width = strides[1];
    const int padding_height = paddings[0];
    const int padding_width = paddings[1];

    const T* input_data = input.data<T>();
C
chengduoZH 已提交
172
    T* output_data = output.mutable_data<T>(context.GetPlace());
173 174 175 176 177 178

    int nthreads = batch_size * output_channels * output_height * output_width;
    int blocks = (nthreads + 1024 - 1) / 1024;
    dim3 threads(1024, 1);
    dim3 grid(blocks, 1);

C
chengduoZH 已提交
179 180 181 182 183 184 185 186 187
    KernelPool2dForward<
        PoolProcess,
        T><<<grid, threads, 0,
             reinterpret_cast<const platform::CUDADeviceContext&>(context)
                 .stream()>>>(nthreads, input_data, output_data, input_channels,
                              input_height, input_width, output_height,
                              output_width, ksize_height, ksize_width,
                              stride_height, stride_width, padding_height,
                              padding_width, pool_process);
188 189 190 191 192 193
  }
};

template <typename PoolProcess, typename T>
class Pool2dBackwardFunctor<platform::GPUPlace, PoolProcess, T> {
 public:
194 195
  void operator()(const platform::DeviceContext& context,
                  const framework::Tensor& input, framework::Tensor& input_grad,
196 197 198
                  const framework::Tensor& output,
                  const framework::Tensor& output_grad, std::vector<int>& ksize,
                  std::vector<int>& strides, std::vector<int>& paddings,
199
                  PoolProcess pool_process) {
200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
    const int batch_size = input.dims()[0];
    const int input_channels = input.dims()[1];
    const int input_height = input.dims()[2];
    const int input_width = input.dims()[3];
    const int output_height = output.dims()[2];
    const int output_width = output.dims()[3];
    const int ksize_height = ksize[0];
    const int ksize_width = ksize[1];
    const int stride_height = strides[0];
    const int stride_width = strides[1];
    const int padding_height = paddings[0];
    const int padding_width = paddings[1];

    const T* input_data = input.data<T>();
    const T* output_data = output.data<T>();
    const T* output_grad_data = output_grad.data<T>();
C
chengduoZH 已提交
216
    T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
217 218 219 220 221 222

    int nthreads = batch_size * input_channels * input_height * input_width;
    int blocks = (nthreads + 1024 - 1) / 1024;
    dim3 threads(1024, 1);
    dim3 grid(blocks, 1);

C
chengduoZH 已提交
223 224 225 226 227
    KernelPool2dBackward<
        PoolProcess,
        T><<<grid, threads, 0,
             reinterpret_cast<const platform::CUDADeviceContext&>(context)
                 .stream()>>>(
228 229 230 231 232 233 234
        nthreads, input_data, output_data, output_grad_data, input_grad_data,
        input_channels, input_height, input_width, output_height, output_width,
        ksize_height, ksize_width, stride_height, stride_width, padding_height,
        padding_width, pool_process);
  }
};

235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280
template <typename T>
class MaxPool2dBackwardFunctor<platform::GPUPlace, T> {
 public:
  void operator()(const platform::DeviceContext& context,
                  const framework::Tensor& input, framework::Tensor& input_grad,
                  const framework::Tensor& output,
                  const framework::Tensor& output_grad, std::vector<int>& ksize,
                  std::vector<int>& strides, std::vector<int>& paddings) {
    const int batch_size = input.dims()[0];
    const int input_channels = input.dims()[1];
    const int input_height = input.dims()[2];
    const int input_width = input.dims()[3];
    const int output_channels = output.dims()[1];
    const int output_height = output.dims()[2];
    const int output_width = output.dims()[3];
    const int ksize_height = ksize[0];
    const int ksize_width = ksize[1];
    const int stride_height = strides[0];
    const int stride_width = strides[1];
    const int padding_height = paddings[0];
    const int padding_width = paddings[1];

    const T* input_data = input.data<T>();
    const T* output_data = output.data<T>();
    const T* output_grad_data = output_grad.data<T>();
    T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());

    int nthreads = batch_size * output_channels * output_height * output_width;
    int blocks = (nthreads + 1024 - 1) / 1024;
    dim3 threads(1024, 1);
    dim3 grid(blocks, 1);

    KernelMaxPool2dBackward<
        T><<<grid, threads, 0,
             reinterpret_cast<const platform::CUDADeviceContext&>(context)
                 .stream()>>>(
        nthreads, input_data, output_data, output_grad_data, input_grad_data,
        input_channels, input_height, input_width, output_height, output_width,
        ksize_height, ksize_width, stride_height, stride_width, padding_height,
        padding_width);
  }
};

template class MaxPool2dBackwardFunctor<platform::GPUPlace, float>;
// template class MaxPool2dBackwardFunctor<platform::GPUPlace, double>;

281 282 283
template class Pool2dForwardFunctor<
    platform::GPUPlace, paddle::operators::math::pool::maxPool<float>, float>;
template class Pool2dForwardFunctor<
284
    platform::GPUPlace, paddle::operators::math::pool::avgPool<float>, float>;
285
template class Pool2dBackwardFunctor<
286 287
    platform::GPUPlace, paddle::operators::math::pool::maxPoolGrad<float>,
    float>;
288
template class Pool2dBackwardFunctor<
289 290
    platform::GPUPlace, paddle::operators::math::pool::avgPoolGrad<float>,
    float>;
291 292 293
template class Pool2dForwardFunctor<
    platform::GPUPlace, paddle::operators::math::pool::maxPool<double>, double>;
template class Pool2dForwardFunctor<
294
    platform::GPUPlace, paddle::operators::math::pool::avgPool<double>, double>;
295
template class Pool2dBackwardFunctor<
296 297
    platform::GPUPlace, paddle::operators::math::pool::maxPoolGrad<double>,
    double>;
298
template class Pool2dBackwardFunctor<
299 300
    platform::GPUPlace, paddle::operators::math::pool::avgPoolGrad<double>,
    double>;
301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365

template <typename PoolProcess, typename T>
__global__ void KernelPool3DForward(
    const int nthreads, const T* input_data, T* output_data, const int channels,
    const int input_depth, const int input_height, const int input_width,
    const int output_depth, const int output_height, const int output_width,
    const int ksize_depth, const int ksize_height, const int ksize_width,
    const int stride_depth, const int stride_height, const int stride_width,
    const int padding_depth, const int padding_height, const int padding_width,
    PoolProcess pool_process) {
  for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
       index += blockDim.x * gridDim.x) {
    int pw = index % output_width;
    int ph = (index / output_width) % output_height;
    int pd = (index / output_width / output_height) % output_depth;
    int c = (index / output_width / output_height / output_depth) % channels;
    int batch_idx =
        index / output_width / output_height / output_depth / channels;
    int dstart = pd * stride_depth - padding_depth;
    int hstart = ph * stride_height - padding_height;
    int wstart = pw * stride_width - padding_width;
    int dend = min(dstart + ksize_depth, input_depth);
    int hend = min(hstart + ksize_height, input_height);
    int wend = min(wstart + ksize_width, input_width);
    dstart = max(dstart, 0);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    T ele = pool_process.initial();
    input_data +=
        (batch_idx * channels + c) * input_depth * input_height * input_width;
    for (int d = dstart; d < dend; ++d) {
      for (int h = hstart; h < hend; ++h) {
        for (int w = wstart; w < wend; ++w) {
          pool_process.process(
              ele, input_data[(d * input_height + h) * input_width + w]);
        }
      }
    }
    int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
    pool_process.finalize(ele, static_cast<T>(pool_size));
    output_data[index] = ele;
  }
}

template <typename PoolProcess, typename T>
__global__ void KernelPool3DBackward(
    const int nthreads, const T* input_data, const T* output_data,
    const T* output_grad, T* input_grad, const int channels,
    const int input_depth, const int input_height, const int input_width,
    const int output_depth, const int output_height, const int output_width,
    const int ksize_depth, const int ksize_height, const int ksize_width,
    const int stride_depth, const int stride_height, const int stride_width,
    const int padding_depth, const int padding_height, const int padding_width,
    PoolProcess pool_process) {
  for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
       index += blockDim.x * gridDim.x) {
    int offsetW = index % input_width + padding_width;
    int offsetH = (index / input_width) % input_height + padding_height;
    int offsetD =
        (index / input_width / input_height) % input_depth + padding_depth;
    int offsetC = (index / input_width / input_height / input_depth) % channels;
    int batch_idx = index / input_width / input_height / input_depth / channels;

    int pdstart = (offsetD < ksize_depth)
                      ? 0
366
                      : (offsetD - ksize_depth) / stride_depth + 1;
367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397
    int phstart = (offsetH < ksize_height)
                      ? 0
                      : (offsetH - ksize_height) / stride_height + 1;
    int pwstart = (offsetW < ksize_width)
                      ? 0
                      : (offsetW - ksize_width) / stride_width + 1;
    int pdend = min((offsetD) / stride_depth + 1, output_depth);
    int phend = min((offsetH) / stride_height + 1, output_height);
    int pwend = min((offsetW) / stride_width + 1, output_width);

    T gradient = 0;
    T input = input_data[index];
    int output_idx = (batch_idx * channels + offsetC) * output_depth *
                     output_height * output_width;
    output_data += output_idx;
    output_grad += output_idx;

    for (int pd = pdstart; pd < pdend; ++pd) {
      for (int ph = phstart; ph < phend; ++ph) {
        for (int pw = pwstart; pw < pwend; ++pw) {
          // figure out the pooling size
          int dstart = pd * stride_depth - padding_depth;
          int hstart = ph * stride_height - padding_height;
          int wstart = pw * stride_width - padding_width;
          int dend = min(dstart + ksize_depth, input_depth);
          int hend = min(hstart + ksize_height, input_height);
          int wend = min(wstart + ksize_width, input_width);
          dstart = max(dstart, 0);
          hstart = max(hstart, 0);
          wstart = max(wstart, 0);
          int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
398
          int output_sub_idx = (pd * output_height + ph) * output_width + pw;
399 400
          pool_process.gradProcess(input, output_data[output_sub_idx],
                                   output_grad[output_sub_idx], gradient,
401
                                   static_cast<T>(1.0 / pool_size));
402 403 404 405 406 407 408
        }
      }
    }
    input_grad[index] = gradient;
  }
}

409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460
template <typename T>
__global__ void KernelMaxPool3DBackward(
    const int nthreads, const T* input_data, const T* output_data,
    const T* output_grad, T* input_grad, const int channels,
    const int input_depth, const int input_height, const int input_width,
    const int output_depth, const int output_height, const int output_width,
    const int ksize_depth, const int ksize_height, const int ksize_width,
    const int stride_depth, const int stride_height, const int stride_width,
    const int padding_depth, const int padding_height,
    const int padding_width) {
  for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
       index += blockDim.x * gridDim.x) {
    int pw = index % output_width;
    int ph = (index / output_width) % output_height;
    int pd = (index / output_width / output_height) % output_depth;
    int c = (index / output_width / output_height / output_depth) % channels;
    int batch_idx =
        index / output_width / output_height / output_depth / channels;
    int dstart = pd * stride_depth - padding_depth;
    int hstart = ph * stride_height - padding_height;
    int wstart = pw * stride_width - padding_width;
    int dend = min(dstart + ksize_depth, input_depth);
    int hend = min(hstart + ksize_height, input_height);
    int wend = min(wstart + ksize_width, input_width);
    dstart = max(dstart, 0);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    T ele = output_data[index];
    bool stop = false;
    int maxIdx = -1;
    input_data +=
        (batch_idx * channels + c) * input_depth * input_height * input_width;
    input_grad +=
        (batch_idx * channels + c) * input_depth * input_height * input_width;

    for (int d = dstart; d < dend && !stop; ++d) {
      for (int h = hstart; h < hend && !stop; ++h) {
        for (int w = wstart; w < wend && !stop; ++w) {
          if (ele == input_data[(d * input_height + h) * input_width + w]) {
            stop = true;
            maxIdx = (d * input_height + h) * input_width + w;
          }
        }
      }
    }
    if (maxIdx != -1) {
      // atomic add
      atomicAdd(input_grad + maxIdx, output_grad[index]);
    }
  }
}

461 462 463
template <typename PoolProcess, class T>
class Pool3dForwardFunctor<platform::GPUPlace, PoolProcess, T> {
 public:
464 465
  void operator()(const platform::DeviceContext& context,
                  const framework::Tensor& input, framework::Tensor& output,
466
                  std::vector<int>& ksize, std::vector<int>& strides,
467
                  std::vector<int>& paddings, PoolProcess pool_process) {
468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487
    const int batch_size = input.dims()[0];
    const int input_channels = input.dims()[1];
    const int input_depth = input.dims()[2];
    const int input_height = input.dims()[3];
    const int input_width = input.dims()[4];
    const int output_channels = output.dims()[1];
    const int output_depth = output.dims()[2];
    const int output_height = output.dims()[3];
    const int output_width = output.dims()[4];
    const int ksize_depth = ksize[0];
    const int ksize_height = ksize[1];
    const int ksize_width = ksize[2];
    const int stride_depth = strides[0];
    const int stride_height = strides[1];
    const int stride_width = strides[2];
    const int padding_depth = paddings[0];
    const int padding_height = paddings[1];
    const int padding_width = paddings[2];

    const T* input_data = input.data<T>();
C
chengduoZH 已提交
488
    T* output_data = output.mutable_data<T>(context.GetPlace());
489 490 491 492 493 494 495

    int nthreads = batch_size * output_channels * output_depth * output_height *
                   output_width;
    int blocks = (nthreads + 1024 - 1) / 1024;
    dim3 threads(1024, 1);
    dim3 grid(blocks, 1);

C
chengduoZH 已提交
496 497 498 499 500
    KernelPool3DForward<
        PoolProcess,
        T><<<grid, threads, 0,
             reinterpret_cast<const platform::CUDADeviceContext&>(context)
                 .stream()>>>(
501 502 503 504 505 506 507 508 509 510 511
        nthreads, input_data, output_data, input_channels, input_depth,
        input_height, input_width, output_depth, output_height, output_width,
        ksize_depth, ksize_height, ksize_width, stride_depth, stride_height,
        stride_width, padding_depth, padding_height, padding_width,
        pool_process);
  }
};

template <typename PoolProcess, class T>
class Pool3dBackwardFunctor<platform::GPUPlace, PoolProcess, T> {
 public:
512 513
  void operator()(const platform::DeviceContext& context,
                  const framework::Tensor& input, framework::Tensor& input_grad,
514 515 516
                  const framework::Tensor& output,
                  const framework::Tensor& output_grad, std::vector<int>& ksize,
                  std::vector<int>& strides, std::vector<int>& paddings,
517
                  PoolProcess pool_process) {
518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539
    const int batch_size = input.dims()[0];
    const int input_channels = input.dims()[1];
    const int input_depth = input.dims()[2];
    const int input_height = input.dims()[3];
    const int input_width = input.dims()[4];
    const int output_channels = output.dims()[1];
    const int output_depth = output.dims()[2];
    const int output_height = output.dims()[3];
    const int output_width = output.dims()[4];
    const int ksize_depth = ksize[0];
    const int ksize_height = ksize[1];
    const int ksize_width = ksize[2];
    const int stride_depth = strides[0];
    const int stride_height = strides[1];
    const int stride_width = strides[2];
    const int padding_depth = paddings[0];
    const int padding_height = paddings[1];
    const int padding_width = paddings[2];

    const T* input_data = input.data<T>();
    const T* output_data = output.data<T>();
    const T* output_grad_data = output_grad.data<T>();
C
chengduoZH 已提交
540
    T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
541

542 543
    int nthreads =
        batch_size * input_channels * input_depth * input_height * input_width;
544 545 546 547
    int blocks = (nthreads + 1024 - 1) / 1024;
    dim3 threads(1024, 1);
    dim3 grid(blocks, 1);

C
chengduoZH 已提交
548 549 550 551 552
    KernelPool3DBackward<
        PoolProcess,
        T><<<grid, threads, 0,
             reinterpret_cast<const platform::CUDADeviceContext&>(context)
                 .stream()>>>(
553 554 555 556 557 558 559 560
        nthreads, input_data, output_data, output_grad_data, input_grad_data,
        input_channels, input_depth, input_height, input_width, output_depth,
        output_height, output_width, ksize_depth, ksize_height, ksize_width,
        stride_depth, stride_height, stride_width, padding_depth,
        padding_height, padding_width, pool_process);
  }
};

561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613
template <class T>
class MaxPool3dBackwardFunctor<platform::GPUPlace, T> {
 public:
  void operator()(const platform::DeviceContext& context,
                  const framework::Tensor& input, framework::Tensor& input_grad,
                  const framework::Tensor& output,
                  const framework::Tensor& output_grad, std::vector<int>& ksize,
                  std::vector<int>& strides, std::vector<int>& paddings) {
    const int batch_size = input.dims()[0];
    const int input_channels = input.dims()[1];
    const int input_depth = input.dims()[2];
    const int input_height = input.dims()[3];
    const int input_width = input.dims()[4];
    const int output_channels = output.dims()[1];
    const int output_depth = output.dims()[2];
    const int output_height = output.dims()[3];
    const int output_width = output.dims()[4];
    const int ksize_depth = ksize[0];
    const int ksize_height = ksize[1];
    const int ksize_width = ksize[2];
    const int stride_depth = strides[0];
    const int stride_height = strides[1];
    const int stride_width = strides[2];
    const int padding_depth = paddings[0];
    const int padding_height = paddings[1];
    const int padding_width = paddings[2];

    const T* input_data = input.data<T>();
    const T* output_data = output.data<T>();
    const T* output_grad_data = output_grad.data<T>();
    T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());

    int nthreads = batch_size * output_channels * output_depth * output_height *
                   output_width;
    int blocks = (nthreads + 1024 - 1) / 1024;
    dim3 threads(1024, 1);
    dim3 grid(blocks, 1);

    KernelMaxPool3DBackward<
        T><<<grid, threads, 0,
             reinterpret_cast<const platform::CUDADeviceContext&>(context)
                 .stream()>>>(
        nthreads, input_data, output_data, output_grad_data, input_grad_data,
        input_channels, input_depth, input_height, input_width, output_depth,
        output_height, output_width, ksize_depth, ksize_height, ksize_width,
        stride_depth, stride_height, stride_width, padding_depth,
        padding_height, padding_width);
  }
};

template class MaxPool3dBackwardFunctor<platform::GPUPlace, float>;
// template class MaxPool3dBackwardFunctor<platform::GPUPlace, double>;

614 615 616
template class Pool3dForwardFunctor<
    platform::GPUPlace, paddle::operators::math::pool::maxPool<float>, float>;
template class Pool3dForwardFunctor<
617
    platform::GPUPlace, paddle::operators::math::pool::avgPool<float>, float>;
618
template class Pool3dBackwardFunctor<
619 620
    platform::GPUPlace, paddle::operators::math::pool::maxPoolGrad<float>,
    float>;
621
template class Pool3dBackwardFunctor<
622 623
    platform::GPUPlace, paddle::operators::math::pool::avgPoolGrad<float>,
    float>;
624 625 626
template class Pool3dForwardFunctor<
    platform::GPUPlace, paddle::operators::math::pool::maxPool<double>, double>;
template class Pool3dForwardFunctor<
627
    platform::GPUPlace, paddle::operators::math::pool::avgPool<double>, double>;
628
template class Pool3dBackwardFunctor<
629 630
    platform::GPUPlace, paddle::operators::math::pool::maxPoolGrad<double>,
    double>;
631
template class Pool3dBackwardFunctor<
632 633
    platform::GPUPlace, paddle::operators::math::pool::avgPoolGrad<double>,
    double>;
634 635 636 637

}  // namespace math
}  // namespace operators
}  // namespace paddle
反馈
建议
客服 返回
顶部