interpolate_op.h 49.3 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
   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. */

#pragma once
X
xiaoting 已提交
13
#include <algorithm>
14
#include <string>
15
#include <vector>
16
#include "paddle/fluid/framework/op_registry.h"
17
#include "paddle/pten/core/hostdevice.h"
18
#include "paddle/pten/kernels/funcs/math_function.h"
19 20 21 22 23 24 25 26

namespace paddle {
namespace operators {

template <typename T, size_t D, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Tensor = framework::Tensor;
27
using DataLayout = framework::DataLayout;
28

29 30 31 32 33 34
inline std::vector<int> get_new_shape(
    const std::vector<const Tensor*>& list_new_shape_tensor) {
  // get tensor from
  std::vector<int> vec_new_shape;
  for (size_t i = 0; i < list_new_shape_tensor.size(); ++i) {
    auto tensor = list_new_shape_tensor[i];
K
Kqnonrime 已提交
35 36 37 38 39
    PADDLE_ENFORCE_EQ(tensor->dims(), framework::make_ddim({1}),
                      platform::errors::InvalidArgument(
                          "The shape of dimension tensor should be [1],"
                          "but received d%.",
                          tensor->dims()));
40 41
    if (platform::is_gpu_place(tensor->place())) {
      framework::Tensor temp;
42
      paddle::framework::TensorCopySync(*tensor, platform::CPUPlace(), &temp);
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
      vec_new_shape.push_back(static_cast<int32_t>(*temp.data<int32_t>()));
    } else {
      vec_new_shape.push_back(static_cast<int32_t>(*tensor->data<int32_t>()));
    }
  }

  return vec_new_shape;
}

template <typename T>
inline std::vector<T> get_new_data_from_tensor(const Tensor* new_data_tensor) {
  std::vector<T> vec_new_data;
  auto* new_data = new_data_tensor->data<T>();
  framework::Tensor cpu_starts_tensor;
  if (platform::is_gpu_place(new_data_tensor->place())) {
58 59
    paddle::framework::TensorCopySync(*new_data_tensor, platform::CPUPlace(),
                                      &cpu_starts_tensor);
60 61 62 63 64 65
    new_data = cpu_starts_tensor.data<T>();
  }
  vec_new_data = std::vector<T>(new_data, new_data + new_data_tensor->numel());
  return vec_new_data;
}

66 67 68 69
inline void ExtractNCDWH(const framework::DDim& dims,
                         const DataLayout& data_layout, int* N, int* C, int* D,
                         int* H, int* W) {
  *N = dims[0];
70 71 72 73 74 75 76

  if (dims.size() == 3) {
    *C = data_layout == DataLayout::kNCHW ? dims[1] : dims[2];
    *D = 1;
    *H = 1;
    *W = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
  } else if (dims.size() == 4) {
77 78 79 80 81 82 83 84 85 86 87 88
    *C = data_layout == DataLayout::kNCHW ? dims[1] : dims[3];
    *D = 1;
    *H = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
    *W = data_layout == DataLayout::kNCHW ? dims[3] : dims[2];
  } else {
    *C = data_layout == DataLayout::kNCHW ? dims[1] : dims[4];
    *D = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
    *H = data_layout == DataLayout::kNCHW ? dims[3] : dims[2];
    *W = data_layout == DataLayout::kNCHW ? dims[4] : dims[3];
  }
}

89 90 91 92
template <typename T>
static void NearestNeighborInterpolate(const Tensor& input, Tensor* output,
                                       const float ratio_h, const float ratio_w,
                                       const int n, const int c,
93
                                       const int out_h, const int out_w,
94 95
                                       const bool align_corners,
                                       const DataLayout& data_layout) {
96 97 98
  auto input_t = EigenTensor<T, 4>::From(input);
  auto output_t = EigenTensor<T, 4>::From(*output);
  for (int k = 0; k < out_h; k++) {  // loop for images
99 100
    int in_k = (align_corners) ? static_cast<int>(ratio_h * k + 0.5)
                               : static_cast<int>(ratio_h * k);
101 102

    for (int l = 0; l < out_w; l++) {
103 104
      int in_l = (align_corners) ? static_cast<int>(ratio_w * l + 0.5)
                                 : static_cast<int>(ratio_w * l);
105 106 107

      for (int i = 0; i < n; i++) {    // loop for batches
        for (int j = 0; j < c; j++) {  // loop for channels
108 109 110 111 112
          if (data_layout == DataLayout::kNCHW) {
            output_t(i, j, k, l) = input_t(i, j, in_k, in_l);
          } else {
            output_t(i, k, l, j) = input_t(i, in_k, in_l, j);
          }
113 114 115 116 117 118
        }
      }
    }
  }
}

119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
template <typename T>
static void LinearInterpolation(const Tensor& input, Tensor* output,
                                const float ratio_w, const int in_w,
                                const int n, const int c, const int out_w,
                                const bool align_corners, const bool align_mode,
                                const DataLayout data_layout) {
  auto input_t = EigenTensor<T, 3>::From(input);
  auto output_t = EigenTensor<T, 3>::From(*output);
  bool align_flag = (align_mode == 0 && !align_corners);

  std::vector<int> vx_w, vx_e;
  std::vector<float> vd_w, vd_e;
  vx_w.reserve(out_w);
  vx_e.reserve(out_w);
  vd_w.reserve(out_w);
  vd_e.reserve(out_w);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int l = 0; l < out_w; l++) {
    int x_w = align_flag ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                         : static_cast<int>(ratio_w * l);
    x_w = (x_w > 0) ? x_w : 0;                       // w
    int x_e = (x_w < (in_w - 1)) ? (x_w + 1) : x_w;  // w_id

    float idx_src_x = ratio_w * (l + 0.5) - 0.5;
    idx_src_x = (idx_src_x > 0) ? idx_src_x : 0;
    float d_w = align_flag ? idx_src_x - x_w : ratio_w * l - x_w;  // w1lambda
    float d_e = 1.f - d_w;                                         // w2lambda
    {
      vx_w[l] = x_w;
      vx_e[l] = x_e;
      vd_w[l] = d_w;
      vd_e[l] = d_e;
    }
  }

#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
  for (int i = 0; i < n; i++) {    // loop for batches
    for (int j = 0; j < c; j++) {  // loop for channels
      for (int l = 0; l < out_w; l++) {
        // linear interpolation
        T out_t;
        if (data_layout == DataLayout::kNCHW) {
          out_t = input_t(i, j, vx_w[l]) * vd_e[l] +
                  input_t(i, j, vx_e[l]) * vd_w[l];
          output_t(i, j, l) = out_t;
        } else {
          out_t = input_t(i, vx_w[l], j) * vd_e[l] +
                  input_t(i, vx_e[l], j) * vd_w[l];
          output_t(i, l, j) = out_t;
        }
      }
    }
  }
}

template <typename T>
static void LinearInterpolationGrad(const Tensor& output_grad,
                                    Tensor* input_grad, const float ratio_w,
                                    const int in_w, const int n, const int c,
                                    const int out_w, const bool align_corners,
                                    const int align_mode,
                                    const DataLayout data_layout) {
  auto input_grad_t = EigenTensor<T, 3>::From(*input_grad);
  auto output_grad_t = EigenTensor<T, 3>::From(output_grad);
  bool align_flag = (align_mode == 0 && !align_corners);
  for (int l = 0; l < out_w; l++) {
    int x_w = align_flag ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                         : static_cast<int>(ratio_w * l);
    x_w = (x_w > 0) ? x_w : 0;                       // w
    int x_e = (x_w < (in_w - 1)) ? (x_w + 1) : x_w;  // w_id

    float idx_src_x = ratio_w * (l + 0.5) - 0.5;
    idx_src_x = (idx_src_x > 0) ? idx_src_x : 0;
    float d_w = align_flag ? idx_src_x - x_w : ratio_w * l - x_w;  // w1lambda
    float d_e = 1.f - d_w;                                         // w2lambda

    for (int i = 0; i < n; i++) {    // loop for batches
      for (int j = 0; j < c; j++) {  // loop for channels
        // linear interpolation grad
        if (data_layout == DataLayout::kNCHW) {
          const T grad = output_grad_t(i, j, l);
          input_grad_t(i, j, x_w) += static_cast<T>(grad * d_e);
          input_grad_t(i, j, x_e) += static_cast<T>(grad * d_w);
        } else {
          const T grad = output_grad_t(i, l, j);
          input_grad_t(i, x_w, j) += static_cast<T>(grad * d_e);
          input_grad_t(i, x_e, j) += static_cast<T>(grad * d_w);
        }
      }
    }
  }
}

216 217 218 219
template <typename T>
static void BilinearInterpolation(const Tensor& input, Tensor* output,
                                  const float ratio_h, const float ratio_w,
                                  const int in_h, const int in_w, const int n,
220 221
                                  const int c, const int out_h, const int out_w,
                                  const bool align_corners,
222 223
                                  const bool align_mode,
                                  const DataLayout data_layout) {
224 225
  auto input_t = EigenTensor<T, 4>::From(input);
  auto output_t = EigenTensor<T, 4>::From(*output);
T
tink2123 已提交
226
  bool align_flag = (align_mode == 0 && !align_corners);
227 228 229 230 231 232 233 234 235 236 237

  std::vector<int> vy_n, vy_s;
  std::vector<float> vd_n, vd_s;
  vy_n.reserve(out_h);
  vy_s.reserve(out_h);
  vd_n.reserve(out_h);
  vd_s.reserve(out_h);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int k = 0; k < out_h; k++) {
T
tink2123 已提交
238 239
    int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
                         : static_cast<int>(ratio_h * k);
T
tink2123 已提交
240
    y_n = (y_n > 0) ? y_n : 0;
241
    int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
242 243 244
    float idx_src_y = ratio_h * (k + 0.5) - 0.5;
    idx_src_y = (idx_src_y > 0) ? idx_src_y : 0;
    float d_n = align_flag ? idx_src_y - y_n : ratio_h * k - y_n;
245
    float d_s = 1.f - d_n;
246 247 248 249 250 251 252
    {
      vy_n[k] = y_n;
      vy_s[k] = y_s;
      vd_n[k] = d_n;
      vd_s[k] = d_s;
    }
  }
253

254 255 256 257 258 259 260 261 262 263 264 265 266 267 268
  std::vector<int> vx_w, vx_e;
  std::vector<float> vd_w, vd_e;
  vx_w.reserve(out_w);
  vx_e.reserve(out_w);
  vd_w.reserve(out_w);
  vd_e.reserve(out_w);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int l = 0; l < out_w; l++) {
    int x_w = (align_mode == 0 && !align_corners)
                  ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                  : static_cast<int>(ratio_w * l);
    x_w = (x_w > 0) ? x_w : 0;
    int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
269 270 271
    float idx_src_x = ratio_w * (l + 0.5) - 0.5;
    idx_src_x = (idx_src_x > 0) ? idx_src_x : 0;
    float d_w = align_flag ? idx_src_x - x_w : ratio_w * l - x_w;
272 273 274 275 276 277 278 279
    float d_e = 1.f - d_w;
    {
      vx_w[l] = x_w;
      vx_e[l] = x_e;
      vd_w[l] = d_w;
      vd_e[l] = d_e;
    }
  }
280

281 282 283 284 285 286 287
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(4)
#endif
  for (int i = 0; i < n; i++) {          // loop for batches
    for (int j = 0; j < c; j++) {        // loop for channels
      for (int k = 0; k < out_h; k++) {  // loop for images
        for (int l = 0; l < out_w; l++) {
288
          // bilinear interpolation
289 290 291
          T out_t;
          if (data_layout == DataLayout::kNCHW) {
            out_t = input_t(i, j, vy_n[k], vx_w[l]) * vd_s[k] * vd_e[l] +
292 293 294
                    input_t(i, j, vy_s[k], vx_w[l]) * vd_n[k] * vd_e[l] +
                    input_t(i, j, vy_n[k], vx_e[l]) * vd_s[k] * vd_w[l] +
                    input_t(i, j, vy_s[k], vx_e[l]) * vd_n[k] * vd_w[l];
295 296 297 298 299 300 301 302 303
            output_t(i, j, k, l) = out_t;

          } else {
            out_t = input_t(i, vy_n[k], vx_w[l], j) * vd_s[k] * vd_e[l] +
                    input_t(i, vy_s[k], vx_w[l], j) * vd_n[k] * vd_e[l] +
                    input_t(i, vy_n[k], vx_e[l], j) * vd_s[k] * vd_w[l] +
                    input_t(i, vy_s[k], vx_e[l], j) * vd_n[k] * vd_w[l];
            output_t(i, k, l, j) = out_t;
          }
304 305 306 307 308 309
        }
      }
    }
  }
}

K
Kaipeng Deng 已提交
310 311 312 313 314
template <typename T>
static void TrilinearInterpolation(
    const Tensor& input, Tensor* output, const float ratio_d,
    const float ratio_h, const float ratio_w, const int in_d, const int in_h,
    const int in_w, const int n, const int c, const int out_d, const int out_h,
315 316
    const int out_w, const bool align_corners, const bool align_mode,
    const DataLayout& data_layout) {
K
Kaipeng Deng 已提交
317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408
  auto input_t = EigenTensor<T, 5>::From(input);
  auto output_t = EigenTensor<T, 5>::From(*output);
  bool align_flag = (align_mode == 0 && !align_corners);

  std::vector<int> vt_f, vt_b;
  std::vector<float> vd_f, vd_b;
  vt_f.reserve(out_d);
  vt_b.reserve(out_d);
  vd_f.reserve(out_d);
  vd_b.reserve(out_d);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int j = 0; j < out_d; j++) {
    int t_f = align_flag ? static_cast<int>(ratio_d * (j + 0.5) - 0.5)
                         : static_cast<int>(ratio_d * j);
    t_f = (t_f > 0) ? t_f : 0;
    int t_b = (t_f + 1) < (in_d - 1) ? (t_f + 1) : (in_d - 1);
    float idx_src_t = ratio_d * (j + 0.5) - 0.5;
    idx_src_t = (idx_src_t > 0) ? idx_src_t : 0;
    float d_f = align_flag ? idx_src_t - t_f : ratio_d * j - t_f;
    float d_b = 1.f - d_f;
    {
      vt_f[j] = t_f;
      vt_b[j] = t_b;
      vd_f[j] = d_f;
      vd_b[j] = d_b;
    }
  }

  std::vector<int> vy_n, vy_s;
  std::vector<float> vd_n, vd_s;
  vy_n.reserve(out_h);
  vy_s.reserve(out_h);
  vd_n.reserve(out_h);
  vd_s.reserve(out_h);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int k = 0; k < out_h; k++) {
    int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
                         : static_cast<int>(ratio_h * k);
    y_n = (y_n > 0) ? y_n : 0;
    int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
    float idx_src_y = ratio_h * (k + 0.5) - 0.5;
    idx_src_y = (idx_src_y > 0) ? idx_src_y : 0;
    float d_n = align_flag ? idx_src_y - y_n : ratio_h * k - y_n;
    float d_s = 1.f - d_n;
    {
      vy_n[k] = y_n;
      vy_s[k] = y_s;
      vd_n[k] = d_n;
      vd_s[k] = d_s;
    }
  }

  std::vector<int> vx_w, vx_e;
  std::vector<float> vd_w, vd_e;
  vx_w.reserve(out_w);
  vx_e.reserve(out_w);
  vd_w.reserve(out_w);
  vd_e.reserve(out_w);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (int l = 0; l < out_w; l++) {
    int x_w = (align_mode == 0 && !align_corners)
                  ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                  : static_cast<int>(ratio_w * l);
    x_w = (x_w > 0) ? x_w : 0;
    int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
    float idx_src_x = ratio_w * (l + 0.5) - 0.5;
    idx_src_x = (idx_src_x > 0) ? idx_src_x : 0;
    float d_w = align_flag ? idx_src_x - x_w : ratio_w * l - x_w;
    float d_e = 1.f - d_w;
    {
      vx_w[l] = x_w;
      vx_e[l] = x_e;
      vd_w[l] = d_w;
      vd_e[l] = d_e;
    }
  }

#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(5)
#endif
  for (int b = 0; b < n; b++) {          // loop for batches
    for (int i = 0; i < c; i++) {        // loop for channels
      for (int j = 0; j < out_d; j++) {  // loop for D, H, W
        for (int k = 0; k < out_h; k++) {
          for (int l = 0; l < out_w; l++) {
            // trilinear interpolation
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
            if (data_layout == DataLayout::kNCHW) {
              T out_t = input_t(b, i, vt_f[j], vy_n[k], vx_w[l]) * vd_b[j] *
                            vd_s[k] * vd_e[l] +
                        input_t(b, i, vt_f[j], vy_n[k], vx_e[l]) * vd_b[j] *
                            vd_s[k] * vd_w[l] +
                        input_t(b, i, vt_f[j], vy_s[k], vx_w[l]) * vd_b[j] *
                            vd_n[k] * vd_e[l] +
                        input_t(b, i, vt_f[j], vy_s[k], vx_e[l]) * vd_b[j] *
                            vd_n[k] * vd_w[l] +
                        input_t(b, i, vt_b[j], vy_n[k], vx_w[l]) * vd_f[j] *
                            vd_s[k] * vd_e[l] +
                        input_t(b, i, vt_b[j], vy_n[k], vx_e[l]) * vd_f[j] *
                            vd_s[k] * vd_w[l] +
                        input_t(b, i, vt_b[j], vy_s[k], vx_w[l]) * vd_f[j] *
                            vd_n[k] * vd_e[l] +
                        input_t(b, i, vt_b[j], vy_s[k], vx_e[l]) * vd_f[j] *
                            vd_n[k] * vd_w[l];
              output_t(b, i, j, k, l) = out_t;
            } else {
              T out_t = input_t(b, vt_f[j], vy_n[k], vx_w[l], i) * vd_b[j] *
                            vd_s[k] * vd_e[l] +
                        input_t(b, vt_f[j], vy_n[k], vx_e[l], i) * vd_b[j] *
                            vd_s[k] * vd_w[l] +
                        input_t(b, vt_f[j], vy_s[k], vx_w[l], i) * vd_b[j] *
                            vd_n[k] * vd_e[l] +
                        input_t(b, vt_f[j], vy_s[k], vx_e[l], i) * vd_b[j] *
                            vd_n[k] * vd_w[l] +
                        input_t(b, vt_b[j], vy_n[k], vx_w[l], i) * vd_f[j] *
                            vd_s[k] * vd_e[l] +
                        input_t(b, vt_b[j], vy_n[k], vx_e[l], i) * vd_f[j] *
                            vd_s[k] * vd_w[l] +
                        input_t(b, vt_b[j], vy_s[k], vx_w[l], i) * vd_f[j] *
                            vd_n[k] * vd_e[l] +
                        input_t(b, vt_b[j], vy_s[k], vx_e[l], i) * vd_f[j] *
                            vd_n[k] * vd_w[l];
              output_t(b, j, k, l, i) = out_t;
            }
K
Kaipeng Deng 已提交
446 447 448 449 450 451 452
          }
        }
      }
    }
  }
}

X
xiaoting 已提交
453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497
template <typename T>
HOSTDEVICE inline T cubic_convolution1(T x, T A) {
  return ((A + 2) * x - (A + 3)) * x * x + 1;
}

template <typename T>
HOSTDEVICE inline T cubic_convolution2(T x, T A) {
  return ((A * x - 5 * A) * x + 8 * A) * x - 4 * A;
}

template <typename T>
HOSTDEVICE inline void get_cubic_upsample_coefficients(T coeffs[4], T t) {
  T A = -0.75;

  T x1 = t;
  coeffs[0] = cubic_convolution2<T>(x1 + 1.0, A);
  coeffs[1] = cubic_convolution1<T>(x1, A);

  // opposite coefficients
  T x2 = 1.0 - t;
  coeffs[2] = cubic_convolution1<T>(x2, A);
  coeffs[3] = cubic_convolution2<T>(x2 + 1.0, A);
}

template <typename T>
static inline T cubic_interp(T x0, T x1, T x2, T x3, T t) {
  T coeffs[4];
  get_cubic_upsample_coefficients<T>(coeffs, t);

  return x0 * coeffs[0] + x1 * coeffs[1] + x2 * coeffs[2] + x3 * coeffs[3];
}

template <typename T>
static void BicubicInterpolation(const Tensor& input, Tensor* output,
                                 const float ratio_h, const float ratio_w,
                                 const int in_h, const int in_w, const int n,
                                 const int c, const int out_h, const int out_w,
                                 const bool align_corners,
                                 const DataLayout data_layout) {
  auto input_t = EigenTensor<T, 4>::From(input);
  auto output_t = EigenTensor<T, 4>::From(*output);

  for (int k = 0; k < out_h; k++) {  // loop for images
    T y_n = align_corners ? static_cast<T>(ratio_h * k)
                          : static_cast<T>(ratio_h * (k + 0.5) - 0.5);
498
    int input_y = floorf(y_n);
X
xiaoting 已提交
499 500 501 502 503
    const T y_t = y_n - input_y;

    for (int l = 0; l < out_w; l++) {
      T x_n = align_corners ? static_cast<T>(ratio_w * l)
                            : static_cast<T>(ratio_w * (l + 0.5) - 0.5);
504
      int input_x = floorf(x_n);
X
xiaoting 已提交
505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552
      const T x_t = x_n - input_x;

      for (int i = 0; i < n; i++) {    // loop for batches
        for (int j = 0; j < c; j++) {  // loop for channels
          T coefficients[4];
          // interp 4 times in x direction
          for (int ii = 0; ii < 4; ii++) {
            int access_y = std::max(std::min(input_y - 1 + ii, in_h - 1),
                                    static_cast<int>(0));
            int access_x_0 =
                std::max(std::min(input_x - 1, in_w - 1), static_cast<int>(0));
            int access_x_1 =
                std::max(std::min(input_x + 0, in_w - 1), static_cast<int>(0));
            int access_x_2 =
                std::max(std::min(input_x + 1, in_w - 1), static_cast<int>(0));
            int access_x_3 =
                std::max(std::min(input_x + 2, in_w - 1), static_cast<int>(0));
            if (data_layout == DataLayout::kNCHW) {
              coefficients[ii] =
                  cubic_interp<T>(input_t(i, j, access_y, access_x_0),
                                  input_t(i, j, access_y, access_x_1),
                                  input_t(i, j, access_y, access_x_2),
                                  input_t(i, j, access_y, access_x_3), x_t);
            } else {
              coefficients[ii] =
                  cubic_interp<T>(input_t(i, access_y, access_x_0, j),
                                  input_t(i, access_y, access_x_1, j),
                                  input_t(i, access_y, access_x_2, j),
                                  input_t(i, access_y, access_x_3, j), x_t);
            }
          }

          // interp y direction
          if (data_layout == DataLayout::kNCHW) {
            output_t(i, j, k, l) =
                cubic_interp<T>(coefficients[0], coefficients[1],
                                coefficients[2], coefficients[3], y_t);
          } else {
            output_t(i, k, l, j) =
                cubic_interp<T>(coefficients[0], coefficients[1],
                                coefficients[2], coefficients[3], y_t);
          }
        }
      }
    }
  }
}

553
template <typename T>
554 555 556
static void NearestNeighborInterpolateGrad(
    const Tensor& output_grad, Tensor* input_grad, const float ratio_h,
    const float ratio_w, const int n, const int c, const int out_h,
557
    const int out_w, const bool align_corners, const DataLayout data_layout) {
558 559
  auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
  auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
560

561
  for (int k = 0; k < out_h; k++) {  // loop for images
562 563
    int in_k = (align_corners) ? static_cast<int>(ratio_h * k + 0.5)
                               : static_cast<int>(ratio_h * k);
564 565

    for (int l = 0; l < out_w; l++) {
566 567
      int in_l = (align_corners) ? static_cast<int>(ratio_w * l + 0.5)
                                 : static_cast<int>(ratio_w * l);
568 569 570

      for (int i = 0; i < n; i++) {    // loop for batches
        for (int j = 0; j < c; j++) {  // loop for channels
571 572 573 574 575
          if (data_layout == DataLayout::kNCHW) {
            input_grad_t(i, j, in_k, in_l) += output_grad_t(i, j, k, l);
          } else {
            input_grad_t(i, in_k, in_l, j) += output_grad_t(i, k, l, j);
          }
576 577 578 579 580 581 582
        }
      }
    }
  }
}

template <typename T>
583 584 585 586 587
static void BilinearInterpolationGrad(
    const Tensor& output_grad, Tensor* input_grad, const float ratio_h,
    const float ratio_w, const int in_h, const int in_w, const int n,
    const int c, const int out_h, const int out_w, const bool align_corners,
    const int align_mode, const DataLayout data_layout) {
588 589
  auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
  auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
T
tink2123 已提交
590
  bool align_flag = (align_mode == 0 && !align_corners);
591
  for (int k = 0; k < out_h; k++) {  // loop for images
T
tink2123 已提交
592 593
    int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
                         : static_cast<int>(ratio_h * k);
T
tink2123 已提交
594
    y_n = (y_n > 0) ? y_n : 0;
595
    int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
596 597 598
    float idx_src_y = ratio_h * (k + 0.5) - 0.5;
    idx_src_y = (idx_src_y > 0) ? idx_src_y : 0;
    float d_n = align_flag ? idx_src_y - y_n : ratio_h * k - y_n;
599 600 601
    float d_s = 1.f - d_n;

    for (int l = 0; l < out_w; l++) {
T
tink2123 已提交
602 603
      int x_w = align_flag ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                           : static_cast<int>(ratio_w * l);
T
tink2123 已提交
604
      x_w = (x_w > 0) ? x_w : 0;
605
      int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
606 607 608
      float idx_src_x = ratio_w * (l + 0.5) - 0.5;
      idx_src_x = (idx_src_x > 0) ? idx_src_x : 0;
      float d_w = align_flag ? idx_src_x - x_w : ratio_w * l - x_w;
609 610 611 612 613
      float d_e = 1.f - d_w;

      for (int i = 0; i < n; i++) {    // loop for batches
        for (int j = 0; j < c; j++) {  // loop for channels
          // bilinear interpolation grad
614 615 616 617 618 619 620 621 622 623 624 625 626
          if (data_layout == DataLayout::kNCHW) {
            const T grad = output_grad_t(i, j, k, l);
            input_grad_t(i, j, y_n, x_w) += static_cast<T>(grad * d_s * d_e);
            input_grad_t(i, j, y_s, x_w) += static_cast<T>(grad * d_n * d_e);
            input_grad_t(i, j, y_n, x_e) += static_cast<T>(grad * d_s * d_w);
            input_grad_t(i, j, y_s, x_e) += static_cast<T>(grad * d_n * d_w);
          } else {
            const T grad = output_grad_t(i, k, l, j);
            input_grad_t(i, y_n, x_w, j) += static_cast<T>(grad * d_s * d_e);
            input_grad_t(i, y_s, x_w, j) += static_cast<T>(grad * d_n * d_e);
            input_grad_t(i, y_n, x_e, j) += static_cast<T>(grad * d_s * d_w);
            input_grad_t(i, y_s, x_e, j) += static_cast<T>(grad * d_n * d_w);
          }
627 628 629 630 631
        }
      }
    }
  }
}
K
Kaipeng Deng 已提交
632

633
template <typename T>
K
Kaipeng Deng 已提交
634 635 636 637
static void TrilinearInterpolationGrad(
    const Tensor& output_grad, Tensor* input_grad, const float ratio_d,
    const float ratio_h, const float ratio_w, const int in_d, const int in_h,
    const int in_w, const int n, const int c, const int out_d, const int out_h,
638 639
    const int out_w, const bool align_corners, const int align_mode,
    const DataLayout data_layout) {
K
Kaipeng Deng 已提交
640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675
  auto input_grad_t = EigenTensor<T, 5>::From(*input_grad);
  auto output_grad_t = EigenTensor<T, 5>::From(output_grad);
  bool align_flag = (align_mode == 0 && !align_corners);
  for (int j = 0; j < out_d; j++) {  // loop for D
    int t_f = align_flag ? static_cast<int>(ratio_d * (j + 0.5) - 0.5)
                         : static_cast<int>(ratio_d * j);
    t_f = (t_f > 0) ? t_f : 0;
    int t_b = (t_f + 1) < (in_d - 1) ? (t_f + 1) : (in_d - 1);
    float idx_src_t = ratio_d * (j + 0.5) - 0.5;
    idx_src_t = (idx_src_t > 0) ? idx_src_t : 0;
    float d_f = align_flag ? idx_src_t - t_f : ratio_d * j - t_f;
    float d_b = 1.f - d_f;

    for (int k = 0; k < out_h; k++) {  // loop for H
      int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
                           : static_cast<int>(ratio_h * k);
      y_n = (y_n > 0) ? y_n : 0;
      int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
      float idx_src_y = ratio_h * (k + 0.5) - 0.5;
      idx_src_y = (idx_src_y > 0) ? idx_src_y : 0;
      float d_n = align_flag ? idx_src_y - y_n : ratio_h * k - y_n;
      float d_s = 1.f - d_n;

      for (int l = 0; l < out_w; l++) {  // loop for W
        int x_w = align_flag ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                             : static_cast<int>(ratio_w * l);
        x_w = (x_w > 0) ? x_w : 0;
        int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
        float idx_src_x = ratio_w * (l + 0.5) - 0.5;
        idx_src_x = (idx_src_x > 0) ? idx_src_x : 0;
        float d_w = align_flag ? idx_src_x - x_w : ratio_w * l - x_w;
        float d_e = 1.f - d_w;

        for (int b = 0; b < n; b++) {    // loop for batches
          for (int i = 0; i < c; i++) {  // loop for channels
            // trilinear interpolation grad
676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712
            if (data_layout == DataLayout::kNCHW) {
              const T grad = output_grad_t(b, i, j, k, l);
              input_grad_t(b, i, t_f, y_n, x_w) +=
                  static_cast<T>(grad * d_b * d_s * d_e);
              input_grad_t(b, i, t_f, y_n, x_e) +=
                  static_cast<T>(grad * d_b * d_s * d_w);
              input_grad_t(b, i, t_f, y_s, x_w) +=
                  static_cast<T>(grad * d_b * d_n * d_e);
              input_grad_t(b, i, t_f, y_s, x_e) +=
                  static_cast<T>(grad * d_b * d_n * d_w);
              input_grad_t(b, i, t_b, y_n, x_w) +=
                  static_cast<T>(grad * d_f * d_s * d_e);
              input_grad_t(b, i, t_b, y_n, x_e) +=
                  static_cast<T>(grad * d_f * d_s * d_w);
              input_grad_t(b, i, t_b, y_s, x_w) +=
                  static_cast<T>(grad * d_f * d_n * d_e);
              input_grad_t(b, i, t_b, y_s, x_e) +=
                  static_cast<T>(grad * d_f * d_n * d_w);
            } else {
              const T grad = output_grad_t(b, j, k, l, i);
              input_grad_t(b, t_f, y_n, x_w, i) +=
                  static_cast<T>(grad * d_b * d_s * d_e);
              input_grad_t(b, t_f, y_n, x_e, i) +=
                  static_cast<T>(grad * d_b * d_s * d_w);
              input_grad_t(b, t_f, y_s, x_w, i) +=
                  static_cast<T>(grad * d_b * d_n * d_e);
              input_grad_t(b, t_f, y_s, x_e, i) +=
                  static_cast<T>(grad * d_b * d_n * d_w);
              input_grad_t(b, t_b, y_n, x_w, i) +=
                  static_cast<T>(grad * d_f * d_s * d_e);
              input_grad_t(b, t_b, y_n, x_e, i) +=
                  static_cast<T>(grad * d_f * d_s * d_w);
              input_grad_t(b, t_b, y_s, x_w, i) +=
                  static_cast<T>(grad * d_f * d_n * d_e);
              input_grad_t(b, t_b, y_s, x_e, i) +=
                  static_cast<T>(grad * d_f * d_n * d_w);
            }
K
Kaipeng Deng 已提交
713 714 715 716 717 718
          }
        }
      }
    }
  }
}
719

X
xiaoting 已提交
720 721 722 723 724 725 726 727 728 729 730 731 732 733
template <typename T>
static void BicubicInterpolationGrad(const Tensor& output_grad,
                                     Tensor* input_grad, const float ratio_h,
                                     const float ratio_w, const int in_h,
                                     const int in_w, const int n, const int c,
                                     const int out_h, const int out_w,
                                     const bool align_corners,
                                     const DataLayout data_layout) {
  auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
  auto output_grad_t = EigenTensor<T, 4>::From(output_grad);

  for (int k = 0; k < out_h; k++) {  // loop for images
    T y_n = align_corners ? static_cast<T>(ratio_h * k)
                          : static_cast<T>(ratio_h * (k + 0.5) - 0.5);
734
    int input_y = floorf(y_n);
X
xiaoting 已提交
735 736 737 738 739
    T y_t = y_n - input_y;

    for (int l = 0; l < out_w; l++) {
      T x_n = align_corners ? static_cast<T>(ratio_w * l)
                            : static_cast<T>(ratio_w * (l + 0.5) - 0.5);
740
      int input_x = floorf(x_n);
X
xiaoting 已提交
741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774
      T x_t = x_n - input_x;

      T x_coeffs[4];
      T y_coeffs[4];

      get_cubic_upsample_coefficients<T>(x_coeffs, x_t);
      get_cubic_upsample_coefficients<T>(y_coeffs, y_t);

      for (int i = 0; i < n; i++) {    // loop for batches
        for (int j = 0; j < c; j++) {  // loop for channels
          // bicubic interpolation grad
          for (int ii = 0; ii < 4; ii++) {
            for (int jj = 0; jj < 4; jj++) {
              int access_x = std::max(std::min(input_x - 1 + ii, in_w - 1),
                                      static_cast<int>(0));
              int access_y = std::max(std::min(input_y - 1 + jj, in_h - 1),
                                      static_cast<int>(0));
              if (data_layout == DataLayout::kNCHW) {
                T grad = output_grad_t(i, j, k, l);
                input_grad_t(i, j, access_y, access_x) +=
                    grad * y_coeffs[jj] * x_coeffs[ii];
              } else {
                T grad = output_grad_t(i, k, l, j);
                input_grad_t(i, access_y, access_x, j) +=
                    grad * y_coeffs[jj] * x_coeffs[ii];
              }
            }
          }
        }
      }
    }
  }
}

775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837
template <typename T>
static void Interpolate1DCPUFwd(const framework::ExecutionContext& ctx,
                                const Tensor& input, Tensor* output) {
  const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
  const DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
  int n, c, in_d, in_h, in_w;
  ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

  auto interp_method = ctx.Attr<std::string>("interp_method");
  bool align_corners = ctx.Attr<bool>("align_corners");
  int align_mode = ctx.Attr<int>("align_mode");

  int out_w = ctx.Attr<int>("out_w");
  auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
  if (list_new_size_tensor.size() > 0) {
    // have size tensor
    auto new_size = get_new_shape(list_new_size_tensor);
    out_w = new_size[0];
  } else {
    float scale;
    auto scale_tensor = ctx.Input<Tensor>("Scale");
    if (scale_tensor != nullptr) {
      auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
      scale = scale_data[0];
    } else {
      scale = ctx.Attr<float>("scale");
    }
    if (scale > 0) {
      out_w = static_cast<int>(in_w * scale);
    }
    auto out_size = ctx.Input<Tensor>("OutSize");
    if (out_size != nullptr) {
      auto out_size_data = get_new_data_from_tensor<int>(out_size);
      out_w = out_size_data[0];
    }
  }
  PADDLE_ENFORCE_GT(out_w, 0, platform::errors::InvalidArgument(
                                  "out_w in Attr(out_shape) of Op(interpolate) "
                                  "should be greater than 0."));
  framework::DDim dim_out;
  if (data_layout == DataLayout::kNCHW) {
    dim_out = {n, c, out_w};
  } else {
    dim_out = {n, out_w, c};
  }
  output->mutable_data<T>(dim_out, ctx.GetPlace());

  if (in_w == out_w) {
    framework::TensorCopy(input, ctx.GetPlace(), output);
    return;
  }

  float ratio_w = 0.f;
  if (out_w > 1) {
    ratio_w = (align_corners) ? static_cast<float>(in_w - 1) / (out_w - 1)
                              : static_cast<float>(in_w) / out_w;
  }
  if ("linear" == interp_method) {
    LinearInterpolation<T>(input, output, ratio_w, in_w, n, c, out_w,
                           align_corners, align_mode, data_layout);
  }
}

K
Kaipeng Deng 已提交
838 839 840
template <typename T>
static void Interpolate2DCPUFwd(const framework::ExecutionContext& ctx,
                                const Tensor& input, Tensor* output) {
841 842 843 844
  const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
  const DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
  int n, c, in_d, in_h, in_w;
  ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
K
Kaipeng Deng 已提交
845 846 847 848 849 850 851

  auto interp_method = ctx.Attr<std::string>("interp_method");
  bool align_corners = ctx.Attr<bool>("align_corners");
  int align_mode = ctx.Attr<int>("align_mode");

  int out_h = ctx.Attr<int>("out_h");
  int out_w = ctx.Attr<int>("out_w");
D
dengkaipeng 已提交
852

853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877
  auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
  if (list_new_size_tensor.size() > 0) {
    // have size tensor
    auto new_size = get_new_shape(list_new_size_tensor);
    out_h = new_size[0];
    out_w = new_size[1];
  } else {
    float scale;
    auto scale_tensor = ctx.Input<Tensor>("Scale");
    if (scale_tensor != nullptr) {
      auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
      scale = scale_data[0];
    } else {
      scale = ctx.Attr<float>("scale");
    }
    if (scale > 0) {
      out_h = static_cast<int>(in_h * scale);
      out_w = static_cast<int>(in_w * scale);
    }
    auto out_size = ctx.Input<Tensor>("OutSize");
    if (out_size != nullptr) {
      auto out_size_data = get_new_data_from_tensor<int>(out_size);
      out_h = out_size_data[0];
      out_w = out_size_data[1];
    }
K
Kaipeng Deng 已提交
878
  }
879 880 881 882 883 884
  PADDLE_ENFORCE_GT(out_h, 0, platform::errors::InvalidArgument(
                                  "out_h in Attr(out_shape) of Op(interpolate) "
                                  "should be greater than 0."));
  PADDLE_ENFORCE_GT(out_w, 0, platform::errors::InvalidArgument(
                                  "out_w in Attr(out_shape) of Op(interpolate) "
                                  "should be greater than 0."));
885 886 887 888 889 890 891
  framework::DDim dim_out;
  if (data_layout == DataLayout::kNCHW) {
    dim_out = {n, c, out_h, out_w};
  } else {
    dim_out = {n, out_h, out_w, c};
  }
  output->mutable_data<T>(dim_out, ctx.GetPlace());
D
dengkaipeng 已提交
892

K
Kaipeng Deng 已提交
893 894 895 896
  if (in_h == out_h && in_w == out_w) {
    framework::TensorCopy(input, ctx.GetPlace(), output);
    return;
  }
897

K
Kaipeng Deng 已提交
898 899 900 901 902 903 904 905 906 907
  float ratio_h = 0.f;
  float ratio_w = 0.f;
  if (out_h > 1) {
    ratio_h = (align_corners) ? static_cast<float>(in_h - 1) / (out_h - 1)
                              : static_cast<float>(in_h) / out_h;
  }
  if (out_w > 1) {
    ratio_w = (align_corners) ? static_cast<float>(in_w - 1) / (out_w - 1)
                              : static_cast<float>(in_w) / out_w;
  }
T
tink2123 已提交
908

K
Kaipeng Deng 已提交
909 910
  if ("bilinear" == interp_method) {
    BilinearInterpolation<T>(input, output, ratio_h, ratio_w, in_h, in_w, n, c,
911 912
                             out_h, out_w, align_corners, align_mode,
                             data_layout);
K
Kaipeng Deng 已提交
913 914
  } else if ("nearest" == interp_method) {
    NearestNeighborInterpolate<T>(input, output, ratio_h, ratio_w, n, c, out_h,
915
                                  out_w, align_corners, data_layout);
X
xiaoting 已提交
916 917 918
  } else if ("bicubic" == interp_method) {
    BicubicInterpolation<T>(input, output, ratio_h, ratio_w, in_h, in_w, n, c,
                            out_h, out_w, align_corners, data_layout);
K
Kaipeng Deng 已提交
919 920
  }
}
921

K
Kaipeng Deng 已提交
922 923 924
template <typename T>
static void Interpolate3DCPUFwd(const framework::ExecutionContext& ctx,
                                const Tensor& input, Tensor* output) {
925 926 927 928
  const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
  const DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
  int n, c, in_d, in_h, in_w;
  ExtractNCDWH(input.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
K
Kaipeng Deng 已提交
929 930 931 932 933 934 935 936 937

  auto interp_method = ctx.Attr<std::string>("interp_method");
  bool align_corners = ctx.Attr<bool>("align_corners");
  int align_mode = ctx.Attr<int>("align_mode");

  int out_d = ctx.Attr<int>("out_d");
  int out_h = ctx.Attr<int>("out_h");
  int out_w = ctx.Attr<int>("out_w");

938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965
  auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
  if (list_new_size_tensor.size() > 0) {
    // have size tensor
    auto new_size = get_new_shape(list_new_size_tensor);
    out_d = new_size[0];
    out_h = new_size[1];
    out_w = new_size[2];
  } else {
    float scale;
    auto scale_tensor = ctx.Input<Tensor>("Scale");
    if (scale_tensor != nullptr) {
      auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
      scale = scale_data[0];
    } else {
      scale = ctx.Attr<float>("scale");
    }
    if (scale > 0) {
      out_d = static_cast<int>(in_d * scale);
      out_h = static_cast<int>(in_h * scale);
      out_w = static_cast<int>(in_w * scale);
    }
    auto out_size = ctx.Input<Tensor>("OutSize");
    if (out_size != nullptr) {
      auto out_size_data = get_new_data_from_tensor<int>(out_size);
      out_d = out_size_data[0];
      out_h = out_size_data[1];
      out_w = out_size_data[2];
    }
K
Kaipeng Deng 已提交
966
  }
967 968 969 970 971 972 973 974 975
  PADDLE_ENFORCE_GT(out_d, 0, platform::errors::InvalidArgument(
                                  "out_d in Attr(out_shape) of Op(interpolate) "
                                  "should be greater than 0."));
  PADDLE_ENFORCE_GT(out_h, 0, platform::errors::InvalidArgument(
                                  "out_h in Attr(out_shape) of Op(interpolate) "
                                  "should be greater than 0."));
  PADDLE_ENFORCE_GT(out_w, 0, platform::errors::InvalidArgument(
                                  "out_w in Attr(out_shape) of Op(interpolate) "
                                  "should be greater than 0."));
976 977 978 979 980 981 982 983 984

  framework::DDim dim_out;
  if (data_layout == DataLayout::kNCHW) {
    dim_out = {n, c, out_d, out_h, out_w};
  } else {
    dim_out = {n, out_d, out_h, out_w, c};
  }

  output->mutable_data<T>(dim_out, ctx.GetPlace());
K
Kaipeng Deng 已提交
985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004

  if (in_d == out_d && in_h == out_h && in_w == out_w) {
    framework::TensorCopy(input, ctx.GetPlace(), output);
    return;
  }

  float ratio_d = 0.f;
  float ratio_h = 0.f;
  float ratio_w = 0.f;
  if (out_d > 1) {
    ratio_d = (align_corners) ? static_cast<float>(in_d - 1) / (out_d - 1)
                              : static_cast<float>(in_d) / out_d;
  }
  if (out_h > 1) {
    ratio_h = (align_corners) ? static_cast<float>(in_h - 1) / (out_h - 1)
                              : static_cast<float>(in_h) / out_h;
  }
  if (out_w > 1) {
    ratio_w = (align_corners) ? static_cast<float>(in_w - 1) / (out_w - 1)
                              : static_cast<float>(in_w) / out_w;
1005
  }
K
Kaipeng Deng 已提交
1006 1007 1008 1009

  if ("trilinear" == interp_method) {
    TrilinearInterpolation<T>(input, output, ratio_d, ratio_h, ratio_w, in_d,
                              in_h, in_w, n, c, out_d, out_h, out_w,
1010
                              align_corners, align_mode, data_layout);
K
Kaipeng Deng 已提交
1011 1012
  }
}
1013

1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059
template <typename T>
static void Interpolate1DCPUBwd(const framework::ExecutionContext& ctx,
                                Tensor* input_grad, const Tensor& output_grad) {
  auto* input = ctx.Input<Tensor>("X");
  const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
  const DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
  int n, c, in_d, in_h, in_w;
  ExtractNCDWH(input->dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);

  auto interp_method = ctx.Attr<std::string>("interp_method");
  bool align_corners = ctx.Attr<bool>("align_corners");
  int align_mode = ctx.Attr<int>("align_mode");

  int out_w = ctx.Attr<int>("out_w");
  float scale;
  auto scale_tensor = ctx.Input<Tensor>("Scale");
  if (scale_tensor != nullptr) {
    auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
    scale = scale_data[0];
  } else {
    scale = ctx.Attr<float>("scale");
  }
  if (scale > 0) {
    out_w = static_cast<int>(in_w * scale);
  }
  auto out_size = ctx.Input<Tensor>("OutSize");
  if (out_size != nullptr) {
    auto out_size_data = get_new_data_from_tensor<int>(out_size);
    out_w = out_size_data[0];
  }
  auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
  if (list_new_size_tensor.size() > 0) {
    // have size tensor
    auto new_size = get_new_shape(list_new_size_tensor);
    out_w = new_size[0];
  }

  framework::DDim dim_grad;
  if (data_layout == DataLayout::kNCHW) {
    dim_grad = {n, c, in_w};
  } else {
    dim_grad = {n, in_w, c};
  }
  input_grad->mutable_data<T>(dim_grad, ctx.GetPlace());

  auto& device_ctx = ctx.template device_context<platform::CPUDeviceContext>();
1060
  pten::funcs::SetConstant<platform::CPUDeviceContext, T> zero;
1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078
  zero(device_ctx, input_grad, static_cast<T>(0.0));

  if (in_w == out_w) {
    framework::TensorCopy(output_grad, ctx.GetPlace(), input_grad);
    return;
  }

  float ratio_w = 0.f;
  if (out_w > 1) {
    ratio_w = (align_corners) ? static_cast<float>(in_w - 1) / (out_w - 1)
                              : static_cast<float>(in_w) / out_w;
  }
  if ("linear" == interp_method) {
    LinearInterpolationGrad<T>(output_grad, input_grad, ratio_w, in_w, n, c,
                               out_w, align_corners, align_mode, data_layout);
  }
}

1079
template <typename T>
K
Kaipeng Deng 已提交
1080 1081 1082
static void Interpolate2DCPUBwd(const framework::ExecutionContext& ctx,
                                Tensor* input_grad, const Tensor& output_grad) {
  auto* input = ctx.Input<Tensor>("X");
1083 1084 1085 1086
  const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
  const DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
  int n, c, in_d, in_h, in_w;
  ExtractNCDWH(input->dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
K
Kaipeng Deng 已提交
1087 1088 1089 1090 1091 1092 1093

  auto interp_method = ctx.Attr<std::string>("interp_method");
  bool align_corners = ctx.Attr<bool>("align_corners");
  int align_mode = ctx.Attr<int>("align_mode");

  int out_h = ctx.Attr<int>("out_h");
  int out_w = ctx.Attr<int>("out_w");
1094 1095 1096 1097 1098 1099 1100 1101
  float scale;
  auto scale_tensor = ctx.Input<Tensor>("Scale");
  if (scale_tensor != nullptr) {
    auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
    scale = scale_data[0];
  } else {
    scale = ctx.Attr<float>("scale");
  }
K
Kaipeng Deng 已提交
1102 1103 1104 1105 1106 1107
  if (scale > 0) {
    out_h = static_cast<int>(in_h * scale);
    out_w = static_cast<int>(in_w * scale);
  }
  auto out_size = ctx.Input<Tensor>("OutSize");
  if (out_size != nullptr) {
1108
    auto out_size_data = get_new_data_from_tensor<int>(out_size);
K
Kaipeng Deng 已提交
1109 1110 1111
    out_h = out_size_data[0];
    out_w = out_size_data[1];
  }
1112 1113 1114 1115 1116 1117 1118
  auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
  if (list_new_size_tensor.size() > 0) {
    // have size tensor
    auto new_size = get_new_shape(list_new_size_tensor);
    out_h = new_size[0];
    out_w = new_size[1];
  }
D
dengkaipeng 已提交
1119

1120 1121 1122 1123 1124 1125 1126 1127
  framework::DDim dim_grad;
  if (data_layout == DataLayout::kNCHW) {
    dim_grad = {n, c, in_h, in_w};
  } else {
    dim_grad = {n, in_h, in_w, c};
  }
  input_grad->mutable_data<T>(dim_grad, ctx.GetPlace());

K
Kaipeng Deng 已提交
1128
  auto& device_ctx = ctx.template device_context<platform::CPUDeviceContext>();
1129
  pten::funcs::SetConstant<platform::CPUDeviceContext, T> zero;
K
Kaipeng Deng 已提交
1130
  zero(device_ctx, input_grad, static_cast<T>(0.0));
D
dengkaipeng 已提交
1131

K
Kaipeng Deng 已提交
1132 1133 1134 1135
  if (in_h == out_h && in_w == out_w) {
    framework::TensorCopy(output_grad, ctx.GetPlace(), input_grad);
    return;
  }
D
dengkaipeng 已提交
1136

K
Kaipeng Deng 已提交
1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150
  float ratio_h = 0.f;
  float ratio_w = 0.f;
  if (out_h > 1) {
    ratio_h = (align_corners) ? static_cast<float>(in_h - 1) / (out_h - 1)
                              : static_cast<float>(in_h) / out_h;
  }
  if (out_w > 1) {
    ratio_w = (align_corners) ? static_cast<float>(in_w - 1) / (out_w - 1)
                              : static_cast<float>(in_w) / out_w;
  }

  if ("bilinear" == interp_method) {
    BilinearInterpolationGrad<T>(output_grad, input_grad, ratio_h, ratio_w,
                                 in_h, in_w, n, c, out_h, out_w, align_corners,
1151
                                 align_mode, data_layout);
K
Kaipeng Deng 已提交
1152 1153
  } else if ("nearest" == interp_method) {
    NearestNeighborInterpolateGrad<T>(output_grad, input_grad, ratio_h, ratio_w,
1154 1155
                                      n, c, out_h, out_w, align_corners,
                                      data_layout);
X
xiaoting 已提交
1156 1157 1158 1159
  } else if ("bicubic" == interp_method) {
    BicubicInterpolationGrad<T>(output_grad, input_grad, ratio_h, ratio_w, in_h,
                                in_w, n, c, out_h, out_w, align_corners,
                                data_layout);
K
Kaipeng Deng 已提交
1160 1161
  }
}
D
dengkaipeng 已提交
1162

K
Kaipeng Deng 已提交
1163 1164 1165 1166
template <typename T>
static void Interpolate3DCPUBwd(const framework::ExecutionContext& ctx,
                                Tensor* input_grad, const Tensor output_grad) {
  auto* input = ctx.Input<Tensor>("X");
1167 1168 1169 1170
  const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
  const DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
  int n, c, in_d, in_h, in_w;
  ExtractNCDWH(input->dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
K
Kaipeng Deng 已提交
1171 1172 1173 1174 1175 1176 1177 1178

  auto interp_method = ctx.Attr<std::string>("interp_method");
  bool align_corners = ctx.Attr<bool>("align_corners");
  int align_mode = ctx.Attr<int>("align_mode");

  int out_d = ctx.Attr<int>("out_d");
  int out_h = ctx.Attr<int>("out_h");
  int out_w = ctx.Attr<int>("out_w");
1179 1180 1181 1182 1183 1184 1185 1186
  float scale;
  auto scale_tensor = ctx.Input<Tensor>("Scale");
  if (scale_tensor != nullptr) {
    auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
    scale = scale_data[0];
  } else {
    scale = ctx.Attr<float>("scale");
  }
K
Kaipeng Deng 已提交
1187 1188 1189 1190 1191 1192 1193
  if (scale > 0) {
    out_d = static_cast<int>(in_d * scale);
    out_h = static_cast<int>(in_h * scale);
    out_w = static_cast<int>(in_w * scale);
  }
  auto out_size = ctx.Input<Tensor>("OutSize");
  if (out_size != nullptr) {
1194
    auto out_size_data = get_new_data_from_tensor<int>(out_size);
K
Kaipeng Deng 已提交
1195 1196 1197 1198
    out_d = out_size_data[0];
    out_h = out_size_data[1];
    out_w = out_size_data[2];
  }
1199 1200 1201 1202 1203 1204 1205 1206
  auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
  if (list_new_size_tensor.size() > 0) {
    // have size tensor
    auto new_size = get_new_shape(list_new_size_tensor);
    out_d = new_size[0];
    out_h = new_size[1];
    out_w = new_size[2];
  }
1207

1208 1209 1210 1211 1212 1213 1214
  framework::DDim dim_grad;
  if (data_layout == DataLayout::kNCHW) {
    dim_grad = {n, c, in_d, in_h, in_w};
  } else {
    dim_grad = {n, in_d, in_h, in_w, c};
  }
  input_grad->mutable_data<T>(dim_grad, ctx.GetPlace());
K
Kaipeng Deng 已提交
1215
  auto& device_ctx = ctx.template device_context<platform::CPUDeviceContext>();
1216
  pten::funcs::SetConstant<platform::CPUDeviceContext, T> zero;
K
Kaipeng Deng 已提交
1217 1218 1219 1220 1221 1222
  zero(device_ctx, input_grad, static_cast<T>(0.0));

  if (in_d == out_d && in_h == out_h && in_w == out_w) {
    framework::TensorCopy(output_grad, ctx.GetPlace(), input_grad);
    return;
  }
1223

K
Kaipeng Deng 已提交
1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238
  float ratio_d = 0.f;
  float ratio_h = 0.f;
  float ratio_w = 0.f;
  if (out_d > 1) {
    ratio_d = (align_corners) ? static_cast<float>(in_d - 1) / (out_d - 1)
                              : static_cast<float>(in_d) / out_d;
  }
  if (out_h > 1) {
    ratio_h = (align_corners) ? static_cast<float>(in_h - 1) / (out_h - 1)
                              : static_cast<float>(in_h) / out_h;
  }
  if (out_w > 1) {
    ratio_w = (align_corners) ? static_cast<float>(in_w - 1) / (out_w - 1)
                              : static_cast<float>(in_w) / out_w;
  }
T
tink2123 已提交
1239

K
Kaipeng Deng 已提交
1240
  if ("trilinear" == interp_method) {
1241 1242 1243
    TrilinearInterpolationGrad<T>(
        output_grad, input_grad, ratio_d, ratio_h, ratio_w, in_d, in_h, in_w, n,
        c, out_d, out_h, out_w, align_corners, align_mode, data_layout);
K
Kaipeng Deng 已提交
1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254
  }
}

template <typename T>
class InterpolateKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* input = ctx.Input<Tensor>("X");
    auto* output = ctx.Output<Tensor>("Out");

    auto input_dims = input->dims();
1255 1256 1257
    if (input_dims.size() == 3) {  // 1D interpolation
      Interpolate1DCPUFwd<T>(ctx, *input, output);
    } else if (input_dims.size() == 4) {  // 2D interpolation
K
Kaipeng Deng 已提交
1258 1259 1260
      Interpolate2DCPUFwd<T>(ctx, *input, output);
    } else if (input_dims.size() == 5) {  // 3D interpolation
      Interpolate3DCPUFwd<T>(ctx, *input, output);
T
tink2123 已提交
1261
    }
K
Kaipeng Deng 已提交
1262 1263 1264 1265 1266 1267 1268 1269 1270
  }
};

template <typename T>
class InterpolateGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
1271

K
Kaipeng Deng 已提交
1272
    auto output_grad_dims = output_grad->dims();
1273 1274 1275
    if (output_grad_dims.size() == 3) {  // 1D interpolation grad
      Interpolate1DCPUBwd<T>(ctx, input_grad, *output_grad);
    } else if (output_grad_dims.size() == 4) {  // 2D interpolation grad
K
Kaipeng Deng 已提交
1276 1277 1278
      Interpolate2DCPUBwd<T>(ctx, input_grad, *output_grad);
    } else if (output_grad_dims.size() == 5) {  // 3D interpolation grad
      Interpolate3DCPUBwd<T>(ctx, input_grad, *output_grad);
1279 1280 1281 1282 1283 1284
    }
  }
};

}  // namespace operators
}  // namespace paddle