bilinear_interp_op.cu 7.2 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11
/* Copyright (c) 2016 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. */

W
wangyang59 已提交
12
#include "paddle/fluid/operators/bilinear_interp_op.h"
H
Helin Wang 已提交
13
#include "paddle/fluid/platform/cuda_primitives.h"
14 15 16 17

namespace paddle {
namespace operators {

W
wangyang59 已提交
18 19
using framework::Tensor;

W
wangyang59 已提交
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
template <typename T>
__global__ void KeBilinearInterpFw(
    const T* in, const size_t in_img_h, const size_t in_img_w,
    const size_t input_h, const size_t input_w, T* out, const size_t out_img_h,
    const size_t out_img_w, const size_t output_h, const size_t output_w,
    const size_t num_channels, const T ratio_h, const T ratioW) {
  int nthreads = output_h * output_w;
  int tid = blockIdx.x * blockDim.x + threadIdx.x;
  if (tid < nthreads) {
    int out_id_h = tid / output_w;
    int out_id_w = tid % output_w;
    int in_img_size = input_w / num_channels;
    int out_img_size = output_w / num_channels;
    int channel_id = out_id_w / out_img_size;

    int out_img_idy = (out_id_w % out_img_size) / out_img_w;
    int in_img_idy = ratio_h * out_img_idy;
    int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
    T h1lambda = ratio_h * out_img_idy - in_img_idy;
    T h2lambda = 1.f - h1lambda;

    int out_img_idx = tid % out_img_w;
    int in_img_idx = ratioW * out_img_idx;
    int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
    T w1lambda = ratioW * out_img_idx - in_img_idx;
    T w2lambda = 1.f - w1lambda;

    const T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
                          in_img_idy * in_img_w + in_img_idx];

    // bilinear interpolation
    out[out_id_h * output_w + out_id_w] =
        h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[w_id]) +
        h1lambda * (w2lambda * in_pos[h_id * in_img_w] +
                    w1lambda * in_pos[h_id * in_img_w + w_id]);
  }
}

template <typename T>
__global__ void KeBilinearInterpBw(
    T* in, const size_t in_img_h, const size_t in_img_w, const size_t input_h,
    const size_t input_w, const T* out, const size_t out_img_h,
    const size_t out_img_w, const size_t output_h, const size_t output_w,
    const size_t num_channels, const T ratio_h, const T ratioW) {
  int nthreads = output_h * output_w;
  int tid = blockIdx.x * blockDim.x + threadIdx.x;
  if (tid < nthreads) {
    int out_id_h = tid / output_w;
    int out_id_w = tid % output_w;
    int in_img_size = input_w / num_channels;
    int out_img_size = output_w / num_channels;
    int channel_id = out_id_w / out_img_size;

    int out_img_idy = (out_id_w % out_img_size) / out_img_w;
    int in_img_idy = ratio_h * out_img_idy;
    int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
    T h1lambda = ratio_h * out_img_idy - in_img_idy;
    T h2lambda = 1.f - h1lambda;

    int out_img_idx = tid % out_img_w;
    int in_img_idx = ratioW * out_img_idx;
    int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
    T w1lambda = ratioW * out_img_idx - in_img_idx;
    T w2lambda = 1.f - w1lambda;

    T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
                    in_img_idy * in_img_w + in_img_idx];
    const T* out_pos = &out[out_id_h * output_w + out_id_w];
    atomicAdd(&in_pos[0], h2lambda * w2lambda * out_pos[0]);
    atomicAdd(&in_pos[w_id], h2lambda * w1lambda * out_pos[0]);
    atomicAdd(&in_pos[h_id * in_img_w], h1lambda * w2lambda * out_pos[0]);
    atomicAdd(&in_pos[h_id * in_img_w + w_id],
              h1lambda * w1lambda * out_pos[0]);
  }
}

96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124
template <typename T>
class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                   "This kernel only runs on GPU device.");
    auto* input_t = ctx.Input<Tensor>("X");      // float tensor
    auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
    auto* input = input_t->data<T>();
    auto* output = output_t->mutable_data<T>(ctx.GetPlace());

    int out_h = ctx.Attr<int>("out_h");
    int out_w = ctx.Attr<int>("out_w");
    int batch_size = input_t->dims()[0];
    int channels = input_t->dims()[1];
    int in_h = input_t->dims()[2];
    int in_w = input_t->dims()[3];

    int in_hw = in_h * in_w;
    int out_hw = out_h * out_w;
    int in_chw = channels * in_hw;
    int out_chw = channels * out_hw;

    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;

    if (in_h == out_h && in_w == out_w) {
      memcpy(output, input, input_t->numel() * sizeof(T));
    } else {
125 126 127 128 129 130 131
      int threadNum = batch_size * out_chw;
      int blocks = (threadNum + 1024 - 1) / 1024;

      KeBilinearInterpFw<
          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
          input, in_h, in_w, batch_size, in_chw, output, out_h, out_w,
          batch_size, out_chw, channels, ratio_h, ratio_w);
132 133 134 135 136 137 138 139 140 141 142 143 144
    }
  }
};

template <typename T>
class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
    auto* d_output = d_output_t->data<T>();

W
wangyang59 已提交
145 146 147 148 149
    auto& device_ctx =
        ctx.template device_context<platform::CUDADeviceContext>();
    math::SetConstant<platform::CUDADeviceContext, T> zero;
    zero(device_ctx, d_input_t, static_cast<T>(0.0));

150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
    int out_h = ctx.Attr<int>("out_h");
    int out_w = ctx.Attr<int>("out_w");
    int batch_size = d_input_t->dims()[0];
    int channels = d_input_t->dims()[1];
    int in_h = d_input_t->dims()[2];
    int in_w = d_input_t->dims()[3];

    int in_hw = in_h * in_w;
    int out_hw = out_h * out_w;
    int in_chw = channels * in_hw;
    int out_chw = channels * out_hw;

    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;

    if (in_h == out_h && in_w == out_w) {
      memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
    } else {
168 169 170 171 172 173 174
      int threadNum = batch_size * out_chw;
      int blocks = (threadNum + 1024 - 1) / 1024;

      KeBilinearInterpBw<
          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
          d_input, in_h, in_w, batch_size, in_chw, d_output, out_h, out_w,
          batch_size, out_chw, channels, ratio_h, ratio_w);
175 176 177 178 179 180 181 182 183 184 185
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(bilinear_interp,
                        ops::BilinearInterpOpCUDAKernel<float>);
REGISTER_OP_CUDA_KERNEL(bilinear_interp_grad,
186
                        ops::BilinearInterpGradOpCUDAKernel<float>);