roi_align_op_xpu.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.

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. */

#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/roi_align_op.h"
#include <memory>
#include <string>

namespace paddle {
namespace operators {

template <typename DeviceContext, typename T>
class XPUROIAlignOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* in = ctx.Input<framework::Tensor>("X");
    auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
    auto* out = ctx.Output<framework::Tensor>("Out");
    auto pooled_height = ctx.Attr<int>("pooled_height");
    auto pooled_width = ctx.Attr<int>("pooled_width");
    auto spatial_scale = ctx.Attr<float>("spatial_scale");
    auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
    auto& dev_ctx = ctx.template device_context<DeviceContext>();
    auto in_dims = in->dims();
    int batch_size = in_dims[0];
    int channels = in_dims[1];
    int height = in_dims[2];
    int width = in_dims[3];
    int rois_num = rois->dims()[0];
    const T* input_data = in->data<T>();
    auto rois_lod = rois->lod().back();
    int rois_batch_size = rois_lod.size() - 1;
    PADDLE_ENFORCE_EQ(
        rois_batch_size, batch_size,
        platform::errors::InvalidArgument(
            "The rois_batch_size and imgs batch_size must be the same."));
    int rois_num_with_lod = rois_lod[rois_batch_size];
    PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
                      platform::errors::InvalidArgument(
                          "The rois_num from input and lod must be the same."));
    T* output_data = out->mutable_data<T>(ctx.GetPlace());
    const T* rois_data = rois->data<T>();
    for (int n = 0; n < rois_batch_size; n++) {
      int cur_batch_rois_num = rois_lod[n + 1] - rois_lod[n];
      if (cur_batch_rois_num != 0) {
        int r = xpu::roi_align(
            dev_ctx.x_context(), input_data + n * channels * height * width,
            rois_data + rois_lod[n] * 4, cur_batch_rois_num, channels, height,
            width, pooled_height, pooled_width, sampling_ratio, spatial_scale,
            output_data +
                rois_lod[n] * channels * pooled_height * pooled_width);
        PADDLE_ENFORCE_EQ(
            r, xpu::Error_t::SUCCESS,
            platform::errors::InvalidArgument("roi_align XPU kernel error!"));
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
    roi_align,
    ops::XPUROIAlignOpKernel<paddle::platform::XPUDeviceContext, float>);

#endif