path: root/tensorflow/core/kernels/non_max_suppression_op.cc

/* Copyright 2015 The TensorFlow 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.
==============================================================================*/

// See docs in ../ops/image_ops.cc

#define EIGEN_USE_THREADS

#include "tensorflow/core/kernels/non_max_suppression_op.h"

#include <queue>
#include <vector>

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/bounds_check.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/gtl/stl_util.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {
namespace {

typedef Eigen::ThreadPoolDevice CPUDevice;

static inline void ParseAndCheckBoxSizes(OpKernelContext* context,
                                         const Tensor& boxes,
                                         const Tensor& scores, int* num_boxes) {
  // The shape of 'boxes' is [num_boxes, 4]
  OP_REQUIRES(context, boxes.dims() == 2,
              errors::InvalidArgument("boxes must be 2-D",
                                      boxes.shape().DebugString()));
  *num_boxes = boxes.dim_size(0);
  OP_REQUIRES(context, boxes.dim_size(1) == 4,
              errors::InvalidArgument("boxes must have 4 columns"));

  // The shape of 'scores' is [num_boxes]
  OP_REQUIRES(context, scores.dims() == 1,
              errors::InvalidArgument("scores must be 1-D",
                                      scores.shape().DebugString()));
  OP_REQUIRES(context, scores.dim_size(0) == *num_boxes,
              errors::InvalidArgument("scores has incompatible shape"));
}

// Return intersection-over-union overlap between boxes i and j
static inline float IOU(typename TTypes<float, 2>::ConstTensor boxes, int i,
                        int j) {
  const float ymin_i = std::min<float>(boxes(i, 0), boxes(i, 2));
  const float xmin_i = std::min<float>(boxes(i, 1), boxes(i, 3));
  const float ymax_i = std::max<float>(boxes(i, 0), boxes(i, 2));
  const float xmax_i = std::max<float>(boxes(i, 1), boxes(i, 3));
  const float ymin_j = std::min<float>(boxes(j, 0), boxes(j, 2));
  const float xmin_j = std::min<float>(boxes(j, 1), boxes(j, 3));
  const float ymax_j = std::max<float>(boxes(j, 0), boxes(j, 2));
  const float xmax_j = std::max<float>(boxes(j, 1), boxes(j, 3));
  const float area_i = (ymax_i - ymin_i) * (xmax_i - xmin_i);
  const float area_j = (ymax_j - ymin_j) * (xmax_j - xmin_j);
  if (area_i <= 0 || area_j <= 0) return 0.0;
  const float intersection_ymin = std::max<float>(ymin_i, ymin_j);
  const float intersection_xmin = std::max<float>(xmin_i, xmin_j);
  const float intersection_ymax = std::min<float>(ymax_i, ymax_j);
  const float intersection_xmax = std::min<float>(xmax_i, xmax_j);
  const float intersection_area =
      std::max<float>(intersection_ymax - intersection_ymin, 0.0) *
      std::max<float>(intersection_xmax - intersection_xmin, 0.0);
  return intersection_area / (area_i + area_j - intersection_area);
}

void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
                           const Tensor& scores, const Tensor& max_output_size,
                           const float iou_threshold,
                           const float score_threshold) {
  OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
              errors::InvalidArgument("iou_threshold must be in [0, 1]"));

  int num_boxes = 0;
  ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
  if (!context->status().ok()) {
    return;
  }

  const int output_size = std::min(max_output_size.scalar<int>()(), num_boxes);
  TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();

  std::vector<float> scores_data(num_boxes);
  std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());

  // Data structure for selection candidate in NMS.
  struct Candidate {
    int box_index;
    float score;
  };

  auto cmp = [](const Candidate bs_i, const Candidate bs_j) {
    return bs_i.score < bs_j.score;
  };
  std::priority_queue<Candidate, std::deque<Candidate>, decltype(cmp)>
      candidate_priority_queue(cmp);
  for (int i = 0; i < scores_data.size(); ++i) {
    if (scores_data[i] > score_threshold) {
      candidate_priority_queue.emplace(Candidate({i, scores_data[i]}));
    }
  }

  std::vector<int> selected;
  std::vector<float> selected_scores;
  Candidate next_candidate;
  float iou, original_score;

  while (selected.size() < output_size && !candidate_priority_queue.empty()) {
    next_candidate = candidate_priority_queue.top();
    original_score = next_candidate.score;
    candidate_priority_queue.pop();

    // Overlapping boxes are likely to have similar scores,
    // therefore we iterate through the previously selected boxes backwards
    // in order to see if `next_candidate` should be suppressed.
    bool should_select = true;
    for (int j = selected.size() - 1; j >= 0; --j) {
      iou = IOU(boxes_data, next_candidate.box_index, selected[j]);
      if (iou > iou_threshold) should_select = false;
    }

    if (should_select) {
      selected.push_back(next_candidate.box_index);
      selected_scores.push_back(next_candidate.score);
    }
  }

  // Allocate output tensors
  Tensor* output_indices = nullptr;
  TensorShape output_shape({static_cast<int>(selected.size())});
  OP_REQUIRES_OK(context,
                 context->allocate_output(0, output_shape, &output_indices));
  TTypes<int, 1>::Tensor output_indices_data = output_indices->tensor<int, 1>();
  std::copy_n(selected.begin(), selected.size(), output_indices_data.data());
}

}  // namespace

template <typename Device>
class NonMaxSuppressionOp : public OpKernel {
 public:
  explicit NonMaxSuppressionOp(OpKernelConstruction* context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("iou_threshold", &iou_threshold_));
  }

  void Compute(OpKernelContext* context) override {
    // boxes: [num_boxes, 4]
    const Tensor& boxes = context->input(0);
    // scores: [num_boxes]
    const Tensor& scores = context->input(1);
    // max_output_size: scalar
    const Tensor& max_output_size = context->input(2);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(max_output_size.shape()),
        errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                max_output_size.shape().DebugString()));

    const float score_threshold_val = std::numeric_limits<float>::lowest();
    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
                          iou_threshold_, score_threshold_val);
  }

 private:
  float iou_threshold_;
};

template <typename Device>
class NonMaxSuppressionV2Op : public OpKernel {
 public:
  explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
      : OpKernel(context) {}

  void Compute(OpKernelContext* context) override {
    // boxes: [num_boxes, 4]
    const Tensor& boxes = context->input(0);
    // scores: [num_boxes]
    const Tensor& scores = context->input(1);
    // max_output_size: scalar
    const Tensor& max_output_size = context->input(2);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(max_output_size.shape()),
        errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                max_output_size.shape().DebugString()));
    // iou_threshold: scalar
    const Tensor& iou_threshold = context->input(3);
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
                errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
                                        iou_threshold.shape().DebugString()));
    const float iou_threshold_val = iou_threshold.scalar<float>()();

    const float score_threshold_val = std::numeric_limits<float>::lowest();
    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
                          iou_threshold_val, score_threshold_val);
  }
};

template <typename Device>
class NonMaxSuppressionV3Op : public OpKernel {
 public:
  explicit NonMaxSuppressionV3Op(OpKernelConstruction* context)
      : OpKernel(context) {}

  void Compute(OpKernelContext* context) override {
    // boxes: [num_boxes, 4]
    const Tensor& boxes = context->input(0);
    // scores: [num_boxes]
    const Tensor& scores = context->input(1);
    // max_output_size: scalar
    const Tensor& max_output_size = context->input(2);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(max_output_size.shape()),
        errors::InvalidArgument("max_output_size must be 0-D, got shape ",
                                max_output_size.shape().DebugString()));
    // iou_threshold: scalar
    const Tensor& iou_threshold = context->input(3);
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
                errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
                                        iou_threshold.shape().DebugString()));
    const float iou_threshold_val = iou_threshold.scalar<float>()();

    // score_threshold: scalar
    const Tensor& score_threshold = context->input(4);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(score_threshold.shape()),
        errors::InvalidArgument("score_threshold must be 0-D, got shape ",
                                score_threshold.shape().DebugString()));
    const float score_threshold_val = score_threshold.scalar<float>()();

    DoNonMaxSuppressionOp(context, boxes, scores, max_output_size,
                          iou_threshold_val, score_threshold_val);
  }
};

REGISTER_KERNEL_BUILDER(Name("NonMaxSuppression").Device(DEVICE_CPU),
                        NonMaxSuppressionOp<CPUDevice>);

REGISTER_KERNEL_BUILDER(Name("NonMaxSuppressionV2").Device(DEVICE_CPU),
                        NonMaxSuppressionV2Op<CPUDevice>);

REGISTER_KERNEL_BUILDER(Name("NonMaxSuppressionV3").Device(DEVICE_CPU),
                        NonMaxSuppressionV3Op<CPUDevice>);

}  // namespace tensorflow