vision.h

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 * to you under the Apache License, Version 2.0 (the
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 *
 *   http://www.apache.org/licenses/LICENSE-2.0
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#ifndef TVM_RELAY_ATTRS_VISION_H_
#define TVM_RELAY_ATTRS_VISION_H_

#include <tvm/ir/attrs.h>
#include <tvm/relay/base.h>

#include <string>

namespace tvm {
namespace relay {

struct MultiBoxPriorAttrs : public tvm::AttrsNode<MultiBoxPriorAttrs> {
  Array<IndexExpr> sizes;
  Array<IndexExpr> ratios;
  Array<IndexExpr> steps;
  Array<IndexExpr> offsets;
  bool clip;

  TVM_DECLARE_ATTRS(MultiBoxPriorAttrs, "relay.attrs.MultiBoxPriorAttrs") {
    TVM_ATTR_FIELD(sizes)
        .set_default(Array<IndexExpr>({static_cast<float>(1.0)}))
        .describe("List of sizes of generated MultiBoxPriores.");
    TVM_ATTR_FIELD(ratios)
        .set_default(Array<IndexExpr>({static_cast<float>(1.0)}))
        .describe("List of aspect ratios of generated MultiBoxPriores.");
    TVM_ATTR_FIELD(steps)
        .set_default(Array<IndexExpr>({static_cast<float>(-1.0), static_cast<float>(-1.0)}))
        .describe("Priorbox step across y and x, -1 for auto calculation.");
    TVM_ATTR_FIELD(offsets)
        .set_default(Array<IndexExpr>({static_cast<float>(0.5), static_cast<float>(0.5)}))
        .describe("Priorbox center offsets, y and x respectively.");
    TVM_ATTR_FIELD(clip).set_default(false).describe("Whether to clip out-of-boundary boxes.");
  }
};

struct MultiBoxTransformLocAttrs : public tvm::AttrsNode<MultiBoxTransformLocAttrs> {
  bool clip;
  double threshold;
  Array<IndexExpr> variances;

  TVM_DECLARE_ATTRS(MultiBoxTransformLocAttrs, "relay.attrs.MultiBoxTransformLocAttrs") {
    TVM_ATTR_FIELD(clip).set_default(true).describe("Clip out-of-boundary boxes.");
    TVM_ATTR_FIELD(threshold).set_default(0.01).describe("Threshold to be a positive prediction.");
    TVM_ATTR_FIELD(variances)
        .set_default(Array<IndexExpr>({0.1f, 0.1f, 0.2f, 0.2f}))
        .describe("Variances to be decoded from box regression output.");
  }
};

struct GetValidCountsAttrs : public tvm::AttrsNode<GetValidCountsAttrs> {
  Optional<FloatImm> score_threshold;
  int id_index;
  int score_index;

  TVM_DECLARE_ATTRS(GetValidCountsAttrs, "relay.attrs.GetValidCountsAttrs") {
    TVM_ATTR_FIELD(score_threshold).describe("Lower limit of score for valid bounding boxes.");
    TVM_ATTR_FIELD(id_index).set_default(0).describe("Axis index of id.");
    TVM_ATTR_FIELD(score_index).set_default(1).describe("Index of the scores/confidence of boxes.");
  }
};

struct NonMaximumSuppressionAttrs : public tvm::AttrsNode<NonMaximumSuppressionAttrs> {
  bool force_suppress;
  int top_k;
  int coord_start;
  int score_index;
  int id_index;
  bool return_indices;
  bool invalid_to_bottom;

  TVM_DECLARE_ATTRS(NonMaximumSuppressionAttrs, "relay.attrs.NonMaximumSuppressionAttrs") {
    TVM_ATTR_FIELD(force_suppress)
        .set_default(false)
        .describe("Suppress all detections regardless of class_id.");
    TVM_ATTR_FIELD(top_k).set_default(-1).describe(
        "Keep maximum top k detections before nms, -1 for no limit.");
    TVM_ATTR_FIELD(coord_start)
        .set_default(2)
        .describe("Start index of the consecutive 4 coordinates.");
    TVM_ATTR_FIELD(score_index).set_default(1).describe("Index of the scores/confidence of boxes.");
    TVM_ATTR_FIELD(id_index).set_default(0).describe("Axis index of id.");
    TVM_ATTR_FIELD(return_indices)
        .set_default(true)
        .describe("Whether to return box indices in input data.");
    TVM_ATTR_FIELD(invalid_to_bottom)
        .set_default(false)
        .describe("Whether to move all invalid bounding boxes to the bottom.");
  }
};

struct AllClassNonMaximumSuppressionAttrs
    : public tvm::AttrsNode<AllClassNonMaximumSuppressionAttrs> {
  std::string output_format;

  TVM_DECLARE_ATTRS(AllClassNonMaximumSuppressionAttrs,
                    "relay.attrs.AllClassNonMaximumSuppressionAttrs") {
    TVM_ATTR_FIELD(output_format)
        .set_default("onnx")
        .describe(
            "Output format, onnx or tensorflow. Returns outputs in a way that can be easily "
            "consumed by each frontend.");
  }
};

struct ROIAlignAttrs : public tvm::AttrsNode<ROIAlignAttrs> {
  Array<IndexExpr> pooled_size;
  double spatial_scale;
  int sample_ratio;
  std::string layout;
  std::string mode;
  TVM_DECLARE_ATTRS(ROIAlignAttrs, "relay.attrs.ROIAlignAttrs") {
    TVM_ATTR_FIELD(pooled_size).describe("Output size of roi align.");
    TVM_ATTR_FIELD(spatial_scale)
        .describe(
            "Ratio of input feature map height (or w) to raw image height (or w). "
            "Equals the reciprocal of total stride in convolutional layers, which should be "
            "in range (0.0, 1.0]");
    TVM_ATTR_FIELD(sample_ratio)
        .set_default(-1)
        .describe("Optional sampling ratio of ROI align, using adaptive size by default.");
    TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
        "Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
        "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
        "dimensions respectively. Convolution is applied on the 'H' and"
        "'W' dimensions.");
    TVM_ATTR_FIELD(mode).set_default("avg").describe(
        "Mode for ROI Align. Can be 'avg' or 'max'. The default mode is 'avg'.");
  }
};

struct ROIPoolAttrs : public tvm::AttrsNode<ROIPoolAttrs> {
  Array<IndexExpr> pooled_size;
  double spatial_scale;
  std::string layout;
  TVM_DECLARE_ATTRS(ROIPoolAttrs, "relay.attrs.ROIPoolAttrs") {
    TVM_ATTR_FIELD(pooled_size).describe("Output size of roi pool.");
    TVM_ATTR_FIELD(spatial_scale)
        .describe(
            "Ratio of input feature map height (or w) to raw image height (or w). "
            "Equals the reciprocal of total stride in convolutional layers, which should be "
            "in range (0.0, 1.0]");
    TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
        "Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
        "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
        "dimensions respectively. Convolution is applied on the 'H' and"
        "'W' dimensions.");
  }
};

struct YoloReorgAttrs : public tvm::AttrsNode<YoloReorgAttrs> {
  Integer stride;

  TVM_DECLARE_ATTRS(YoloReorgAttrs, "relay.attrs.YoloReorgAttrs") {
    TVM_ATTR_FIELD(stride).set_default(1).describe("Stride value for yolo reorg");
  }
};

struct ProposalAttrs : public tvm::AttrsNode<ProposalAttrs> {
  Array<IndexExpr> scales;
  Array<IndexExpr> ratios;
  int feature_stride;
  double threshold;
  int rpn_pre_nms_top_n;
  int rpn_post_nms_top_n;
  int rpn_min_size;
  bool iou_loss;

  TVM_DECLARE_ATTRS(ProposalAttrs, "relay.attrs.ProposalAttrs") {
    TVM_ATTR_FIELD(scales)
        .set_default(Array<IndexExpr>({4.0f, 8.0f, 16.0f, 32.0f}))
        .describe("Used to generate anchor windows by enumerating scales");
    TVM_ATTR_FIELD(ratios)
        .set_default(Array<IndexExpr>({0.5f, 1.0f, 2.0f}))
        .describe("Used to generate anchor windows by enumerating ratios");
    TVM_ATTR_FIELD(feature_stride)
        .set_default(16)
        .describe(
            "The size of the receptive field each unit in the convolution layer of the rpn,"
            "for example the product of all stride's prior to this layer.");
    TVM_ATTR_FIELD(threshold).set_default(0.7).describe(
        "IoU threshold of non-maximum suppresion (suppress boxes with IoU >= this threshold)");
    TVM_ATTR_FIELD(rpn_pre_nms_top_n)
        .set_default(6000)
        .describe("Number of top scoring boxes to apply NMS. -1 to use all boxes");
    TVM_ATTR_FIELD(rpn_post_nms_top_n)
        .set_default(300)
        .describe("Number of top scoring boxes to keep after applying NMS to RPN proposals");
    TVM_ATTR_FIELD(rpn_min_size).set_default(16).describe("Minimum height or width in proposal");
    TVM_ATTR_FIELD(iou_loss).set_default(false).describe("Usage of IoU Loss");
  }
};

}  // namespace relay
}  // namespace tvm
#endif  // TVM_RELAY_ATTRS_VISION_H_