text: # Copyright (c) Facebook, Inc. and its affiliates.
  
  from typing import Any, Dict, List, Tuple
  import torch
  from torch.nn import functional as F
  
  from detectron2.structures import BoxMode, Instances
  
  from densepose.converters import ToChartResultConverter
  from densepose.converters.base import IntTupleBox, make_int_box
  from densepose.structures import DensePoseDataRelative, DensePoseList
  
  
  class DensePoseBaseSampler:
      """
      Base DensePose sampler to produce DensePose data from DensePose predictions.
      Samples for each class are drawn according to some distribution over all pixels estimated
      to belong to that class.
      """
  
      def __init__(self, count_per_class: int = 8):
          """
          Constructor
  
          Args:
            count_per_class (int): the sampler produces at most `count_per_class`
                samples for each category
          """
          self.count_per_class = count_per_class
  
      def __call__(self, instances: Instances) -> DensePoseList:
          """
          Convert DensePose predictions (an instance of `DensePoseChartPredictorOutput`)
          into DensePose annotations data (an instance of `DensePoseList`)
          """
          boxes_xyxy_abs = instances.pred_boxes.tensor.clone().cpu()
          boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
          dp_datas = []
          for i in range(len(boxes_xywh_abs)):
              annotation_i = self._sample(instances[i], make_int_box(boxes_xywh_abs[i]))
              annotation_i[DensePoseDataRelative.S_KEY] = self._resample_mask(  # pyre-ignore[6]
                  instances[i].pred_densepose
              )
              dp_datas.append(DensePoseDataRelative(annotation_i))
          # create densepose annotations on CPU
          dp_list = DensePoseList(dp_datas, boxes_xyxy_abs, instances.image_size)
          return dp_list
  
      def _sample(self, instance: Instances, bbox_xywh: IntTupleBox) -> Dict[str, List[Any]]:
          """
          Sample DensPoseDataRelative from estimation results
          """
          labels, dp_result = self._produce_labels_and_results(instance)
          annotation = {
              DensePoseDataRelative.X_KEY: [],
              DensePoseDataRelative.Y_KEY: [],
              DensePoseDataRelative.U_KEY: [],
              DensePoseDataRelative.V_KEY: [],
              DensePoseDataRelative.I_KEY: [],
          }
          n, h, w = dp_result.shape
          for part_id in range(1, DensePoseDataRelative.N_PART_LABELS + 1):
              # indices - tuple of 3 1D tensors of size k
              # 0: index along the first dimension N
              # 1: index along H dimension
              # 2: index along W dimension
              indices = torch.nonzero(labels.expand(n, h, w) == part_id, as_tuple=True)
              # values - an array of size [n, k]
              # n: number of channels (U, V, confidences)
              # k: number of points labeled with part_id
              values = dp_result[indices].view(n, -1)
              k = values.shape[1]
              count = min(self.count_per_class, k)
              if count <= 0:
                  continue
              index_sample = self._produce_index_sample(values, count)
              sampled_values = values[:, index_sample]
              sampled_y = indices[1][index_sample] + 0.5
              sampled_x = indices[2][index_sample] + 0.5
              # prepare / normalize data
              x = (sampled_x / w * 256.0).cpu().tolist()
              y = (sampled_y / h * 256.0).cpu().tolist()
              u = sampled_values[0].clamp(0, 1).cpu().tolist()
              v = sampled_values[1].clamp(0, 1).cpu().tolist()
              fine_segm_labels = [part_id] * count
              # extend annotations
              annotation[DensePoseDataRelative.X_KEY].extend(x)
              annotation[DensePoseDataRelative.Y_KEY].extend(y)
              annotation[DensePoseDataRelative.U_KEY].extend(u)
              annotation[DensePoseDataRelative.V_KEY].extend(v)
              annotation[DensePoseDataRelative.I_KEY].extend(fine_segm_labels)
          return annotation
  
      def _produce_index_sample(self, values: torch.Tensor, count: int):
          """
          Abstract method to produce a sample of indices to select data
          To be implemented in descendants
  
          Args:
              values (torch.Tensor): an array of size [n, k] that contains
                  estimated values (U, V, confidences);
                  n: number of channels (U, V, confidences)
                  k: number of points labeled with part_id
              count (int): number of samples to produce, should be positive and <= k
  
          Return:
              list(int): indices of values (along axis 1) selected as a sample
          """
          raise NotImplementedError
  
      def _produce_labels_and_results(self, instance: Instances) -> Tuple[torch.Tensor, torch.Tensor]:
          """
          Method to get labels and DensePose results from an instance
  
          Args:
              instance (Instances): an instance of `DensePoseChartPredictorOutput`
  
          Return:
              labels (torch.Tensor): shape [H, W], DensePose segmentation labels
              dp_result (torch.Tensor): shape [2, H, W], stacked DensePose results u and v
          """
          converter = ToChartResultConverter
          chart_result = converter.convert(instance.pred_densepose, instance.pred_boxes)
          labels, dp_result = chart_result.labels.cpu(), chart_result.uv.cpu()
          return labels, dp_result
  
      def _resample_mask(self, output: Any) -> torch.Tensor:
          """
          Convert DensePose predictor output to segmentation annotation - tensors of size
          (256, 256) and type `int64`.
  
          Args:
              output: DensePose predictor output with the following attributes:
               - coarse_segm: tensor of size [N, D, H, W] with unnormalized coarse
                 segmentation scores
               - fine_segm: tensor of size [N, C, H, W] with unnormalized fine
                 segmentation scores
          Return:
              Tensor of size (S, S) and type `int64` with coarse segmentation annotations,
              where S = DensePoseDataRelative.MASK_SIZE
          """
          sz = DensePoseDataRelative.MASK_SIZE
          S = (
              F.interpolate(output.coarse_segm, (sz, sz), mode="bilinear", align_corners=False)
              .argmax(dim=1)
              .long()
          )
          I = (
              (
                  F.interpolate(
                      output.fine_segm, (sz, sz), mode="bilinear", align_corners=False
                  ).argmax(dim=1)
                  * (S > 0).long()
              )
              .squeeze()
              .cpu()
          )
          # Map fine segmentation results to coarse segmentation ground truth
          # TODO: extract this into separate classes
          # coarse segmentation: 1 = Torso, 2 = Right Hand, 3 = Left Hand,
          # 4 = Left Foot, 5 = Right Foot, 6 = Upper Leg Right, 7 = Upper Leg Left,
          # 8 = Lower Leg Right, 9 = Lower Leg Left, 10 = Upper Arm Left,
          # 11 = Upper Arm Right, 12 = Lower Arm Left, 13 = Lower Arm Right,
          # 14 = Head
          # fine segmentation: 1, 2 = Torso, 3 = Right Hand, 4 = Left Hand,
          # 5 = Left Foot, 6 = Right Foot, 7, 9 = Upper Leg Right,
          # 8, 10 = Upper Leg Left, 11, 13 = Lower Leg Right,
          # 12, 14 = Lower Leg Left, 15, 17 = Upper Arm Left,
          # 16, 18 = Upper Arm Right, 19, 21 = Lower Arm Left,
          # 20, 22 = Lower Arm Right, 23, 24 = Head
          FINE_TO_COARSE_SEGMENTATION = {
              1: 1,
              2: 1,
              3: 2,
              4: 3,
              5: 4,
              6: 5,
              7: 6,
              8: 7,
              9: 6,
              10: 7,
              11: 8,
              12: 9,
              13: 8,
              14: 9,
              15: 10,
              16: 11,
              17: 10,
              18: 11,
              19: 12,
              20: 13,
              21: 12,
              22: 13,
              23: 14,
              24: 14,
          }
          mask = torch.zeros((sz, sz), dtype=torch.int64, device=torch.device("cpu"))
          for i in range(DensePoseDataRelative.N_PART_LABELS):
              mask[I == i + 1] = FINE_TO_COARSE_SEGMENTATION[i + 1]
          return mask
  