class_docstr: DensePose results for chart-based methods represented by labels and quantized
  inner coordinates (U, V) of individual charts. Each chart is a 2D manifold
  that has an associated label and is parameterized by two coordinates U and V.
  Both U and V take values in [0, 1].
  Quantized coordinates Uq and Vq have uint8 values which are obtained as:
    Uq = U * 255 (hence 0 <= Uq <= 255)
    Vq = V * 255 (hence 0 <= Vq <= 255)
  Thus the results are represented by one tensor:
  - labels_uv_uint8 (tensor [3, H, W] of uint8): contains estimated label
      and quantized coordinates Uq and Vq for each pixel of the detection
      bounding box of size (H, W)
class_docstr_tok: ["DensePose","results","for","chart-based","methods","represented","by","labels","and","quantized","inner","coordinates","(","U",",","V",")","of","individual","charts",".","Each","chart","is","a","2D","manifold","that","has","an","associated","label","and","is","parameterized","by","two","coordinates","U","and","V.","Both","U","and","V","take","values","in","[","0",",","1","]",".","Quantized","coordinates","Uq","and","Vq","have","uint8","values","which","are","obtained","as",":","Uq","=","U","*","255","(","hence","0","<","=","Uq","<","=","255",")","Vq","=","V","*","255","(","hence","0","<","=","Vq","<","=","255",")","Thus","the","results","are","represented","by","one","tensor",":","-","labels_uv_uint8","(","tensor","[","3",",","H",",","W","]","of","uint8",")",":","contains","estimated","label","and","quantized","coordinates","Uq","and","Vq","for","each","pixel","of","the","detection","bounding","box","of","size","(","H",",","W",")"]
docstr: Transfers all tensors to the given device
docstr_tok: ["Transfers","all","tensors","to","the","given","device"]
code: def to(self, device: torch.device):
      """
      Transfers all tensors to the given device
      """
      labels_uv_uint8 = self.labels_uv_uint8.to(device)
      return DensePoseChartResultQuantized(
          labels_uv_uint8=labels_uv_uint8
      )
  
file_path: detectron2/projects/DensePose/densepose/structures/chart_result.py
imports: from dataclasses import dataclass
  from typing import Any, Optional, Tuple
  import torch
label_desc: Use image node_id 1 for calling the DensePoseChartResultQuantized obj's underlying member method code with example usage: obj.to(device) and returns: DensePoseChartResultQuantized