code: def __getitem__(self, index):
      idx = self.imgidx[index]
      group = self.imggroup[index]
      if group == 0:
          imgrec = self.imgrec
      elif group == 1:
          imgrec = self.imgrec2
      elif group == 2:
          imgrec = self.imgrec3
  
      s = imgrec.read_idx(idx)
      header, img = mx.recordio.unpack(s)
      hlabel = header.label
      img = mx.image.imdecode(img).asnumpy()  # rgb numpy
  
      label_verts = np.array(
          hlabel[: 1220 * 3], dtype=np.float32
      ).reshape(-1, 3)
      label_Rt = np.array(
          hlabel[1220 * 3 : 1220 * 3 + 16], dtype=np.float32
      ).reshape(4, 4)
      label_tform = np.array(
          hlabel[1220 * 3 + 16 : 1220 * 3 + 16 + 6], dtype=np.float32
      ).reshape(2, 3)
      label_6dof = Rt26dof(label_Rt, self.degrees_6dof)
      if self.norm_6dof:
          label_6dof = (
              label_6dof - self.label_6dof_mean
          ) / self.label_6dof_std
      label_6dof_tensor = torch.tensor(label_6dof, dtype=torch.float32)
      el_inv = None
      er_inv = None
      if self.use_eye:
          a = 1220 * 3 + 16 + 6
          el_inv = np.array(
              hlabel[a : a + 481 * 3], dtype=np.float32
          ).reshape(-1, 3)
          a += 481 * 3
          er_inv = np.array(
              hlabel[a : a + 481 * 3], dtype=np.float32
          ).reshape(-1, 3)
          # el_inv = torch.tensor(el_inv, dtype=torch.float32)
          # er_inv = torch.tensor(er_inv, dtype=torch.float32)
          # eye_verts = [el_inv, er_inv]
          eye_verts = np.concatenate((el_inv, er_inv), axis=0)
  
      # img_local = None
      img_raw = None
      # if self.task==0 or self.task==2:
      #    img_raw = img[:,self.input_size:,:]
      # if self.task==0 or self.task==1 or self.task==3:
      #    img_local = img[:,:self.input_size,:]
      assert (
          img.shape[0] == img.shape[1]
          and img.shape[0] >= self.input_size
      )
      if img.shape[0] > self.input_size:
          scale = float(self.input_size) / img.shape[0]
          # print('scale:', scale)
          # src_pts = np.float32([
          #    [0, 0],
          #    [0, 799],
          #    [799, 0]
          # ])
          # tform = cv2.getAffineTransform(src_pts, self.dst_pts)
          # new_tform = np.identity(3)
          # new_tform[:2,:3] = tform
          # label_tform = np.dot(new_tform, label_tform.T).T
  
          src_pts = np.float32([[0, 0, 1], [0, 799, 1], [799, 0, 1]])
          dst_pts = np.dot(label_tform, src_pts.T).T
          dst_pts *= scale
          dst_pts = dst_pts.copy()
          src_pts = src_pts[:, :2].copy()
          # print('index:', index)
          # print(src_pts.shape, dst_pts.shape)
          # print(label_tform.shape)
          # print(src_pts.dtype)
          # print(dst_pts.dtype)
          tform = cv2.getAffineTransform(src_pts, dst_pts)
          label_tform = tform
  
          img = cv2.resize(img, (self.input_size, self.input_size))
  
      img_local = img
      need_points2d = self.task == 0 or self.task == 3
  
      if need_points2d:
          ones = np.ones([label_verts.shape[0], 1])
          verts_homo = np.concatenate([label_verts, ones], axis=1)
          verts = verts_homo @ label_Rt @ self.M_proj @ self.M1
          w_ = verts[:, [3]]
          verts = verts / w_
          points2d = verts[:, :3]
          points2d[:, 1] = 800.0 - points2d[:, 1]
          verts2d = points2d[:, :2].copy()
          points2d[:, 2] = 1.0
          points2d = np.dot(label_tform, points2d.T).T
      else:
          points2d = np.ones((1, 2), dtype=np.float32) * (
              self.input_size // 2
          )
      if self.use_eye:
          verts_homo = eye_verts
          if verts_homo.shape[1] == 3:
              ones = np.ones([verts_homo.shape[0], 1])
              verts_homo = np.concatenate([verts_homo, ones], axis=1)
          verts_out = verts_homo @ label_Rt @ self.M_proj @ self.M1
          w_ = verts_out[:, [3]]
          verts_out = verts_out / w_
          _points2d = verts_out[:, :3]
          _points2d[:, 1] = 800.0 - _points2d[:, 1]
          _points2d[:, 2] = 1.0
          _points2d = np.dot(label_tform, _points2d.T).T
          eye_points = _points2d
      # if img.shape[0]!=self.input_size:
      #    assert img.shape[0]>self.input_size
      # img = cv2.resize(img, (self.input_size, self.input_size))
      # scale = float(self.input_size) / img.shape[0]
      # points2d *= scale
  
      if self.transform is not None:
          if img_raw is not None:
              img_raw = self.transform(
                  image=img_raw, keypoints=points2d
              )["image"]
          if img_local is not None:
              height, width = img_local.shape[:2]
              x = self.transform(image=img_local, keypoints=points2d)
              img_local = x["image"]
              points2d = x["keypoints"]
              points2d = np.array(points2d, dtype=np.float32)
              if self.is_test_aug:
                  for trans in x["replay"]["transforms"]:
                      if (
                          trans["__class_fullname__"]
                          == "ShiftScaleRotate"
                          and trans["applied"]
                      ):
                          param = trans["params"]
                          dx, dy, angle, scale = (
                              param["dx"],
                              param["dy"],
                              param["angle"],
                              param["scale"],
                          )
                          center = (width / 2, height / 2)
                          matrix = cv2.getRotationMatrix2D(
                              center, angle, scale
                          )
                          matrix[0, 2] += dx * width
                          matrix[1, 2] += dy * height
                          new_matrix = np.identity(3)
                          new_matrix[:2, :3] = matrix
                          old_tform = np.identity(3)
                          old_tform[:2, :3] = label_tform
                          # new_tform = np.dot(old_tform, new_matrix)
                          new_tform = np.dot(new_matrix, old_tform)
                          # print('label_tform:')
                          # print(label_tform.flatten())
                          # print(new_matrix.flatten())
                          # print(new_tform.flatten())
                          label_tform = new_tform[:2, :3]
                          break
                          # print('trans param:', param)
  
      if self.loss_pip:
          target_map = np.zeros(
              (
                  self.num_verts,
                  int(self.input_size / self.net_stride),
                  int(self.input_size / self.net_stride),
              )
          )
          target_local_x = np.zeros(
              (
                  self.num_verts,
                  int(self.input_size / self.net_stride),
                  int(self.input_size / self.net_stride),
              )
          )
          target_local_y = np.zeros(
              (
                  self.num_verts,
                  int(self.input_size / self.net_stride),
                  int(self.input_size / self.net_stride),
              )
          )
          target = points2d / self.input_size
          target_map, target_local_x, target_local_y = gen_target_pip(
              target, target_map, target_local_x, target_local_y
          )
          target_map_tensor = torch.tensor(
              target_map, dtype=torch.float32
          )
          target_x_tensor = torch.tensor(
              target_local_x, dtype=torch.float32
          )
          target_y_tensor = torch.tensor(
              target_local_y, dtype=torch.float32
          )
          d["pip_map"] = target_map_tensor
          d["pip_x"] = target_x_tensor
          d["pip_y"] = target_y_tensor
  
      if (
          self.is_train
          and self.enable_flip
          and np.random.random() < 0.5
      ):
          # if self.local_rank==0:
          #    print('XXX:', label_verts[:5,:2])
          img_local = img_local.flip([2])
          x_of_central = 0.0
          # x_of_central = label_verts[self.verts3d_central_index,0]
          # x_of_central = np.mean(x_of_central)
          label_verts = label_verts[self.flipindex, :]
          label_verts[:, 0] -= x_of_central
          label_verts[:, 0] *= -1.0
          label_verts[:, 0] += x_of_central
  
          if need_points2d:
              flipped_p2d = points2d[self.flipindex, :].copy()
              flipped_p2d[:, 0] = (
                  self.input_size - 1 - flipped_p2d[:, 0]
              )
              points2d = flipped_p2d
          if self.use_eye:
              flipped_p2d = eye_points[self.flipindex, :].copy()
              flipped_p2d[:, 0] = (
                  self.input_size - 1 - flipped_p2d[:, 0]
              )
              eye_points = flipped_p2d
      label_verts_tensor = torch.tensor(
          label_verts * 10.0, dtype=torch.float32
      )
      d = {}
      d["img_local"] = img_local
      d["verts"] = label_verts_tensor
      d["6dof"] = label_6dof_tensor
      d["rt"] = torch.tensor(label_Rt, dtype=torch.float32)
      if need_points2d:
          points2d = points2d / (self.input_size // 2) - 1.0
          points2d_tensor = torch.tensor(points2d, dtype=torch.float32)
          d["points2d"] = points2d_tensor
      if self.use_eye:
          d["eye_verts"] = torch.tensor(eye_verts, dtype=torch.float32)
          eye_points = eye_points / (self.input_size // 2) - 1.0
          eye_points_tensor = torch.tensor(
              eye_points, dtype=torch.float32
          )
          d["eye_points"] = eye_points_tensor
  
      loss_weight = 1.0
      if group != 0:
          loss_weight = 0.0
      loss_weight_tensor = torch.tensor(
          loss_weight, dtype=torch.float32
      )
      d["loss_weight"] = loss_weight_tensor
      label_tform_tensor = torch.tensor(
          label_tform, dtype=torch.float32
      )
      d["tform"] = label_tform_tensor
  
      # if img_local is None:
      #    image = (img_raw,)
      # elif img_raw is None:
      #    image = (img_local,)
      # else:
      #    image = (img_local,img_raw)
      # ret = image + (label_verts_tensor, label_6dof_tensor, points2d_tensor)
      if not self.is_train:
          idx_tensor = torch.tensor([idx], dtype=torch.long)
          d["idx"] = idx_tensor
          d["verts2d"] = torch.tensor(verts2d, dtype=torch.float32)
      return d
  
imports: import numbers
  import os
  import os.path
  import pickle
  import queue
  import threading
  import logging
  import numbers
  import math
  import pandas
  from scipy.spatial.transform import Rotation
  import mxnet
  from pathlib import Path
  import numpy
  import torch
  from torch.utils.data import DataLoader, Dataset
  from skimage import transform
  import cv2
  import albumentations
  from albumentations.pytorch import ToTensorV2
  from augs import *
label_desc: Use image node_id 3 for calling the MXFaceDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: d