code: """ 
  @Date: 2021/08/12
  @description:
  """
  
  import torch
  import torch.nn as nn
  import numpy as np
  
  from visualization.grad import get_all
  
  
  class GradLoss(nn.Module):
      def __init__(self):
          super().__init__()
          self.loss = nn.L1Loss()
          self.cos = nn.CosineSimilarity(dim=-1, eps=0)
  
          self.grad_conv = nn.Conv1d(1, 1, kernel_size=3, stride=1, padding=0, bias=False, padding_mode='circular')
          self.grad_conv.weight = nn.Parameter(torch.tensor([[[1, 0, -1]]]).float())
          self.grad_conv.weight.requires_grad = False
  
      def forward(self, gt, dt):
          gt_direction, _, gt_angle_grad = get_all(gt['depth'], self.grad_conv)
          dt_direction, _, dt_angle_grad = get_all(dt['depth'], self.grad_conv)
  
          normal_loss = (1 - self.cos(gt_direction, dt_direction)).mean()
          grad_loss = self.loss(gt_angle_grad, dt_angle_grad)
          return [normal_loss, grad_loss]
  
  
  if __name__ == '__main__':
      from dataset.mp3d_dataset import MP3DDataset
      from utils.boundary import depth2boundaries
      from utils.conversion import uv2xyz
      from visualization.boundary import draw_boundaries
      from visualization.floorplan import draw_floorplan
  
      def show_boundary(image, depth, ratio):
          boundary_list = depth2boundaries(ratio, depth, step=None)
          draw_boundaries(image.transpose(1, 2, 0), boundary_list=boundary_list, show=True)
          draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=True, center_color=0.8)
  
      mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode='train', patch_num=256)
      gt = mp3d_dataset.__getitem__(1)
      gt['depth'] = torch.from_numpy(gt['depth'][np.newaxis])  # batch size is 1
      dummy_dt = {
          'depth': gt['depth'].clone(),
      }
      # dummy_dt['depth'][..., 20] *= 3  # some different
  
      # show_boundary(gt['image'], gt['depth'][0].numpy(), gt['ratio'])
      # show_boundary(gt['image'], dummy_dt['depth'][0].numpy(), gt['ratio'])
  
      grad_loss = GradLoss()
      loss = grad_loss(gt, dummy_dt)
      print(loss)
  
apis: torch.nn.L1Loss torch.tensor torch.nn.Conv1d torch.from_numpy torch.nn.CosineSimilarity