text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from .BasePIFuNet import BasePIFuNet
  
  
  class VhullPIFuNet(BasePIFuNet):
      '''
      Vhull Piximp network is a minimal network demonstrating how the template works
      also, it helps debugging the training/test schemes
      It does the following:
          1. Compute the masks of images and stores under self.im_feats
          2. Calculate calibration and indexing
          3. Return if the points fall into the intersection of all masks
      '''
  
      def __init__(self,
                   num_views,
                   projection_mode='orthogonal',
                   error_term=nn.MSELoss(),
                   ):
          super(VhullPIFuNet, self).__init__(
              projection_mode=projection_mode,
              error_term=error_term)
          self.name = 'vhull'
  
          self.num_views = num_views
  
          self.im_feat = None
  
      def filter(self, images):
          '''
          Filter the input images
          store all intermediate features.
          :param images: [B, C, H, W] input images
          '''
          # If the image has alpha channel, use the alpha channel
          if images.shape[1] > 3:
              self.im_feat = images[:, 3:4, :, :]
          # Else, tell if it's not white
          else:
              self.im_feat = images[:, 0:1, :, :]
  
      def query(self, points, calibs, transforms=None, labels=None):
          '''
          Given 3D points, query the network predictions for each point.
          Image features should be pre-computed before this call.
          store all intermediate features.
          query() function may behave differently during training/testing.
          :param points: [B, 3, N] world space coordinates of points
          :param calibs: [B, 3, 4] calibration matrices for each image
          :param transforms: Optional [B, 2, 3] image space coordinate transforms
          :param labels: Optional [B, Res, N] gt labeling
          :return: [B, Res, N] predictions for each point
          '''
          if labels is not None:
              self.labels = labels
  
          xyz = self.projection(points, calibs, transforms)
          xy = xyz[:, :2, :]
  
          point_local_feat = self.index(self.im_feat, xy)
          local_shape = point_local_feat.shape
          point_feat = point_local_feat.view(
              local_shape[0] // self.num_views,
              local_shape[1] * self.num_views,
              -1)
          pred = torch.prod(point_feat, dim=1)
  
          self.preds = pred.unsqueeze(1)
  