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| 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) | |