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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)
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