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| import torch | |
| import torch.nn as nn | |
| from monoscene.modules import ( | |
| Process, | |
| ASPP, | |
| ) | |
| class CPMegaVoxels(nn.Module): | |
| def __init__(self, feature, size, n_relations=4, bn_momentum=0.0003): | |
| super().__init__() | |
| self.size = size | |
| self.n_relations = n_relations | |
| print("n_relations", self.n_relations) | |
| self.flatten_size = size[0] * size[1] * size[2] | |
| self.feature = feature | |
| self.context_feature = feature * 2 | |
| self.flatten_context_size = (size[0] // 2) * (size[1] // 2) * (size[2] // 2) | |
| padding = ((size[0] + 1) % 2, (size[1] + 1) % 2, (size[2] + 1) % 2) | |
| self.mega_context = nn.Sequential( | |
| nn.Conv3d( | |
| feature, self.context_feature, stride=2, padding=padding, kernel_size=3 | |
| ), | |
| ) | |
| self.flatten_context_size = (size[0] // 2) * (size[1] // 2) * (size[2] // 2) | |
| self.context_prior_logits = nn.ModuleList( | |
| [ | |
| nn.Sequential( | |
| nn.Conv3d( | |
| self.feature, | |
| self.flatten_context_size, | |
| padding=0, | |
| kernel_size=1, | |
| ), | |
| ) | |
| for i in range(n_relations) | |
| ] | |
| ) | |
| self.aspp = ASPP(feature, [1, 2, 3]) | |
| self.resize = nn.Sequential( | |
| nn.Conv3d( | |
| self.context_feature * self.n_relations + feature, | |
| feature, | |
| kernel_size=1, | |
| padding=0, | |
| bias=False, | |
| ), | |
| Process(feature, nn.BatchNorm3d, bn_momentum, dilations=[1]), | |
| ) | |
| def forward(self, input): | |
| ret = {} | |
| bs = input.shape[0] | |
| x_agg = self.aspp(input) | |
| # get the mega context | |
| x_mega_context_raw = self.mega_context(x_agg) | |
| x_mega_context = x_mega_context_raw.reshape(bs, self.context_feature, -1) | |
| x_mega_context = x_mega_context.permute(0, 2, 1) | |
| # get context prior map | |
| x_context_prior_logits = [] | |
| x_context_rels = [] | |
| for rel in range(self.n_relations): | |
| # Compute the relation matrices | |
| x_context_prior_logit = self.context_prior_logits[rel](x_agg) | |
| x_context_prior_logit = x_context_prior_logit.reshape( | |
| bs, self.flatten_context_size, self.flatten_size | |
| ) | |
| x_context_prior_logits.append(x_context_prior_logit.unsqueeze(1)) | |
| x_context_prior_logit = x_context_prior_logit.permute(0, 2, 1) | |
| x_context_prior = torch.sigmoid(x_context_prior_logit) | |
| # Multiply the relation matrices with the mega context to gather context features | |
| x_context_rel = torch.bmm(x_context_prior, x_mega_context) # bs, N, f | |
| x_context_rels.append(x_context_rel) | |
| x_context = torch.cat(x_context_rels, dim=2) | |
| x_context = x_context.permute(0, 2, 1) | |
| x_context = x_context.reshape( | |
| bs, x_context.shape[1], self.size[0], self.size[1], self.size[2] | |
| ) | |
| x = torch.cat([input, x_context], dim=1) | |
| x = self.resize(x) | |
| x_context_prior_logits = torch.cat(x_context_prior_logits, dim=1) | |
| ret["P_logits"] = x_context_prior_logits | |
| ret["x"] = x | |
| return ret | |