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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
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