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'''
-----------------------------------------------------------------------------
Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
-----------------------------------------------------------------------------
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
import numpy as np
from DIT.attention import SelfAttention, CrossAttention
# import kiui
class DummyLatent:
def __init__(self, mean):
self.mean = mean
def sample(self):
return self.mean
def mode(self):
return self.mean
def kl(self):
# just an l2 penalty
return 0.5 * torch.sum(torch.pow(self.mean, 2))
class PointEmbed(nn.Module):
def __init__(self, dim=512, freq_embed_dim=48):
super().__init__()
# frequency embedding
assert freq_embed_dim % 6 == 0
self.freq_embed_dim = freq_embed_dim
e = torch.pow(2, torch.arange(self.freq_embed_dim // 6)).float() * np.pi
e = torch.stack([
torch.cat([e, torch.zeros(self.freq_embed_dim // 6), torch.zeros(self.freq_embed_dim // 6)]),
torch.cat([torch.zeros(self.freq_embed_dim // 6), e, torch.zeros(self.freq_embed_dim // 6)]),
torch.cat([torch.zeros(self.freq_embed_dim // 6), torch.zeros(self.freq_embed_dim // 6), e]),
])
self.register_buffer('basis', e) # [3, 48]
self.mlp = nn.Linear(self.freq_embed_dim+3, dim)
@staticmethod
def embed(input, basis):
projections = torch.einsum('bnd,de->bne', input, basis.to(input.dtype))
embeddings = torch.cat([projections.sin(), projections.cos()], dim=2)
return embeddings
def forward(self, input):
# input: B x N x 3
embed = self.embed(input, self.basis) # B x N x C
embed = torch.cat([embed, input], dim=2).to(input.dtype)
embed = self.mlp(embed) # B x N x C
return embed
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim = -1)
return x * F.gelu(gates)
class FeedForward(nn.Module):
def __init__(self, dim, mult=4):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, dim * mult * 2),
GEGLU(),
nn.Linear(dim * mult, dim)
)
def forward(self, x):
return self.net(x)
class ResAttBlock(nn.Module):
def __init__(self, dim, num_heads, gradient_checkpointing=True):
super().__init__()
self.gradient_checkpointing = gradient_checkpointing
self.ln1 = nn.LayerNorm(dim)
self.att = SelfAttention(dim, num_heads)
self.ln2 = nn.LayerNorm(dim)
self.mlp = FeedForward(dim)
def forward(self, x):
if self.training and self.gradient_checkpointing:
return checkpoint(self._forward, x, use_reentrant=False)
else:
return self._forward(x)
def _forward(self, x):
x = x + self.att(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class ResCrossAttBlock(nn.Module):
def __init__(self, dim, num_heads, gradient_checkpointing=True):
super().__init__()
self.gradient_checkpointing = gradient_checkpointing
self.ln1 = nn.LayerNorm(dim)
self.att = CrossAttention(dim, num_heads)
self.ln2 = nn.LayerNorm(dim)
self.mlp = FeedForward(dim)
def forward(self, x, c):
if self.training and self.gradient_checkpointing:
return checkpoint(self._forward, x, c, use_reentrant=False)
else:
return self._forward(x, c)
def _forward(self, x, c):
x = x + self.att(self.ln1(x), c)
x = x + self.mlp(self.ln2(x))
return x
class PointEncoder(nn.Module):
def __init__(self, hidden_dim=1024, num_heads=16, latent_size=2048, latent_dim=64, gradient_checkpointing=True):
super().__init__()
self.latent_size = latent_size
# self.query_embed = nn.Parameter(torch.randn(1, latent_size, hidden_dim) / hidden_dim ** 0.5)
self.latent_dim=latent_dim
self.point_embed = PointEmbed(dim=hidden_dim)
self.ln = nn.LayerNorm(hidden_dim)
self.cross_att = ResCrossAttBlock(hidden_dim, num_heads, gradient_checkpointing)
self.linear = nn.Linear(hidden_dim, latent_dim)
def forward(self, pc):
# pc: [B, N, 3]
# return: latent [B, L, D]
B, N, C = pc.shape
# embed
x = self.ln(self.point_embed(pc)) # [B, N, D], condition (kv)
###### fps
import torch_cluster
pc_flattened = pc.view(B*N, C)
batch_indices = torch.arange(B, device=pc.device).repeat_interleave(N)
fps_indices = torch_cluster.fps(pc_flattened, batch_indices, ratio=self.latent_size / N)
query_pc = pc_flattened[fps_indices].view(B, self.latent_size, C)
q = self.point_embed(query_pc)
######
# att
l = self.cross_att(q, x)
# out
l = self.linear(l) # [B, L, D]
posterior = DummyLatent(l)
return posterior
class PointEncoderEmbed(nn.Module):
def __init__(self, hidden_dim=1024, num_heads=16, latent_size=2048, latent_dim=64, gradient_checkpointing=True):
super().__init__()
self.latent_size = latent_size
self.query_embed = nn.Parameter(torch.randn(1, latent_size, hidden_dim) / hidden_dim ** 0.5)
self.point_embed = PointEmbed(dim=hidden_dim)
self.ln = nn.LayerNorm(hidden_dim)
self.cross_att = ResCrossAttBlock(hidden_dim, num_heads, gradient_checkpointing)
self.linear = nn.Linear(hidden_dim, latent_dim)
def forward(self, x):
# x: [B, N, 3]
# return: latent [B, L, D]
B, N, C = x.shape
# embed
x = self.ln(self.point_embed(x)) # [B, N, D], condition (kv)
# downsample x to q
q = self.query_embed.repeat(B, 1, 1) # query
# att
l = self.cross_att(q, x)
# out
l = self.linear(l) # [B, L, D]
posterior = DummyLatent(l)
return posterior |