LION/models/latent_points_ada.py

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2023-01-23 05:14:49 +00:00
# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
import torch
from loguru import logger
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .pvcnn2_ada import \
create_pointnet2_sa_components, create_pointnet2_fp_modules, LinearAttention, create_mlp_components, SharedMLP
# the building block of encode and decoder for VAE
class PVCNN2Unet(nn.Module):
"""
copied and modified from https://github.com/alexzhou907/PVD/blob/9747265a5f141e5546fd4f862bfa66aa59f1bd33/model/pvcnn_generation.py#L172
"""
def __init__(self,
num_classes, embed_dim, use_att, dropout=0.1,
extra_feature_channels=3,
input_dim=3,
width_multiplier=1,
voxel_resolution_multiplier=1,
time_emb_scales=1.0,
verbose=True,
condition_input=False,
point_as_feat=1, cfg={},
sa_blocks={}, fp_blocks={},
clip_forge_enable=0,
clip_forge_dim=512
):
super().__init__()
logger.info('[Build Unet] extra_feature_channels={}, input_dim={}',
extra_feature_channels, input_dim)
self.input_dim = input_dim
self.clip_forge_enable = clip_forge_enable
self.sa_blocks = sa_blocks
self.fp_blocks = fp_blocks
self.point_as_feat = point_as_feat
self.condition_input = condition_input
assert extra_feature_channels >= 0
self.time_emb_scales = time_emb_scales
self.embed_dim = embed_dim
## assert(self.embed_dim == 0)
if self.embed_dim > 0: # has time embedding
# for prior model, we have time embedding, for VAE model, no time embedding
self.embedf = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.LeakyReLU(0.1, inplace=True),
nn.Linear(embed_dim, embed_dim),
)
if self.clip_forge_enable:
self.clip_forge_mapping = nn.Linear(clip_forge_dim, embed_dim)
style_dim = cfg.latent_pts.style_dim
self.style_clip = nn.Linear(style_dim + embed_dim, style_dim)
self.in_channels = extra_feature_channels + 3
sa_layers, sa_in_channels, channels_sa_features, _ = \
create_pointnet2_sa_components(
input_dim=input_dim,
sa_blocks=self.sa_blocks,
extra_feature_channels=extra_feature_channels,
with_se=True,
embed_dim=embed_dim, # time embedding dim
use_att=use_att, dropout=dropout,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier,
verbose=verbose, cfg=cfg
)
self.sa_layers = nn.ModuleList(sa_layers)
self.global_att = None if not use_att else LinearAttention(channels_sa_features, 8, verbose=verbose)
# only use extra features in the last fp module
sa_in_channels[0] = extra_feature_channels + input_dim - 3
fp_layers, channels_fp_features = create_pointnet2_fp_modules(
fp_blocks=self.fp_blocks, in_channels=channels_sa_features,
sa_in_channels=sa_in_channels,
with_se=True, embed_dim=embed_dim,
use_att=use_att, dropout=dropout,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier,
verbose=verbose, cfg=cfg
)
self.fp_layers = nn.ModuleList(fp_layers)
layers, _ = create_mlp_components(
in_channels=channels_fp_features,
out_channels=[128, dropout, num_classes], # was 0.5
classifier=True, dim=2, width_multiplier=width_multiplier,
cfg=cfg)
self.classifier = nn.ModuleList(layers)
def get_timestep_embedding(self, timesteps, device):
if len(timesteps.shape) == 2 and timesteps.shape[1] == 1:
timesteps = timesteps[:,0]
assert(len(timesteps.shape) == 1), f'get shape: {timesteps.shape}'
timesteps = timesteps * self.time_emb_scales
half_dim = self.embed_dim // 2
emb = np.log(10000) / (half_dim - 1)
emb = torch.from_numpy(np.exp(np.arange(0, half_dim) * -emb)).float().to(device)
emb = timesteps[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if self.embed_dim % 2 == 1: # zero pad
emb = nn.functional.pad(emb, (0, 1), "constant", 0)
assert emb.shape == torch.Size([timesteps.shape[0], self.embed_dim])
return emb
def forward(self, inputs, **kwargs):
# Input: coords: B3N
B = inputs.shape[0]
coords = inputs[:, :self.input_dim, :].contiguous()
features = inputs
temb = kwargs.get('t', None)
if temb is not None:
t = temb
if t.ndim == 0 and not len(t.shape) == 1:
t = t.view(1).expand(B)
temb = self.embedf(self.get_timestep_embedding(t, inputs.device
))[:,:,None].expand(-1,-1,inputs.shape[-1])
temb_ori = temb # B,embed_dim,Npoint
style = kwargs['style']
if self.clip_forge_enable:
clip_feat = kwargs['clip_feat']
assert(clip_feat is not None), f'require clip_feat as input'
clip_feat = self.clip_forge_mapping(clip_feat)
style = torch.cat([style, clip_feat], dim=1).contiguous()
style = self.style_clip(style)
coords_list, in_features_list = [], []
for i, sa_blocks in enumerate(self.sa_layers):
in_features_list.append(features)
coords_list.append(coords)
if i > 0 and temb is not None:
#TODO: implement a sa_blocks forward function; check if is PVConv layer and kwargs get grid_emb, take as additional input
features = torch.cat([features,temb],dim=1)
features, coords, temb, _ = \
sa_blocks ((features,
coords, temb, style))
else: # i == 0 or temb is None
features, coords, temb, _ = \
sa_blocks ((features, coords, temb, style))
in_features_list[0] = inputs[:, 3:, :].contiguous()
if self.global_att is not None:
features = self.global_att(features)
for fp_idx, fp_blocks in enumerate(self.fp_layers):
if temb is not None:
features, coords, temb, _ = fp_blocks((
coords_list[-1-fp_idx], coords,
torch.cat([features,temb],dim=1),
in_features_list[-1-fp_idx], temb, style))
else:
features, coords, temb, _ = fp_blocks((
coords_list[-1-fp_idx], coords,
features,
in_features_list[-1-fp_idx], temb, style))
for l in self.classifier:
if isinstance(l, SharedMLP):
features = l(features, style)
else:
features = l(features)
return features
class PointTransPVC(nn.Module):
# encoder : B,N,3 -> B,N,2*D
sa_blocks = [ # conv_configs, sa_configs
((32, 2, 32), (1024, 0.1, 32, (32, 64))),
((64, 3, 16), (256, 0.2, 32, (64, 128))),
((128, 3, 8), (64, 0.4, 32, (128, 256))),
(None, (16, 0.8, 32, (128, 128, 128))),
]
fp_blocks = [
((128, 128), (128, 3, 8)), # fp_configs, conv_configs
((128, 128), (128, 3, 8)),
((128, 128), (128, 2, 16)),
((128, 128, 64), (64, 2, 32)),
]
def __init__(self, zdim, input_dim, args={}):
super().__init__()
self.zdim = zdim
self.layers = PVCNN2Unet(2*zdim+input_dim*2,
embed_dim=0, use_att=1, extra_feature_channels=0,
input_dim=args.ddpm.input_dim, cfg=args,
sa_blocks=self.sa_blocks, fp_blocks=self.fp_blocks,
dropout=args.ddpm.dropout)
self.skip_weight = args.latent_pts.skip_weight
self.pts_sigma_offset = args.latent_pts.pts_sigma_offset
self.input_dim = input_dim
def forward(self, inputs):
x, style = inputs
B,N,D = x.shape
output = self.layers(x.permute(0,2,1).contiguous(), style=style).permute(0,2,1).contiguous() # BND
pt_mu_1d = output[:,:,:self.input_dim].contiguous()
pt_sigma_1d = output[:,:,self.input_dim:2*self.input_dim].contiguous() - self.pts_sigma_offset
pt_mu_1d = self.skip_weight * pt_mu_1d + x
if self.zdim > 0:
ft_mu_1d = output[:,:,2*self.input_dim:-self.zdim].contiguous()
ft_sigma_1d = output[:,:,-self.zdim:].contiguous()
mu_1d = torch.cat([pt_mu_1d, ft_mu_1d], dim=2).view(B,-1).contiguous()
sigma_1d = torch.cat([pt_sigma_1d, ft_sigma_1d], dim=2).view(B,-1).contiguous()
else:
mu_1d = pt_mu_1d.view(B,-1).contiguous()
sigma_1d = pt_sigma_1d.view(B,-1).contiguous()
return {'mu_1d': mu_1d, 'sigma_1d': sigma_1d}
class LatentPointDecPVC(nn.Module):
""" input x: [B,Npoint,D] with [B,Npoint,3]
"""
sa_blocks = [ # conv_configs, sa_configs
((32, 2, 32), (1024, 0.1, 32, (32, 64))),
((64, 3, 16), (256, 0.2, 32, (64, 128))),
((128, 3, 8), (64, 0.4, 32, (128, 256))),
(None, (16, 0.8, 32, (128, 128, 128))),
]
fp_blocks = [
((128, 128), (128, 3, 8)), # fp_configs, conv_configs
((128, 128), (128, 3, 8)),
((128, 128), (128, 2, 16)),
((128, 128, 64), (64, 2, 32)),
]
def __init__(self, point_dim, context_dim, num_points=None, args={}, **kwargs):
super().__init__()
self.point_dim = point_dim
logger.info('[Build Dec] point_dim={}, context_dim={}', point_dim, context_dim)
self.context_dim = context_dim + self.point_dim
# self.num_points = num_points
if num_points is None:
self.num_points = args.data.tr_max_sample_points
else:
self.num_points = num_points
self.layers = PVCNN2Unet(point_dim, embed_dim=0, use_att=1,
extra_feature_channels=context_dim,
input_dim=args.ddpm.input_dim, cfg=args,
sa_blocks=self.sa_blocks, fp_blocks=self.fp_blocks,
dropout=args.ddpm.dropout)
self.skip_weight = args.latent_pts.skip_weight
def forward(self, x, beta, context, style):
"""
Args:
x: Point clouds at some timestep t, (B, N, d). [not used]
beta: Time. (B, ). [not used]
context: Latent points, (B,N_pts*D_latent_pts), D_latent_pts = D_input + D_extra
style: Shape latents. (B,d).
Returns:
points: (B,N,3)
"""
# CHECKDIM(context, 1, self.num_points*self.context_dim)
assert(context.shape[1] == self.num_points*self.context_dim)
context = context.view(-1,self.num_points,self.context_dim) # BND
x = context[:,:,:self.point_dim]
output = self.layers(context.permute(0,2,1).contiguous(), style=style).permute(0,2,1).contiguous() # BN3
output = output * self.skip_weight + x
return output