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Zero
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from utils import zero_init, EMANorm, create_rays
import einops
from .render import gaussian_render
from utils import quaternion_to_matrix
def inverse_sigmoid(x):
if type(x) == torch.Tensor:
return torch.log(x/(1-x))
else:
return math.log(x/(1-x))
def inverse_softplus(x, beta=1):
if type(x) == torch.Tensor:
return (torch.exp(beta * x) - 1).log() / beta
else:
return math.log((math.exp(beta * x) - 1)) / beta
import copy
import math
import torch
import torch.nn as nn
import numpy as np
from .autoencoder_kl_wan import WanCausalConv3d, WanRMS_norm, unpatchify
class WANDecoderPixelAligned3DGSReconstructionModel(nn.Module):
def __init__(self,
vae_model,
feat_dim,
# num_remove_decoder_up_blocks=0,
# num_points_per_pixel=4,
use_network_checkpointing=True,
use_render_checkpointing=True
):
super().__init__()
self.decoder = copy.deepcopy(vae_model.decoder).requires_grad_(True)
self.post_quant_conv = copy.deepcopy(vae_model.post_quant_conv).requires_grad_(True)
self.extra_conv_in = WanCausalConv3d(feat_dim, self.decoder.conv_in.weight.shape[0], 3, padding=1)
time_pad = self.extra_conv_in._padding[4]
self.extra_conv_in.padding = (0, self.extra_conv_in._padding[2], self.extra_conv_in._padding[0])
self.extra_conv_in._padding = (0, 0, 0, 0, 0, 0)
self.extra_conv_in.weight = torch.nn.Parameter(self.extra_conv_in.weight[:, :, time_pad:].clone())
with torch.no_grad():
self.extra_conv_in.weight.data.zero_()
self.extra_conv_in.bias.data.zero_()
# remove one block
# self.decoder.up_blocks = self.decoder.up_blocks[:-1]
dims = [self.decoder.dim * u for u in [self.decoder.dim_mult[-1]] + self.decoder.dim_mult[::-1]]
# self.decoder.up_blocks[-1].upsampler.mode = None
# self.decoder.up_blocks[-1].upsampler.resample = nn.Identity()
# self.decoder.up_blocks[-1].avg_shortcut = None
self.decoder.norm_out = WanRMS_norm(dims[-1], images=False, bias=False)
self.decoder.conv_out = nn.Identity()
# add ema_norm for vae
# for i_level in reversed(range(len(self.decoder.up_blocks))):
# if self.decoder.up_blocks[i_level].upsampler is not None:
# self.decoder.up_blocks[i_level].upsampler.resample = nn.Sequential(
# self.decoder.up_blocks[i_level].upsampler.resample,
# )
self.patch_size = vae_model.config.patch_size
# assert dims[-1] % 4 == 0
self.gs_head = PixelAligned3DGS(dims[-1], num_points_per_pixel=2)
del self.decoder.up_blocks[0].upsampler.time_conv
del self.decoder.up_blocks[1].upsampler.time_conv
self.decoder.conv_out = nn.Identity()
self.network_checkpointing = use_network_checkpointing
self.render_checkpointing = use_render_checkpointing
def decode(self, feats, z):
## conv1
x = self.decoder.conv_in(self.post_quant_conv(z)) + self.extra_conv_in(feats)
## middle
if self.network_checkpointing and torch.is_grad_enabled():
x = torch.utils.checkpoint.checkpoint(self.decoder.mid_block, x, None, [0], use_reentrant=False)
else:
x = self.decoder.mid_block(x, None, [0])
## upsamples
for i, up_block in enumerate(self.decoder.up_blocks):
if self.network_checkpointing and torch.is_grad_enabled():
x = torch.utils.checkpoint.checkpoint(up_block, x, None, [0], True, use_reentrant=False)
else:
x = up_block(x, None, [0], first_chunk=True)
# head
x = self.decoder.norm_out(x)
x = self.decoder.nonlinearity(x)
x = self.decoder.conv_out(x)
# if self.patch_size is not None:
# x = unpatchify(x, patch_size=self.patch_size)
return x
def forward(self, feats, z, cameras):
x = self.decode(feats, z).squeeze(2)
gaussian_params = self.gs_head(x, cameras.flatten(0, 1)).unflatten(0, (cameras.shape[0], cameras.shape[1]))
return gaussian_params
# def forward(self, images, cameras, scene_chunk_lens):
# x, z, feats = self.encode(images)
# return self.reconstruct(x, z, feats, cameras, scene_chunk_lens)
@torch.amp.autocast(device_type='cuda', enabled=False)
def render(self, gaussian_params, camerass, height, width, bg_mode='random'):
camerass = camerass.to(torch.float32)
test_c2ws = torch.eye(4, device=camerass.device)[None][None].repeat(camerass.shape[0], camerass.shape[1], 1, 1).float()
test_c2ws[:, :, :3, :3] = quaternion_to_matrix(camerass[:, :, :4])
test_c2ws[:, :, :3, 3] = camerass[:, :, 4:7]
test_intr = torch.eye(3, device=camerass.device)[None, None].repeat(camerass.shape[0], camerass.shape[1], 1, 1).float()
fx, fy, cx, cy = camerass[:, :, 7:11].split([1, 1, 1, 1], dim=-1)
test_intr = torch.cat([fx * width, fy * height, cx * width, cy * height], dim=-1)
return gaussian_render(gaussian_params, test_c2ws, test_intr, width, height, use_checkpoint=self.render_checkpointing, sh_degree=self.gs_head.sh_degree, bg_mode=bg_mode)
from torch.autograd import Function
class _trunc_exp(Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return torch.exp(x)
@staticmethod
def backward(ctx, g):
x = ctx.saved_tensors[0]
return g * torch.exp(x.clamp(-10, 10))
trunc_exp = _trunc_exp.apply
class PixelAligned3DGS(nn.Module):
def __init__(
self,
embed_dim,
sh_degree=2,
use_mask=False,
scale_range=(0, 16), # related to pixel size
num_points_per_pixel=1,
):
super().__init__()
self.sh_degree = sh_degree
# sh, uv_offset, depth, opacity, scales, rotations
# TODO: handle different sh_degree
self.gaussian_channels = [3 * (self.sh_degree + 1) ** 2, 2, 1, 1, 3, 4, (1 if use_mask else 0)]
self.gs_proj = nn.Conv2d(embed_dim, num_points_per_pixel * sum(self.gaussian_channels), 3, 1, 1)
self.register_buffer("lrs_mul", torch.Tensor(
[1] * 3 + # sh 0
[0.5] * 3 * ((self.sh_degree + 1) ** 2 - 1) + # other sh
[0.01] * 2 + # uv_offset
[1] * 1 + # depth
[1] * 1 + # opacity
[1] * 3 + # scales
[1] * 4 + # rotations
[0.1] * (1 if use_mask else 0) # mask
).repeat(num_points_per_pixel), persistent=True)
self.lrs_mul = self.lrs_mul / self.lrs_mul.max()
self.use_mask = use_mask
self.scale_range = scale_range
with torch.no_grad():
self.gs_proj.weight.data.zero_()
self.gs_proj.bias = nn.Parameter(torch.Tensor(
[0.0] * 3 * (self.sh_degree + 1) ** 2 + # sh
[0.0] * 2 + # uv_offset
[math.log(1)] * 1 + # depth
# [inverse_softplus(1)] * 1 + # depth
[inverse_sigmoid(0.1)] * 1 + # opacity
[inverse_sigmoid((1 - scale_range[0]) / (scale_range[1] - scale_range[0]))] * 3 + # scales (default: 1 hence the gaussian scale is equal to pixel size)
# [inverse_softplus(0.005)] * 3 + # scales (default: 1 hence the gaussian scale is equal to pixel size)
[1., 0, 0, 0] + # rotations
[inverse_sigmoid(0.9)] * (1 if use_mask else 0) # mask (default: 0.9)
).repeat(num_points_per_pixel) / self.lrs_mul)
self.num_points_per_pixel = num_points_per_pixel
@torch.amp.autocast(device_type='cuda', enabled=False)
def forward(self, x, cameras):
x = x.to(torch.float32)
cameras = cameras.to(torch.float32)
BN, _, h, w = x.shape
local_gaussian_params = F.conv2d(x, self.gs_proj.weight * self.lrs_mul[:, None, None, None], self.gs_proj.bias * self.lrs_mul, stride=1, padding=1).unflatten(1, (self.num_points_per_pixel, -1))
# local_gaussian_params = F.conv2d(x, self.gs_proj.weight, self.gs_proj.bias, stride=1, padding=1).unflatten(1, (self.num_points_per_pixel, -1))
# batch * n_frame, num_points_per_pixel, c, h, w -> batch * n_frame, num_points_per_pixel, h, w, c
local_gaussian_params = local_gaussian_params.permute(0, 1, 3, 4, 2)
features, uv_offset, depth, opacity, scales, rotations, mask = local_gaussian_params.split(self.gaussian_channels, dim=-1)
rays_o, rays_d = create_rays(cameras[:, None].repeat(1, self.num_points_per_pixel, 1), uv_offset=uv_offset, h=h, w=w)
depth = trunc_exp(depth)
# depth = F.softplus(depth, beta=1)
xyz = (rays_o + depth * rays_d)
# features = features.unflatten(-1, (-1, 3))
opacity = torch.sigmoid(opacity)
if self.use_mask:
if torch.is_grad_enabled():
mask = torch.sigmoid(mask)
hard_mask = (mask > torch.rand_like(mask)).float()
opacity = opacity * (mask + (hard_mask - mask).detach())
else:
mask = torch.sigmoid(mask)
hard_mask = (mask > torch.rand_like(mask)).float()
opacity = opacity * hard_mask
fx, fy = cameras[:, 7:9].split([1, 1], dim=-1)
fx, fy = fx / w, fy / h
pixel_size = torch.sqrt(fx.pow(2) + fy.pow(2))[:, None, None, None] * depth
scales = (torch.sigmoid(scales) * (self.scale_range[1] - self.scale_range[0]) + self.scale_range[0]) * pixel_size
# scales = F.softplus(scales, beta=1)
# It’s not required to be normalized for gspalt rasterization?
rotations = torch.nn.functional.normalize(rotations, dim=-1)
gaussian_params = torch.cat([xyz, opacity, scales, rotations, features], dim=-1)
return gaussian_params |