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Zero
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from typing import *
from torch import Tensor
from lpips import LPIPS
from skimage.metrics import structural_similarity as calculate_ssim
import numpy as np
from torch import nn
import torch.nn.functional as tF
from einops import rearrange
from src.models.networks.attention import *
from src.models.gs_render import GaussianRenderer
from src.options import Options
from src.utils import plucker_ray, patchify, unpatchify
class GSRecon(nn.Module):
def __init__(self, opt: Options):
super().__init__()
self.opt = opt
# Image tokenizer
in_channels = 3 + 6 # RGB + plucker
if opt.input_normal:
in_channels += 3
if opt.input_coord:
in_channels += 3
if opt.input_mr:
in_channels += 2
self.x_embedder = nn.Linear(in_channels * (opt.patch_size**2), opt.dim)
# Transformer backbone
self.transformer = Transformer(opt.num_blocks, opt.dim, opt.num_heads, llama_style=opt.llama_style)
self.ln_out = nn.LayerNorm(opt.dim)
if opt.grad_checkpoint:
self.transformer.set_grad_checkpointing()
# Output heads
self.inter_res = opt.input_res // opt.patch_size
self.out_depth = nn.Linear(opt.dim, 1 * (opt.patch_size**2), bias=False)
self.out_rgb = nn.Linear(opt.dim, 3 * (opt.patch_size**2), bias=False)
self.out_scale = nn.Linear(opt.dim, 3 * (opt.patch_size**2), bias=False)
self.out_rotation = nn.Linear(opt.dim, 4 * (opt.patch_size**2), bias=False)
self.out_opacity = nn.Linear(opt.dim, 1 * (opt.patch_size**2), bias=False)
# Rendering
self.gs_renderer = GaussianRenderer(opt)
# Initialize weights
nn.init.xavier_uniform_(self.x_embedder.weight)
nn.init.zeros_(self.x_embedder.bias)
nn.init.zeros_(self.out_depth.weight) # zero init.
nn.init.xavier_uniform_(self.out_rgb.weight)
nn.init.zeros_(self.out_scale.weight) # zero init.
nn.init.xavier_uniform_(self.out_rotation.weight)
nn.init.zeros_(self.out_opacity.weight) # zero init.
def forward(self, *args, func_name="compute_loss", **kwargs):
# To support different forward functions for models wrapped by `accelerate`
return getattr(self, func_name)(*args, **kwargs)
def compute_loss(self, data: Dict[str, Tensor], lpips_loss: LPIPS, step: int, dtype: torch.dtype = torch.float32):
outputs = {}
color_name = "albedo" if self.opt.input_albedo else "image"
images = data[color_name].to(dtype) # (B, V, 3, H, W)
masks = data["mask"].to(dtype) # (B, V, 1, H, W)
C2W = data["C2W"].to(dtype) # (B, V, 4, 4)
fxfycxcy = data["fxfycxcy"].to(dtype) # (B, V, 4)
# Input views
V_in = self.opt.num_input_views
input_images = images[:, :V_in, ...]
input_C2W = C2W[:, :V_in, ...]
input_fxfycxcy = fxfycxcy[:, :V_in, ...]
if self.opt.input_normal:
input_images = torch.cat([input_images, data["normal"][:, :V_in, ...]], dim=2)
if self.opt.input_coord:
input_images = torch.cat([input_images, data["coord"][:, :V_in, ...]], dim=2)
if self.opt.input_mr:
input_images = torch.cat([input_images, data["mr"][:, :V_in, :2]], dim=2)
model_outputs = self.forward_gaussians(input_images, input_C2W, input_fxfycxcy)
render_outputs = self.gs_renderer.render(model_outputs, input_C2W, input_fxfycxcy, C2W, fxfycxcy)
for k in render_outputs.keys():
render_outputs[k] = render_outputs[k].to(dtype)
render_images = render_outputs["image"] # (B, V, 3, H, W)
render_masks = render_outputs["alpha"] # (B, V, 1, H, W)
render_coords = render_outputs["coord"] # (B, V, 3, H, W)
render_normals = render_outputs["normal"] # (B, V, 3, H, W)
# For visualization
outputs["images_render"] = render_images
outputs["images_gt"] = images
if self.opt.vis_coords:
outputs["images_coord"] = render_coords
if self.opt.load_coord:
outputs["images_gt_coord"] = data["coord"]
if self.opt.vis_normals:
outputs["images_normal"] = render_normals
if self.opt.load_normal:
outputs["images_gt_normal"] = data["normal"]
# if self.opt.input_mr:
# outputs["images_mr"] = data["mr"]
################################ Compute reconstruction losses/metrics ################################
outputs["image_mse"] = image_mse = tF.mse_loss(images, render_images)
outputs["mask_mse"] = mask_mse = tF.mse_loss(masks, render_masks)
loss = image_mse + mask_mse
# Coord & Normal
if self.opt.coord_weight > 0:
assert self.opt.load_coord
outputs["coord_mse"] = coord_mse = tF.mse_loss(data["coord"], render_coords)
loss += self.opt.coord_weight * coord_mse
if self.opt.normal_weight > 0:
assert self.opt.load_normal
outputs["normal_cosim"] = normal_cosim = tF.cosine_similarity(data["normal"], render_normals, dim=2).mean()
loss += self.opt.normal_weight * (1. - normal_cosim)
# LPIPS
if step < self.opt.lpips_warmup_start:
lpips_weight = 0.
elif step > self.opt.lpips_warmup_end:
lpips_weight = self.opt.lpips_weight
else:
lpips_weight = self.opt.lpips_weight * (step - self.opt.lpips_warmup_start) / (
self.opt.lpips_warmup_end - self.opt.lpips_warmup_start)
if lpips_weight > 0.:
outputs["lpips"] = lpips = lpips_loss(
# Downsampled to at most 256 to reduce memory cost
tF.interpolate(
rearrange(images, "b v c h w -> (b v) c h w") * 2. - 1.,
(self.opt.lpips_resize, self.opt.lpips_resize), mode="bilinear", align_corners=False
) if self.opt.lpips_resize > 0 else rearrange(images, "b v c h w -> (b v) c h w") * 2. - 1.,
tF.interpolate(
rearrange(render_images, "b v c h w -> (b v) c h w") * 2. - 1.,
(self.opt.lpips_resize, self.opt.lpips_resize), mode="bilinear", align_corners=False
) if self.opt.lpips_resize > 0 else rearrange(render_images, "b v c h w -> (b v) c h w") * 2. - 1.,
).mean()
loss += lpips_weight * lpips
outputs["loss"] = loss
# Metric: PSNR, SSIM and LPIPS
with torch.no_grad():
outputs["psnr"] = -10 * torch.log10(torch.mean((images - render_images.detach()) ** 2))
outputs["ssim"] = torch.tensor(calculate_ssim(
(rearrange(images, "b v c h w -> (b v c) h w")
.cpu().float().numpy() * 255.).astype(np.uint8),
(rearrange(render_images.detach(), "b v c h w -> (b v c) h w")
.cpu().float().numpy() * 255.).astype(np.uint8),
channel_axis=0,
), device=images.device)
if lpips_weight <= 0.:
outputs["lpips"] = lpips = lpips_loss(
# Downsampled to at most 256 to reduce memory cost
tF.interpolate(
rearrange(images, "b v c h w -> (b v) c h w") * 2. - 1.,
(self.opt.lpips_resize, self.opt.lpips_resize), mode="bilinear", align_corners=False
) if self.opt.lpips_resize > 0 else rearrange(images, "b v c h w -> (b v) c h w") * 2. - 1.,
tF.interpolate(
rearrange(render_images.detach(), "b v c h w -> (b v) c h w") * 2. - 1.,
(256, 256), mode="bilinear", align_corners=False
) if self.opt.lpips_resize > 0 else rearrange(render_images.detach(), "b v c h w -> (b v) c h w") * 2. - 1.,
).mean()
return outputs
def forward_gaussians(self, input_images: Tensor, input_C2W: Tensor, input_fxfycxcy: Tensor):
"""
Inputs:
- `input_images`: (B, V_in, C, H, W)
- `input_C2W`: (B, V_in, 4, 4)
- `input_fxycxcy`: (B, V_in, 4)
"""
_, V_in, _, H, W = input_images.shape
plucker, _ = plucker_ray(H, W, input_C2W, input_fxfycxcy) # (B, V_in, 6, H, W)
images_plucker = torch.cat([input_images * 2. - 1., plucker], dim=2)
images_plucker = rearrange(images_plucker, "b v c h w -> (b v) c h w")
x = patchify(images_plucker, self.opt.patch_size) # (B*V_in, N, C)
x = rearrange(x, "(b v) n c -> b v n c", v=V_in)
x = self.x_embedder(x) # (B, V_in, N, D)
x = rearrange(x, "b v n d -> b (v n) d")
x = self.transformer(x)
x = self.ln_out(x)
def _reshape_feature(features: Tensor):
features = rearrange(features, "b (v h w) d -> (b v) (h w) d", v=V_in, h=self.inter_res)
features = unpatchify(features, self.opt.patch_size, int(features.shape[1]**0.5))
features = rearrange(features, "(b v) c h w -> b v c h w", v=V_in) # (B, V_in, `dim`, H, W)
return features
depth = _reshape_feature(self.out_depth(x))
rgb = _reshape_feature(self.out_rgb(x))
scale = _reshape_feature(self.out_scale(x))
rotation = _reshape_feature(self.out_rotation(x))
opacity = _reshape_feature(self.out_opacity(x))
depth = torch.sigmoid(depth) * 2. - 1. # [0, 1] -> [-1, 1]
rgb = torch.sigmoid(rgb) * 2. - 1. # [0, 1] -> [-1, 1]
scale = torch.sigmoid(scale) * 2. - 1. # [0, 1] -> [-1, 1]
rotation = tF.normalize(rotation, p=2, dim=2) # L2 normalize [-1, 1]
opacity = torch.sigmoid(opacity - 2.) * 2. - 1. # [0, 1] -> [-1, 1]; `-2.` cf. GS-LRM Appendix A.4
return {
"depth": depth,
"rgb": rgb,
"scale": scale,
"rotation": rotation,
"opacity": opacity,
}
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