mirror of
https://github.com/deepseek-ai/DreamCraft3D
synced 2024-12-04 18:15:11 +00:00
459 lines
15 KiB
Python
459 lines
15 KiB
Python
import math
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from collections import defaultdict
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from igl import fast_winding_number_for_meshes, point_mesh_squared_distance, read_obj
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from torch.autograd import Function
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from torch.cuda.amp import custom_bwd, custom_fwd
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import threestudio
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from threestudio.utils.typing import *
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def dot(x, y):
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return torch.sum(x * y, -1, keepdim=True)
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def reflect(x, n):
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return 2 * dot(x, n) * n - x
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ValidScale = Union[Tuple[float, float], Num[Tensor, "2 D"]]
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def scale_tensor(
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dat: Num[Tensor, "... D"], inp_scale: ValidScale, tgt_scale: ValidScale
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):
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if inp_scale is None:
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inp_scale = (0, 1)
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if tgt_scale is None:
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tgt_scale = (0, 1)
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if isinstance(tgt_scale, Tensor):
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assert dat.shape[-1] == tgt_scale.shape[-1]
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dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])
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dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]
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return dat
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class _TruncExp(Function): # pylint: disable=abstract-method
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# Implementation from torch-ngp:
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# https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, x): # pylint: disable=arguments-differ
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ctx.save_for_backward(x)
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return torch.exp(x)
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@staticmethod
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@custom_bwd
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def backward(ctx, g): # pylint: disable=arguments-differ
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x = ctx.saved_tensors[0]
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return g * torch.exp(torch.clamp(x, max=15))
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class SpecifyGradient(Function):
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# Implementation from stable-dreamfusion
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# https://github.com/ashawkey/stable-dreamfusion
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@staticmethod
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@custom_fwd
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def forward(ctx, input_tensor, gt_grad):
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ctx.save_for_backward(gt_grad)
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# we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward.
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return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype)
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_scale):
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(gt_grad,) = ctx.saved_tensors
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gt_grad = gt_grad * grad_scale
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return gt_grad, None
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trunc_exp = _TruncExp.apply
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def get_activation(name) -> Callable:
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if name is None:
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return lambda x: x
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name = name.lower()
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if name == "none":
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return lambda x: x
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elif name == "lin2srgb":
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return lambda x: torch.where(
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x > 0.0031308,
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torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055,
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12.92 * x,
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).clamp(0.0, 1.0)
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elif name == "exp":
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return lambda x: torch.exp(x)
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elif name == "shifted_exp":
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return lambda x: torch.exp(x - 1.0)
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elif name == "trunc_exp":
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return trunc_exp
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elif name == "shifted_trunc_exp":
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return lambda x: trunc_exp(x - 1.0)
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elif name == "sigmoid":
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return lambda x: torch.sigmoid(x)
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elif name == "tanh":
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return lambda x: torch.tanh(x)
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elif name == "shifted_softplus":
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return lambda x: F.softplus(x - 1.0)
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elif name == "scale_-11_01":
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return lambda x: x * 0.5 + 0.5
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else:
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try:
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return getattr(F, name)
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except AttributeError:
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raise ValueError(f"Unknown activation function: {name}")
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def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any:
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if chunk_size <= 0:
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return func(*args, **kwargs)
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B = None
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for arg in list(args) + list(kwargs.values()):
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if isinstance(arg, torch.Tensor):
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B = arg.shape[0]
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break
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assert (
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B is not None
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), "No tensor found in args or kwargs, cannot determine batch size."
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out = defaultdict(list)
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out_type = None
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# max(1, B) to support B == 0
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for i in range(0, max(1, B), chunk_size):
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out_chunk = func(
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*[
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arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
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for arg in args
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],
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**{
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k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
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for k, arg in kwargs.items()
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},
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)
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if out_chunk is None:
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continue
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out_type = type(out_chunk)
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if isinstance(out_chunk, torch.Tensor):
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out_chunk = {0: out_chunk}
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elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list):
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chunk_length = len(out_chunk)
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out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)}
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elif isinstance(out_chunk, dict):
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pass
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else:
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print(
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f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}."
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)
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exit(1)
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for k, v in out_chunk.items():
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v = v if torch.is_grad_enabled() else v.detach()
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out[k].append(v)
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if out_type is None:
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return None
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out_merged: Dict[Any, Optional[torch.Tensor]] = {}
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for k, v in out.items():
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if all([vv is None for vv in v]):
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# allow None in return value
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out_merged[k] = None
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elif all([isinstance(vv, torch.Tensor) for vv in v]):
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out_merged[k] = torch.cat(v, dim=0)
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else:
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raise TypeError(
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f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}"
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)
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if out_type is torch.Tensor:
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return out_merged[0]
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elif out_type in [tuple, list]:
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return out_type([out_merged[i] for i in range(chunk_length)])
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elif out_type is dict:
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return out_merged
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def get_ray_directions(
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H: int,
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W: int,
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focal: Union[float, Tuple[float, float]],
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principal: Optional[Tuple[float, float]] = None,
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use_pixel_centers: bool = True,
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) -> Float[Tensor, "H W 3"]:
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"""
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Get ray directions for all pixels in camera coordinate.
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Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/
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ray-tracing-generating-camera-rays/standard-coordinate-systems
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Inputs:
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H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers
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Outputs:
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directions: (H, W, 3), the direction of the rays in camera coordinate
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"""
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pixel_center = 0.5 if use_pixel_centers else 0
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if isinstance(focal, float):
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fx, fy = focal, focal
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cx, cy = W / 2, H / 2
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else:
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fx, fy = focal
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assert principal is not None
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cx, cy = principal
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i, j = torch.meshgrid(
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torch.arange(W, dtype=torch.float32) + pixel_center,
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torch.arange(H, dtype=torch.float32) + pixel_center,
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indexing="xy",
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)
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directions: Float[Tensor, "H W 3"] = torch.stack(
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[(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1
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)
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return directions
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def get_rays(
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directions: Float[Tensor, "... 3"],
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c2w: Float[Tensor, "... 4 4"],
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keepdim=False,
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noise_scale=0.0,
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normalize=True,
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) -> Tuple[Float[Tensor, "... 3"], Float[Tensor, "... 3"]]:
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# Rotate ray directions from camera coordinate to the world coordinate
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assert directions.shape[-1] == 3
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if directions.ndim == 2: # (N_rays, 3)
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if c2w.ndim == 2: # (4, 4)
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c2w = c2w[None, :, :]
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assert c2w.ndim == 3 # (N_rays, 4, 4) or (1, 4, 4)
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rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1) # (N_rays, 3)
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rays_o = c2w[:, :3, 3].expand(rays_d.shape)
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elif directions.ndim == 3: # (H, W, 3)
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assert c2w.ndim in [2, 3]
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if c2w.ndim == 2: # (4, 4)
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rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum(
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-1
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) # (H, W, 3)
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rays_o = c2w[None, None, :3, 3].expand(rays_d.shape)
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elif c2w.ndim == 3: # (B, 4, 4)
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rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
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-1
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) # (B, H, W, 3)
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rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
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elif directions.ndim == 4: # (B, H, W, 3)
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assert c2w.ndim == 3 # (B, 4, 4)
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rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
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-1
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) # (B, H, W, 3)
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rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
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# add camera noise to avoid grid-like artifect
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# https://github.com/ashawkey/stable-dreamfusion/blob/49c3d4fa01d68a4f027755acf94e1ff6020458cc/nerf/utils.py#L373
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if noise_scale > 0:
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rays_o = rays_o + torch.randn(3, device=rays_o.device) * noise_scale
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rays_d = rays_d + torch.randn(3, device=rays_d.device) * noise_scale
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if normalize:
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rays_d = F.normalize(rays_d, dim=-1)
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if not keepdim:
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rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)
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return rays_o, rays_d
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def get_projection_matrix(
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fovy: Float[Tensor, "B"], aspect_wh: float, near: float, far: float
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) -> Float[Tensor, "B 4 4"]:
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batch_size = fovy.shape[0]
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proj_mtx = torch.zeros(batch_size, 4, 4, dtype=torch.float32)
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proj_mtx[:, 0, 0] = 1.0 / (torch.tan(fovy / 2.0) * aspect_wh)
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proj_mtx[:, 1, 1] = -1.0 / torch.tan(
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fovy / 2.0
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) # add a negative sign here as the y axis is flipped in nvdiffrast output
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proj_mtx[:, 2, 2] = -(far + near) / (far - near)
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proj_mtx[:, 2, 3] = -2.0 * far * near / (far - near)
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proj_mtx[:, 3, 2] = -1.0
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return proj_mtx
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def get_mvp_matrix(
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c2w: Float[Tensor, "B 4 4"], proj_mtx: Float[Tensor, "B 4 4"]
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) -> Float[Tensor, "B 4 4"]:
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# calculate w2c from c2w: R' = Rt, t' = -Rt * t
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# mathematically equivalent to (c2w)^-1
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w2c: Float[Tensor, "B 4 4"] = torch.zeros(c2w.shape[0], 4, 4).to(c2w)
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w2c[:, :3, :3] = c2w[:, :3, :3].permute(0, 2, 1)
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w2c[:, :3, 3:] = -c2w[:, :3, :3].permute(0, 2, 1) @ c2w[:, :3, 3:]
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w2c[:, 3, 3] = 1.0
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# calculate mvp matrix by proj_mtx @ w2c (mv_mtx)
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mvp_mtx = proj_mtx @ w2c
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return mvp_mtx
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def get_full_projection_matrix(
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c2w: Float[Tensor, "B 4 4"], proj_mtx: Float[Tensor, "B 4 4"]
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) -> Float[Tensor, "B 4 4"]:
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return (c2w.unsqueeze(0).bmm(proj_mtx.unsqueeze(0))).squeeze(0)
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def binary_cross_entropy(input, target):
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"""
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F.binary_cross_entropy is not numerically stable in mixed-precision training.
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"""
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return -(target * torch.log(input) + (1 - target) * torch.log(1 - input)).mean()
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def tet_sdf_diff(
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vert_sdf: Float[Tensor, "Nv 1"], tet_edges: Integer[Tensor, "Ne 2"]
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) -> Float[Tensor, ""]:
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sdf_f1x6x2 = vert_sdf[:, 0][tet_edges.reshape(-1)].reshape(-1, 2)
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mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
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sdf_f1x6x2 = sdf_f1x6x2[mask]
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sdf_diff = F.binary_cross_entropy_with_logits(
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sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()
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) + F.binary_cross_entropy_with_logits(
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sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float()
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)
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return sdf_diff
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# Implementation from Latent-NeRF
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# https://github.com/eladrich/latent-nerf/blob/f49ecefcd48972e69a28e3116fe95edf0fac4dc8/src/latent_nerf/models/mesh_utils.py
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class MeshOBJ:
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dx = torch.zeros(3).float()
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dx[0] = 1
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dy, dz = dx[[1, 0, 2]], dx[[2, 1, 0]]
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dx, dy, dz = dx[None, :], dy[None, :], dz[None, :]
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def __init__(self, v: np.ndarray, f: np.ndarray):
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self.v = v
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self.f = f
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self.dx, self.dy, self.dz = MeshOBJ.dx, MeshOBJ.dy, MeshOBJ.dz
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self.v_tensor = torch.from_numpy(self.v)
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vf = self.v[self.f, :]
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self.f_center = vf.mean(axis=1)
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self.f_center_tensor = torch.from_numpy(self.f_center).float()
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e1 = vf[:, 1, :] - vf[:, 0, :]
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e2 = vf[:, 2, :] - vf[:, 0, :]
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self.face_normals = np.cross(e1, e2)
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self.face_normals = (
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self.face_normals / np.linalg.norm(self.face_normals, axis=-1)[:, None]
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)
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self.face_normals_tensor = torch.from_numpy(self.face_normals)
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def normalize_mesh(self, target_scale=0.5):
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verts = self.v
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# Compute center of bounding box
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# center = torch.mean(torch.column_stack([torch.max(verts, dim=0)[0], torch.min(verts, dim=0)[0]]))
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center = verts.mean(axis=0)
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verts = verts - center
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scale = np.max(np.linalg.norm(verts, axis=1))
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verts = (verts / scale) * target_scale
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return MeshOBJ(verts, self.f)
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def winding_number(self, query: torch.Tensor):
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device = query.device
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shp = query.shape
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query_np = query.detach().cpu().reshape(-1, 3).numpy()
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target_alphas = fast_winding_number_for_meshes(
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self.v.astype(np.float32), self.f, query_np
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)
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return torch.from_numpy(target_alphas).reshape(shp[:-1]).to(device)
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def gaussian_weighted_distance(self, query: torch.Tensor, sigma):
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device = query.device
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shp = query.shape
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query_np = query.detach().cpu().reshape(-1, 3).numpy()
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distances, _, _ = point_mesh_squared_distance(
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query_np, self.v.astype(np.float32), self.f
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)
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distances = torch.from_numpy(distances).reshape(shp[:-1]).to(device)
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weight = torch.exp(-(distances / (2 * sigma**2)))
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return weight
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def ce_pq_loss(p, q, weight=None):
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def clamp(v, T=0.0001):
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return v.clamp(T, 1 - T)
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p = p.view(q.shape)
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ce = -1 * (p * torch.log(clamp(q)) + (1 - p) * torch.log(clamp(1 - q)))
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if weight is not None:
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ce *= weight
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return ce.sum()
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class ShapeLoss(nn.Module):
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def __init__(self, guide_shape):
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super().__init__()
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self.mesh_scale = 0.7
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self.proximal_surface = 0.3
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self.delta = 0.2
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self.shape_path = guide_shape
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v, _, _, f, _, _ = read_obj(self.shape_path, float)
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mesh = MeshOBJ(v, f)
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matrix_rot = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) @ np.array(
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[[0, 0, 1], [0, 1, 0], [-1, 0, 0]]
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)
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self.sketchshape = mesh.normalize_mesh(self.mesh_scale)
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self.sketchshape = MeshOBJ(
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np.ascontiguousarray(
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(matrix_rot @ self.sketchshape.v.transpose(1, 0)).transpose(1, 0)
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),
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f,
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)
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def forward(self, xyzs, sigmas):
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mesh_occ = self.sketchshape.winding_number(xyzs)
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if self.proximal_surface > 0:
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weight = 1 - self.sketchshape.gaussian_weighted_distance(
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xyzs, self.proximal_surface
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)
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else:
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weight = None
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indicator = (mesh_occ > 0.5).float()
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nerf_occ = 1 - torch.exp(-self.delta * sigmas)
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nerf_occ = nerf_occ.clamp(min=0, max=1.1)
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loss = ce_pq_loss(
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nerf_occ, indicator, weight=weight
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) # order is important for CE loss + second argument may not be optimized
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return loss
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def shifted_expotional_decay(a, b, c, r):
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return a * torch.exp(-b * r) + c
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def shifted_cosine_decay(a, b, c, r):
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return a * torch.cos(b * r + c) + a
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|
|
|
def perpendicular_component(x: Float[Tensor, "B C H W"], y: Float[Tensor, "B C H W"]):
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# get the component of x that is perpendicular to y
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|
eps = torch.ones_like(x[:, 0, 0, 0]) * 1e-6
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|
return (
|
|
x
|
|
- (
|
|
torch.mul(x, y).sum(dim=[1, 2, 3])
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/ torch.maximum(torch.mul(y, y).sum(dim=[1, 2, 3]), eps)
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|
).view(-1, 1, 1, 1)
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|
* y
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|
)
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|
|
|
|
|
def validate_empty_rays(ray_indices, t_start, t_end):
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|
if ray_indices.nelement() == 0:
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|
threestudio.warn("Empty rays_indices!")
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|
ray_indices = torch.LongTensor([0]).to(ray_indices)
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|
t_start = torch.Tensor([0]).to(ray_indices)
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|
t_end = torch.Tensor([0]).to(ray_indices)
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|
return ray_indices, t_start, t_end |