mirror of
https://github.com/deepseek-ai/DreamCraft3D.git
synced 2025-02-23 14:28:55 -05:00
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
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import random
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from dataclasses import dataclass, field
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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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import threestudio
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from threestudio.models.materials.base import BaseMaterial
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from threestudio.models.networks import get_encoding, get_mlp
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from threestudio.utils.ops import dot, get_activation
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from threestudio.utils.typing import *
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@threestudio.register("no-material")
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class NoMaterial(BaseMaterial):
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@dataclass
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class Config(BaseMaterial.Config):
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n_output_dims: int = 3
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color_activation: str = "sigmoid"
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input_feature_dims: Optional[int] = None
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mlp_network_config: Optional[dict] = None
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requires_normal: bool = False
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cfg: Config
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def configure(self) -> None:
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self.use_network = False
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if (
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self.cfg.input_feature_dims is not None
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and self.cfg.mlp_network_config is not None
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):
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self.network = get_mlp(
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self.cfg.input_feature_dims,
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self.cfg.n_output_dims,
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self.cfg.mlp_network_config,
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)
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self.use_network = True
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self.requires_normal = self.cfg.requires_normal
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def forward(
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self, features: Float[Tensor, "B ... Nf"], **kwargs
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) -> Float[Tensor, "B ... Nc"]:
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if not self.use_network:
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assert (
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features.shape[-1] == self.cfg.n_output_dims
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), f"Expected {self.cfg.n_output_dims} output dims, only got {features.shape[-1]} dims input."
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color = get_activation(self.cfg.color_activation)(features)
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else:
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color = self.network(features.view(-1, features.shape[-1])).view(
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*features.shape[:-1], self.cfg.n_output_dims
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)
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color = get_activation(self.cfg.color_activation)(color)
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return color
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def export(self, features: Float[Tensor, "*N Nf"], **kwargs) -> Dict[str, Any]:
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color = self(features, **kwargs).clamp(0, 1)
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assert color.shape[-1] >= 3, "Output color must have at least 3 channels"
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if color.shape[-1] > 3:
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threestudio.warn(
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"Output color has >3 channels, treating the first 3 as RGB"
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)
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return {"albedo": color[..., :3]}
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