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A10G
Running
on
A10G
File size: 3,399 Bytes
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import tyro
from dataclasses import dataclass
from typing import Tuple, Literal, Dict, Optional
@dataclass
class Options:
### model
# Unet image input size
input_size: int = 256
# Unet definition
down_channels: Tuple[int, ...] = (64, 128, 256, 512, 1024, 1024)
down_attention: Tuple[bool, ...] = (False, False, False, True, True, True)
mid_attention: bool = True
up_channels: Tuple[int, ...] = (1024, 1024, 512, 256)
up_attention: Tuple[bool, ...] = (True, True, True, False)
# Unet output size, dependent on the input_size and U-Net structure!
splat_size: int = 64
# gaussian render size
output_size: int = 256
### dataset
# data mode (only support s3 now)
data_mode: Literal['s3'] = 's3'
# fovy of the dataset
fovy: float = 49.1
# camera near plane
znear: float = 0.5
# camera far plane
zfar: float = 2.5
# number of all views (input + output)
num_views: int = 12
# number of views
num_input_views: int = 4
# camera radius
cam_radius: float = 1.5 # to better use [-1, 1]^3 space
# num workers
num_workers: int = 8
### training
# workspace
workspace: str = './workspace'
# resume
resume: Optional[str] = None
# batch size (per-GPU)
batch_size: int = 8
# gradient accumulation
gradient_accumulation_steps: int = 1
# training epochs
num_epochs: int = 30
# lpips loss weight
lambda_lpips: float = 1.0
# gradient clip
gradient_clip: float = 1.0
# mixed precision
mixed_precision: str = 'bf16'
# learning rate
lr: float = 4e-4
# augmentation prob for grid distortion
prob_grid_distortion: float = 0.5
# augmentation prob for camera jitter
prob_cam_jitter: float = 0.5
### testing
# test image path
test_path: Optional[str] = None
### misc
# nvdiffrast backend setting
force_cuda_rast: bool = False
# render fancy video with gaussian scaling effect
fancy_video: bool = False
# all the default settings
config_defaults: Dict[str, Options] = {}
config_doc: Dict[str, str] = {}
config_doc['lrm'] = 'the default settings for LGM'
config_defaults['lrm'] = Options()
config_doc['small'] = 'small model with lower resolution Gaussians'
config_defaults['small'] = Options(
input_size=256,
splat_size=64,
output_size=256,
batch_size=8,
gradient_accumulation_steps=1,
mixed_precision='bf16',
)
config_doc['big'] = 'big model with higher resolution Gaussians'
config_defaults['big'] = Options(
input_size=256,
up_channels=(1024, 1024, 512, 256, 128), # one more decoder
up_attention=(True, True, True, False, False),
splat_size=128,
output_size=512, # render & supervise Gaussians at a higher resolution.
batch_size=8,
num_views=8,
gradient_accumulation_steps=1,
mixed_precision='bf16',
)
config_doc['tiny'] = 'tiny model for ablation'
config_defaults['tiny'] = Options(
input_size=256,
down_channels=(32, 64, 128, 256, 512),
down_attention=(False, False, False, False, True),
up_channels=(512, 256, 128),
up_attention=(True, False, False, False),
splat_size=64,
output_size=256,
batch_size=16,
num_views=8,
gradient_accumulation_steps=1,
mixed_precision='bf16',
)
AllConfigs = tyro.extras.subcommand_type_from_defaults(config_defaults, config_doc)
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