MeshDiffusion/configs/default_configs.py

88 wiersze
2.4 KiB
Python

import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 64
training.n_iters = 2400001
training.snapshot_freq = 50000
training.log_freq = 50
training.eval_freq = 100
## store additional checkpoints for preemption in cloud computing environments
training.snapshot_freq_for_preemption = 5000
## produce samples at each snapshot.
training.snapshot_sampling = True
training.likelihood_weighting = False
training.continuous = True
training.reduce_mean = False
training.iter_size = 1
training.loss_type = 'l2'
training.train_dir = "PLACEHOLDER"
# sampling
config.sampling = sampling = ml_collections.ConfigDict()
sampling.n_steps_each = 1
sampling.noise_removal = True
sampling.probability_flow = False
sampling.snr = 0.075
# evaluation
config.eval = evaluate = ml_collections.ConfigDict()
evaluate.begin_ckpt = 50
evaluate.end_ckpt = 96
evaluate.batch_size = 512
evaluate.enable_sampling = True
evaluate.num_samples = 50000
evaluate.enable_loss = True
evaluate.enable_bpd = False
evaluate.bpd_dataset = 'test'
evaluate.ckpt_path = "PLACEHOLDER"
evaluate.partial_dmtet_path = "PLACEHOLDER"
evaluate.tet_path = "PLACEHOLDER"
evaluate.freeze_iters = 950
# data
config.data = data = ml_collections.ConfigDict()
data.dataset = 'LSUN'
data.image_size = 256
data.random_flip = True
data.uniform_dequantization = False
data.centered = False
data.num_channels = 3
data.num_workers = 4
data.normalize_sdf = True
data.meta_path = "PLACEHOLDER" ### metadata for all dataset files
data.filter_meta_path = "PLACEHOLDER" ### metadata for the list of training samples
# model
config.model = model = ml_collections.ConfigDict()
model.sigma_max = 378
model.sigma_min = 0.01
model.num_scales = 2000
model.beta_min = 0.1
model.beta_max = 20.
model.dropout = 0.
model.embedding_type = 'fourier'
model.deform_scale = 1.0
# optimization
config.optim = optim = ml_collections.ConfigDict()
optim.weight_decay = 0
optim.optimizer = 'Adam'
optim.lr = 2e-4
optim.beta1 = 0.9
optim.eps = 1e-8
optim.warmup = 5000
optim.grad_clip = 1.
config.seed = 42
config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# rendering
config.render = render = ml_collections.ConfigDict()
return config