kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
90 wiersze
2.5 KiB
Python
90 wiersze
2.5 KiB
Python
import ml_collections
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import torch
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def get_default_configs():
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config = ml_collections.ConfigDict()
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# training
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config.training = training = ml_collections.ConfigDict()
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config.training.batch_size = 64
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training.n_iters = 2400001
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training.snapshot_freq = 50000
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training.log_freq = 50
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training.eval_freq = 100
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## store additional checkpoints for preemption in cloud computing environments
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training.snapshot_freq_for_preemption = 5000
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## produce samples at each snapshot.
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training.snapshot_sampling = True
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training.likelihood_weighting = False
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training.continuous = True
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training.reduce_mean = False
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training.iter_size = 1
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training.loss_type = 'l2'
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training.train_dir = "PLACEHOLDER"
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# sampling
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config.sampling = sampling = ml_collections.ConfigDict()
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sampling.n_steps_each = 1
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sampling.noise_removal = True
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sampling.probability_flow = False
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sampling.snr = 0.075
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# evaluation
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config.eval = evaluate = ml_collections.ConfigDict()
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evaluate.begin_ckpt = 50
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evaluate.end_ckpt = 96
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evaluate.batch_size = 512
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evaluate.enable_sampling = True
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evaluate.num_samples = 50000
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evaluate.enable_loss = True
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evaluate.enable_bpd = False
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evaluate.bpd_dataset = 'test'
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evaluate.ckpt_path = "PLACEHOLDER"
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evaluate.partial_dmtet_path = "PLACEHOLDER"
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evaluate.tet_path = "PLACEHOLDER"
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evaluate.freeze_iters = 950
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# data
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config.data = data = ml_collections.ConfigDict()
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data.dataset = 'LSUN'
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data.image_size = 256
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data.random_flip = True
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data.uniform_dequantization = False
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data.centered = False
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data.num_channels = 3
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data.num_workers = 4
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data.normalize_sdf = True
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data.meta_path = "PLACEHOLDER" ### metadata for all dataset files
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data.filter_meta_path = "PLACEHOLDER" ### metadata for the list of training samples
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data.extension = 'pt' ### either 'pt' or 'npy', depending how the data are stored
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# model
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config.model = model = ml_collections.ConfigDict()
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model.sigma_max = 378
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model.sigma_min = 0.01
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model.num_scales = 2000
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model.beta_min = 0.1
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model.beta_max = 20.
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model.dropout = 0.
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model.embedding_type = 'fourier'
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model.deform_scale = 1.0
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# optimization
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config.optim = optim = ml_collections.ConfigDict()
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optim.weight_decay = 0
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optim.optimizer = 'Adam'
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optim.lr = 2e-4
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optim.beta1 = 0.9
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optim.eps = 1e-8
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optim.warmup = 5000
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optim.grad_clip = 1.
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config.seed = 42
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config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
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# rendering
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config.render = render = ml_collections.ConfigDict()
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return config
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