kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
63 wiersze
1.5 KiB
Python
63 wiersze
1.5 KiB
Python
"""Config file for reproducing the results of DDPM on bedrooms."""
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from configs.default_configs import get_default_configs
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def get_config():
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config = get_default_configs()
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# training
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training = config.training
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training.sde = 'vpsde'
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training.continuous = False
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training.reduce_mean = True
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training.batch_size = 8
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training.lip_scale = None
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training.iter_size = 4
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training.snapshot_freq_for_preemption = 1000
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# sampling
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sampling = config.sampling
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sampling.method = 'pc'
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sampling.predictor = 'ancestral_sampling'
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sampling.corrector = 'none'
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# data
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data = config.data
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data.dataset = 'ShapeNet'
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data.centered = True
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data.image_size = 128
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data.num_channels = 4
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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.num_workers = 8
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data.aug = True
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# model
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model = config.model
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model.name = 'ddpm_res128_v2'
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model.scale_by_sigma = False
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model.num_scales = 1000
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model.ema_rate = 0.9999
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model.normalization = 'GroupNorm'
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model.nonlinearity = 'swish'
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model.nf = 128
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model.ch_mult = (1, 1, 2, 4, 4, 4)
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model.num_res_blocks_first = 2
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model.num_res_blocks = 2
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model.attn_resolutions = (16,)
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model.resamp_with_conv = True
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model.conditional = True
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model.dropout = 0.1
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# optim
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optim = config.optim
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optim.lr = 7e-5 / training.iter_size * 2.0
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config.eval.batch_size = 7
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config.seed = 42
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return config
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