142 lines
3.8 KiB
Python
142 lines
3.8 KiB
Python
"""Training PGSN on Community Small Dataset with GraphGDP"""
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import ml_collections
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import torch
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def get_config():
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config = ml_collections.ConfigDict()
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# general
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config.resume = False
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config.resume_ckpt_path = './exp'
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config.folder_name = 'tr_scorenet'
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config.task = 'tr_scorenet'
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config.exp_name = None
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config.model_type = 'sde'
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# training
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config.training = training = ml_collections.ConfigDict()
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training.sde = 'vesde'
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training.continuous = True
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training.reduce_mean = True
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training.batch_size = 256
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training.eval_batch_size = 1000
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training.n_iters = 1000000
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training.snapshot_freq = 10000
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training.log_freq = 200
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training.eval_freq = 10000
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## store additional checkpoints for preemption
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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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# sampling
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config.sampling = sampling = ml_collections.ConfigDict()
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sampling.method = 'pc'
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sampling.predictor = 'euler_maruyama'
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sampling.corrector = 'none'
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sampling.rtol = 1e-5
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sampling.atol = 1e-5
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sampling.ode_method = 'dopri5' # 'rk4'
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sampling.ode_step = 0.01
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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 = 0.16
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sampling.vis_row = 4
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sampling.vis_col = 4
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sampling.alpha = 0.5
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sampling.qtype = 'threshold'
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# evaluation
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config.eval = evaluate = ml_collections.ConfigDict()
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evaluate.begin_ckpt = 5
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evaluate.end_ckpt = 20
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evaluate.batch_size = 1024
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evaluate.enable_sampling = True
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evaluate.num_samples = 1024
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evaluate.mmd_distance = 'RBF'
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evaluate.max_subgraph = False
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evaluate.save_graph = False
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# data
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config.data = data = ml_collections.ConfigDict()
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data.centered = True
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data.dequantization = False
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data.root = './data/ofa/data_score_model/ofa_database_500000.pt'
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data.name = 'ofa'
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data.split_ratio = 0.9
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data.dataset_idx = 'random'
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data.max_node = 20
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data.n_vocab = 9 # 10 #
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data.START_TYPE = 0
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data.END_TYPE = 1
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data.num_graphs = 100000
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data.num_channels = 1
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data.except_inout = False
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data.triu_adj = True
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data.connect_prev = False
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data.label_list = None
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data.tg_dataset = None
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data.node_rule_type = 2
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# aug_mask
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data.aug_mask_algo = 'none'
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# model
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config.model = model = ml_collections.ConfigDict()
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model.name = 'CATE'
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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.num_gnn_layers = 4
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model.size_cond = False
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model.embedding_type = 'positional'
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model.rw_depth = 16
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model.graph_layer = 'PosTransLayer'
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model.edge_th = -1.
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model.heads = 8
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model.attn_clamp = False
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model.num_scales = 1000
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model.sigma_min = 0.1
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model.sigma_max = 1.0
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model.dropout = 0.1
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model.pos_enc_type = 2
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# graph encoder
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config.model.graph_encoder = graph_encoder = ml_collections.ConfigDict()
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graph_encoder.n_layers = 12
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graph_encoder.d_model = 64
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graph_encoder.n_head = 8
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graph_encoder.d_ff = 128
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graph_encoder.dropout = 0.1
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graph_encoder.n_vocab = 9 #10 # 30
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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-5
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optim.beta1 = 0.9
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optim.eps = 1e-8
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optim.warmup = 1000
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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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# log
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config.log = log = ml_collections.ConfigDict()
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log.use_wandb = True
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log.wandb_project_name = 'DiffusionNAG'
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log.log_valid_sample_prop = False
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log.num_graphs_to_visualize = 20
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return config
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