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NAS-Bench-201/configs/eval_scorenet.py

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+"""Evaluate trained score network"""
+
+import ml_collections
+import torch
+
+from all_path import SCORENET_CKPT_PATH
+
+def get_config():
+    config = ml_collections.ConfigDict()
+
+    # general
+    config.folder_name = 'test'
+    config.model_type = 'scorenet'
+    config.task = 'eval_scorenet'
+    config.exp_name = None
+    config.seed = 42
+    config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
+    config.resume = False
+    config.scorenet_ckpt_path = SCORENET_CKPT_PATH
+
+    # training
+    config.training = training = ml_collections.ConfigDict()
+    training.sde = 'vesde'
+    training.continuous = True
+    training.reduce_mean = True
+    training.noised = True
+
+    # sampling
+    config.sampling = sampling = ml_collections.ConfigDict()
+    sampling.method = 'pc'
+    sampling.predictor = 'euler_maruyama'
+    sampling.corrector = 'langevin'
+    sampling.n_steps_each = 1
+    sampling.noise_removal = True
+    sampling.probability_flow = False
+    sampling.snr = 0.16
+
+    # evaluation
+    config.eval = evaluate = ml_collections.ConfigDict()
+    evaluate.batch_size = 256
+    evaluate.enable_sampling = True
+    evaluate.num_samples = 256
+
+    # data
+    config.data = data = ml_collections.ConfigDict()
+    data.centered = True
+    data.dequantization = False
+
+    data.root = '../data/transfer_nag/nasbench201_info.pt'
+    data.name = 'NASBench201'
+    data.split_ratio = 1.0
+    data.dataset_idx = 'random' # 'sorted' | 'random'
+    data.max_node = 8
+    data.n_vocab = 7 # number of operations
+    data.START_TYPE = 0
+    data.END_TYPE = 1
+    data.num_graphs = 15625
+    data.num_channels = 1
+    data.label_list = ['test-acc']
+    data.tg_dataset = 'cifar10'
+    # aug_mask
+    data.aug_mask_algo = 'floyd' # 'long_range' | 'floyd'
+
+    # model
+    config.model = model = ml_collections.ConfigDict()
+    model.num_scales = 1000
+    model.beta_min = 0.1
+    model.beta_max = 5.0
+    model.sigma_min = 0.1
+    model.sigma_max = 5.0
+
+    return config