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- import random
-
- import torch
-
-
- class Config:
- DEBUG = False
-
- batch_size = 32
- eval_batch_size = 128
-
- test_percent = 20
- val_percent = 10
-
- learning_rate = 0.0001
- decay_rate = 1 # 0.99**50=0.6, 0.99**100=0.36
- n_epoch = 2 if DEBUG else 20
-
- available_device = "cuda" if torch.cuda.is_available() and not DEBUG else "cpu"
- print(f"Device: {available_device}")
-
- workers = 1 if DEBUG else 40
-
- # learned from evaluate_image_patcher_and_visualize.py
- laplacian_threshold = 298
-
- # RANDOM SEED
- seed = 115
-
- @staticmethod
- def reset_random_seeds():
- random.seed(Config.seed)
- torch.manual_seed(Config.seed)
-
- class_names = ["BENIGN", "MALIGNANT"]
- class_idx_dict = {"BENIGN": 0, "MALIGNANT": 1}
-
- train_val_acc_max_distance_for_best_epoch = 6 # Percent
- n_epoch_for_image_patcher = 60
-
- train_phase = False
- evaluate_phase = False
-
-
- Config.reset_random_seeds()
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