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29 lines
746 B
Python
29 lines
746 B
Python
import torch
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import albumentations as A
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from albumentations.pytorch import ToTensorV2
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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TRAIN_DIR = "data/train"
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VAL_DIR = "data/val"
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BATCH_SIZE = 1
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LEARNING_RATE = 1e-5
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LAMBDA_IDENTITY = 0.0
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LAMBDA_CYCLE = 10
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NUM_WORKERS = 4
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NUM_EPOCHS = 10
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LOAD_MODEL = True
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SAVE_MODEL = True
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CHECKPOINT_GEN_H = "genh.pth.tar"
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CHECKPOINT_GEN_Z = "genz.pth.tar"
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CHECKPOINT_CRITIC_H = "critich.pth.tar"
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CHECKPOINT_CRITIC_Z = "criticz.pth.tar"
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transforms = A.Compose(
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[
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A.Resize(width=256, height=256),
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A.HorizontalFlip(p=0.5),
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A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_pixel_value=255),
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ToTensorV2(),
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],
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additional_targets={"image0": "image"},
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) |