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21 lines
597 B
Python
21 lines
597 B
Python
import cv2
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import torch
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from math import log2
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START_TRAIN_AT_IMG_SIZE = 4
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DATASET = 'celeb_dataset'
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CHECKPOINT_GEN = "generator.pth"
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CHECKPOINT_CRITIC = "critic.pth"
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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SAVE_MODEL = True
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LOAD_MODEL = False
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LEARNING_RATE = 1e-3
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BATCH_SIZES = [32, 32, 32, 16, 16, 16, 16, 8, 4]
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CHANNELS_IMG = 3
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Z_DIM = 256 # should be 512 in original paper
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IN_CHANNELS = 256 # should be 512 in original paper
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CRITIC_ITERATIONS = 1
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LAMBDA_GP = 10
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PROGRESSIVE_EPOCHS = [30] * len(BATCH_SIZES)
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FIXED_NOISE = torch.randn(8, Z_DIM, 1, 1).to(DEVICE)
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NUM_WORKERS = 4 |