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b157024 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import random
import numpy as np
import torch
def seed_everything(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def show_gpu_memory():
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
allocated = torch.cuda.memory_allocated(i) / 1024**3
reserved = torch.cuda.memory_reserved(i) / 1024**3
print(
f"GPU {i}: "
f"allocated: {allocated:.2f} GB "
f"reserved: {reserved:.2f} GB"
)
else:
print(
f"No GPU detected"
)
def count_parameters(model):
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
non_trainable_params = total_params - trainable_params
return total_params, trainable_params, non_trainable_params
def denormalize_image(image):
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = image * std + mean
return np.clip(image, 0, 1)
# Test
if __name__ == "__main__":
seed_everything()
show_gpu_memory()
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