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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
import random, torch, os, numpy as np
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import torch.nn as nn
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import config
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import copy
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def save_checkpoint(model, optimizer, filename="my_checkpoint.pth.tar"):
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print("=> Saving checkpoint")
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checkpoint = {
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"state_dict": model.state_dict(),
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"optimizer": optimizer.state_dict(),
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}
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torch.save(checkpoint, filename)
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def load_checkpoint(checkpoint_file, model, optimizer, lr):
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print("=> Loading checkpoint")
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checkpoint = torch.load(checkpoint_file, map_location=config.DEVICE)
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model.load_state_dict(checkpoint["state_dict"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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# If we don't do this then it will just have learning rate of old checkpoint
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# and it will lead to many hours of debugging \:
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for param_group in optimizer.param_groups:
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param_group["lr"] = lr
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def seed_everything(seed=42):
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os.environ["PYTHONHASHSEED"] = str(seed)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False |