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import numpy as np
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import torch
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from torch.utils import data
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def InfiniteSampler(n):
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i = n - 1
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order = np.random.permutation(n)
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while True:
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yield order[i]
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i += 1
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if i >= n:
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np.random.seed()
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order = np.random.permutation(n)
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i = 0
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class InfiniteSamplerWrapper(data.sampler.Sampler):
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def __init__(self, data_source):
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self.num_samples = len(data_source)
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def __iter__(self):
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return iter(InfiniteSampler(self.num_samples))
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def __len__(self):
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return 2 ** 31
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def save_checkpoint(encoder1,encoder2, transModule, decoder, optimizer, scheduler, epoch,
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log_c, log_s, log_id1, log_id2, log_all, loss_count_interval, save_path):
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checkpoint = {
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'encoder1': encoder1.state_dict() if not encoder1 is None else None,
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'encoder2': encoder2.state_dict() if not encoder2 is None else None,
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'transModule': transModule.state_dict() if not transModule is None else None,
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'decoder': decoder.state_dict() if not decoder is None else None,
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'optimizer': optimizer.state_dict() if not optimizer is None else None,
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'scheduler': scheduler.state_dict() if not scheduler is None else None,
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'epoch': epoch if not epoch is None else None,
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'log_c': log_c if not log_c is None else None,
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'log_s': log_s if not log_s is None else None,
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'log_id1': log_id1 if not log_id1 is None else None,
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'log_id2': log_id2 if not log_id2 is None else None,
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'log_all': log_all if not log_all is None else None,
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'loss_count_interval': loss_count_interval if not loss_count_interval is None else None
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}
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torch.save(checkpoint, save_path) |
