38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
import glob
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import os
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import torch
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from torch.nn.utils import weight_norm
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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def apply_weight_norm(m):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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weight_norm(m)
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size*dilation - dilation)/2)
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def load_checkpoint(filepath, device):
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assert os.path.isfile(filepath)
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print("Loading '{}'".format(filepath))
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checkpoint_dict = torch.load(filepath, map_location=device)
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print("Complete.")
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return checkpoint_dict
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def save_checkpoint(filepath, obj):
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print("Saving checkpoint to {}".format(filepath))
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torch.save(obj, filepath)
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print("Complete.")
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def scan_checkpoint(cp_dir, prefix):
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pattern = os.path.join(cp_dir, prefix + '????????')
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cp_list = glob.glob(pattern)
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if len(cp_list) == 0:
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return None
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return sorted(cp_list)[-1]
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