51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
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import torch
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import os
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import time
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ROOT_PATH = ''
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BASE_ADV_PATH = os.path.join(ROOT_PATH, 'advimages')
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class AverageMeter:
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def accuracy(output, target, topk=(1,)):
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.reshape(1, -1).expand_as(pred))
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return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]
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class Logger(object):
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def __init__(self, path, header):
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self.log_file = open(path, 'w')
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self.logger = csv.writer(self.log_file, delimiter='\t')
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self.logger.writerow(header)
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self.header = header
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def __del(self):
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self.log_file.close()
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def log(self, values):
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write_values = []
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for col in self.header:
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assert col in values
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write_values.append(values[col])
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self.logger.writerow(write_values)
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self.log_file.flush()
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