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utils.py
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utils.py
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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import shutil
import numpy as np
import torch
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def count_parameters_in_MB(model):
return sum(np.prod(v.size()) for name, v in model.named_parameters())/1e6
def create_exp_dir(path):
if not os.path.exists(path):
os.makedirs(path)
print('Experiment dir : {}'.format(path))
def load_pretrained_model(model, pretrained_dict):
model_dict = model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(model_dict)
def transform_time(s):
m, s = divmod(int(s), 60)
h, m = divmod(m, 60)
return h,m,s
def save_checkpoint(state, is_best, save_root):
save_path = os.path.join(save_root, 'checkpoint.pth.tar')
torch.save(state, save_path)
if is_best:
best_save_path = os.path.join(save_root, 'model_best.pth.tar')
shutil.copyfile(save_path, best_save_path)
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res