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train_utils.py
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train_utils.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import shutil
import numpy as np
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
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 accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
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 = torch.flatten(correct[:k], start_dim=0).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def init_logfile(filename: str, text: str):
f = open(filename, 'w')
f.write(text+"\n")
f.close()
def measurement(n_measure, dim):
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
aa = torch.normal(0, np.sqrt(1 / n_measure), size=(dim, n_measure)).cuda()
return aa
def log(filename: str, text: str):
f = open(filename, 'a')
f.write(text+"\n")
f.close()
def requires_grad_(model:torch.nn.Module, requires_grad:bool) -> None:
for param in model.parameters():
param.requires_grad_(requires_grad)
def copy_code(outdir):
"""Copies files to the outdir to store complete script with each experiment"""
# embed()
code = []
exclude = set([])
for root, _, files in os.walk("./code", topdown=True):
for f in files:
if not f.endswith('.py'):
continue
code += [(root,f)]
for r, f in code:
codedir = os.path.join(outdir,r)
if not os.path.exists(codedir):
os.mkdir(codedir)
shutil.copy2(os.path.join(r,f), os.path.join(codedir,f))
print("Code copied to '{}'".format(outdir))