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validation.py
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validation.py
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import time
from tqdm import *
from torch.autograd import Variable
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 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
def validation(net, loader, loss_func, use_cuda, topk = (1,)):
print("this module is deprecated...use TrainTest.py")
net.eval()
test_loss = 0
batch_time = AverageMeter()
losses = AverageMeter()
topk_lst = []
for k in topk:
topk_lst.append(AverageMeter())
total = 0
end = time.time()
for (inputs, targets) in tqdm(loader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs, volatile=True), Variable(targets)
# compute output
outputs = net(inputs)
loss = loss_func(outputs, targets)
# measure accuracy and record loss
topk_rtn = accuracy(outputs.data, targets.data, topk)
for idx, topk_val in enumerate(topk_rtn):
topk_lst[idx].update(topk_val[0], inputs.size(0))
losses.update(loss.data[0], inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Save checkpoint.
print("validation finished")
return (batch_time.avg, losses.avg) + tuple(topk_val.avg for topk_val in topk_lst)