本文整理汇总了Python中torch.utils.AvgrageMeter方法的典型用法代码示例。如果您正苦于以下问题：Python utils.AvgrageMeter方法的具体用法？Python utils.AvgrageMeter怎么用？Python utils.AvgrageMeter使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.utils的用法示例。
在下文中一共展示了utils.AvgrageMeter方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def valid(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda()
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data, n)
top1.update(prec1.data, n)
top5.update(prec5.data, n)
if (step+1) % 100 == 0:
logging.info('valid %03d %e %f %f', step+1, objs.avg, top1.avg, top5.avg)
return top1.avg, top5.avg, objs.avg 示例2: nao_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def nao_valid(queue, model):
pa = utils.AvgrageMeter()
hs = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda()
decoder_target = decoder_target.cuda()
predict_value, logits, arch = model(encoder_input)
n = encoder_input.size(0)
pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(),
predict_value.data.squeeze().tolist())
hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
pa.update(pairwise_acc, n)
hs.update(hamming_dis, n)
return pa.avg, hs.avg 示例3: valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def valid(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda()
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data, n)
top1.update(prec1.data, n)
top5.update(prec5.data, n)
if (step+1) % 100 == 0:
logging.info('valid %03d %e %f %f', step+1, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg 示例4: nao_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def nao_valid(queue, model):
pa = utils.AvgrageMeter()
hs = utils.AvgrageMeter()
mse = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda()
decoder_target = decoder_target.cuda()
predict_value, logits, arch = model(encoder_input)
n = encoder_input.size(0)
pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(),
predict_value.data.squeeze().tolist())
hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
mse.update(F.mse_loss(predict_value.data.squeeze(), encoder_target.data.squeeze()), n)
pa.update(pairwise_acc, n)
hs.update(hamming_dis, n)
return mse.avg, pa.avg, hs.avg 示例5: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda(non_blocking=True)
logits = model(input, discrete=True)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
if args.debug:
break
return top1.avg, objs.avg 示例6: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda(non_blocking=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg 示例7: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda(non_blocking=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
if args.debug:
break
return top1.avg, objs.avg 示例8: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, top5.avg, objs.avg 示例9: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg 示例10: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, top5.avg, objs.avg 示例11: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(test_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(test_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg 示例12: nao_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def nao_valid(queue, model):
pa = utils.AvgrageMeter()
hs = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda()
decoder_target = decoder_target.cuda()
predict_value, logits, arch = model(encoder_input)
n = encoder_input.size(0)
pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(), predict_value.data.squeeze().tolist())
hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
pa.update(pairwise_acc, n)
hs.update(hamming_dis, n)
return pa.avg, hs.avg 示例13: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg 示例14: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg 示例15: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import AvgrageMeter [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
if args.debug:
break
return top1.avg, objs.avg