本文整理汇总了Python中torch.utils.accuracy方法的典型用法代码示例。如果您正苦于以下问题：Python utils.accuracy方法的具体用法？Python utils.accuracy怎么用？Python utils.accuracy使用的例子？那么恭喜您, 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.accuracy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: child_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [as 别名]
def child_valid(valid_queue, model, arch_pool, criterion):
valid_acc_list = []
with torch.no_grad():
model.eval()
for i, arch in enumerate(arch_pool):
# for step, (input, target) in enumerate(valid_queue):
inputs, targets = next(iter(valid_queue))
inputs = inputs.cuda()
targets = targets.cuda()
logits, _ = model(inputs, arch, bn_train=True)
loss = criterion(logits, targets)
prec1, prec5 = utils.accuracy(logits, targets, topk=(1, 5))
valid_acc_list.append(prec1.data/100)
if (i+1) % 100 == 0:
logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', ' '.join(map(str, arch[0] + arch[1])), loss, prec1, prec5)
return valid_acc_list 示例2: valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [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3: valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [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: child_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [as 别名]
def child_valid(valid_queue, model, arch_pool, criterion):
valid_acc_list = []
with torch.no_grad():
model.eval()
for i, arch in enumerate(arch_pool):
#for step, (inputs, targets) in enumerate(valid_queue):
inputs, targets = next(iter(valid_queue))
inputs = inputs.cuda()
targets = targets.cuda()
logits, _ = model(inputs, arch, bn_train=True)
loss = criterion(logits, targets)
prec1, prec5 = utils.accuracy(logits, targets, topk=(1, 5))
valid_acc_list.append(prec1.data/100)
if (i+1) % 100 == 0:
logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', ' '.join(map(str, arch[0] + arch[1])), loss, prec1, prec5)
return valid_acc_list 示例5: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [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 accuracy [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 accuracy [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 accuracy [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 accuracy [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: child_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [as 别名]
def child_valid(self, valid_queue, model, arch_pool, criterion):
valid_acc_list = []
with torch.no_grad():
model.eval()
for i, arch in enumerate(arch_pool):
# for step, (inputs, targets) in enumerate(valid_queue):
inputs, targets = valid_queue.next_batch()
inputs = inputs.cuda()
targets = targets.cuda()
arch_l = arch
arch = self.process_arch(arch)
logits, _ = model(inputs, arch, bn_train=True)
loss = criterion(logits, targets)
prec1, prec5 = self.utils.accuracy(logits, targets, topk=(1, 5))
valid_acc_list.append(prec1.data / 100)
if (i + 1) % 100 == 0:
logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', self.process_archname(arch_l),
loss, prec1, prec5)
return valid_acc_list 示例11: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [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12: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [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13: infer
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [as 别名]
def infer(valid_queue, model, criterion):
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
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: valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [as 别名]
def valid(valid_queue, model, criterion):
objs = search_policies.cnn.utils.AverageMeter()
top1 = search_policies.cnn.utils.AverageMeter()
top5 = search_policies.cnn.utils.AverageMeter()
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15: child_valid
# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import accuracy [as 别名]
def child_valid(self,valid_queue, model, arch_pool, criterion):
valid_acc_list = []
with torch.no_grad():
model.eval()
for i, arch in enumerate(arch_pool):
# for step, (inputs, targets) in enumerate(valid_queue):
inputs, targets = next(iter(valid_queue))
inputs = inputs.cuda()
targets = targets.cuda()
arch_l = arch
arch = self.process_arch(arch)
logits, _ = model(inputs, arch, bn_train=True)
loss = criterion(logits, targets)
prec1, prec5 = self.utils.accuracy(logits, targets, topk=(1, 5))
valid_acc_list.append(prec1.data / 100)
if (i + 1) % 100 == 0:
logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', self.process_archname(arch_l),
loss, prec1, prec5)
return valid_acc_list