本文整理汇总了Python中util.AverageMeter方法的典型用法代码示例。如果您正苦于以下问题:Python util.AverageMeter方法的具体用法?Python util.AverageMeter怎么用?Python util.AverageMeter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util
的用法示例。
在下文中一共展示了util.AverageMeter方法的9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: train
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def train(epoch, net, trainloader, device, optimizer, loss_fn, max_grad_norm):
print('\nEpoch: %d' % epoch)
net.train()
loss_meter = util.AverageMeter()
with tqdm(total=len(trainloader.dataset)) as progress_bar:
for x, _ in trainloader:
x = x.to(device)
optimizer.zero_grad()
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
loss.backward()
util.clip_grad_norm(optimizer, max_grad_norm)
optimizer.step()
progress_bar.set_postfix(loss=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg))
progress_bar.update(x.size(0))
示例2: train
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def train(epoch, net, trainloader, device, optimizer, scheduler, loss_fn, max_grad_norm):
global global_step
print('\nEpoch: %d' % epoch)
net.train()
loss_meter = util.AverageMeter()
with tqdm(total=len(trainloader.dataset)) as progress_bar:
for x, _ in trainloader:
x = x.to(device)
optimizer.zero_grad()
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
loss.backward()
if max_grad_norm > 0:
util.clip_grad_norm(optimizer, max_grad_norm)
optimizer.step()
scheduler.step(global_step)
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg),
lr=optimizer.param_groups[0]['lr'])
progress_bar.update(x.size(0))
global_step += x.size(0)
示例3: test
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def test(epoch, net, testloader, device, loss_fn, num_samples):
global best_loss
net.eval()
loss_meter = util.AverageMeter()
with torch.no_grad():
with tqdm(total=len(testloader.dataset)) as progress_bar:
for x, _ in testloader:
x = x.to(device)
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
progress_bar.set_postfix(loss=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg))
progress_bar.update(x.size(0))
# Save checkpoint
if loss_meter.avg < best_loss:
print('Saving...')
state = {
'net': net.state_dict(),
'test_loss': loss_meter.avg,
'epoch': epoch,
}
os.makedirs('ckpts', exist_ok=True)
torch.save(state, 'ckpts/best.pth.tar')
best_loss = loss_meter.avg
# Save samples and data
images = sample(net, num_samples, device)
os.makedirs('samples', exist_ok=True)
images_concat = torchvision.utils.make_grid(images, nrow=int(num_samples ** 0.5), padding=2, pad_value=255)
torchvision.utils.save_image(images_concat, 'samples/epoch_{}.png'.format(epoch))
示例4: validate
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def validate(data_loader, model, criterion, epoch, monitors, args):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
batch_time = AverageMeter()
total_sample = len(data_loader.sampler)
batch_size = data_loader.batch_size
steps_per_epoch = math.ceil(total_sample / batch_size)
logger.info('Validation: %d samples (%d per mini-batch)', total_sample, batch_size)
model.eval()
end_time = time.time()
for batch_idx, (inputs, targets) in enumerate(data_loader):
with t.no_grad():
inputs = inputs.to(args.device.type)
targets = targets.to(args.device.type)
outputs = model(inputs)
loss = criterion(outputs, targets)
acc1, acc5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1.item(), inputs.size(0))
top5.update(acc5.item(), inputs.size(0))
batch_time.update(time.time() - end_time)
end_time = time.time()
if (batch_idx + 1) % args.log.print_freq == 0:
for m in monitors:
m.update(epoch, batch_idx + 1, steps_per_epoch, 'Validation', {
'Loss': losses,
'Top1': top1,
'Top5': top5,
'BatchTime': batch_time
})
logger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n', top1.avg, top5.avg, losses.avg)
return top1.avg, top5.avg, losses.avg
示例5: test
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def test(epoch, net, testloader, device, loss_fn, num_samples):
global best_loss
net.eval()
loss_meter = util.AverageMeter()
with tqdm(total=len(testloader.dataset)) as progress_bar:
for x, _ in testloader:
x = x.to(device)
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg))
progress_bar.update(x.size(0))
# Save checkpoint
if loss_meter.avg < best_loss:
print('Saving...')
state = {
'net': net.state_dict(),
'test_loss': loss_meter.avg,
'epoch': epoch,
}
os.makedirs('ckpts', exist_ok=True)
torch.save(state, 'ckpts/best.pth.tar')
best_loss = loss_meter.avg
# Save samples and data
images = sample(net, num_samples, device)
os.makedirs('samples', exist_ok=True)
images_concat = torchvision.utils.make_grid(images, nrow=int(num_samples ** 0.5), padding=2, pad_value=255)
torchvision.utils.save_image(images_concat, 'samples/epoch_{}.png'.format(epoch))
示例6: validate
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def validate(val_loader, model, epoch, output_writers):
global args
batch_time = AverageMeter()
flow2_EPEs = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
target = target.to(device)
input = torch.cat(input,1).to(device)
# compute output
output = model(input)
flow2_EPE = args.div_flow*realEPE(output, target, sparse=args.sparse)
# record EPE
flow2_EPEs.update(flow2_EPE.item(), target.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i < len(output_writers): # log first output of first batches
if epoch == 0:
mean_values = torch.tensor([0.45,0.432,0.411], dtype=input.dtype).view(3,1,1)
output_writers[i].add_image('GroundTruth', flow2rgb(args.div_flow * target[0], max_value=10), 0)
output_writers[i].add_image('Inputs', (input[0,:3].cpu() + mean_values).clamp(0,1), 0)
output_writers[i].add_image('Inputs', (input[0,3:].cpu() + mean_values).clamp(0,1), 1)
output_writers[i].add_image('FlowNet Outputs', flow2rgb(args.div_flow * output[0], max_value=10), epoch)
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t Time {2}\t EPE {3}'
.format(i, len(val_loader), batch_time, flow2_EPEs))
print(' * EPE {:.3f}'.format(flow2_EPEs.avg))
return flow2_EPEs.avg
示例7: train
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def train(train_loader, model, criterion, optimizer, lr_scheduler, epoch, monitors, args):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
batch_time = AverageMeter()
total_sample = len(train_loader.sampler)
batch_size = train_loader.batch_size
steps_per_epoch = math.ceil(total_sample / batch_size)
logger.info('Training: %d samples (%d per mini-batch)', total_sample, batch_size)
model.train()
end_time = time.time()
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs = inputs.to(args.device.type)
targets = targets.to(args.device.type)
outputs = model(inputs)
loss = criterion(outputs, targets)
acc1, acc5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1.item(), inputs.size(0))
top5.update(acc5.item(), inputs.size(0))
if lr_scheduler is not None:
lr_scheduler.step(epoch=epoch, batch=batch_idx)
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end_time)
end_time = time.time()
if (batch_idx + 1) % args.log.print_freq == 0:
for m in monitors:
m.update(epoch, batch_idx + 1, steps_per_epoch, 'Training', {
'Loss': losses,
'Top1': top1,
'Top5': top5,
'BatchTime': batch_time,
'LR': optimizer.param_groups[0]['lr']
})
logger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n',
top1.avg, top5.avg, losses.avg)
return top1.avg, top5.avg, losses.avg
示例8: evaluate
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def evaluate(model, data_loader, device, eval_file, max_len, use_squad_v2):
nll_meter = util.AverageMeter()
model.eval()
pred_dict = {}
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(data_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
# Forward
log_p1, log_p2 = model(cw_idxs, qw_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, max_len, use_squad_v2)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
preds, _ = util.convert_tokens(gold_dict,
ids.tolist(),
starts.tolist(),
ends.tolist(),
use_squad_v2)
pred_dict.update(preds)
model.train()
results = util.eval_dicts(gold_dict, pred_dict, use_squad_v2)
results_list = [('NLL', nll_meter.avg),
('F1', results['F1']),
('EM', results['EM'])]
if use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
return results, pred_dict
示例9: train
# 需要导入模块: import util [as 别名]
# 或者: from util import AverageMeter [as 别名]
def train(train_loader, model, optimizer, epoch, train_writer):
global n_iter, args
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
flow2_EPEs = AverageMeter()
epoch_size = len(train_loader) if args.epoch_size == 0 else min(len(train_loader), args.epoch_size)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.to(device)
input = torch.cat(input,1).to(device)
# compute output
output = model(input)
if args.sparse:
# Since Target pooling is not very precise when sparse,
# take the highest resolution prediction and upsample it instead of downsampling target
h, w = target.size()[-2:]
output = [F.interpolate(output[0], (h,w)), *output[1:]]
loss = multiscaleEPE(output, target, weights=args.multiscale_weights, sparse=args.sparse)
flow2_EPE = args.div_flow * realEPE(output[0], target, sparse=args.sparse)
# record loss and EPE
losses.update(loss.item(), target.size(0))
train_writer.add_scalar('train_loss', loss.item(), n_iter)
flow2_EPEs.update(flow2_EPE.item(), target.size(0))
# compute gradient and do optimization step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t Time {3}\t Data {4}\t Loss {5}\t EPE {6}'
.format(epoch, i, epoch_size, batch_time,
data_time, losses, flow2_EPEs))
n_iter += 1
if i >= epoch_size:
break
return losses.avg, flow2_EPEs.avg