def validate():
model.eval()
top1 = AverageMeter()
for batch_idx, (data, target) in enumerate(val_loader):
data, target = data.cuda(gpu), target.cuda(gpu)
with torch.no_grad():
output = model(data)
acc1 = accuracy(output, target, topk=(1, ))
top1.update(acc1[0], data.size(0))
return top1.avg
def train(epoch):
train_sampler.set_epoch(epoch)
model.train()
losses = AverageMeter()
top1 = AverageMeter()
global best_pred, acclist_train
for batch_idx, (data, target) in enumerate(train_loader):
scheduler(optimizer, batch_idx, epoch, best_pred)
if not args.mixup:
data, target = data.cuda(args.gpu), target.cuda(args.gpu)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if not args.mixup:
acc1 = accuracy_multilabel(output, target)
top1.update(acc1, data.size(0))
losses.update(loss.item(), data.size(0))
if batch_idx % 100 == 0 and args.gpu == 0:
if args.mixup:
print('Batch: %d| Loss: %.3f' % (batch_idx, losses.avg))
else:
print('Batch: %d| Loss: %.3f | Top1: %.3f' %
(batch_idx, losses.avg, top1.avg))
acclist_train += [top1.avg]
def validate(epoch):
model.eval()
top1 = AverageMeter()
top5 = AverageMeter()
global best_pred, acclist_train, acclist_val
is_best = False
for batch_idx, (data, target) in enumerate(val_loader):
data, target = data.cuda(args.gpu), target.cuda(args.gpu)
with torch.no_grad():
output = model(data)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
top1.update(acc1[0], data.size(0))
top5.update(acc5[0], data.size(0))
# sum all
sum1, cnt1, sum5, cnt5 = torch_dist_sum(args.gpu, top1.sum, top1.count, top5.sum, top5.count)
if args.gpu == 0:
top1_acc = sum(sum1) / sum(cnt1)
top5_acc = sum(sum5) / sum(cnt5)
print('Validation: Top1: %.3f | Top5: %.3f'%(top1_acc, top5_acc))
# save checkpoint
acclist_val += [top1_acc]
if top1_acc > best_pred:
best_pred = top1_acc
is_best = True
encoding.utils.save_checkpoint({
'epoch': epoch,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
'acclist_train':acclist_train,
'acclist_val':acclist_val,
}, args=args, is_best=is_best)
def train(epoch):
# train_sampler.set_epoch(epoch)
model.train()
train_loss, correct, total = 0, 0, 0
# losses = AverageMeter()
top1 = AverageMeter()
global best_pred, acclist_train
for batch_idx, (data, target) in enumerate(train_loader):
scheduler(optimizer, batch_idx, epoch, best_pred)
if not args.mixup:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output, embed = model(data)
loss1 = criterion(output, target)
loss2 = criterion_triplet(embed, target)
loss = loss1 + 0.1 * loss2
loss.backward()
optimizer.step()
# -----另一种计算方式
train_loss += loss.item()
pred = output.data.max(1)[1]
correct += pred.eq(target.data).cpu().sum()
total += target.size(0)
# ------end------
if not args.mixup:
acc1 = accuracy(output, target, topk=(1, ))
# print("acc1:")
# print(acc1)
top1.update(acc1[0], data.size(0))
# losses.update(loss.item(), data.size(0))
if batch_idx % 100 == 0:
if args.mixup:
print('Batch: %d| Loss: %.3f' % (batch_idx, train_loss /
(batch_idx + 1)))
else:
print('Batch: %d| Loss: %.3f | Top1: %.3f' %
(batch_idx, train_loss / (batch_idx + 1), top1.avg))
print(' Train set, Accuracy:({:.0f}%)\n'.format(100. * correct /
total))
print(' Top1: %.3f' % top1.avg)
acclist_train += [top1.avg]
def validate(epoch):
model.eval()
top1 = AverageMeter()
top5 = AverageMeter()
global best_pred, acclist_train, acclist_val
is_best = False
correct, total = 0, 0
for batch_idx, (data, target) in enumerate(val_loader):
data, target = data.cuda(), target.cuda()
with torch.no_grad():
output = model(data)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
top1.update(acc1[0], data.size(0))
top5.update(acc5[0], data.size(0))
pred = output.data.max(1)[1]
correct += pred.eq(target.data).cpu().sum()
total += target.size(0)
# sum all
# sum1, cnt1, sum5, cnt5 = torch_dist_sum( top1.sum, top1.count, top5.sum, top5.count)
# sum1, cnt1, sum5, cnt5 = top1.sum, top1.count, top5.sum, top5.count
# if args.eval:
# top1_acc = sum1 / cnt1
# top5_acc = sum5 / cnt5
# print('Validation: Top1: %.3f | Top5: %.3f' % (top1_acc, top5_acc))
# return
top1_acc = top1.avg
top5_acc = top5.avg
# print('Validation: Top1: %.3f | Top5: %.3f' % (100. * top1_acc, 100. * top5_acc))
# print('Valid set, Accuracy: %.3f' %(100. * top1_acc))
print("Validation correct:%d total:%d" % (correct, total))
print('Valid set, Accuracy: %.3f' % (correct / total))
print('Validation: Top1: %.3f | Top5: %.3f' % (top1_acc, top5_acc))
# save checkpoint
acclist_val += [top1_acc]
if top1_acc > best_pred:
best_pred = top1_acc
is_best = True
encoding.utils.save_checkpoint(
{
'epoch': epoch,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
'acclist_train': acclist_train,
'acclist_val': acclist_val,
},
args=args,
is_best=is_best)
def validate(auto_policy):
model.eval()
top1 = AverageMeter()
_, transform_val = get_transform(args.dataset, args.base_size,
args.crop_size)
if auto_policy is not None:
transform_val.transforms.insert(0, Augmentation(auto_policy))
valset.transform = transform_val
val_loader = torch.utils.data.DataLoader(valset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
for batch_idx, (data, target) in enumerate(val_loader):
data, target = data.cuda(gpu), target.cuda(gpu)
with torch.no_grad():
output = model(data)
acc1 = accuracy(output, target, topk=(1, ))
top1.update(acc1[0], data.size(0))
return top1.avg
def train(epoch):
model.train()
top1 = AverageMeter()
for batch_idx, (data, target) in enumerate(train_loader):
lr_scheduler(optimizer, batch_idx, epoch, 0)
data, target = data.cuda(gpu), target.cuda(gpu)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
if args.amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
def train(epoch):
train_sampler.set_epoch(epoch)
model.train()
losses = AverageMeter()
top1 = AverageMeter()
global best_pred, acclist_train
tic = time.time()
for batch_idx, (data, target) in enumerate(train_loader):
scheduler(optimizer, batch_idx, epoch, best_pred)
if not args.mixup:
data, target = data.cuda(args.gpu), target.cuda(args.gpu)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
if args.amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
if not args.mixup:
acc1 = accuracy(output, target, topk=(1, ))
top1.update(acc1[0], data.size(0))
losses.update(loss.item(), data.size(0))
if batch_idx % 100 == 0 and args.gpu == 0:
iter_per_sec = 100.0 / (time.time() -
tic) if batch_idx != 0 else 1.0 / (
time.time() - tic)
tic = time.time()
if args.mixup:
#print('Batch: %d| Loss: %.3f'%(batch_idx, losses.avg))
print('Epoch: {}, Iter: {}, Speed: {:.3f} iter/sec, Train loss: {:.3f}'. \
format(epoch, batch_idx, iter_per_sec, losses.avg.item()))
else:
#print('Batch: %d| Loss: %.3f | Top1: %.3f'%(batch_idx, losses.avg, top1.avg))
print('Epoch: {}, Iter: {}, Speed: {:.3f} iter/sec, Top1: {:.3f}'. \
format(epoch, batch_idx, iter_per_sec, top1.avg.item()))
acclist_train += [top1.avg]
def main():
# init the args
args = Options().parse()
args.cuda = not args.no_cuda and torch.cuda.is_available()
print(args)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# init dataloader
_, transform_val = encoding.transforms.get_transform(
args.dataset, args.base_size, args.crop_size)
valset = encoding.datasets.get_dataset(
args.dataset,
root=os.path.expanduser('~/.encoding/data'),
transform=transform_val,
train=False,
download=True)
val_loader = torch.utils.data.DataLoader(
valset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True if args.cuda else False)
# init the model
model_kwargs = {'pretrained': True}
if args.rectify:
model_kwargs['rectified_conv'] = True
model_kwargs['rectify_avg'] = args.rectify_avg
model = encoding.models.get_model(args.model, **model_kwargs)
print(model)
if args.cuda:
model.cuda()
# Please use CUDA_VISIBLE_DEVICES to control the number of gpus
model = nn.DataParallel(model)
# checkpoint
if args.verify:
if os.path.isfile(args.verify):
print("=> loading checkpoint '{}'".format(args.verify))
model.module.load_state_dict(torch.load(args.verify))
else:
raise RuntimeError ("=> no verify checkpoint found at '{}'".\
format(args.verify))
elif args.resume is not None:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.module.load_state_dict(checkpoint['state_dict'])
else:
raise RuntimeError ("=> no resume checkpoint found at '{}'".\
format(args.resume))
model.eval()
top1 = AverageMeter()
top5 = AverageMeter()
is_best = False
tbar = tqdm(val_loader, desc='\r')
for batch_idx, (data, target) in enumerate(tbar):
if args.cuda:
data, target = data.cuda(), target.cuda()
with torch.no_grad():
output = model(data)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
top1.update(acc1[0], data.size(0))
top5.update(acc5[0], data.size(0))
tbar.set_description('Top1: %.3f | Top5: %.3f' % (top1.avg, top5.avg))
print('Top1 Acc: %.3f | Top5 Acc: %.3f ' % (top1.avg, top5.avg))
if args.export:
torch.save(model.module.state_dict(), args.export + '.pth')