def main(args):
global best_acc
global idx
# idx is the index of joints used to compute accuracy
if args.dataset in ['mpii', 'lsp']:
idx = [1, 2, 3, 4, 5, 6, 11, 12, 15, 16]
elif args.dataset == 'coco':
idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
else:
print("Unknown dataset: {}".format(args.dataset))
assert False
# create checkpoint dir
if not isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# create model
njoints = datasets.__dict__[args.dataset].njoints
print("==> creating model '{}', stacks={}, blocks={}".format(
args.arch, args.stacks, args.blocks))
model = models.__dict__[args.arch](num_stacks=args.stacks,
num_blocks=args.blocks,
num_classes=njoints,
resnet_layers=args.resnet_layers)
model = torch.nn.DataParallel(model).to(device)
# define loss function (criterion) and optimizer
criterion = losses.JointsMSELoss().to(device)
if args.solver == 'rms':
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.solver == 'adam':
optimizer = torch.optim.Adam(
model.parameters(),
lr=args.lr,
)
else:
print('Unknown solver: {}'.format(args.solver))
assert False
# optionally resume from a checkpoint
title = args.dataset + ' ' + args.arch
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
logger = Logger(join(args.checkpoint, 'log.txt'),
title=title,
resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
print(' Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
# create data loader
train_dataset = datasets.__dict__[args.dataset](is_train=True,
**vars(args))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_dataset = datasets.__dict__[args.dataset](is_train=False, **vars(args))
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# evaluation only
if args.evaluate:
print('\nEvaluation only')
loss, acc, predictions = validate(val_loader, model, criterion,
njoints, args.debug, args.flip)
save_pred(predictions, checkpoint=args.checkpoint)
return
# train and eval
lr = args.lr
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule,
args.gamma)
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr))
# decay sigma
if args.sigma_decay > 0:
train_loader.dataset.sigma *= args.sigma_decay
val_loader.dataset.sigma *= args.sigma_decay
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, args.debug, args.flip)
# evaluate on validation set
valid_loss, valid_acc, predictions = validate(val_loader, model,
criterion, njoints,
args.debug, args.flip)
# append logger file
logger.append(
[epoch + 1, lr, train_loss, valid_loss, train_acc, valid_acc])
# remember best acc and save checkpoint
is_best = valid_acc > best_acc
best_acc = max(valid_acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
},
predictions,
is_best,
checkpoint=args.checkpoint,
snapshot=args.snapshot)
logger.close()
logger.plot(['Train Acc', 'Val Acc'])
savefig(os.path.join(args.checkpoint, 'log.eps'))
def main(args):
global best_acc
# create checkpoint dir
if not isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# create model
print("==> creating model '{}', stacks={}, blocks={}".format(args.arch, args.stacks, args.blocks))
model = models.__dict__[args.arch](num_stacks=args.stacks, num_blocks=args.blocks, num_classes=args.num_classes)
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = torch.nn.MSELoss(size_average=True).cuda()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
title = 'mpii-' + args.arch
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
logger = Logger(join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
# Data loading code
train_loader = torch.utils.data.DataLoader(
datasets.Mpii('data/mpii/mpii_annotations.json', 'data/mpii/images',
sigma=args.sigma, label_type=args.label_type),
batch_size=args.train_batch, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.Mpii('data/mpii/mpii_annotations.json', 'data/mpii/images',
sigma=args.sigma, label_type=args.label_type, train=False),
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
print('\nEvaluation only')
loss, acc, predictions = validate(val_loader, model, criterion, args.num_classes, args.debug, args.flip)
save_pred(predictions, checkpoint=args.checkpoint)
return
lr = args.lr
for epoch in range(args.start_epoch, args.epochs):
from time import sleep
sleep(2)
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule, args.gamma)
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr))
# decay sigma
if args.sigma_decay > 0:
train_loader.dataset.sigma *= args.sigma_decay
val_loader.dataset.sigma *= args.sigma_decay
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion, optimizer, args.debug, args.flip)
# evaluate on validation set
valid_loss, valid_acc, predictions = validate(val_loader, model, criterion, args.num_classes,
args.debug, args.flip)
# append logger file
logger.append([epoch + 1, lr, train_loss, valid_loss, train_acc, valid_acc])
# remember best acc and save checkpoint
is_best = valid_acc > best_acc
best_acc = max(valid_acc, best_acc)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer' : optimizer.state_dict(),
}, predictions, is_best, checkpoint=args.checkpoint)
logger.close()
logger.plot(['Train Acc', 'Val Acc'])
savefig(os.path.join(args.checkpoint, 'log.eps'))
def main(args):
global best_acc
# create checkpoint dir
if not isdir(args.checkpoint):
mkdir_p(args.checkpoint)
_logger = log.get_logger(__name__, args)
_logger.info(print_args(args))
# create model
print("==> creating model '{}', stacks={}, blocks={}".format(
args.arch, args.stacks, args.blocks))
model = models.__dict__[args.arch](num_stacks=args.stacks,
num_blocks=args.blocks,
num_classes=len(args.index_classes))
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = models.loss.UniLoss(a_points=args.a_points)
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
title = 'mpii-' + args.arch
if args.resume:
if isfile(args.resume):
_logger.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
print(param_group['lr'])
_logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
logger = Logger(join(args.checkpoint, 'log.txt'),
title=title,
resume=False)
logger.set_names([
'Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'
])
else:
_logger.info("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
cudnn.benchmark = True
_logger.info(' Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
# Data loading code
train_loader = torch.utils.data.DataLoader(
datasets.Mpii('data/mpii/mpii_annotations.json',
'data/mpii/images',
sigma=args.sigma,
label_type=args.label_type,
_idx=args.index_classes,
direct=True,
n_points=args.n_points),
batch_size=args.train_batch,
shuffle=True,
collate_fn=datasets.mpii.mycollate,
num_workers=args.workers,
pin_memory=False)
val_loader = torch.utils.data.DataLoader(
datasets.Mpii('data/mpii/mpii_annotations.json',
'data/mpii/images',
sigma=args.sigma,
label_type=args.label_type,
_idx=args.index_classes,
train=False,
direct=True),
batch_size=args.test_batch,
shuffle=False,
collate_fn=datasets.mpii.mycollate,
num_workers=args.workers,
pin_memory=False)
if args.evaluate:
_logger.warning('\nEvaluation only')
loss, acc, predictions = validate(val_loader,
model,
criterion,
len(args.index_classes),
False,
args.flip,
_logger,
evaluate_only=True)
save_pred(predictions, checkpoint=args.checkpoint)
return
# multi-thread
inqueues = []
outqueues = []
valid_accs = []
lr = args.lr
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule,
args.gamma)
_logger.warning('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr))
# decay sigma
if args.sigma_decay > 0:
train_loader.dataset.sigma *= args.sigma_decay
val_loader.dataset.sigma *= args.sigma_decay
# train for one epoch
train_loss, train_acc = train(inqueues, outqueues, train_loader, model,
criterion, optimizer, args.debug,
args.flip, args.clip, _logger)
# evaluate on validation set
with torch.no_grad():
valid_loss, valid_acc, predictions = validate(
val_loader, model, criterion, len(args.index_classes),
args.debug, args.flip, _logger)
# append logger file
logger.append(
[epoch + 1, lr, train_loss, valid_loss, train_acc, valid_acc])
valid_accs.append(valid_acc)
if args.schedule[0] == -1:
if len(valid_accs) > 8:
if sum(valid_accs[-4:]) / 4 * 0.99 < sum(
valid_accs[-8:-4]) / 4:
args.schedule.append(epoch + 1)
valid_accs = []
# remember best acc and save checkpoint
is_best = valid_acc > best_acc
best_acc = max(valid_acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
},
predictions,
is_best,
checkpoint=args.checkpoint,
snapshot=1)
logger.close()
logger.plot(['Train Acc', 'Val Acc'])
savefig(os.path.join(args.checkpoint, 'log.eps'))
def main(args):
num_datasets = len(args.data_dir) #number of datasets
for item in [
args.training_set_percentage, args.meta_dir, args.anno_type,
args.ratio
]:
if len(item) == 1:
for i in range(num_datasets - 1):
item.append(item[0])
assert len(item) == num_datasets
scales = [0.7, 0.85, 1, 1.3, 1.6]
if args.meta_dir == '':
args.meta_dir = args.data_dir #if not specified, assume meta info is stored in data dir.
# create checkpoint dir
if not isdir(args.checkpoint):
mkdir_p(args.checkpoint)
#create the log file not exist
file = open(join(args.checkpoint, 'log.txt'), 'w+')
file.close()
if args.evaluate: #creatng path for evaluation
if not isdir(args.save_result_dir):
mkdir_p(args.save_result_dir)
folders_to_create = ['preds', 'visualization']
if args.save_heatmap:
folders_to_create.append('heatmaps')
for folder_name in folders_to_create:
if not os.path.isdir(
os.path.join(args.save_result_dir, folder_name)):
print('creating path: ' +
os.path.join(args.save_result_dir, folder_name))
os.mkdir(os.path.join(args.save_result_dir, folder_name))
idx = range(args.num_classes)
global best_acc
cams = ['FusionCameraActor3_2']
# create model
print("==> creating model '{}', stacks={}, blocks={}".format(
args.arch, args.stacks, args.blocks))
model = models.__dict__[args.arch](num_stacks=args.stacks,
num_blocks=args.blocks,
num_classes=args.num_classes)
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = torch.nn.MSELoss(size_average=True).cuda()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
title = 'arm-' + args.arch
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
logger = Logger(join(args.checkpoint, 'log.txt'),
title=title,
resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
cudnn.benchmark = True
print(' Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
train_set_list = []
val_set_list = []
for i in range(num_datasets):
train_set_list.append(
datasets.Arm(
args.data_dir[i],
args.meta_dir[i],
args.random_bg_dir,
cams[0],
args.anno_type[i],
train=True,
training_set_percentage=args.training_set_percentage[i],
replace_bg=args.replace_bg))
val_set_list.append(
datasets.Arm(
args.data_dir[i],
args.meta_dir[i],
args.random_bg_dir,
cams[0],
args.anno_type[i],
train=False,
training_set_percentage=args.training_set_percentage[i],
scales=scales,
multi_scale=args.multi_scale,
ignore_invis_pts=args.ignore_invis_pts))
# Data loading code
if not args.evaluate:
train_loader = torch.utils.data.DataLoader(datasets.Concat(
datasets=train_set_list, ratio=args.ratio),
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
print("No. minibatches in training set:{}".format(len(train_loader)))
if args.multi_scale: #multi scale testing
args.test_batch = args.test_batch * len(scales)
val_loader = torch.utils.data.DataLoader(datasets.Concat(
datasets=val_set_list, ratio=None),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print("No. minibatches in validation set:{}".format(len(val_loader)))
if args.evaluate:
print('\nEvaluation only')
# if not args.compute_3d:
loss, acc = validate(val_loader, model, criterion, args.num_classes,
idx, args.save_result_dir, args.meta_dir,
args.anno_type, args.flip, args.evaluate, scales,
args.multi_scale, args.save_heatmap)
if args.compute_3d:
preds = []
gts = []
hit, d3_pred, file_name_list = d2tod3(
data_dir=args.save_result_dir,
meta_dir=args.meta_dir[0],
cam_type=args.camera_type,
pred_from_heatmap=False,
em_test=False)
# validate the 3d reconstruction accuracy
with open(os.path.join(args.save_result_dir, 'd3_pred.json'),
'r') as f:
obj = json.load(f)
hit, d3_pred, file_name_list = obj['hit'], obj['d3_pred'], obj[
'file_name_list']
for file_name in file_name_list:
preds.append(d3_pred[file_name]['preds']) #predicted x
with open(os.path.join(args.data_dir[0], 'angles', file_name),
'r') as f:
gts.append(json.load(f))
print('average error in angle: [base, elbow, ankle, wrist]:{}'.
format(d3_acc(preds, gts)))
return
lr = args.lr
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule,
args.gamma)
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr))
# decay sigma
if args.sigma_decay > 0:
train_loader.dataset.sigma *= args.sigma_decay
val_loader.dataset.sigma *= args.sigma_decay
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, idx, args.flip)
# evaluate on validation set
valid_loss, valid_acc = validate(val_loader, model, criterion,
args.num_classes, idx,
args.save_result_dir, args.meta_dir,
args.anno_type, args.flip,
args.evaluate)
#If concatenated dataset is used, re-random after each epoch
train_loader.dataset.reset(), val_loader.dataset.reset()
# append logger file
logger.append(
[epoch + 1, lr, train_loss, valid_loss, train_acc, valid_acc])
# remember best acc and save checkpoint
is_best = valid_acc > best_acc
best_acc = max(valid_acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
},
is_best,
checkpoint=args.checkpoint)
logger.close()
logger.plot(['Train Acc', 'Val Acc'])
savefig(os.path.join(args.checkpoint, 'log.eps'))
def main(args):
global best_acc
global idx
# idx is the index of joints used to compute accuracy
if args.dataset in ['mpii', 'lsp']:
idx = [1, 2, 3, 4, 5, 6, 11, 12, 15, 16]
elif args.dataset == 'coco':
idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
elif args.dataset == 'homes':
idx = list(range(1, 71))
else:
print("Unknown dataset: {}".format(args.dataset))
assert False
# create checkpoint dir
if not isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# create model
njoints = datasets.__dict__[args.dataset].njoints
print("==> creating model '{}', stacks={}, blocks={}".format(
args.arch, args.stacks, args.blocks))
model = models.__dict__[args.arch](
num_stacks=args.stacks,
num_blocks=args.blocks,
num_classes=16, # read as 16, the nmodified to 70
resnet_layers=args.resnet_layers)
model = torch.nn.DataParallel(model).to(device)
#print(list(model.children())); exit(1)
# define loss function (criterion) and optimizer
#criterion = losses.JointsMSELoss().to(device)
#criterion = torch.nn.BCEWithLogitsLoss().to(device)
criterion = torch.nn.MSELoss().to(device)
print('==> loss function: %s' % criterion.__str__())
if args.solver == 'rms':
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.solver == 'adam':
optimizer = torch.optim.Adam(
model.parameters(),
lr=args.lr,
)
else:
print('Unknown solver: {}'.format(args.solver))
assert False
# optionally resume from a checkpoint
title = args.dataset + ' ' + args.arch
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
logger = Logger(join(args.checkpoint, 'log.txt'),
title=title,
resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
print(' Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
# update the final score layer and fine tuning => update number of output channels to 70
#print(list(model.children())[-1].score[0]); exit(1)
model_net = list(model.children())
model_net[-1].score[0] = nn.Conv2d(256,
70,
kernel_size=(1, 1),
stride=(1, 1)).to(device)
model = nn.Sequential(*model_net)
# create data loader
train_dataset = datasets.__dict__[args.dataset](**vars(args))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_dataset = datasets.__dict__[args.dataset](is_train=False,
is_valid=True,
**vars(args))
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# evaluation only
if args.evaluate:
print('\nEvaluation only')
loss, acc, predictions = validate(val_loader, model, criterion,
njoints, args.debug, args.flip)
save_pred(predictions, checkpoint=args.checkpoint)
return
# train and eval
df_loss = pd.DataFrame()
train_epo_loss, val_epo_loss = [], []
lr = args.lr
best_vel = np.float('inf')
for epoch in range(args.start_epoch, args.epochs + args.start_epoch):
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule,
args.gamma)
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr))
# decay sigma
if args.sigma_decay > 0:
train_loader.dataset.sigma *= args.sigma_decay
val_loader.dataset.sigma *= args.sigma_decay
# train for one epoch
train_loss, train_acc, tel = train(train_loader, model, criterion,
optimizer, args.debug, args.flip)
# evaluate on validation set
valid_loss, valid_acc, predictions, vel = validate(
val_loader, model, criterion, njoints, args.debug, args.flip)
# save epoch loss to csv
train_epo_loss += [tel]
val_epo_loss += [vel]
df_loss.assign(train=train_epo_loss,
val=val_epo_loss).to_csv('./loss.csv')
# append logger file
logger.append([
epoch + 1, lr, train_loss, valid_loss,
train_acc.item(),
valid_acc.item()
])
# remember best acc and save checkpoint
#is_best = valid_acc > best_acc
#best_acc = max(valid_acc, best_acc)
is_best = vel < best_vel
best_vel = min(best_vel, vel)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
},
predictions,
is_best,
checkpoint=args.checkpoint,
snapshot=args.snapshot)
logger.close()