def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}')
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=0,
pin_memory=True,
)
valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=0,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
# sample_weight = labels.mean(0)
# sample_weight = labels[labels!=2].reshape((labels.shape[0], labels.shape[1])).mean(0)
sample_weight = np.nanmean(np.where(labels!=2,labels,np.nan), axis=0)
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter, nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
if torch.cuda.is_available():
param_groups = [{'params': model.module.finetune_params(), 'lr': args.lr_ft},
{'params': model.module.fresh_params(), 'lr': args.lr_new}]
else:
param_groups = [{'params': model.finetune_params(), 'lr': args.lr_ft},
{'params': model.fresh_params(), 'lr': args.lr_new}]
optimizer = torch.optim.SGD(param_groups, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
# Added for logging purposes
user = getpass.getuser()
fixed_time_str = time_str()
stdout_file = os.path.join(
log_dir, "_".join(['stdout', user, f'{fixed_time_str}.txt']))
save_model_path = os.path.join(
model_dir, "_".join(['ckpt_max', user, f'{fixed_time_str}.pth']))
trackitems_dir = os.path.join(
log_dir, "_".join(['trackitems', user, f'{fixed_time_str}.txt']))
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
#train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_set = AttrDataset_new(args=args,
split=args.train_split,
transformation_dict=args.train_transform)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=8,
pin_memory=True,
)
#valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_set = AttrDataset_new(args=args,
split=args.valid_split,
transformation_dict=args.valid_transform)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=8,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
# sample_weight = labels.mean(0)
sample_weight = np.nanmean(np.where(labels != 2, labels, np.nan), axis=0)
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter,
nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
if torch.cuda.is_available():
param_groups = [{
'params': model.module.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.module.fresh_params(),
'lr': args.lr_new
}]
else:
param_groups = [{
'params': model.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.fresh_params(),
'lr': args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
# Added for logging purposes
with open(trackitems_dir, "a") as f:
code, line_no = inspect.getsourcelines(get_transform)
for line in code:
f.write(str(line))
f.write(str("\n\n"))
f.write(str(args.__dict__))
f.write(str("\n\n"))
f.write(str(lr_scheduler.__dict__))
f.write(str("\n\n"))
model_str = str(model).lower()
have_dropout = 'dropout' in model_str
f.write('dropout: %s' % (have_dropout))
f.write(str("\n\n"))
have_leaky_relu = 'leaky_relu' in model_str
f.write('leaky_relu: %s' % (have_leaky_relu))
f.write(str("\n\n"))
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
measure="f1")
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
# Added for logging purposes
with open(trackitems_dir, "a") as f:
f.write(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
f.write("\n\n")
