def main():
if len(sys.argv) > 1:
name = ' '.join(sys.argv[1:])
else:
from datetime import datetime
name = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
target_name = os.path.join('logs', '{}.pth'.format(name))
print('will save to {}'.format(target_name))
cudnn.benchmark = True
train_loader = data.get_loader(train=True)
val_loader = data.get_loader(val=True)
net = nn.DataParallel(model.Net(train_loader.dataset.num_tokens)).cuda()
optimizer = optim.Adam([p for p in net.parameters() if p.requires_grad])
net_var = nn.DataParallel(model.Uncertainty(config.max_answers)).cuda()
optimizer_var = optim.SGD(
[p for p in net_var.parameters() if p.requires_grad], lr=0.0002)
tracker = utils.Tracker()
ckp = torch.load('logs/2019-03-19_22:49:23.pth_9.pth')
net.load_state_dict(ckp['weights'])
net_var.load_state_dict(ckp['weights_var'])
run(net,
net_var,
val_loader,
optimizer,
optimizer_var,
tracker,
train=False,
prefix='val')
def main():
if len(sys.argv) > 1:
name = ' '.join(sys.argv[1:])
else:
from datetime import datetime
name = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
target_name = os.path.join('logs', '{}'.format(name))
print('will save to {}'.format(target_name))
cudnn.benchmark = True
train_loader = data.get_loader(train=True)
val_loader = data.get_loader(val=True)
net = nn.DataParallel(model.Net(train_loader.dataset.num_tokens)).cuda()
optimizer = optim.Adam([p for p in net.parameters() if p.requires_grad])
tracker = utils.Tracker()
config_as_dict = {k: v for k, v in vars(config).items() if not k.startswith('__')}
net_dis = nn.DataParallel(model.Net_Discriminitor(196)).cuda()
optimizer_dis = optim.Adam([p for p in net_dis.parameters() if p.requires_grad],lr = 0.0002, betas = (0.5, 0.999))
# Uncomment the following lines while evaluating or want to start by loading checkpoint
# ----------------------------------------
ckp = torch.load('logs/2018-10-28_12:47:45_180.pth')
name = ckp['name']
# tracker = ckp['tracker']
config_as_dict = ckp['config']
net.load_state_dict(ckp['weights'])
net_dis.load_state_dict(ckp['weights_grad'])
train_loader.dataset.vocab = ckp['vocab']
# ----------------------------------------
for i in range(0, config.epochs):
_ = run(net,net_dis, train_loader, optimizer,optimizer_dis, tracker, train=True, prefix='train', epoch=i)
r = run(net,net_dis, val_loader, optimizer, optimizer_dis,tracker, train=False, prefix='val', epoch=i)
results = {
'name': name,
'tracker': tracker.to_dict(),
'config': config_as_dict,
'weights': net.state_dict(),
'weights_grad': net_dis.state_dict(),
'eval': {
'answers': r[0],
'accuracies': r[1],
'idx': r[2],
},
'vocab': train_loader.dataset.vocab,
}
torch.save(results, target_name+"_"+str(i)+".pth")