args = parser.parse_args()
hp = HParam(args.config)
graphs = [
read_graph(hp.model.graph0),
read_graph(hp.model.graph1),
read_graph(hp.model.graph2),
]
print('Loading model from checkpoint...')
model = RandWire(hp, graphs).cuda()
checkpoint = torch.load(args.checkpoint_path)
model.load_state_dict(checkpoint['model'])
step = checkpoint['step']
dataset = hp.data.type
switcher = {
'MNIST': MNIST_dataloader,
'CIFAR10': CIFAR10_dataloader,
'ImageNet': create_dataloader,
}
assert dataset in switcher.keys(), 'Dataset type currently not supported'
dl_func = switcher[dataset]
valset = dl_func(hp, args, False)
print('Validating...')
test_avg_loss, accuracy = validate(model, valset)
print('Result on step %d:' % step)
print('Average test loss: %.4f' % test_avg_loss)
print('Accuracy: %.3f' % accuracy)
def train(out_dir, chkpt_path, trainset, valset, writer, logger, hp, hp_str, graphs, in_channels=3):
model = RandWire(hp, graphs, in_channels=in_channels).cuda()
#print(net)
#print(count_parameters(model))
#with torch.no_grad():
# x = torch.randn(1,1,28,28).cuda()
# y = model(x)
# Save onnx model for visualization
#torch.onnx.export(model, x, "onnx/CElegans.onnx")
#print('Model saved to disk')
if hp.train.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=hp.train.adam*10)
elif hp.train.optimizer == 'adabound':
optimizer = adabound.AdaBound(model.parameters(),
lr=hp.train.adabound.initial,
final_lr=hp.train.adabound.final)
else:
raise Exception("Optimizer not supported: %s" % hp.train.optimizer)
step = 0
if chkpt_path is not None:
logger.info("Resuming from checkpoint: %s" % chkpt_path)
checkpoint = torch.load(chkpt_path)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
step = checkpoint['step']
if hp_str != checkpoint['hp_str']:
logger.warning("New hparams are different from checkpoint.")
logger.warning("Will use new hparams.")
# hp = load_hparam_str(hp_str)
else:
logger.info("Starting new training run")
logger.info("Writing graph to tensorboardX...")
#writer.write_graph(model, torch.randn(1, 1, 28, 28).cuda())
logger.info("Finished.")
try:
model.train()
epoch = 0
best_acc = 0
while epoch < 20: # TODO Change this to one epoch but make sure logging still works
print(type(model.dagly3.weighted_adj))
print(model.dagly3.weighted_adj)
np.savetxt('adj/unweighted.txt'.format(epoch), model.dagly3.unweighted_adj)
np.savetxt('adj/after_{}_epochs.txt'.format(epoch), model.dagly3.weighted_adj)
for data, target in trainset:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
step += 1
loss = loss.item()
if loss > 1e8 or math.isnan(loss):
logger.error("Loss exploded to %.02f at step %d!" % (loss, step))
raise Exception("Loss exploded")
if step % hp.train.summary_interval == 0:
writer.log_training(loss, step)
logger.info("Wrote summary at step %d, epoch %d" % (step, epoch))
if step % len(trainset) == 0: #step % hp.train.checkpoint_interval == 0:
save_path = os.path.join(out_dir, 'chkpt_%07d.pt' % step)
torch.save({
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': step,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
if step % len(trainset) == 0: #step % hp.train.evaluation_interval == 0:
test_loss, accuracy = validate(model, valset, writer, step)
if accuracy > best_acc:
best_acc = accuracy
logger.info("Evaluation saved at step %d, epoch %d | test_loss: %.5f | accuracy: %.4f"
% (step, epoch, test_loss, accuracy))
if step % hp.train.decay.step == 0:
temp = optimizer.state_dict()
temp['param_groups'][0]['lr'] *= hp.train.decay.gamma
optimizer.load_state_dict(temp)
epoch += 1
writer.log_best_acc(best_acc)
print(type(model.dagly3.weighted_adj))
print(model.dagly3.weighted_adj)
np.savetxt('adj/after_{}_epochs.txt'.format(epoch), model.dagly3.weighted_adj)
except Exception as e:
logger.info("Exiting due to exception: %s" % e)
traceback.print_exc()
def train(out_dir, chkpt_path, trainset, valset, writer, logger, hp, hp_str):
model = RandWire(hp).cuda()
if hp.train.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=hp.train.adam)
elif hp.train.optimizer == 'adabound':
optimizer = AdaBound(model.parameters(),
lr=hp.train.adabound.initial,
final_lr=hp.train.adabound.final)
else:
raise Exception("Optimizer not supported: %s" % hp.train.optimizer)
step = 0
if chkpt_path is not None:
logger.info("Resuming from checkpoint: %s" % chkpt_path)
checkpoint = torch.load(chkpt_path)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
step = checkpoint['step']
if hp_str != checkpoint['hp_str']:
logger.warning("New hparams are different from checkpoint.")
logger.warning("Will use new hparams.")
# hp = load_hparam_str(hp_str)
else:
logger.info("Starting new training run")
try:
model.train()
while True:
for batch in trainset:
batch = batch.cuda()
data, target = batch
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
step += 1
loss = loss.item()
if loss > 1e8 or math.isnan(loss):
logger.error("Loss exploded to %.02f at step %d!" %
(loss, step))
raise Exception("Loss exploded")
if step % hp.train.summary_interval == 0:
writer.log_training(loss, step)
logger.info("Wrote summary at step %d" % step)
if step % hp.train.checkpoint_interval == 0:
save_path = os.path.join(out_dir, 'chkpt_%07d.pt' % step)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': step,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
if step % hp.train.evaluation_interval == 0:
test_loss, accuracy = validate(model, valset, writer, step)
logger.info(
"Evaluation saved at step %d | test_loss: %.5f | accuracy: %.2f(%)"
% (test_loss, accuracy))
except Exception as e:
logger.info("Exiting due to exception: %s" % e)
traceback.print_exc()
def train(out_dir, chkpt_path, trainset, valset, writer, logger, hp, hp_str,
graphs):
model = RandWire(hp, graphs).cuda()
if hp.train.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=hp.train.adam)
elif hp.train.optimizer == 'adabound':
optimizer = adabound.AdaBound(model.parameters(),
lr=hp.train.adabound.initial,
final_lr=hp.train.adabound.final)
elif hp.train.optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
lr=hp.train.sgd.lr,
momentum=hp.train.sgd.momentum,
weight_decay=hp.train.sgd.weight_decay)
else:
raise Exception("Optimizer not supported: %s" % hp.train.optimizer)
step = 0
if chkpt_path is not None:
logger.info("Resuming from checkpoint: %s" % chkpt_path)
checkpoint = torch.load(chkpt_path)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
step = checkpoint['step']
if hp_str != checkpoint['hp_str']:
logger.warning("New hparams are different from checkpoint.")
logger.warning("Will use new hparams.")
# hp = load_hparam_str(hp_str)
else:
logger.info("Starting new training run")
logger.info("Writing graph to tensorboardX...")
writer.write_graph(
model,
torch.randn(7, hp.model.input_maps, 224, 224).cuda())
logger.info("Finished.")
try:
model.train()
while True:
for data, target in trainset:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
step += 1
loss = loss.item()
if loss > 1e8 or math.isnan(loss):
logger.error("Loss exploded to %.02f at step %d!" %
(loss, step))
raise Exception("Loss exploded")
if step % hp.train.summary_interval == 0:
writer.log_training(loss, step)
logger.info("Wrote summary at step %d" % step)
if step % hp.train.checkpoint_interval == 0:
save_path = os.path.join(out_dir, 'chkpt_%07d.pt' % step)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': step,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
if step % hp.train.evaluation_interval == 0:
test_loss, accuracy = validate(model, valset, writer, step)
logger.info(
"Evaluation saved at step %d | test_loss: %.5f | accuracy: %.4f"
% (step, test_loss, accuracy))
if step % hp.train.decay.step == 0:
temp = optimizer.state_dict()
temp['param_groups'][0]['lr'] *= hp.train.decay.gamma
optimizer.load_state_dict(temp)
except Exception as e:
logger.info("Exiting due to exception: %s" % e)
traceback.print_exc()
def train(out_dir, chkpt_path, trainset, valset, writer, logger, hp, hp_str,
graphs):
model = RandWire(hp, graphs).cuda()
if hp.train.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=hp.train.adam)
elif hp.train.optimizer == 'adabound':
optimizer = adabound.AdaBound(model.parameters(),
lr=hp.train.adabound.initial,
final_lr=hp.train.adabound.final)
elif hp.train.optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
lr=hp.train.sgd.lr,
momentum=hp.train.sgd.momentum,
weight_decay=hp.train.sgd.weight_decay)
else:
raise Exception("Optimizer not supported: %s" % hp.train.optimizer)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, hp.train.epoch)
init_epoch = -1
step = 0
if chkpt_path is not None:
logger.info("Resuming from checkpoint: %s" % chkpt_path)
checkpoint = torch.load(chkpt_path)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
step = checkpoint['step']
init_epoch = checkpoint['epoch']
if hp_str != checkpoint['hp_str']:
logger.warning("New hparams are different from checkpoint.")
logger.warning("Will use new hparams.")
# hp = load_hparam_str(hp_str)
else:
logger.info("Starting new training run")
logger.info("Writing graph to tensorboardX...")
#print(model)
# parameters = 0
# for p in list(model.parameters()):
# nn =1
# for s in list(p.size()):
# nn = nn * s
# parameters += nn
#print("Parameters",parameters)
#print("model",hp.model)
# summary(model,(1,224,224))
writer.write_graph(
model,
torch.randn(7, hp.model.input_maps, 224, 224).cuda())
#Batch??, input_channels, width,depths
logger.info("Finished.")
try:
model.train()
for epoch in itertools.count(init_epoch + 1):
loader = tqdm.tqdm(trainset, desc='Train data loader')
for data, target in loader:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
loss = loss.item()
if loss > 1e8 or math.isnan(loss):
logger.error("Loss exploded to %.02f at step %d!" %
(loss, step))
raise Exception("Loss exploded")
writer.log_training(loss, step)
loader.set_description('Loss %.02f at step %d' % (loss, step))
step += 1
save_path = os.path.join(out_dir, 'chkpt_%03d.pt' % epoch)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'step': step,
'epoch': epoch,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
validate(model, valset, writer, epoch)
lr_scheduler.step()
except Exception as e:
logger.info("Exiting due to exception: %s" % e)
traceback.print_exc()
