class Logger:
def __init__(self, send_logs, tags, parameters, experiment=None):
self.stations = 5
self.send_logs = send_logs
if self.send_logs:
if experiment is None:
json_loc = glob.glob("./**/comet_token.json")[0]
with open(json_loc, "r") as f:
kwargs = json.load(f)
self.experiment = OfflineExperiment(**kwargs)
else:
self.experiment = experiment
self.sent_mb = 0
self.speed_window = deque(maxlen=100)
self.step_time = None
self.current_speed = 0
if self.send_logs:
if tags is not None:
self.experiment.add_tags(tags)
if parameters is not None:
self.experiment.log_parameters(parameters)
def begin_logging(self, episode_count, steps_per_ep, sigma, theta, step_time):
self.step_time = step_time
if self.send_logs:
self.experiment.log_parameter("Episode count", episode_count)
self.experiment.log_parameter("Steps per episode", steps_per_ep)
self.experiment.log_parameter("theta", theta)
self.experiment.log_parameter("sigma", sigma)
def log_round(self, states, reward, cumulative_reward, info, loss, observations, step):
self.experiment.log_histogram_3d(states, name="Observations", step=step)
info = [[j for j in i.split("|")] for i in info]
info = np.mean(np.array(info, dtype=np.float32), axis=0)
try:
round_mb = info[0]
except Exception as e:
print(info)
print(reward)
raise e
self.speed_window.append(round_mb)
self.current_speed = np.mean(np.asarray(self.speed_window)/self.step_time)
self.sent_mb += round_mb
CW = info[1]
CW_ax = info[2]
self.stations = info[3]
fairness = info[4]
if self.send_logs:
self.experiment.log_metric("Round reward", np.mean(reward), step=step)
self.experiment.log_metric("Per-ep reward", np.mean(cumulative_reward), step=step)
self.experiment.log_metric("Megabytes sent", self.sent_mb, step=step)
self.experiment.log_metric("Round megabytes sent", round_mb, step=step)
self.experiment.log_metric("Chosen CW for legacy devices", CW, step=step)
self.experiment.log_metric("Chosen CW for 802.11ax devices", CW_ax, step=step)
self.experiment.log_metric("Station count", self.stations, step=step)
self.experiment.log_metric("Current throughput", self.current_speed, step=step)
self.experiment.log_metric("Fairness index", fairness, step=step)
for i, obs in enumerate(observations):
self.experiment.log_metric(f"Observation {i}", obs, step=step)
self.experiment.log_metrics(loss, step=step)
def log_episode(self, cumulative_reward, speed, step):
if self.send_logs:
self.experiment.log_metric("Cumulative reward", cumulative_reward, step=step)
self.experiment.log_metric("Speed", speed, step=step)
self.sent_mb = 0
self.last_speed = speed
self.speed_window = deque(maxlen=100)
self.current_speed = 0
def end(self):
if self.send_logs:
self.experiment.end()
class CometLogger(Logger):
def __init__(
self,
batch_size: int,
snapshot_dir: Optional[str] = None,
snapshot_mode: str = "last",
snapshot_gap: int = 1,
exp_set: Optional[str] = None,
use_print_exp: bool = False,
saved_exp: Optional[str] = None,
**kwargs,
):
"""
:param kwargs: passed to comet's Experiment at init.
"""
if use_print_exp:
self.experiment = PrintExperiment()
else:
from comet_ml import Experiment, ExistingExperiment, OfflineExperiment
if saved_exp:
self.experiment = ExistingExperiment(
previous_experiment=saved_exp, **kwargs
)
else:
try:
self.experiment = Experiment(**kwargs)
except ValueError: # no API key
log_dir = Path.home() / "logs"
log_dir.mkdir(exist_ok=True)
self.experiment = OfflineExperiment(offline_directory=str(log_dir))
self.experiment.log_parameter("complete", False)
if exp_set:
self.experiment.log_parameter("exp_set", exp_set)
if snapshot_dir:
snapshot_dir = Path(snapshot_dir) / self.experiment.get_key()
# log_traj_window (int): How many trajectories to hold in deque for computing performance statistics.
self.log_traj_window = 100
self._cum_metrics = {
"n_unsafe_actions": 0,
"constraint_used": 0,
"cum_completed_trajs": 0,
"logging_time": 0,
}
self._new_completed_trajs = 0
self._last_step = 0
self._start_time = self._last_time = time()
self._last_snapshot_upload = 0
self._snaphot_upload_time = 30 * 60
super().__init__(batch_size, snapshot_dir, snapshot_mode, snapshot_gap)
def log_fast(
self,
step: int,
traj_infos: Sequence[Dict[str, float]],
opt_info: Optional[Tuple[Sequence[float], ...]] = None,
test: bool = False,
) -> None:
if not traj_infos:
return
start = time()
self._new_completed_trajs += len(traj_infos)
self._cum_metrics["cum_completed_trajs"] += len(traj_infos)
# TODO: do we need to support sum(t[k]) if key in k?
# without that, this doesn't include anything from extra eval samplers
for key in self._cum_metrics:
if key == "cum_completed_trajs":
continue
self._cum_metrics[key] += sum(t.get(key, 0) for t in traj_infos)
self._cum_metrics["logging_time"] += time() - start
def log(
self,
step: int,
traj_infos: Sequence[Dict[str, float]],
opt_info: Optional[Tuple[Sequence[float], ...]] = None,
test: bool = False,
):
self.log_fast(step, traj_infos, opt_info, test)
start = time()
with (self.experiment.test() if test else nullcontext()):
step *= self.batch_size
if opt_info is not None:
# grad norm is left on the GPU for some reason
# https://github.com/astooke/rlpyt/issues/163
self.experiment.log_metrics(
{
k: np.mean(v)
for k, v in opt_info._asdict().items()
if k != "gradNorm"
},
step=step,
)
if traj_infos:
agg_vals = {}
for key in traj_infos[0].keys():
if key in self._cum_metrics:
continue
agg_vals[key] = sum(t[key] for t in traj_infos) / len(traj_infos)
self.experiment.log_metrics(agg_vals, step=step)
if not test:
now = time()
self.experiment.log_metrics(
{
"new_completed_trajs": self._new_completed_trajs,
"steps_per_second": (step - self._last_step)
/ (now - self._last_time),
},
step=step,
)
self._last_time = now
self._last_step = step
self._new_completed_trajs = 0
self.experiment.log_metrics(self._cum_metrics, step=step)
self._cum_metrics["logging_time"] += time() - start
def log_metric(self, name, val):
self.experiment.log_metric(name, val)
def log_parameters(self, parameters):
self.experiment.log_parameters(parameters)
def log_config(self, config):
self.experiment.log_parameter("config", json.dumps(convert_dict(config)))
def upload_snapshot(self):
if self.snapshot_dir:
self.experiment.log_asset(self._previous_snapshot_fname)
def save_itr_params(
self, step: int, params: Dict[str, Any], metric: Optional[float] = None
) -> None:
super().save_itr_params(step, params, metric)
now = time()
if now - self._last_snapshot_upload > self._snaphot_upload_time:
self._last_snapshot_upload = now
self.upload_snapshot()
def shutdown(self, error: bool = False) -> None:
if not error:
self.upload_snapshot()
self.experiment.log_parameter("complete", True)
self.experiment.end()
class CometWriter:
def __init__(self,
logger,
project_name: Optional[str] = None,
experiment_name: Optional[str] = None,
api_key: Optional[str] = None,
log_dir: Optional[str] = None,
offline: bool = False,
**kwargs):
if not _COMET_AVAILABLE:
raise ImportError(
"You want to use `comet_ml` logger which is not installed yet,"
" install it with `pip install comet-ml`.")
self.project_name = project_name
self.experiment_name = experiment_name
self.kwargs = kwargs
self.timer = Timer()
if (api_key is not None) and (log_dir is not None):
self.mode = "offline" if offline else "online"
self.api_key = api_key
self.log_dir = log_dir
elif api_key is not None:
self.mode = "online"
self.api_key = api_key
self.log_dir = None
elif log_dir is not None:
self.mode = "offline"
self.log_dir = log_dir
else:
logger.warning(
"CometLogger requires either api_key or save_dir during initialization."
)
if self.mode == "online":
self.experiment = CometExperiment(
api_key=self.api_key,
project_name=self.project_name,
**self.kwargs,
)
else:
self.experiment = CometOfflineExperiment(
offline_directory=self.log_dir,
project_name=self.project_name,
**self.kwargs,
)
if self.experiment_name:
self.experiment.set_name(self.experiment_name)
def set_step(self, step, epoch=None, mode='train') -> None:
self.mode = mode
self.step = step
self.epoch = epoch
if step == 0:
self.timer.reset()
else:
duration = self.timer.check()
self.add_scalar({'steps_per_sec': 1 / duration})
def log_hyperparams(self, params: Dict[str, Any]) -> None:
self.experiment.log_parameters(params)
def log_code(self, file_name=None, folder='models/') -> None:
self.experiment.log_code(file_name=file_name, folder=folder)
def add_scalar(self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None,
epoch: Optional[int] = None) -> None:
metrics_renamed = {}
for key, val in metrics.items():
tag = '{}/{}'.format(key, self.mode)
if is_tensor(val):
metrics_renamed[tag] = val.cpu().detach()
else:
metrics_renamed[tag] = val
if epoch is None:
self.experiment.log_metrics(metrics_renamed,
step=self.step,
epoch=self.epoch)
else:
self.experiment.log_metrics(metrics_renamed, epoch=epoch)
def add_plot(self, figure_name, figure):
"""
Primarily for log gate plots
"""
self.experiment.log_figure(figure_name=figure_name, figure=figure)
def add_hist3d(self, hist, name):
"""
Primarily for log gate plots
"""
self.experiment.log_histogram_3d(hist, name=name)
def reset_experiment(self):
self.experiment = None
def finalize(self) -> None:
self.experiment.end()
self.reset_experiment()
def main(args):
print('Pretrain? ', not args.not_pretrain)
print(args.model)
start_time = time.time()
if opt['local_comet_dir']:
comet_exp = OfflineExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="selfcifar",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False,
offline_directory=opt['local_comet_dir'])
else:
comet_exp = CometExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="selfcifar",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False)
comet_exp.log_parameters(vars(args))
comet_exp.set_name(args.name)
# Build model
# path = "/misc/kcgscratch1/ChoGroup/resnick/spaceofmotion/zeping/bsn"
linear_cls = NonLinearModel if args.do_nonlinear else LinearModel
if args.model == "amdim":
hparams = load_hparams_from_tags_csv(
'/checkpoint/cinjon/amdim/meta_tags.csv')
# hparams = load_hparams_from_tags_csv(os.path.join(path, "meta_tags.csv"))
model = AMDIMModel(hparams)
if not args.not_pretrain:
# _path = os.path.join(path, "_ckpt_epoch_434.ckpt")
_path = '/checkpoint/cinjon/amdim/_ckpt_epoch_434.ckpt'
model.load_state_dict(torch.load(_path)["state_dict"])
else:
print("AMDIM not loading checkpoint") # Debug
linear_model = linear_cls(AMDIM_OUTPUT_DIM, args.num_classes)
elif args.model == "ccc":
model = CCCModel(None)
if not args.not_pretrain:
# _path = os.path.join(path, "TimeCycleCkpt14.pth")
_path = '/checkpoint/cinjon/spaceofmotion/bsn/TimeCycleCkpt14.pth'
checkpoint = torch.load(_path)
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint['state_dict'].items())
}
model.load_state_dict(base_dict)
else:
print("CCC not loading checkpoint") # Debug
linear_model = linaer_cls(CCC_OUTPUT_DIM,
args.num_classes) #.to(device)
elif args.model == "corrflow":
model = CORRFLOWModel(None)
if not args.not_pretrain:
_path = '/checkpoint/cinjon/spaceofmotion/supercons/corrflow.kineticsmodel.pth'
# _path = os.path.join(path, "corrflow.kineticsmodel.pth")
checkpoint = torch.load(_path)
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint['state_dict'].items())
}
model.load_state_dict(base_dict)
else:
print("CorrFlow not loading checkpoing") # Debug
linear_model = linear_cls(CORRFLOW_OUTPUT_DIM, args.num_classes)
elif args.model == "resnet":
if not args.not_pretrain:
resnet = torchvision.models.resnet50(pretrained=True)
else:
resnet = torchvision.models.resnet50(pretrained=False)
print("ResNet not loading checkpoint") # Debug
modules = list(resnet.children())[:-1]
model = nn.Sequential(*modules)
linear_model = linear_cls(RESNET_OUTPUT_DIM, args.num_classes)
else:
raise Exception("model type has to be amdim, ccc, corrflow or resnet")
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model).to(device)
linear_model = nn.DataParallel(linear_model).to(device)
else:
model = model.to(device)
linear_model = linear_model.to(device)
# model = model.to(device)
# linear_model = linear_model.to(device)
# Freeze model
for p in model.parameters():
p.requires_grad = False
model.eval()
if args.optimizer == "Adam":
optimizer = optim.Adam(linear_model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
print("Optimizer: Adam with weight decay: {}".format(
args.weight_decay))
elif args.optimizer == "SGD":
optimizer = optim.SGD(linear_model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
print("Optimizer: SGD with weight decay: {} momentum: {}".format(
args.weight_decay, args.momentum))
else:
raise Exception("optimizer should be Adam or SGD")
optimizer.zero_grad()
# Set up log dir
now = datetime.datetime.now()
log_dir = '/checkpoint/cinjon/spaceofmotion/bsn/cifar-%d-weights/%s/%s' % (
args.num_classes, args.model, args.name)
# log_dir = "{}{:%Y%m%dT%H%M}".format(args.model, now)
# log_dir = os.path.join("weights", log_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
print("Saving to {}".format(log_dir))
batch_size = args.batch_size * torch.cuda.device_count()
# CIFAR-10
if args.num_classes == 10:
data_path = ("/private/home/cinjon/cifar-data/cifar-10-batches-py")
_train_dataset = CIFAR_dataset(glob(os.path.join(data_path, "data*")),
args.num_classes, args.model, True)
# _train_acc_dataset = CIFAR_dataset(
# glob(os.path.join(data_path, "data*")),
# args.num_classes,
# args.model,
# False)
train_dataloader = data.DataLoader(_train_dataset,
shuffle=True,
batch_size=batch_size,
num_workers=args.num_workers)
# train_split = int(len(_train_dataset) * 0.8)
# train_dev_split = int(len(_train_dataset) - train_split)
# train_dataset, train_dev_dataset = data.random_split(
# _train_dataset, [train_split, train_dev_split])
# train_acc_dataloader = data.DataLoader(
# train_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# train_dev_acc_dataloader = data.DataLoader(
# train_dev_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# train_dataset = data.Subset(_train_dataset, list(range(train_split)))
# train_dataloader = data.DataLoader(
# train_dataset, shuffle=True, batch_size=batch_size, num_workers=args.num_workers)
# train_acc_dataset = data.Subset(
# _train_acc_dataset, list(range(train_split)))
# train_acc_dataloader = data.DataLoader(
# train_acc_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# train_dev_acc_dataset = data.Subset(
# _train_acc_dataset, list(range(train_split, len(_train_acc_dataset))))
# train_dev_acc_dataloader = data.DataLoader(
# train_dev_acc_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
_val_dataset = CIFAR_dataset([os.path.join(data_path, "test_batch")],
args.num_classes, args.model, False)
val_dataloader = data.DataLoader(_val_dataset,
shuffle=False,
batch_size=batch_size,
num_workers=args.num_workers)
# val_split = int(len(_val_dataset) * 0.8)
# val_dev_split = int(len(_val_dataset) - val_split)
# val_dataset, val_dev_dataset = data.random_split(
# _val_dataset, [val_split, val_dev_split])
# val_dataloader = data.DataLoader(
# val_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# val_dev_dataloader = data.DataLoader(
# val_dev_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# CIFAR-100
elif args.num_classes == 100:
data_path = ("/private/home/cinjon/cifar-data/cifar-100-python")
_train_dataset = CIFAR_dataset([os.path.join(data_path, "train")],
args.num_classes, args.model, True)
train_dataloader = data.DataLoader(_train_dataset,
shuffle=True,
batch_size=batch_size)
_val_dataset = CIFAR_dataset([os.path.join(data_path, "test")],
args.num_classes, args.model, False)
val_dataloader = data.DataLoader(_val_dataset,
shuffle=False,
batch_size=batch_size)
else:
raise Exception("num_classes should be 10 or 100")
best_acc = 0.0
best_epoch = 0
# Training
for epoch in range(1, args.epochs + 1):
current_lr = max(3e-4, args.lr *\
math.pow(0.5, math.floor(epoch / args.lr_interval)))
linear_model.train()
if args.optimizer == "Adam":
optimizer = optim.Adam(linear_model.parameters(),
lr=current_lr,
weight_decay=args.weight_decay)
elif args.optimizer == "SGD":
optimizer = optim.SGD(
linear_model.parameters(),
lr=current_lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
####################################################
# Train
t = time.time()
train_acc = 0
train_loss_sum = 0.0
for iter, input in enumerate(train_dataloader):
if time.time(
) - start_time > args.time * 3600 - 300 and comet_exp is not None:
comet_exp.end()
sys.exit(-1)
imgs = input[0].to(device)
if args.model != "resnet":
imgs = imgs.unsqueeze(1)
lbls = input[1].flatten().to(device)
# output = model(imgs)
# output = linear_model(output)
output = linear_model(model(imgs))
loss = F.cross_entropy(output, lbls)
train_loss_sum += float(loss.data)
train_acc += int(sum(torch.argmax(output, dim=1) == lbls))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# log_text = "train epoch {}/{}\titer {}/{} loss:{} {:.3f}s/iter"
if iter % 1500 == 0:
log_text = "train epoch {}/{}\titer {}/{} loss:{}"
print(log_text.format(epoch, args.epochs, iter + 1,
len(train_dataloader), loss.data,
time.time() - t),
flush=False)
t = time.time()
train_acc /= len(_train_dataset)
train_loss_sum /= len(train_dataloader)
with comet_exp.train():
comet_exp.log_metrics({
'acc': train_acc,
'loss': train_loss_sum
},
step=(epoch + 1) * len(train_dataloader),
epoch=epoch + 1)
print("train acc epoch {}/{} loss:{} train_acc:{}".format(
epoch, args.epochs, train_loss_sum, train_acc),
flush=True)
#######################################################################
# Train acc
# linear_model.eval()
# train_acc = 0
# train_loss_sum = 0.0
# for iter, input in enumerate(train_acc_dataloader):
# imgs = input[0].to(device)
# if args.model != "resnet":
# imgs = imgs.unsqueeze(1)
# lbls = input[1].flatten().to(device)
#
# # output = model(imgs)
# # output = linear_model(output)
# output = linear_model(model(imgs))
# loss = F.cross_entropy(output, lbls)
# train_loss_sum += float(loss.data)
# train_acc += int(sum(torch.argmax(output, dim=1) == lbls))
#
# print("train acc epoch {}/{}\titer {}/{} loss:{} {:.3f}s/iter".format(
# epoch,
# args.epochs,
# iter+1,
# len(train_acc_dataloader),
# loss.data,
# time.time() - t),
# flush=True)
# t = time.time()
#
#
# train_acc /= len(train_acc_dataset)
# train_loss_sum /= len(train_acc_dataloader)
# print("train acc epoch {}/{} loss:{} train_acc:{}".format(
# epoch, args.epochs, train_loss_sum, train_acc), flush=True)
#######################################################################
# Train dev acc
# # linear_model.eval()
# train_dev_acc = 0
# train_dev_loss_sum = 0.0
# for iter, input in enumerate(train_dev_acc_dataloader):
# imgs = input[0].to(device)
# if args.model != "resnet":
# imgs = imgs.unsqueeze(1)
# lbls = input[1].flatten().to(device)
#
# output = model(imgs)
# output = linear_model(output)
# # output = linear_model(model(imgs))
# loss = F.cross_entropy(output, lbls)
# train_dev_loss_sum += float(loss.data)
# train_dev_acc += int(sum(torch.argmax(output, dim=1) == lbls))
#
# print("train dev acc epoch {}/{}\titer {}/{} loss:{} {:.3f}s/iter".format(
# epoch,
# args.epochs,
# iter+1,
# len(train_dev_acc_dataloader),
# loss.data,
# time.time() - t),
# flush=True)
# t = time.time()
#
# train_dev_acc /= len(train_dev_acc_dataset)
# train_dev_loss_sum /= len(train_dev_acc_dataloader)
# print("train dev epoch {}/{} loss:{} train_dev_acc:{}".format(
# epoch, args.epochs, train_dev_loss_sum, train_dev_acc), flush=True)
#######################################################################
# Val dev
# # linear_model.eval()
# val_dev_acc = 0
# val_dev_loss_sum = 0.0
# for iter, input in enumerate(val_dev_dataloader):
# imgs = input[0].to(device)
# if args.model != "resnet":
# imgs = imgs.unsqueeze(1)
# lbls = input[1].flatten().to(device)
#
# output = model(imgs)
# output = linear_model(output)
# loss = F.cross_entropy(output, lbls)
# val_dev_loss_sum += float(loss.data)
# val_dev_acc += int(sum(torch.argmax(output, dim=1) == lbls))
#
# print("val dev epoch {}/{} iter {}/{} loss:{} {:.3f}s/iter".format(
# epoch,
# args.epochs,
# iter+1,
# len(val_dev_dataloader),
# loss.data,
# time.time() - t),
# flush=True)
# t = time.time()
#
# val_dev_acc /= len(val_dev_dataset)
# val_dev_loss_sum /= len(val_dev_dataloader)
# print("val dev epoch {}/{} loss:{} val_dev_acc:{}".format(
# epoch, args.epochs, val_dev_loss_sum, val_dev_acc), flush=True)
#######################################################################
# Val
linear_model.eval()
val_acc = 0
val_loss_sum = 0.0
for iter, input in enumerate(val_dataloader):
if time.time(
) - start_time > args.time * 3600 - 300 and comet_exp is not None:
comet_exp.end()
sys.exit(-1)
imgs = input[0].to(device)
if args.model != "resnet":
imgs = imgs.unsqueeze(1)
lbls = input[1].flatten().to(device)
output = model(imgs)
output = linear_model(output)
loss = F.cross_entropy(output, lbls)
val_loss_sum += float(loss.data)
val_acc += int(sum(torch.argmax(output, dim=1) == lbls))
# log_text = "val epoch {}/{} iter {}/{} loss:{} {:.3f}s/iter"
if iter % 1500 == 0:
log_text = "val epoch {}/{} iter {}/{} loss:{}"
print(log_text.format(epoch, args.epochs, iter + 1,
len(val_dataloader), loss.data,
time.time() - t),
flush=False)
t = time.time()
val_acc /= len(_val_dataset)
val_loss_sum /= len(val_dataloader)
print("val epoch {}/{} loss:{} val_acc:{}".format(
epoch, args.epochs, val_loss_sum, val_acc))
with comet_exp.test():
comet_exp.log_metrics({
'acc': val_acc,
'loss': val_loss_sum
},
step=(epoch + 1) * len(train_dataloader),
epoch=epoch + 1)
if val_acc > best_acc:
best_acc = val_acc
best_epoch = epoch
linear_save_path = os.path.join(log_dir,
"{}.linear.pth".format(epoch))
model_save_path = os.path.join(log_dir,
"{}.model.pth".format(epoch))
torch.save(linear_model.state_dict(), linear_save_path)
torch.save(model.state_dict(), model_save_path)
# Check bias and variance
print(
"Epoch {} lr {} total: train_loss:{} train_acc:{} val_loss:{} val_acc:{}"
.format(epoch, current_lr, train_loss_sum, train_acc, val_loss_sum,
val_acc),
flush=True)
# print("Epoch {} lr {} total: train_acc:{} train_dev_acc:{} val_dev_acc:{} val_acc:{}".format(
# epoch, current_lr, train_acc, train_dev_acc, val_dev_acc, val_acc), flush=True)
print("The best epoch: {} acc: {}".format(best_epoch, best_acc))