def trainval(exp_dict, savedir, args):
"""
exp_dict: dictionary defining the hyperparameters of the experiment
savedir: the directory where the experiment will be saved
args: arguments passed through the command line
"""
# -- Datasets
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=args.datadir)
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=args.datadir)
# -- Model
model = models.Model(exp_dict, device=torch.device('cuda'))
# -- Train & Val Loop
score_list = []
for e in range(0, 50):
# Compute metrics
score_dict = {"epoch": e}
score_dict["train_loss"] = model.val_on_dataset(
val_set, metric_name='softmax_loss')
score_dict["val_acc"] = model.val_on_dataset(val_set,
metric_name='softmax_acc')
score_list += [score_dict]
# Train model for one epoch
model.train_on_dataset(train_set)
# Visualize
images = model.vis_on_dataset(val_set,
fname=os.path.join(
savedir, 'images', 'results.png'))
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.save_pkl(os.path.join(savedir, 'score_list.pkl'), score_list)
hu.torch_save(os.path.join(savedir, 'model.pth'), model.state_dict())
print("Checkpoint Saved: %s" % savedir)
print('Experiment completed et epoch %d' % e)
def trainval():
print("train")
num_epochs = 50
results = {}
train_dl = datasets.get_dataset(dataroot="data", image_size=64, batch_size=32, num_workers=2)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = models.Model(device)
score_list = []
for epoch in range(0, num_epochs):
print(f'epoch {epoch} of {num_epochs}')
lossD, lossG = model.train_on_dataset(train_dl)
results["lossD"] = lossD
results["lossG"] = lossG
model.vis_on_dataset(fname=os.path.join('training_image_results', f'epcoch{epoch}_results.png'))
def get_stats(exp_dict):
dataset_name = exp_dict['dataset']['name']
n_classes = exp_dict['dataset']['n_classes']
stat_list = []
print('')
print(dataset_name, '-', 'n_classes: %d' % n_classes)
print('===========')
fname = '.tmp/covid_stats/%s_c%d.csv' % (dataset_name, n_classes)
if not os.path.exists(fname):
for split in ['train', 'val', 'test']:
dataset = datasets.get_dataset(dataset_dict={'name': dataset_name},
datadir=None,
split=split,
exp_dict=exp_dict)
loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
num_workers=100,
collate_fn=ut.collate_fn)
for i, b in enumerate(tqdm.tqdm(loader)):
u_list = np.unique(b['masks'])
stat_dict = {'split': split}
b['points'][b['points'] == 0] = 255
for c in range(n_classes):
if c in u_list:
stat_dict['class_%d' % c] = 1
else:
stat_dict['class_%d' % c] = 0
for c in range(n_classes):
if c == 0:
continue
stat_dict['n_regions_c%d' %
c] = (b['points'] == c).sum().item()
# stat_dict['n_regions_2'] = (b['points'] == 2).sum().item()
stat_list += [stat_dict]
stats = pd.DataFrame(stat_list).groupby('split').sum()
stats.to_csv(fname)
else:
stats = pd.read_csv(fname)
return stats
def train(cfg):
print(cfg.pretty())
train_config_validator(cfg)
fix_seed(cfg.seed)
writer = SummaryWriter(log_dir='logs')
controller = load_pretrained_weights(
NAO(**cfg.controller).to(0), cfg.pretrained_model_path)
dataset = get_dataset(writer=writer, seed=cfg.seed, **cfg.dataset)
optimizer = get_optimizer(parameters=_get_target_parameters(
controller, cfg.freeze_encoder_decoder),
**cfg.optimizer)
lr_scheduler = get_scheduler(optimizer=optimizer, **cfg.scheduler)
end_of_epoch_hook = ModelSaverHook().end_of_epoch_hook
get_trainer(
controller=controller,
dataset=dataset,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
writer=writer,
end_of_epoch_hook=end_of_epoch_hook,
**cfg.trainer,
).train()
def pretrain(cfg):
print(cfg.pretty())
pretrain_config_validator(cfg)
fix_seed(cfg.seed)
controller = load_pretrained_weights(
NAO(**cfg.controller).to(0), cfg.pretrained_model_path)
models = {'trunk': controller}
dataset = get_dataset(seed=cfg.seed, **cfg.dataset)
optimizers = {
'trunk_optimizer':
get_optimizer(parameters=models['trunk'].parameters(), **cfg.optimizer)
}
lr_schedulers = {
'trunk_scheduler_by_iteration':
get_scheduler(optimizer=optimizers['trunk_optimizer'], **cfg.scheduler)
}
loss_funcs = {
'reconstruction_loss': torch.nn.NLLLoss(),
'metric_loss': get_loss(**cfg.loss)
}
mining_funcs = {"tuple_miner": get_miner(**cfg.miner)}
visualizers = [umap.UMAP(**params) for params in cfg.visualizers]
end_of_iteration_hook = TensorboardHook(visualizers).end_of_iteration_hook
end_of_epoch_hook = ModelSaverHook().end_of_epoch_hook
get_trainer(
models=models,
optimizers=optimizers,
lr_schedulers=lr_schedulers,
loss_funcs=loss_funcs,
mining_funcs=mining_funcs,
dataset=dataset,
end_of_iteration_hook=end_of_iteration_hook,
end_of_epoch_hook=end_of_epoch_hook,
**cfg.trainer,
).train()
"model": {
'name': 'lcfcn',
'base': "fcn8_vgg16"
},
"batch_size": 1,
"max_epoch": 100,
'dataset_size': {
'train': 1,
'val': 1
},
'optimizer': 'adam',
'lr': 1e-5
}
train_set = datasets.get_dataset(dataset_dict=exp_dict['dataset'],
datadir='/mnt/public/datasets/Trancos',
split="test",
exp_dict=exp_dict)
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
batch = train_set[0]
batch['images'] = batch['images'][None]
batch['points'] = batch['points'][None]
# train for several iterations
for i in range(1000):
loss = model.train_on_batch(batch)
print(i, '- loss:', float(loss['train_loss']))
# visualize blobs and heatmap
model.vis_on_batch(batch, savedir_image='result.png')
def trainval(exp_dict, savedir_base, datadir_base, reset=False):
# bookkeeping stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# load train and acrtive set
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
datadir_base=datadir_base,
exp_dict=exp_dict)
active_set = ActiveLearningDataset(train_set, random_state=42)
# val set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="val",
datadir_base=datadir_base,
exp_dict=exp_dict)
val_loader = DataLoader(val_set, batch_size=exp_dict["batch_size"])
# Model
# ==================
model = models.get_model(model_name=exp_dict['model']['name'],
exp_dict=exp_dict).cuda()
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
active_set.load_state_dict(
hu.load_pkl(os.path.join(savedir, "active_set.pkl")))
score_list = hu.load_pkl(score_list_path)
inner_s_epoch = score_list[-1]['inner_epoch'] + 1
s_cycle = score_list[-1]['cycle']
else:
# restart experiment
score_list = []
inner_s_epoch = 0
s_cycle = 0
# Train & Val
# ==================
print("Starting experiment at cycle %d epoch %d" %
(s_cycle, inner_s_epoch))
for c in range(s_cycle, exp_dict['max_cycle']):
# Set seed
np.random.seed(c)
torch.manual_seed(c)
torch.cuda.manual_seed_all(c)
if inner_s_epoch == 0:
active_set.label_next_batch(model)
hu.save_pkl(os.path.join(savedir, "active_set.pkl"),
active_set.state_dict())
train_loader = DataLoader(active_set,
sampler=samplers.get_sampler(
exp_dict['sampler']['train'],
active_set),
batch_size=exp_dict["batch_size"])
# Visualize the model
model.vis_on_loader(vis_loader,
savedir=os.path.join(savedir, "images"))
for e in range(inner_s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
if e == 0:
score_dict.update(model.val_on_loader(val_loader))
# Train the model
score_dict.update(model.train_on_loader(train_loader))
# Validate the model
score_dict["epoch"] = len(score_list)
score_dict["inner_epoch"] = e
score_dict["cycle"] = c
score_dict['n_ratio'] = active_set.n_labelled_ratio
score_dict["n_train"] = len(train_loader.dataset)
score_dict["n_pool"] = len(train_loader.dataset.pool)
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
inner_s_epoch = 0
def trainval(exp_dict, savedir, datadir, metrics_flag=True):
# TODO: Do we get similar results with different seeds?
# Set seed
np.random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
pprint.pprint(exp_dict)
# Load Train Dataset
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=datadir,
exp_dict=exp_dict)
train_loader = DataLoader(train_set,
drop_last=True,
shuffle=True,
batch_size=exp_dict["batch_size"])
# Load Val Dataset
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=datadir,
exp_dict=exp_dict)
# Load model
model = models.get_model(exp_dict["model"], train_set=train_set).cuda()
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# Load Optimizer
n_batches_per_epoch = len(train_set) / float(exp_dict["batch_size"])
opt = optimizers.get_optimizer(opt=exp_dict["opt"],
params=model.parameters(),
n_batches_per_epoch=n_batches_per_epoch)
# Resume from last saved state_dict
if (not os.path.exists(savedir + "/run_dict.pkl")
or not os.path.exists(savedir + "/score_list.pkl")):
ut.save_pkl(savedir + "/run_dict.pkl", {"running": 1})
score_list = []
s_epoch = 0
else:
score_list = ut.load_pkl(savedir + "/score_list.pkl")
model.load_state_dict(torch.load(savedir + "/model_state_dict.pth"))
opt.load_state_dict(torch.load(savedir + "/opt_state_dict.pth"))
s_epoch = score_list[-1]["epoch"] + 1
for epoch in range(s_epoch, exp_dict["max_epoch"]):
# Set seed
np.random.seed(epoch)
torch.manual_seed(epoch)
torch.cuda.manual_seed_all(epoch)
score_dict = {"epoch": epoch}
if metrics_flag:
# 1. Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model, train_set, metric_name=exp_dict["loss_func"])
# 2. Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model, val_set, metric_name=exp_dict["acc_func"])
# 3. Train over train loader
model.train()
print("%d - Training model with %s..." %
(epoch, exp_dict["loss_func"]))
s_time = time.time()
for images, labels in tqdm.tqdm(train_loader):
images, labels = images.cuda(), labels.cuda()
opt.zero_grad()
if exp_dict["opt"]["name"] in exp_configs.ours_opt_list + ["l4"]:
closure = lambda: loss_function(
model, images, labels, backwards=False)
opt.step(closure)
else:
loss = loss_function(model, images, labels)
loss.backward()
opt.step()
e_time = time.time()
# Record step size and batch size
score_dict["step_size"] = opt.state["step_size"]
score_dict["n_forwards"] = opt.state["n_forwards"]
score_dict["n_backwards"] = opt.state["n_backwards"]
score_dict["batch_size"] = train_loader.batch_size
score_dict["train_epoch_time"] = e_time - s_time
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
ut.save_pkl(savedir + "/score_list.pkl", score_list)
ut.torch_save(savedir + "/model_state_dict.pth", model.state_dict())
ut.torch_save(savedir + "/opt_state_dict.pth", opt.state_dict())
print("Saved: %s" % savedir)
return score_list
datadir = '/mnt/public/datasets/DeepFish/'
score_list = []
for hash_id in hash_list:
fname = os.path.join('/mnt/public/predictions/habitat/%s.pkl' %
hash_id)
exp_dict = hu.load_json(
os.path.join(savedir_base, hash_id, 'exp_dict.json'))
if os.path.exists(fname):
print('FOUND:', fname)
val_dict = hu.load_pkl(fname)
else:
train_set = datasets.get_dataset(
dataset_dict=exp_dict["dataset"],
split='train',
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
test_set = datasets.get_dataset(
dataset_dict=exp_dict["dataset"],
split='test',
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
test_loader = DataLoader(test_set,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=0)
pprint.pprint(exp_dict)
def trainval(exp_dict, savedir, args):
"""
exp_dict: dictionary defining the hyperparameters of the experiment
savedir: the directory where the experiment will be saved
args: arguments passed through the command line
"""
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
if args.use_cuda:
device = 'cuda'
torch.cuda.manual_seed_all(seed)
assert torch.cuda.is_available(
), 'cuda is not, available please run with "-c 0"'
else:
device = 'cpu'
print('Running on device: %s' % device)
# Dataset
# Load val set and train set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="val",
transform=exp_dict.get("transform"),
datadir=args.datadir)
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
transform=exp_dict.get("transform"),
datadir=args.datadir)
# Load train loader, val loader, and vis loader
train_loader = DataLoader(train_set,
sampler=RandomSampler(
train_set,
replacement=True,
num_samples=max(min(500, len(train_set)),
len(val_set))),
batch_size=exp_dict["batch_size"])
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=exp_dict["batch_size"])
vis_loader = DataLoader(val_set,
sampler=ut.SubsetSampler(train_set,
indices=[0, 1, 2]),
batch_size=1)
# Create model, opt, wrapper
model_original = models.get_model(exp_dict["model"],
exp_dict=exp_dict).cuda()
opt = torch.optim.Adam(model_original.parameters(),
lr=1e-5,
weight_decay=0.0005)
model = wrappers.get_wrapper(exp_dict["wrapper"],
model=model_original,
opt=opt).cuda()
score_list = []
# Checkpointing
# =============
score_list_path = os.path.join(savedir, "score_list.pkl")
model_path = os.path.join(savedir, "model_state_dict.pth")
opt_path = os.path.join(savedir, "opt_state_dict.pth")
if os.path.exists(score_list_path):
# resume experiment
score_list = hu.load_pkl(score_list_path)
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_path))
s_epoch = score_list[-1]["epoch"] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Run training and validation
for epoch in range(s_epoch, exp_dict["max_epoch"]):
score_dict = {"epoch": epoch}
# visualize
model.vis_on_loader(vis_loader,
savedir=os.path.join(savedir, "images"))
# validate
score_dict.update(model.val_on_loader(val_loader))
# train
score_dict.update(model.train_on_loader(train_loader))
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved in %s" % savedir)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
model = eval(args.model)(dataset=args.dataset, device=args.device)
# for fine-tuning a pre-trained model, we strip out the last fc layer
if args.save_path:
saved_dict = torch.load(args.save_path)
del saved_dict["model.module.fc.weight"]
del saved_dict["model.module.fc.bias"]
model.load_state_dict(saved_dict, strict=False)
model.train()
train_loader = DataLoader(get_dataset(args.dataset, "train"),
shuffle=True,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=False)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4,
nesterov=True)
annealer = optim.lr_scheduler.CosineAnnealingLR(optimizer, args.num_epochs)
loss_meter = meter.AverageValueMeter()
time_meter = meter.TimeMeter(unit=False)
def trainval(exp_dict, savedir_base, datadir, reset=False, num_workers=0):
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# test set
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="test",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(
val_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=num_workers)
test_loader = DataLoader(
test_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
# model.opt = optimizers.get_optim(exp_dict['opt'], model)
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
model.waiting = 0
model.val_score_best = -np.inf
train_sampler = torch.utils.data.RandomSampler(train_set,
replacement=True,
num_samples=2 *
len(test_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
collate_fn=ut.collate_fn,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate the model
val_dict = model.val_on_loader(val_loader)
score_dict["val_score"] = val_dict["val_score"]
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = e
score_dict["waiting"] = model.waiting
model.waiting += 1
# Add to score_list and save checkpoint
score_list += [score_dict]
# Save Best Checkpoint
score_df = pd.DataFrame(score_list)
if score_dict["val_score"] >= model.val_score_best:
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
score_dict.update(test_dict)
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
# score_df.to_csv(os.path.join(savedir, "score_best_df.csv"))
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
model.waiting = 0
model.val_score_best = score_dict["val_score"]
print("Saved Best: %s" % savedir)
# Report & Save
score_df = pd.DataFrame(score_list)
# score_df.to_csv(os.path.join(savedir, "score_df.csv"))
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
if model.waiting > 100:
break
print('Experiment completed et epoch %d' % e)
def trainval_svrg(exp_dict, savedir, datadir, metrics_flag=True):
'''
SVRG-specific training and validation loop.
'''
pprint.pprint(exp_dict)
# Load Train Dataset
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=datadir,
exp_dict=exp_dict)
train_loader = DataLoader(train_set,
drop_last=False,
shuffle=True,
batch_size=exp_dict["batch_size"])
# Load Val Dataset
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=datadir,
exp_dict=exp_dict)
# Load model
model = models.get_model(exp_dict["model"], train_set=train_set).cuda()
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# lookup the learning rate
lr = get_svrg_step_size(exp_dict)
# Load Optimizer
opt = get_svrg_optimizer(model,
loss_function,
train_loader=train_loader,
lr=lr)
# Resume from last saved state_dict
if (not os.path.exists(savedir + "/run_dict.pkl")
or not os.path.exists(savedir + "/score_list.pkl")):
ut.save_pkl(savedir + "/run_dict.pkl", {"running": 1})
score_list = []
s_epoch = 0
else:
score_list = ut.load_pkl(savedir + "/score_list.pkl")
model.load_state_dict(torch.load(savedir + "/model_state_dict.pth"))
opt.load_state_dict(torch.load(savedir + "/opt_state_dict.pth"))
s_epoch = score_list[-1]["epoch"] + 1
for epoch in range(s_epoch, exp_dict["max_epoch"]):
score_dict = {"epoch": epoch}
if metrics_flag:
# 1. Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model, train_set, metric_name=exp_dict["loss_func"])
# 2. Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model, val_set, metric_name=exp_dict["acc_func"])
# 3. Train over train loader
model.train()
print("%d - Training model with %s..." %
(epoch, exp_dict["loss_func"]))
s_time = time.time()
for images, labels in tqdm.tqdm(train_loader):
images, labels = images.cuda(), labels.cuda()
opt.zero_grad()
closure = lambda svrg_model: loss_function(
svrg_model, images, labels, backwards=True)
opt.step(closure)
e_time = time.time()
# Record step size and batch size
score_dict["step_size"] = opt.state["step_size"]
score_dict["batch_size"] = train_loader.batch_size
score_dict["train_epoch_time"] = e_time - s_time
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
ut.save_pkl(savedir + "/score_list.pkl", score_list)
ut.torch_save(savedir + "/model_state_dict.pth", model.state_dict())
ut.torch_save(savedir + "/opt_state_dict.pth", opt.state_dict())
print("Saved: %s" % savedir)
return score_list
def trainval(exp_dict, savedir_base, datadir, reset=False, num_workers=0):
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=1,
num_workers=num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
# model.opt = optimizers.get_optim(exp_dict['opt'], model)
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
train_sampler = torch.utils.data.RandomSampler(train_set,
replacement=True,
num_samples=2 *
len(val_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate and Visualize the model
val_dict = model.val_on_loader(val_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
score_dict.update(val_dict)
# model.vis_on_loader(
# vis_loader, savedir=os.path.join(savedir, "images"))
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = len(score_list)
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# Save Best Checkpoint
if e == 0 or (score_dict.get("val_score", 0) >
score_df["val_score"][:-1].fillna(0).max()):
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
print('Experiment completed et epoch %d' % e)
def trainval(exp_dict, savedir, args):
"""
exp_dict: dictionary defining the hyperparameters of the experiment
savedir: the directory where the experiment will be saved
args: arguments passed through the command line
"""
datadir = args.datadir
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# test set
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="test",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(val_set,
# sampler=val_sampler,
batch_size=exp_dict["batch_size"],
collate_fn=ut.collate_fn,
num_workers=args.num_workers,
drop_last=False)
test_loader = DataLoader(test_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=args.num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
chk_dict = hw.get_checkpoint(savedir)
score_list = chk_dict['score_list']
# Train & Val
# ==================
model.waiting = 0
model.val_score_best = -np.inf
sampler = exp_dict['dataset'].get('sampler', 'random')
if sampler == 'random':
train_sampler = torch.utils.data.RandomSampler(
train_set, replacement=True,
num_samples=len(val_set))
elif sampler == 'balanced':
train_sampler = samplers.BalancedSampler(
train_set, n_samples=len(val_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
collate_fn=ut.collate_fn,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=args.num_workers)
for e in range(chk_dict['epoch'], exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate the model
val_dict = model.val_on_loader(val_loader,
savedir_images=os.path.join(savedir, "images"), n_images=5)
score_dict.update(val_dict)
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = e
score_dict["waiting"] = model.waiting
model.waiting += 1
# Add to score_list and save checkpoint
score_list += [score_dict]
# Save Best Checkpoint
score_df = pd.DataFrame(score_list)
if score_dict["val_score"] >= model.val_score_best:
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(savedir, "images"),
n_images=3)
score_dict.update(test_dict)
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"), score_list)
# score_df.to_csv(os.path.join(savedir, "score_best_df.csv"))
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
model.waiting = 0
model.val_score_best = score_dict["val_score"]
print("Saved Best: %s" % savedir)
# Report & Save
hw.save_checkpoint(savedir, score_list=score_list)
if model.waiting > 100:
break
print('Experiment completed et epoch %d' % e)
def train(exp_dict, savedir_base, reset, compute_fid=False):
# Book keeping
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
ut.rmtree(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
print('Experiment saved in %s' % savedir)
device = \
torch.device('cuda:' + exp_dict['gpu'] if torch.cuda.is_available() else 'cpu')
# 1. Load dataset and loader
train_set, test_set, num_channels, num_train_classes, num_test_classes = \
datasets.get_dataset(exp_dict['dataset'],
dataset_path=savedir_base,
image_size=exp_dict['image_size'])
train_loader, test_loader = \
dataloaders.get_dataloader(exp_dict['dataloader'],
train_set, test_set, exp_dict)
# 2. Fetch model to train
model = models.get_model(exp_dict['model'], num_train_classes,
num_test_classes, num_channels, device, exp_dict)
# 3. Resume experiment or start from scratch
score_list_path = os.path.join(savedir, 'score_list.pkl')
if os.path.exists(score_list_path):
# Resume experiment if it exists
model_path = os.path.join(savedir, 'model_state_dict.pth')
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
meta_dict_path = os.path.join(savedir, 'meta_dict.pkl')
meta_dict = hu.load_pkl(meta_dict_path)
print('Resuming experiment at episode %d epoch %d' %
(meta_dict['episode'], meta_dict['epoch']))
else:
# Start experiment from scratch
meta_dict = {'episode': 1, 'epoch': 1}
score_list = []
# Remove TensorBoard logs from previous runs
ut.rmtree(os.path.join(savedir, 'tensorboard_logs'))
print('Starting experiment at episode %d epoch %d' %
(meta_dict['episode'], meta_dict['epoch']))
# 4. Train and eval loop
s_epoch = meta_dict['epoch']
for e in range(s_epoch, exp_dict['num_epochs'] + 1):
# 0. Initialize dicts
score_dict = {'epoch': e}
meta_dict['epoch'] = e
# 1. Train on loader
train_dict = model.train_on_loader(train_loader)
# 1b. Compute FID
if compute_fid == 1:
if e % 20 == 0 or e == 1 or e == exp_dict['num_epochs']:
print('Starting FID computation...')
train_dict['fid'] = fid(model, train_loader.dataset,
train_loader.sampler, save_dir)
score_dict.update(train_dict)
# 2. Eval on loader
eval_dict = model.val_on_loader(test_loader, savedir, e)
score_dict.update(eval_dict)
# 3. Report and save model state, optimizer state, and scores
score_list += [score_dict]
score_df = pd.DataFrame(score_list)
print('\n', score_df.tail(), '\n')
if e % 10 == 0:
hu.torch_save(os.path.join(savedir, 'model_state_dict.pth'),
model.get_state_dict())
hu.save_pkl(os.path.join(savedir, 'score_list.pkl'), score_list)
hu.save_pkl(os.path.join(savedir, 'meta_dict.pkl'), meta_dict)
def trainval(exp_dict, savedir, args):
"""
exp_dict: dictionary defining the hyperparameters of the experiment
savedir: the directory where the experiment will be saved
args: arguments passed through the command line
"""
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
#helen commented out the following lines to hard code in that the device was 'cpu' to resolve errors
#if args.use_cuda:
#device = 'cuda'
#torch.cuda.manual_seed_all(seed)
#assert torch.cuda.is_available(), 'cuda is not, available please run with "-c 0"'
#else:
device = 'cpu'
print('Running on device: %s' % device)
# Dataset
# Load val set and train set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="val",
transform=exp_dict.get("transform"),
datadir=args.datadir)
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
transform=exp_dict.get("transform"),
datadir=args.datadir)
# Load train loader, val loader, and vis loader
train_loader = DataLoader(train_set,
sampler=RandomSampler(
train_set,
replacement=True,
num_samples=max(min(500, len(train_set)),
len(val_set))),
batch_size=exp_dict["batch_size"])
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=exp_dict["batch_size"])
vis_loader = DataLoader(val_set,
sampler=ut.SubsetSampler(train_set,
indices=[0, 1, 2]),
batch_size=1)
# Create model, opt, wrapper
model_original = models.get_model(exp_dict["model"],
exp_dict=exp_dict).cuda()
opt = torch.optim.Adam(model_original.parameters(),
lr=1e-5,
weight_decay=0.0005)
model = wrappers.get_wrapper(exp_dict["wrapper"],
model=model_original,
opt=opt).cuda()
score_list = []
# Checkpointing
# =============
#score_list_path = os.path.join(savedir, "score_list.pkl") #helen commented out these three lines and hard coded the model and opt paths to resolve errors
#model_path = os.path.join(savedir, "model_state_dict.pth")
#opt_path = os.path.join(savedir, "opt_state_dict.pth")
score_list_path = '/Users/helenpropson/Documents/git/marepesca/results/testresults/score_list.pkl' #helen added this
model_path = '/Users/helenpropson/Documents/git/marepesca/results/testresults/model_state_dict.pth' #helen added this
opt_path = '/Users/helenpropson/Documents/git/marepesca/results/testresults/opt_state_dict.pth' #helen added this
#helen hard coded that the experiment would resume instead of restarting from epoch 0
#if os.path.exists(score_list_path):
# resume experiment
score_list = hu.load_pkl(
score_list_path
) #helen changed this from ut.load_pkl to hu.load_pkl to resolve error
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_path))
s_epoch = score_list[-1]["epoch"] + 1
#else:
# restart experiment
#score_list = []
#s_epoch = 0
# *************** helen added this code
im = Image.open("/Users/helenpropson/Documents/git/marepesca/tank.jpg")
# im.show() #this line will display the image you are running the model on if uncommented
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
normalize_transform = transforms.Normalize(mean=mean, std=std)
data_transform = transforms.Compose(
[transforms.ToTensor(),
normalize_transform]) #transformations we will use on our image
im_new = data_transform(
im) #transforms the image into a tensor and normalizes it
im_final = im_new.unsqueeze(
0) #adds another dimension so image is the correct shape for the model
print("now trying helen's code") #print statement for debugging
#model.vis_on_batch_helen(im_final, f'im_new') #uncomment this line to run model on image
# *************** this is the end of helen's code
# Run training and validation
for epoch in range(s_epoch, exp_dict["max_epoch"]):
score_dict = {"epoch": epoch}
# visualize
model.vis_on_loader(vis_loader,
savedir=os.path.join(savedir, "images"))
print("after vis_on_loader"
) #helen add this print statement as an update while iterating
# validate
score_dict.update(model.val_on_loader(val_loader))
print("after validate")
# train
score_dict.update(model.train_on_loader(train_loader))
print("after train")
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved in %s" % savedir)
exp_dict = exp_configs.EXP_GROUPS[exp_group][0]
dataset_name = exp_dict['dataset']['name']
n_classes = exp_dict['dataset']['n_classes']
stat_list = []
print('')
print(dataset_name, '-', 'n_classes: %d' % n_classes)
print('===========')
fname = '.tmp/covid_stats/%s_c%d.csv' % (dataset_name, n_classes)
if not os.path.exists(fname):
for split in ['train', 'val', 'test']:
dataset = datasets.get_dataset(
dataset_dict={'name': dataset_name},
datadir=None,
split=split,
exp_dict=exp_dict)
loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
num_workers=100,
collate_fn=ut.collate_fn)
for i, b in enumerate(tqdm.tqdm(loader)):
u_list = np.unique(b['masks'])
stat_dict = {'split': split}
b['points'][b['points'] == 0] = 255
for c in range(n_classes):
if c in u_list:
stat_dict['class_%d' % c] = 1
else:
# lcfcn loss with_affinity=True
# hash_dir = '84ced18cf5c1fb3ad5820cc1b55a38fa'
# point level
# hash_dir = 'd7040c9534b08e765f48c6cb034b26b2'
# LCFCN
hash_dir = 'bcba046296675e9e3af5cd9f353d217b'
savedir = '/mnt/public/predictions'
datadir = '/mnt/public/datasets/DeepFish/'
split = 'test'
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split=split,
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
test_loader = DataLoader(
test_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=0)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=test_set).cuda()
def newminimum(exp_id,
savedir_base,
datadir,
name,
exp_dict,
metrics_flag=True):
# bookkeeping
# ---------------
# get experiment directory
old_modeldir = os.path.join(savedir_base, exp_id)
savedir = os.path.join(savedir_base, exp_id, name)
old_exp_dict = hu.load_json(os.path.join(old_modeldir, 'exp_dict.json'))
# TODO: compare exp dict for possible errors:
# optimizer have to be the same
# same network, dataset
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
# set seed
# ---------------
seed = 42 + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# -----------
# Load Train Dataset
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=datadir,
exp_dict=exp_dict)
train_loader = torch.utils.data.DataLoader(
train_set,
drop_last=True,
shuffle=True,
batch_size=exp_dict["batch_size"])
# Load Val Dataset
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=datadir,
exp_dict=exp_dict)
# Model
# -----------
model = models.get_model(exp_dict["model"], train_set=train_set)
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# Load Optimizer
n_batches_per_epoch = len(train_set) / float(exp_dict["batch_size"])
opt = optimizers.get_optimizer(opt=exp_dict["opt"],
params=model.parameters(),
n_batches_per_epoch=n_batches_per_epoch)
# Checkpoint
# -----------
model_path = os.path.join(savedir, 'model.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
opt_path = os.path.join(savedir, 'opt_state_dict.pth')
old_model_path = os.path.join(old_modeldir, 'model.pth')
old_score_list_path = os.path.join(old_modeldir, 'score_list.pkl')
old_opt_path = os.path.join(old_modeldir, 'opt_state_dict.pth')
score_list = hu.load_pkl(old_score_list_path)
model.load_state_dict(torch.load(old_model_path))
opt.load_state_dict(torch.load(old_opt_path))
s_epoch = score_list[-1]['epoch'] + 1
# save current model state for comparison
minimum = []
for param in model.parameters():
minimum.append(param.clone())
# Train & Val
# ------------
print('Starting experiment at epoch %d/%d' %
(s_epoch, exp_dict['max_epoch']))
for epoch in range(s_epoch, exp_dict['max_epoch']):
# Set seed
np.random.seed(exp_dict['runs'] + epoch)
torch.manual_seed(exp_dict['runs'] + epoch)
# torch.cuda.manual_seed_all(exp_dict['runs']+epoch) not needed since no cuda available
score_dict = {"epoch": epoch}
if metrics_flag:
# 1. Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model, train_set, metric_name='softmax_loss')
# metric_name=exp_dict["loss_func"])
# TODO: which loss should be used? (normal or with reguralizer?)
# 2. Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model, val_set, metric_name=exp_dict["acc_func"])
# 3. Train over train loader
model.train()
print("%d - Training model with %s..." %
(epoch, exp_dict["loss_func"]))
s_time = time.time()
for images, labels in tqdm.tqdm(train_loader):
# images, labels = images.cuda(), labels.cuda() no cuda available
opt.zero_grad()
loss = loss_function(model, images, labels, minimum,
0.1) # just works for custom loss function
loss.backward()
opt.step()
e_time = time.time()
# Record metrics
score_dict["step_size"] = opt.state["step_size"]
score_dict["n_forwards"] = opt.state["n_forwards"]
score_dict["n_backwards"] = opt.state["n_backwards"]
score_dict["batch_size"] = train_loader.batch_size
score_dict["train_epoch_time"] = e_time - s_time
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved: %s" % savedir)
with torch.nograd():
print('Current distance: %f',
metrics.computedistance(minimum, model))
print('Experiment completed')
def test(exp_dict,
savedir_base,
datadir,
num_workers=0,
model_path=None,
scan_id=None):
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# val set
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
if str(scan_id) != 'None':
test_set.active_data = test_set.get_scan(scan_id)
test_sampler = torch.utils.data.SequentialSampler(test_set)
test_loader = DataLoader(test_set,
sampler=test_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=num_workers)
# Model
# ==================
# chk = torch.load('best_model.ckpt')
model = models.get_model_for_onnx_export(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=test_set).cuda()
epoch = -1
if str(model_path) != 'None':
model_path = model_path
model.load_state_dict(hu.torch_load(model_path))
else:
try:
exp_dict_train = copy.deepcopy(exp_dict)
del exp_dict_train['test_mode']
savedir_train = os.path.join(savedir_base,
hu.hash_dict(exp_dict_train))
model_path = os.path.join(savedir_train, "model_best.pth")
score_list = hu.load_pkl(
os.path.join(savedir_train, 'score_list_best.pkl'))
epoch = score_list[-1]['epoch']
print('Loaded model at epoch %d with score %.3f' % epoch)
model.load_state_dict(hu.torch_load(model_path))
except:
pass
s_time = time.time()
savedir_images = os.path.join(savedir, 'images')
# delete image folder if exists
if os.path.exists(savedir_images):
shutil.rmtree(savedir_images)
os.makedirs(savedir_images, exist_ok=True)
# for i in range(20):
# score_dict = model.train_on_loader(test_loader)
score_dict = model.val_on_loader(test_loader,
savedir_images=savedir_images,
n_images=30000,
save_preds=True)
score_dict['epoch'] = epoch
score_dict["time"] = time.time() - s_time
score_dict["saved_at"] = hu.time_to_montreal()
# save test_score_list
test_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(test_path):
test_score_list = [
sd for sd in hu.load_pkl(test_path) if sd['epoch'] != epoch
]
else:
test_score_list = []
# append score_dict to last result
test_score_list += [score_dict]
hu.save_pkl(test_path, test_score_list)
print('Final Score is ', str(score_dict["val_score"]) + "\n")
'lr': 1e-06,
'max_epoch': 100,
'model': {
'base': 'fcn8_vgg16',
'loss': 'point_level',
'n_channels': 3,
'n_classes': 2,
'name': 'semseg'
},
'num_channels': 1,
'optimizer': 'adam'
}
pprint.pprint(exp_dict)
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir='/mnt/public/datasets/DeepFish',
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
model_seam = resnet38_SEAM.Net().cuda()
model_seam.load_state_dict(
torch.load(os.path.join('/mnt/public/weights', 'resnet38_SEAM.pth')))
model_aff = resnet38_aff.Net().cuda()
model_aff.load_state_dict(torch.load(
os.path.join('/mnt/public/weights', 'resnet38_aff_SEAM.pth')),
strict=False)
# ut.generate_seam_segmentation(train_set,
# path_base='/mnt/datasets/public/issam/seam',
# # path_base='D:/Issam/SEAM_model/'
def trainval(exp_dict, savedir_base, datadir_base, reset=False,
num_workers=0, pin_memory=False, ngpu=1, cuda_deterministic=False):
# bookkeeping
# ==================
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
if DEVICE.type == "cuda":
if cuda_deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
else:
cudnn.benchmark = True
# Dataset
# ==================
trainset = get_dataset(exp_dict['dataset'], 'train',
exp_dict=exp_dict, datadir_base=datadir_base,
n_samples=exp_dict['dataset_size']['train'],
transform_lvl=exp_dict['dataset']['transform_lvl'],
colorjitter=exp_dict['dataset'].get('colorjitter')
)
valset = get_dataset(exp_dict['dataset'], 'validation',
exp_dict=exp_dict, datadir_base=datadir_base,
n_samples=exp_dict['dataset_size']['train'],
transform_lvl=0,
val_transform=exp_dict['dataset']['val_transform'])
testset = get_dataset(exp_dict['dataset'], 'test',
exp_dict=exp_dict, datadir_base=datadir_base,
n_samples=exp_dict['dataset_size']['test'],
transform_lvl=0,
val_transform=exp_dict['dataset']['val_transform'])
print("Dataset defined.")
# define dataloaders
if exp_dict['dataset']['name'] == 'bach':
testloader = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory)
else:
testloader = torch.utils.data.DataLoader(testset, batch_size=exp_dict['batch']['size'],
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory)
print("Testloader defined.")
# Model
# ==================
model = get_model(exp_dict, trainset, device=DEVICE)
print("Model loaded")
model_path = os.path.join(savedir, 'model.pth')
model_best_path = os.path.join(savedir, 'model_best.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
# checkpoint management
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = len(score_list)
else:
# restart experiment
score_list = []
s_epoch = 0
# define and log random seed for reproducibility
assert('fixedSeed' in exp_dict)
seed = exp_dict['fixedSeed']
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
print("Seed defined.")
# Train & Val
# ==================
print("Starting experiment at epoch %d/%d" % (s_epoch, exp_dict['niter']))
for epoch in range(s_epoch, exp_dict['niter']):
s_time = time.time()
# Sample new train val
trainloader, valloader = get_train_val_dataloader(exp_dict,
trainset, valset,
mixtrainval=exp_dict['mixTrainVal'],
num_workers=num_workers,
pin_memory=pin_memory)
# Train & validate
train_dict = model.train_on_loader(trainloader, valloader, epoch=epoch,
exp_dict=exp_dict)
# Test phase
train_dict_2 = model.test_on_loader(trainloader)
val_dict = model.test_on_loader(valloader)
test_dict = model.test_on_loader(testloader)
# Vis phase
model.vis_on_loader('train', trainset, savedir_images=os.path.join(
savedir, 'images'), epoch=epoch)
score_dict = {}
score_dict["epoch"] = epoch
score_dict["test_acc"] = test_dict['acc']
score_dict["val_acc"] = val_dict['acc']
score_dict["train_acc"] = train_dict_2['acc']
score_dict["train_loss"] = train_dict['loss']
score_dict["time_taken"] = time.time() - s_time
score_dict["netC_lr"] = train_dict['netC_lr']
if exp_dict['model']['netA'] is not None:
if 'transformations_mean' in train_dict:
for i in range(len(train_dict['transformations_mean'])):
score_dict[str(
i) + "_mean"] = train_dict['transformations_mean'][i].item()
if 'transformations_std' in train_dict:
for i in range(len(train_dict['transformations_std'])):
score_dict[str(
i) + "_std"] = train_dict['transformations_std'][i].item()
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# Update best score
if epoch == 0 or (score_dict["test_acc"] >= score_df["test_acc"][:-1].max()):
hu.save_pkl(os.path.join(
savedir, "score_list_best.pkl"), score_list)
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
print('experiment completed')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--datadir',
type=str,
default='/mnt/public/datasets/DeepFish')
parser.add_argument("-e", "--exp_config", default='loc')
parser.add_argument("-uc", "--use_cuda", type=int, default=0)
args = parser.parse_args()
device = torch.device('cuda' if args.use_cuda else 'cpu')
exp_dict = exp_configs.EXP_GROUPS[args.exp_config][0]
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
transform=exp_dict.get("transform"),
datadir=args.datadir)
# Create model, opt, wrapper
model_original = models.get_model(exp_dict["model"],
exp_dict=exp_dict).to('cpu') #.cuda()
opt = torch.optim.Adam(model_original.parameters(),
lr=1e-5,
weight_decay=0.0005)
model = wrappers.get_wrapper(exp_dict["wrapper"],
model=model_original,
opt=opt).to('cpu') #.cuda()
if args.exp_config == 'loc':
batch = torch.utils.data.dataloader.default_collate([train_set[3]])
'pascal': '/mnt/datasets/public/issam',
'kitti': '/mnt/datasets/public/issam'
}
if __name__ == "__main__":
for exp_group in [
'weakly_covid19_v1_c2', 'weakly_covid19_v2_mixed_c2',
'weakly_covid19_v2_sep_c2', 'weakly_covid19_v2_mixed_c3',
'weakly_covid19_v2_sep_c3', 'weakly_covid19_v3_mixed_c2'
]:
exp_dict = exp_configs.EXP_GROUPS[exp_group][0]
dataset_name = exp_dict['dataset']['name']
n_classes = exp_dict['dataset']['n_classes']
train_set = datasets.get_dataset(dataset_dict={'name': dataset_name},
datadir=None,
split="test",
exp_dict=exp_dict)
for i, b in enumerate(train_set):
if b['masks'].sum() == 0:
print(i)
continue
break
batch = ut.collate_fn([b])
image = batch['images']
gt = np.asarray(batch['masks'], np.float32)
gt /= (gt.max() + 1e-8)
image = F.interpolate(image,
size=gt.shape[-2:],
mode='bilinear',
argparser.add_argument("--data-parallel", action="store_true")
argparser.add_argument("--use-val-set", action="store_true")
argparser.add_argument("--focal", action="store_true")
argparser.add_argument('--output-dir', type=str, default=os.getenv("PT_OUTPUT_DIR"))
args = argparser.parse_args()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
model = eval(args.model)(dataset=args.dataset, device=args.device, precision=args.precision,
norm_layer=args.norm_layer, focal=args.focal)
model = DataParallelWrapper(model) if args.data_parallel else model
if not args.use_val_set:
train_dataset = get_dataset(args.dataset, "train", args.precision)
train_loader = get_dataloader(train_dataset, True, args.batch_size, args.num_workers)
#_, subset_idxs = split_hold_out_set(train_dataset.targets, 10000)
subset_idxs = np.random.choice(len(train_dataset), 10000, replace=False)
train_subset_dataset = Subset(train_dataset, list(subset_idxs))
train_subset_loader = get_dataloader(train_subset_dataset, False,
args.batch_size, args.num_workers)
test_dataset = get_dataset(args.eval_dataset or args.dataset, "test", args.precision)
test_loader = get_dataloader(test_dataset, False, args.batch_size, args.num_workers)
eval_loaders_and_datasets = ((train_subset_loader, len(train_subset_dataset), "train"),
(test_loader, len(test_dataset), "test"))
else:
def trainval(exp_dict, savedir_base, reset=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
# set seed
# ---------------
seed = 42 + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# -----------
# train loader
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=savedir_base,
exp_dict=exp_dict)
train_loader = torch.utils.data.DataLoader(
train_set,
drop_last=True,
shuffle=True,
batch_size=exp_dict["batch_size"])
# val set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=savedir_base,
exp_dict=exp_dict)
# Model
# -----------
model = models.get_model(exp_dict["model"], train_set=train_set).cuda()
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# Compute fstar
# -------------
if exp_dict['opt'].get('fstar_flag'):
ut.compute_fstar(train_set, loss_function, savedir_base, exp_dict)
# Load Optimizer
n_batches_per_epoch = len(train_set) / float(exp_dict["batch_size"])
opt = optimizers.get_optimizer(opt_dict=exp_dict["opt"],
params=model.parameters(),
n_batches_per_epoch=n_batches_per_epoch)
# Checkpoint
# -----------
model_path = os.path.join(savedir, 'model.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
opt_path = os.path.join(savedir, 'opt_state_dict.pth')
if os.path.exists(score_list_path):
# resume experiment
score_list = hu.load_pkl(score_list_path)
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_path))
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ------------
print('Starting experiment at epoch %d/%d' %
(s_epoch, exp_dict['max_epoch']))
for e in range(s_epoch, exp_dict['max_epoch']):
# Set seed
seed = e + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
score_dict = {}
# Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model, train_set, metric_name=exp_dict["loss_func"])
# Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model, val_set, metric_name=exp_dict["acc_func"])
# Train over train loader
model.train()
print("%d - Training model with %s..." % (e, exp_dict["loss_func"]))
# train and validate
s_time = time.time()
for batch in tqdm.tqdm(train_loader):
images, labels = batch["images"].cuda(), batch["labels"].cuda()
opt.zero_grad()
# closure
def closure():
return loss_function(model, images, labels, backwards=True)
opt.step(closure)
e_time = time.time()
# Record metrics
score_dict["epoch"] = e
score_dict["step_size"] = opt.state["step_size"]
score_dict["step_size_avg"] = opt.state["step_size_avg"]
score_dict["n_forwards"] = opt.state["n_forwards"]
score_dict["n_backwards"] = opt.state["n_backwards"]
score_dict["grad_norm"] = opt.state["grad_norm"]
score_dict["batch_size"] = train_loader.batch_size
score_dict["train_epoch_time"] = e_time - s_time
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved: %s" % savedir)
print('Experiment completed')
def trainval(exp_dict,
savedir_base,
reset,
metrics_flag=True,
datadir=None,
cuda=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
print(pprint.pprint(exp_dict))
print('Experiment saved in %s' % savedir)
# set seed
# ==================
seed = 42 + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
if cuda:
device = 'cuda'
torch.cuda.manual_seed_all(seed)
else:
device = 'cpu'
print('Running on device: %s' % device)
# Dataset
# ==================
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=datadir,
exp_dict=exp_dict)
train_loader = DataLoader(train_set,
drop_last=True,
shuffle=True,
sampler=None,
batch_size=exp_dict["batch_size"])
# Load Val Dataset
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=datadir,
exp_dict=exp_dict)
# Model
# ==================
use_backpack = exp_dict['opt'].get("backpack", False)
model = models.get_model(exp_dict["model"],
train_set=train_set,
backpack=use_backpack).to(device=device)
if use_backpack:
assert exp_dict['opt']['name'] in ['nus_wrapper', 'adaptive_second']
from backpack import extend
model = extend(model)
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# Load Optimizer
# ==============
n_batches_per_epoch = len(train_set) / float(exp_dict["batch_size"])
opt = optimizers.get_optimizer(opt=exp_dict["opt"],
params=model.parameters(),
n_batches_per_epoch=n_batches_per_epoch,
n_train=len(train_set),
train_loader=train_loader,
model=model,
loss_function=loss_function,
exp_dict=exp_dict,
batch_size=exp_dict["batch_size"])
# Checkpointing
# =============
score_list_path = os.path.join(savedir, "score_list.pkl")
model_path = os.path.join(savedir, "model_state_dict.pth")
opt_path = os.path.join(savedir, "opt_state_dict.pth")
if os.path.exists(score_list_path):
# resume experiment
score_list = ut.load_pkl(score_list_path)
if use_backpack:
model.load_state_dict(torch.load(model_path), strict=False)
else:
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_path))
s_epoch = score_list[-1]["epoch"] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Start Training
# ==============
n_train = len(train_loader.dataset)
n_batches = len(train_loader)
batch_size = train_loader.batch_size
for epoch in range(s_epoch, exp_dict["max_epoch"]):
# Set seed
seed = epoch + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
score_dict = {"epoch": epoch}
# Validate
# --------
if metrics_flag:
# 1. Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model,
train_set,
metric_name=exp_dict["loss_func"],
batch_size=exp_dict['batch_size'])
# 2. Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model,
val_set,
metric_name=exp_dict["acc_func"],
batch_size=exp_dict['batch_size'])
# Train
# -----
model.train()
print("%d - Training model with %s..." %
(epoch, exp_dict["loss_func"]))
s_time = time.time()
train_on_loader(model, train_set, train_loader, opt, loss_function,
epoch, use_backpack)
e_time = time.time()
# Record step size and batch size
score_dict["step"] = opt.state.get("step",
0) / int(n_batches_per_epoch)
score_dict["step_size"] = opt.state.get("step_size", {})
score_dict["step_size_avg"] = opt.state.get("step_size_avg", {})
score_dict["n_forwards"] = opt.state.get("n_forwards", {})
score_dict["n_backwards"] = opt.state.get("n_backwards", {})
score_dict["grad_norm"] = opt.state.get("grad_norm", {})
score_dict["batch_size"] = batch_size
score_dict["train_epoch_time"] = e_time - s_time
score_dict.update(opt.state["gv_stats"])
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
ut.save_pkl(score_list_path, score_list)
ut.torch_save(model_path, model.state_dict())
ut.torch_save(opt_path, opt.state_dict())
print("Saved: %s" % savedir)
return score_list
argparser.add_argument("--sigma", default=0.0, type=float)
argparser.add_argument("--noise", default="Clean", type=str)
argparser.add_argument("--k", default=None, type=int)
argparser.add_argument("--j", default=None, type=int)
argparser.add_argument("--a", default=None, type=int)
argparser.add_argument("--lambd", default=None, type=float)
argparser.add_argument("--adv", default=2, type=int)
argparser.add_argument("--experiment-name", default="cifar", type=str)
argparser.add_argument("--dataset", default="cifar", type=str)
argparser.add_argument("--model", default="WideResNet", type=str)
argparser.add_argument("--output-dir",
type=str,
default=os.getenv("PT_OUTPUT_DIR"))
args = argparser.parse_args()
test_dataset = get_dataset(args.dataset, "test")
test_loader = DataLoader(
test_dataset,
shuffle=False,
batch_size=args.batch_size, # todo: fix
num_workers=args.num_workers)
save_path = f"{args.output_dir}/{args.experiment_name}/model_ckpt.torch"
model = eval(args.model)(dataset=args.dataset, device=args.device)
model.load_state_dict(torch.load(save_path))
model.eval()
noise = parse_noise_from_args(args,
device=args.device,
dim=get_dim(args.dataset))
def trainval(exp_dict,
savedir_base,
reset=False,
num_workers=0,
run_ssl=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
# load datasets
# ==========================
train_set = datasets.get_dataset(
dataset_name=exp_dict["dataset_train"],
data_root=exp_dict["dataset_train_root"],
split="train",
transform=exp_dict["transform_train"],
classes=exp_dict["classes_train"],
support_size=exp_dict["support_size_train"],
query_size=exp_dict["query_size_train"],
n_iters=exp_dict["train_iters"],
unlabeled_size=exp_dict["unlabeled_size_train"])
val_set = datasets.get_dataset(
dataset_name=exp_dict["dataset_val"],
data_root=exp_dict["dataset_val_root"],
split="val",
transform=exp_dict["transform_val"],
classes=exp_dict["classes_val"],
support_size=exp_dict["support_size_val"],
query_size=exp_dict["query_size_val"],
n_iters=exp_dict["val_iters"],
unlabeled_size=exp_dict["unlabeled_size_val"])
test_set = datasets.get_dataset(
dataset_name=exp_dict["dataset_test"],
data_root=exp_dict["dataset_test_root"],
split="test",
transform=exp_dict["transform_val"],
classes=exp_dict["classes_test"],
support_size=exp_dict["support_size_test"],
query_size=exp_dict["query_size_test"],
n_iters=exp_dict["test_iters"],
unlabeled_size=exp_dict["unlabeled_size_test"])
# get dataloaders
# ==========================
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=exp_dict["batch_size"],
shuffle=True,
num_workers=num_workers,
collate_fn=ut.get_collate(exp_dict["collate_fn"]),
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=1,
shuffle=False,
num_workers=num_workers,
collate_fn=lambda x: x,
drop_last=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=num_workers,
collate_fn=lambda x: x,
drop_last=True)
# create model and trainer
# ==========================
# Create model, opt, wrapper
backbone = backbones.get_backbone(
backbone_name=exp_dict['model']["backbone"], exp_dict=exp_dict)
model = models.get_model(model_name=exp_dict["model"]['name'],
backbone=backbone,
n_classes=exp_dict["n_classes"],
exp_dict=exp_dict)
if run_ssl:
# runs the SSL experiments
score_list_path = os.path.join(savedir, 'score_list.pkl')
if not os.path.exists(score_list_path):
test_dict = model.test_on_loader(test_loader, max_iter=None)
hu.save_pkl(score_list_path, [test_dict])
return
# Checkpoint
# -----------
checkpoint_path = os.path.join(savedir, 'checkpoint.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(checkpoint_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Run training and validation
for epoch in range(s_epoch, exp_dict["max_epoch"]):
score_dict = {"epoch": epoch}
score_dict.update(model.get_lr())
# train
score_dict.update(model.train_on_loader(train_loader))
# validate
score_dict.update(model.val_on_loader(val_loader))
score_dict.update(model.test_on_loader(test_loader))
# Add score_dict to score_list
score_list += [score_dict]
# Report
score_df = pd.DataFrame(score_list)
print(score_df.tail())
# Save checkpoint
hu.save_pkl(score_list_path, score_list)
hu.torch_save(checkpoint_path, model.get_state_dict())
print("Saved: %s" % savedir)
if "accuracy" in exp_dict["target_loss"]:
is_best = score_dict[exp_dict["target_loss"]] >= score_df[
exp_dict["target_loss"]][:-1].max()
else:
is_best = score_dict[exp_dict["target_loss"]] <= score_df[
exp_dict["target_loss"]][:-1].min()
# Save best checkpoint
if is_best:
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
hu.torch_save(os.path.join(savedir, "checkpoint_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
# Check for end of training conditions
if model.is_end_of_training():
break
