def predict_on_eval_using_fold(fold, train_eval_data):
_, eval_indices = indices_for_fold(fold, train_eval_data)
eval_dataset = TrainEvalDataset(train_eval_data.iloc[eval_indices],
transform=eval_transform)
eval_data_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=config.batch_size,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
model = Model(config.model, NUM_CLASSES)
model = model.to(DEVICE)
model.load_state_dict(
torch.load(
os.path.join(args.experiment_path, 'model_{}.pth'.format(fold))))
model.eval()
with torch.no_grad():
fold_labels = []
fold_logits = []
fold_exps = []
fold_ids = []
for images, feats, exps, labels, ids in tqdm(
eval_data_loader, desc='fold {} evaluation'.format(fold)):
images, feats, labels = images.to(DEVICE), feats.to(
DEVICE), labels.to(DEVICE)
b, n, c, h, w = images.size()
images = images.view(b * n, c, h, w)
feats = feats.view(b, 1, 2).repeat(1, n, 1).view(b * n, 2)
logits = model(images, feats)
logits = logits.view(b, n, NUM_CLASSES)
fold_labels.append(labels)
fold_logits.append(logits)
fold_exps.extend(exps)
fold_ids.extend(ids)
fold_labels = torch.cat(fold_labels, 0)
fold_logits = torch.cat(fold_logits, 0)
return fold_labels, fold_logits, fold_exps, fold_ids
def predict_on_test_using_fold(fold, test_data):
test_dataset = TestDataset(test_data, transform=test_transform)
test_data_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=config.batch_size // 2,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
model = Model(config.model, NUM_CLASSES)
model = model.to(DEVICE)
model.load_state_dict(
torch.load(
os.path.join(args.experiment_path, 'model_{}.pth'.format(fold))))
model.eval()
with torch.no_grad():
fold_logits = []
fold_exps = []
fold_plates = []
fold_ids = []
for images, feats, exps, plates, ids in tqdm(
test_data_loader, desc='fold {} inference'.format(fold)):
images, feats = images.to(DEVICE), feats.to(DEVICE)
b, n, c, h, w = images.size()
images = images.view(b * n, c, h, w)
feats = feats.view(b, 1, 2).repeat(1, n, 1).view(b * n, 2)
logits = model(images, feats)
logits = logits.view(b, n, NUM_CLASSES)
fold_logits.append(logits)
fold_exps.extend(exps)
fold_plates.extend(plates)
fold_ids.extend(ids)
fold_logits = torch.cat(fold_logits, 0)
torch.save((fold_logits, fold_exps, fold_ids),
'./test_{}.pth'.format(fold))
return fold_logits, fold_exps, fold_plates, fold_ids
def compute_features_using_fold(fold, data):
dataset = TestDataset(data, transform=test_transform)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=config.batch_size //
2,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
model = Model(config.model, NUM_CLASSES, return_features=True)
model = model.to(DEVICE)
model.load_state_dict(
torch.load(
os.path.join(args.experiment_path, 'model_{}.pth'.format(fold))))
model.eval()
with torch.no_grad():
fold_embs = []
fold_exps = []
fold_ids = []
for images, feats, exps, ids in tqdm(
data_loader, desc='fold {} inference'.format(fold)):
images, feats = images.to(DEVICE), feats.to(DEVICE)
b, n, c, h, w = images.size()
images = images.view(b * n, c, h, w)
feats = feats.view(b, 1, 2).repeat(1, n, 1).view(b * n, 2)
_, embds = model(images, feats)
embds = embds.view(b, n, embds.size(1))
fold_embs.append(embds)
fold_exps.extend(exps)
fold_ids.extend(ids)
fold_embs = torch.cat(fold_embs, 0)
return fold_embs, fold_exps, fold_ids
def train_fold(fold, train_eval_data, unsup_data):
train_indices, eval_indices = indices_for_fold(fold, train_eval_data)
train_dataset = TrainEvalDataset(train_eval_data.iloc[train_indices],
transform=train_transform)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.batch_size,
drop_last=True,
shuffle=True,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
eval_dataset = TrainEvalDataset(train_eval_data.iloc[eval_indices],
transform=eval_transform)
eval_data_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=config.batch_size,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
unsup_data = TestDataset(unsup_data, transform=unsup_transform)
unsup_data_loader = torch.utils.data.DataLoader(
unsup_data,
batch_size=config.batch_size // 2,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
model = Model(config.model, NUM_CLASSES)
model = model.to(DEVICE)
if args.restore_path is not None:
model.load_state_dict(
torch.load(
os.path.join(args.restore_path, 'model_{}.pth'.format(fold))))
optimizer = build_optimizer(config.opt, model.parameters())
if config.sched.type == 'onecycle':
scheduler = lr_scheduler_wrapper.StepWrapper(
OneCycleScheduler(optimizer,
lr=(config.opt.lr / 20, config.opt.lr),
beta_range=config.sched.onecycle.beta,
max_steps=len(train_data_loader) * config.epochs,
annealing=config.sched.onecycle.anneal,
peak_pos=config.sched.onecycle.peak_pos,
end_pos=config.sched.onecycle.end_pos))
elif config.sched.type == 'step':
scheduler = lr_scheduler_wrapper.EpochWrapper(
torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=config.sched.step.step_size,
gamma=config.sched.step.decay))
elif config.sched.type == 'cyclic':
step_size_up = len(
train_data_loader) * config.sched.cyclic.step_size_up
step_size_down = len(
train_data_loader) * config.sched.cyclic.step_size_down
scheduler = lr_scheduler_wrapper.StepWrapper(
torch.optim.lr_scheduler.CyclicLR(
optimizer,
0.,
config.opt.lr,
step_size_up=step_size_up,
step_size_down=step_size_down,
mode='triangular2',
gamma=config.sched.cyclic.decay**(
1 / (step_size_up + step_size_down)),
cycle_momentum=True,
base_momentum=0.85,
max_momentum=0.95))
elif config.sched.type == 'cawr':
scheduler = lr_scheduler_wrapper.StepWrapper(
torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer, T_0=len(train_data_loader), T_mult=2))
elif config.sched.type == 'plateau':
scheduler = lr_scheduler_wrapper.ScoreWrapper(
torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode='max',
factor=config.sched.plateau.decay,
patience=config.sched.plateau.patience,
verbose=True))
else:
raise AssertionError('invalid sched {}'.format(config.sched.type))
best_score = 0
for epoch in range(1, config.epochs + 1):
optimizer.train()
train_epoch(model=model,
optimizer=optimizer,
scheduler=scheduler,
data_loader=train_data_loader,
unsup_data_loader=unsup_data_loader,
fold=fold,
epoch=epoch)
gc.collect()
optimizer.eval()
metric = eval_epoch(model=model,
data_loader=eval_data_loader,
fold=fold,
epoch=epoch)
gc.collect()
score = metric['accuracy@1']
scheduler.step_epoch()
scheduler.step_score(score)
if score > best_score:
best_score = score
torch.save(
model.state_dict(),
os.path.join(args.experiment_path,
'model_{}.pth'.format(fold)))
def train_fold(fold, train_eval_data):
model = Model(config.model, NUM_CLASSES)
model = model.to(DEVICE)
if args.restore_path is not None:
model.load_state_dict(
torch.load(
os.path.join(args.restore_path, 'model_{}.pth'.format(fold))))
optimizer = build_optimizer(config.opt, model.parameters())
if config.sched.type == 'onecycle':
scheduler = lr_scheduler_wrapper.EpochWrapper(
OneCycleScheduler(optimizer,
lr=(config.opt.lr / 20, config.opt.lr),
beta_range=config.sched.onecycle.beta,
max_steps=config.epochs,
annealing=config.sched.onecycle.anneal,
peak_pos=config.sched.onecycle.peak_pos,
end_pos=config.sched.onecycle.end_pos))
elif config.sched.type == 'cyclic':
step_size_up = len(
train_data_loader) * config.sched.cyclic.step_size_up
step_size_down = len(
train_data_loader) * config.sched.cyclic.step_size_down
scheduler = lr_scheduler_wrapper.StepWrapper(
torch.optim.lr_scheduler.CyclicLR(
optimizer,
0.,
config.opt.lr,
step_size_up=step_size_up,
step_size_down=step_size_down,
mode='exp_range',
gamma=config.sched.cyclic.decay**(
1 / (step_size_up + step_size_down)),
cycle_momentum=True,
base_momentum=0.75,
max_momentum=0.95))
elif config.sched.type == 'cawr':
scheduler = lr_scheduler_wrapper.StepWrapper(
torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer, T_0=len(train_data_loader), T_mult=2))
elif config.sched.type == 'plateau':
scheduler = lr_scheduler_wrapper.ScoreWrapper(
torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode='max',
factor=config.sched.plateau.decay,
patience=config.sched.plateau.patience,
verbose=True))
else:
raise AssertionError('invalid sched {}'.format(config.sched.type))
best_score = 0
for epoch in range(1, config.epochs + 1):
train_indices, eval_indices = indices_for_fold(fold, train_eval_data)
eval_pl = pd.read_csv(
'./tf_log/cells/tmp-512-progres-crop-norm-la/eval_{}.csv'.format(
fold))
eval_pl['root'] = os.path.join(args.dataset_path, 'train')
test_pl = pd.read_csv(
'./tf_log/cells/tmp-512-progres-crop-norm-la/test.csv')
test_pl['root'] = os.path.join(args.dataset_path, 'test')
pl = pd.concat([eval_pl, test_pl])
pl_size = len(pl)
pl = pl.sample(frac=np.linspace(1., 0., config.epochs)[epoch -
1].item())
print('frac: {:.4f}, lr: {:.8f}'.format(
len(pl) / pl_size, scheduler.get_lr()))
train_dataset = TrainEvalDataset(pd.concat(
[train_eval_data.iloc[train_indices], pl]),
transform=train_transform)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.batch_size,
drop_last=True,
shuffle=True,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
eval_dataset = TrainEvalDataset(train_eval_data.iloc[eval_indices],
transform=eval_transform)
eval_data_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=config.batch_size,
num_workers=args.workers,
worker_init_fn=worker_init_fn)
train_epoch(model=model,
optimizer=optimizer,
scheduler=scheduler,
data_loader=train_data_loader,
fold=fold,
epoch=epoch)
gc.collect()
metric = eval_epoch(model=model,
data_loader=eval_data_loader,
fold=fold,
epoch=epoch)
gc.collect()
score = metric['accuracy@1']
scheduler.step_epoch()
scheduler.step_score(score)
if score > best_score:
best_score = score
torch.save(
model.state_dict(),
os.path.join(args.experiment_path,
'model_{}.pth'.format(fold)))