def experiment(logdir: Path, device: torch.device) -> None:
"""Experiment function
Args:
logdir (Path): directory where should be placed logs
device (str): device name to use
"""
tb_dir = logdir / "tensorboard"
main_metric = "gap"
minimize_metric = False
seed_all()
model = EncoderWithHead(
EfficientNetEncoder("efficientnet-b0", EMBEDDING_SIZE, bias=False),
CosFace(EMBEDDING_SIZE, NUM_CLASSESS, None),
)
load_checkpoint("./logs/full_set5/stage_0/best.pth", model)
# model.head.s = 64.0 # np.sqrt(2) * np.log(NUM_CLASSESS - 1)
# model.head.m = 0.35
# # freeze backbone of encoder
# for parameter in model.encoder.base._blocks.parameters():
# parameter.requires_grad = False
model = nn.DataParallel(model)
model = model.to(device)
optimizer = optim.SGD(model.parameters(),
lr=1e-3,
momentum=0.9,
weight_decay=1e-5)
criterion = nn.CrossEntropyLoss(weight=get_class_weights()).to(device)
scheduler = None
with TensorboardLogger(tb_dir) as tb:
stage = "stage_0"
n_epochs = 5
print(f"Stage - '{stage}'")
checkpointer = CheckpointManager(
logdir=logdir / stage,
metric=main_metric,
metric_minimization=minimize_metric,
save_n_best=5,
)
train_loader, valid_loader = get_loaders(stage,
train_bs=32,
valid_bs=64)
for epoch in range(1, n_epochs + 1):
epoch_start_time = time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime())
print(f"[{epoch_start_time}]\n[Epoch {epoch}/{n_epochs}]")
train_metrics = train_fn(model, train_loader, device, criterion,
optimizer)
log_metrics(stage, train_metrics, tb, "train", epoch)
valid_metrics = valid_fn(model, valid_loader, device, criterion)
log_metrics(stage, valid_metrics, tb, "valid", epoch)
checkpointer.process(
metric_value=valid_metrics[main_metric],
epoch=epoch,
checkpoint=make_checkpoint(
stage,
epoch,
model,
optimizer,
scheduler,
metrics={
"train": train_metrics,
"valid": valid_metrics
},
),
)
def experiment(logdir, device) -> None:
"""Experiment function
Args:
logdir (Path): directory where should be placed logs
device (str): device name to use
"""
tb_dir = logdir / "tensorboard"
main_metric = "loss"
minimize_metric = True
seed_all()
history_n_frames = cfg["model_params"]["history_num_frames"]
future_n_frames = cfg["model_params"]["future_num_frames"]
n_trajectories = 3
model = ModelWithConfidence(
backbone=resnet18(
pretrained=True,
in_channels=3 + 2 * (history_n_frames + 1),
num_classes=2 * future_n_frames * n_trajectories + n_trajectories,
),
future_num_frames=future_n_frames,
num_trajectories=n_trajectories,
)
# model = nn.DataParallel(model)
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = neg_multi_log_likelihood_batch
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)
with TensorboardLogger(tb_dir) as tb:
stage = "stage_0"
n_epochs = 1
print(f"Stage - {stage}")
checkpointer = CheckpointManager(
logdir=logdir / stage,
metric=main_metric,
metric_minimization=minimize_metric,
save_n_best=5,
)
train_loader, valid_loader = get_loaders(train_batch_size=32,
valid_batch_size=32)
for epoch in range(1, n_epochs + 1):
epoch_start_time = time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime())
print(f"[{epoch_start_time}]\n[Epoch {epoch}/{n_epochs}]")
train_metrics = train_fn(model, train_loader, device, criterion,
optimizer)
log_metrics(stage, train_metrics, tb, "train", epoch)
valid_metrics = valid_fn(model, valid_loader, device, criterion)
log_metrics(stage, valid_metrics, tb, "valid", epoch)
checkpointer.process(
metric_value=valid_metrics[main_metric],
epoch=epoch,
checkpoint=make_checkpoint(
stage,
epoch,
model,
optimizer,
scheduler,
metrics={
"train": train_metrics,
"valid": valid_metrics
},
),
)
scheduler.step()
def train_fn(
model,
loader,
device,
loss_fn,
optimizer,
scheduler=None,
accumulation_steps=1,
verbose=True,
tensorboard_logger=None,
logdir=None,
):
"""Train step.
Args:
model (nn.Module): model to train
loader (DataLoader): loader with data
device (str or torch.device): device to use for placing batches
loss_fn (nn.Module): loss function, should be callable
optimizer (torch.optim.Optimizer): model parameters optimizer
scheduler ([type], optional): batch scheduler to use.
Default is `None`.
accumulation_steps (int, optional): number of steps to accumulate gradients.
Default is `1`.
verbose (bool, optional): verbosity mode.
Default is True.
Returns:
dict with metics computed during the training on loader
"""
model.train()
metrics = {"loss": 0.0}
n_batches = len(loader)
indices_to_save = [
int(n_batches * pcnt) for pcnt in np.arange(0.1, 1, 0.1)
]
with tqdm(total=len(loader), desc="train",
disable=not verbose) as progress:
for idx, batch in enumerate(loader):
images, targets, target_availabilities = t2d(
(
batch["image"],
batch["target_positions"],
batch["target_availabilities"],
),
device,
)
zero_grad(optimizer)
predictions, confidences = model(images)
loss = loss_fn(targets, predictions, confidences,
target_availabilities)
_loss = loss.detach().item()
metrics["loss"] += _loss
if tensorboard_logger is not None:
tensorboard_logger.metric("loss", _loss, idx)
loss.backward()
progress.set_postfix_str(f"loss - {_loss:.5f}")
progress.update(1)
if (idx + 1) in indices_to_save and logdir is not None:
checkpoint = make_checkpoint("train", idx + 1, model)
save_checkpoint(checkpoint, logdir, f"train_{idx}.pth")
if (idx + 1) % accumulation_steps == 0:
optimizer.step()
if scheduler is not None:
scheduler.step()
if idx == DEBUG:
break
metrics["loss"] /= idx + 1
return metrics
def experiment(logdir, device) -> None:
"""Experiment function
Args:
logdir (Path): directory where should be placed logs
device (str): device name to use
"""
tb_dir = logdir / "tensorboard"
main_metric = "loss"
minimize_metric = True
seed_all()
history_n_frames = cfg["model_params"]["history_num_frames"]
future_n_frames = cfg["model_params"]["future_num_frames"]
n_trajectories = 3
model = ModelWithConfidence(
backbone=resnet34_accel(
pretrained=True,
in_channels=3 + 3,
num_classes=2 * future_n_frames * n_trajectories + n_trajectories,
in_accel_features=(history_n_frames - 1) * 2,
num_accel_features=32,
),
future_num_frames=future_n_frames,
num_trajectories=n_trajectories,
)
load_checkpoint(
"./logs/resnet34_frast_fulldata_confidence_25hist_accel/epoch_1/train_689999.pth",
model,
)
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = neg_multi_log_likelihood_batch
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)
with TensorboardLogger(tb_dir) as tb:
stage = "stage_0"
n_epochs = 1
print(f"Stage - {stage}")
checkpointer = CheckpointManager(
logdir=logdir / stage,
metric=main_metric,
metric_minimization=minimize_metric,
save_n_best=5,
)
train_loader, (valid_loader, valid_gt_path) = get_loaders(
train_batch_size=32, valid_batch_size=32
)
valid_func = partial(
valid_fn,
loader=valid_loader,
ground_truth_file=valid_gt_path,
logdir=logdir,
verbose=True,
)
for epoch in range(1, n_epochs + 1):
epoch_start_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
print(f"[{epoch_start_time}]\n[Epoch {epoch}/{n_epochs}]")
# try:
train_metrics = train_fn(
model,
train_loader,
device,
criterion,
optimizer,
tensorboard_logger=tb,
logdir=logdir / f"epoch_{epoch}",
validation_fn=valid_func,
)
log_metrics(stage, train_metrics, tb, "train", epoch)
# except BaseException:
# train_metrics = {"message": "An exception occured!"}
valid_metrics = valid_fn(model, valid_loader, device, valid_gt_path, logdir)
log_metrics(stage, valid_metrics, tb, "valid", epoch)
checkpointer.process(
metric_value=valid_metrics["score"],
epoch=epoch,
checkpoint=make_checkpoint(
stage,
epoch,
model,
optimizer,
scheduler,
metrics={"train": train_metrics, "valid": valid_metrics},
),
)
def experiment(logdir: Path, device: torch.device) -> None:
"""Experiment function
Args:
logdir (Path): directory where should be placed logs
device (str): device name to use
"""
tb_dir = logdir / "tensorboard"
main_metric = "gap"
minimize_metric = False
seed_all()
model = EncoderWithHead(
EfficientNetEncoder("efficientnet-b5", EMBEDDING_SIZE, bias=True),
CosFace(
EMBEDDING_SIZE,
NUM_CLASSESS,
device_id=None,
s=np.sqrt(2) * np.log(NUM_CLASSESS - 1),
m=0.1,
),
)
model = nn.DataParallel(model)
model = model.to(device)
optimizer = optim.SGD(
model.parameters(),
lr=1e-3,
momentum=0.9,
weight_decay=1e-5,
)
criterion = nn.CrossEntropyLoss()
scheduler = None
with TensorboardLogger(tb_dir) as tb:
stage = "stage_0"
n_epochs = 10
print(f"Stage - '{stage}'")
checkpointer = CheckpointManager(
logdir=logdir / stage,
metric=main_metric,
metric_minimization=minimize_metric,
save_n_best=5,
)
train_loader, valid_loader = get_loaders(stage,
train_bs=48,
valid_bs=128)
for epoch in range(1, n_epochs + 1):
epoch_start_time = time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime())
print(f"[{epoch_start_time}]\n[Epoch {epoch}/{n_epochs}]")
train_metrics = train_fn(model, train_loader, device, criterion,
optimizer)
log_metrics(stage, train_metrics, tb, "train", epoch)
valid_metrics = valid_fn(model, valid_loader, device, criterion)
log_metrics(stage, valid_metrics, tb, "valid", epoch)
checkpointer.process(
metric_value=valid_metrics[main_metric],
epoch=epoch,
checkpoint=make_checkpoint(
stage,
epoch,
model,
optimizer,
scheduler,
metrics={
"train": train_metrics,
"valid": valid_metrics
},
),
)
def train_fn(
model,
loader,
device,
loss_fn,
optimizer,
scheduler=None,
accumulation_steps=1,
verbose=True,
tensorboard_logger=None,
logdir=None,
validation_fn=None,
):
"""Train step.
Args:
model (nn.Module): model to train
loader (DataLoader): loader with data
device (str or torch.device): device to use for placing batches
loss_fn (nn.Module): loss function, should be callable
optimizer (torch.optim.Optimizer): model parameters optimizer
scheduler ([type], optional): batch scheduler to use.
Default is `None`.
accumulation_steps (int, optional): number of steps to accumulate gradients.
Default is `1`.
verbose (bool, optional): verbosity mode.
Default is True.
Returns:
dict with metics computed during the training on loader
"""
model.train()
metrics = {"regression_loss": 0.0, "mask_loss": 0.0, "loss": 0.0}
n_batches = len(loader)
indices_to_save = [
int(n_batches * pcnt) for pcnt in np.arange(0.1, 1, 0.1)
]
last_score = 0.0
with tqdm(total=len(loader), desc="train",
disable=not verbose) as progress:
for idx, batch in enumerate(loader):
(images, targets, target_availabilities, masks) = t2d(
(
batch["image"],
batch["target_positions"],
batch["target_availabilities"],
batch["mask"],
),
device,
)
zero_grad(optimizer)
predictions, confidences, masks_logits = model(images)
rloss = loss_fn(targets, predictions, confidences,
target_availabilities)
mloss = 1e4 * F.binary_cross_entropy_with_logits(
masks_logits, masks)
loss = rloss + mloss
_rloss = rloss.detach().item()
_mloss = mloss.detach().item()
_loss = loss.detach().item()
metrics["regression_loss"] += _rloss
metrics["mask_loss"] += _mloss
metrics["mask_loss"] += _loss
if (idx + 1) % 30_000 == 0 and validation_fn is not None:
score = validation_fn(model=model, device=device)
model.train()
last_score = score
if logdir is not None:
checkpoint = make_checkpoint("train", idx + 1, model)
save_checkpoint(checkpoint, logdir, f"train_{idx}.pth")
else:
score = None
if tensorboard_logger is not None:
tensorboard_logger.metric("regression_loss", _rloss, idx)
tensorboard_logger.metric("mask_loss", _mloss, idx)
tensorboard_logger.metric("loss", _loss, idx)
if score is not None:
tensorboard_logger.metric("score", score, idx)
if (idx + 1) % 1_000 == 0:
# masks_gt - (bs)x(1)x(h)x(w)
# masks - (bs)x(1)x(h)x(w)
tensorboard_logger.writer.add_images(
"gt_vs_mask",
torch.cat([masks, torch.sigmoid(masks_logits)],
dim=-1),
idx,
)
loss.backward()
progress.set_postfix_str(f"rloss - {_rloss:.5f}, "
f"mloss - {_mloss:.5f}, "
f"loss - {_loss:.5f}, "
f"score - {last_score:.5f}")
progress.update(1)
if (idx + 1) % accumulation_steps == 0:
optimizer.step()
if scheduler is not None:
scheduler.step()
if idx == DEBUG:
break
def experiment(logdir, device) -> None:
"""Experiment function
Args:
logdir (Path): directory where should be placed logs
device (str): device name to use
"""
tb_dir = logdir / "tensorboard"
main_metric = "score"
minimize_metric = True
seed_all()
history_n_frames = cfg["model_params"]["history_num_frames"]
future_n_frames = cfg["model_params"]["future_num_frames"]
n_trajectories = 3
model = ModelWithConfidence(
backbone=resnet18(
pretrained=True,
in_channels=3 + 2 * (history_n_frames + 1),
num_classes=2 * future_n_frames * n_trajectories + n_trajectories,
),
future_num_frames=future_n_frames,
num_trajectories=n_trajectories,
)
# model = nn.DataParallel(model)
model = model.to(device)
# optimizer = optim.Adam(model.parameters(), lr=1e-3)
optimizer = optim.SGD(model.parameters(), lr=1e-4)
scheduler = optim.lr_scheduler.CyclicLR(
optimizer,
base_lr=1e-4,
max_lr=1e-3,
step_size_up=120_000,
cycle_momentum=True,
mode="triangular2",
)
load_checkpoint(
"./logs/resnet18_bigerimages_continue4_chopped/epoch_1/train_25868.pth",
model,
# optimizer,
)
criterion = neg_multi_log_likelihood_batch
with TensorboardLogger(tb_dir) as tb:
stage = "stage_0"
n_epochs = 1
print(f"Stage - {stage}")
checkpointer = CheckpointManager(
logdir=logdir / stage,
metric=main_metric,
metric_minimization=minimize_metric,
save_n_best=5,
)
train_loader, (valid_loader, valid_gt_path) = get_loaders(
train_batch_size=32, valid_batch_size=32
)
valid_func = partial(
valid_fn,
loader=valid_loader,
ground_truth_file=valid_gt_path,
logdir=logdir,
verbose=True,
)
for epoch in range(1, n_epochs + 1):
epoch_start_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
print(f"[{epoch_start_time}]\n[Epoch {epoch}/{n_epochs}]")
try:
train_metrics = train_fn(
model,
train_loader,
device,
criterion,
optimizer,
scheduler=scheduler,
tensorboard_logger=tb,
logdir=logdir / f"epoch_{epoch}",
validation_fn=valid_func,
)
log_metrics(stage, train_metrics, tb, "train", epoch)
except BaseException:
train_metrics = {"message": "An exception occured!"}
# valid_metrics = train_metrics
valid_metrics = valid_fn(model, valid_loader, device, valid_gt_path, logdir)
log_metrics(stage, valid_metrics, tb, "valid", epoch)
checkpointer.process(
metric_value=valid_metrics[main_metric],
epoch=epoch,
checkpoint=make_checkpoint(
stage,
epoch,
model,
optimizer,
scheduler,
metrics={"train": train_metrics, "valid": valid_metrics},
),
)