def _test(with_eval, with_optim):
trainer = Engine(lambda e, b: b)
evaluators = None
optimizers = None
if with_eval:
evaluator = Engine(lambda e, b: None)
acc_scores = [0.1, 0.2, 0.3, 0.4, 0.3, 0.3, 0.2, 0.1, 0.1, 0.0]
@trainer.on(Events.EPOCH_COMPLETED)
def validate(engine):
evaluator.run(
[0,]
)
@evaluator.on(Events.EPOCH_COMPLETED)
def set_eval_metric(engine):
engine.state.metrics = {"acc": acc_scores[trainer.state.epoch - 1]}
evaluators = {"validation": evaluator}
if with_optim:
t = torch.tensor([0,])
optimizers = {"optimizer": torch.optim.SGD([t,], lr=0.01)}
setup_tb_logging(dirname, trainer, optimizers=optimizers, evaluators=evaluators, log_every_iters=1)
handlers = trainer._event_handlers[Events.ITERATION_COMPLETED]
for cls in [
tb_logger_module.OutputHandler,
]:
assert any([isinstance(h[0], cls) for h in handlers]), "{}".format(handlers)
if with_optim:
handlers = trainer._event_handlers[Events.ITERATION_STARTED]
for cls in [
tb_logger_module.OptimizerParamsHandler,
]:
assert any([isinstance(h[0], cls) for h in handlers]), "{}".format(handlers)
if with_eval:
handlers = evaluator._event_handlers[Events.COMPLETED]
for cls in [
tb_logger_module.OutputHandler,
]:
assert any([isinstance(h[0], cls) for h in handlers]), "{}".format(handlers)
data = [0, 1, 2]
trainer.run(data, max_epochs=10)
tb_files = list(os.listdir(dirname))
assert len(tb_files) == 1
for v in [
"events",
]:
assert any([v in c for c in tb_files]), "{}".format(tb_files)
def training(local_rank, config):
# Setup dataflow and
train_loader, val_loader = get_dataflow(config)
model, optimizer, criterion, lr_scheduler = initialize(config)
# Setup model trainer and evaluator
trainer = create_trainer(model, optimizer, criterion, lr_scheduler, config)
evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()}, device=idist.device())
# Run model evaluation every 3 epochs and show results
@trainer.on(Events.EPOCH_COMPLETED(every=3))
def evaluate_model():
state = evaluator.run(val_loader)
if idist.get_rank() == 0:
print(state.metrics)
# Setup tensorboard experiment tracking
if idist.get_rank() == 0:
tb_logger = common.setup_tb_logging(
config.get("output_path", "output"), trainer, optimizer, evaluators={"validation": evaluator},
)
trainer.run(train_loader, max_epochs=config.get("max_epochs", 3))
if idist.get_rank() == 0:
tb_logger.close()
def training(config,
local_rank=None,
with_mlflow_logging=False,
with_plx_logging=False):
if not getattr(config, "use_fp16", True):
raise RuntimeError("This training script uses by default fp16 AMP")
set_seed(config.seed + local_rank)
torch.cuda.set_device(local_rank)
device = 'cuda'
torch.backends.cudnn.benchmark = True
train_loader = config.train_loader
train_sampler = getattr(train_loader, "sampler", None)
assert train_sampler is not None, "Train loader of type '{}' " \
"should have attribute 'sampler'".format(type(train_loader))
assert hasattr(train_sampler, 'set_epoch') and callable(train_sampler.set_epoch), \
"Train sampler should have a callable method `set_epoch`"
train_eval_loader = config.train_eval_loader
val_loader = config.val_loader
model = config.model.to(device)
optimizer = config.optimizer
model, optimizer = amp.initialize(model,
optimizer,
opt_level=getattr(
config, "fp16_opt_level", "O2"),
num_losses=1)
model = DDP(model, delay_allreduce=True)
criterion = config.criterion.to(device)
prepare_batch = getattr(config, "prepare_batch", _prepare_batch)
non_blocking = getattr(config, "non_blocking", True)
# Setup trainer
accumulation_steps = getattr(config, "accumulation_steps", 1)
model_output_transform = getattr(config, "model_output_transform",
lambda x: x)
def train_update_function(engine, batch):
model.train()
x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
y_pred = model(x)
y_pred = model_output_transform(y_pred)
loss = criterion(y_pred, y) / accumulation_steps
with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss:
scaled_loss.backward()
if engine.state.iteration % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
return {
'supervised batch loss': loss.item(),
}
trainer = Engine(train_update_function)
common.setup_common_distrib_training_handlers(
trainer,
train_sampler,
to_save={
'model': model,
'optimizer': optimizer
},
save_every_iters=1000,
output_path=config.output_path.as_posix(),
lr_scheduler=config.lr_scheduler,
with_gpu_stats=True,
output_names=[
'supervised batch loss',
],
with_pbars=True,
with_pbar_on_iters=with_mlflow_logging,
log_every_iters=1)
if getattr(config, "benchmark_dataflow", False):
benchmark_dataflow_num_iters = getattr(config,
"benchmark_dataflow_num_iters",
1000)
DataflowBenchmark(benchmark_dataflow_num_iters,
prepare_batch=prepare_batch,
device=device).attach(trainer, train_loader)
# Setup evaluators
val_metrics = {
"Accuracy": Accuracy(device=device),
"Top-5 Accuracy": TopKCategoricalAccuracy(k=5, device=device),
}
if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
model_output_transform = getattr(config, "model_output_transform",
lambda x: x)
evaluator_args = dict(model=model,
metrics=val_metrics,
device=device,
non_blocking=non_blocking,
prepare_batch=prepare_batch,
output_transform=lambda x, y, y_pred: (
model_output_transform(y_pred),
y,
))
train_evaluator = create_supervised_evaluator(**evaluator_args)
evaluator = create_supervised_evaluator(**evaluator_args)
if dist.get_rank() == 0 and with_mlflow_logging:
ProgressBar(persist=False,
desc="Train Evaluation").attach(train_evaluator)
ProgressBar(persist=False, desc="Val Evaluation").attach(evaluator)
def run_validation(_):
train_evaluator.run(train_eval_loader)
evaluator.run(val_loader)
if getattr(config, "start_by_validation", False):
trainer.add_event_handler(Events.STARTED, run_validation)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(every=getattr(config, "val_interval", 1)),
run_validation)
trainer.add_event_handler(Events.COMPLETED, run_validation)
score_metric_name = "Accuracy"
if hasattr(config, "es_patience"):
common.add_early_stopping_by_val_score(config.es_patience,
evaluator,
trainer,
metric_name=score_metric_name)
if dist.get_rank() == 0:
tb_logger = common.setup_tb_logging(config.output_path.as_posix(),
trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
})
if with_mlflow_logging:
common.setup_mlflow_logging(trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
})
if with_plx_logging:
common.setup_plx_logging(trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
})
common.save_best_model_by_val_score(config.output_path.as_posix(),
evaluator,
model,
metric_name=score_metric_name,
trainer=trainer)
# Log train/val predictions:
tb_logger.attach(
evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(once=len(val_loader) // 2))
tb_logger.attach(train_evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="training"),
event_name=Events.ITERATION_COMPLETED(
once=len(train_eval_loader) // 2))
trainer.run(train_loader, max_epochs=config.num_epochs)
def training(local_rank, config):
rank = idist.get_rank()
manual_seed(config["seed"] + rank)
device = idist.device()
logger = setup_logger(name="CIFAR10-Training", distributed_rank=local_rank)
log_basic_info(logger, config)
output_path = config["output_path"]
if rank == 0:
if config["stop_iteration"] is None:
now = datetime.now().strftime("%Y%m%d-%H%M%S")
else:
now = f"stop-on-{config['stop_iteration']}"
folder_name = f"{config['model']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}"
output_path = Path(output_path) / folder_name
if not output_path.exists():
output_path.mkdir(parents=True)
config["output_path"] = output_path.as_posix()
logger.info(f"Output path: {config['output_path']}")
if "cuda" in device.type:
config["cuda device name"] = torch.cuda.get_device_name(local_rank)
if config["with_clearml"]:
try:
from clearml import Task
except ImportError:
# Backwards-compatibility for legacy Trains SDK
from trains import Task
task = Task.init("CIFAR10-Training", task_name=output_path.stem)
task.connect_configuration(config)
# Log hyper parameters
hyper_params = [
"model",
"batch_size",
"momentum",
"weight_decay",
"num_epochs",
"learning_rate",
"num_warmup_epochs",
]
task.connect({k: config[k] for k in hyper_params})
# Setup dataflow, model, optimizer, criterion
train_loader, test_loader = get_dataflow(config)
config["num_iters_per_epoch"] = len(train_loader)
model, optimizer, criterion, lr_scheduler = initialize(config)
# Create trainer for current task
trainer = create_trainer(model, optimizer, criterion, lr_scheduler, train_loader.sampler, config, logger)
# Let's now setup evaluator engine to perform model's validation and compute metrics
metrics = {
"Accuracy": Accuracy(),
"Loss": Loss(criterion),
}
# We define two evaluators as they wont have exactly similar roles:
# - `evaluator` will save the best model based on validation score
evaluator = create_evaluator(model, metrics=metrics, config=config)
train_evaluator = create_evaluator(model, metrics=metrics, config=config)
def run_validation(engine):
epoch = trainer.state.epoch
state = train_evaluator.run(train_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics)
state = evaluator.run(test_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics)
trainer.add_event_handler(Events.EPOCH_COMPLETED(every=config["validate_every"]) | Events.COMPLETED, run_validation)
if rank == 0:
# Setup TensorBoard logging on trainer and evaluators. Logged values are:
# - Training metrics, e.g. running average loss values
# - Learning rate
# - Evaluation train/test metrics
evaluators = {"training": train_evaluator, "test": evaluator}
tb_logger = common.setup_tb_logging(output_path, trainer, optimizer, evaluators=evaluators)
# Store 2 best models by validation accuracy starting from num_epochs / 2:
best_model_handler = Checkpoint(
{"model": model},
get_save_handler(config),
filename_prefix="best",
n_saved=2,
global_step_transform=global_step_from_engine(trainer),
score_name="test_accuracy",
score_function=Checkpoint.get_default_score_fn("Accuracy"),
)
evaluator.add_event_handler(
Events.COMPLETED(lambda *_: trainer.state.epoch > config["num_epochs"] // 2), best_model_handler
)
# In order to check training resuming we can stop training on a given iteration
if config["stop_iteration"] is not None:
@trainer.on(Events.ITERATION_STARTED(once=config["stop_iteration"]))
def _():
logger.info(f"Stop training on {trainer.state.iteration} iteration")
trainer.terminate()
try:
trainer.run(train_loader, max_epochs=config["num_epochs"])
except Exception as e:
logger.exception("")
raise e
if rank == 0:
tb_logger.close()
def training(local_rank, config, logger, with_clearml):
rank = idist.get_rank()
manual_seed(config.seed + local_rank)
train_loader = config.train_loader
val_loader = config.val_loader
train_eval_loader = config.train_eval_loader
model, optimizer, criterion = utils.initialize(config)
# Setup trainer for this specific task
trainer = create_trainer(model, optimizer, criterion, train_loader.sampler, config, logger, with_clearml)
# Setup evaluators
num_classes = config.num_classes
cm_metric = ConfusionMatrix(num_classes=num_classes)
val_metrics = {
"IoU": IoU(cm_metric),
"mIoU_bg": mIoU(cm_metric),
}
if ("val_metrics" in config) and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="val")
train_evaluator = create_evaluator(model, val_metrics, config, with_clearml, tag="train")
val_interval = config.get("val_interval", 1)
# Run validation on every val_interval epoch, in the end of the training
# and in the begining if config.start_by_validation is True
event = Events.EPOCH_COMPLETED(every=val_interval)
if config.num_epochs % val_interval != 0:
event |= Events.COMPLETED
if config.get("start_by_validation", False):
event |= Events.STARTED
@trainer.on(event)
def run_validation():
epoch = trainer.state.epoch
state = train_evaluator.run(train_eval_loader)
utils.log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics)
state = evaluator.run(val_loader)
utils.log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics)
score_metric_name = "mIoU_bg"
if "es_patience" in config:
common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name)
# Store 2 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=utils.get_save_handler(config.output_path.as_posix(), with_clearml),
evaluator=evaluator,
models=model,
metric_name=score_metric_name,
n_saved=2,
trainer=trainer,
tag="val",
)
# Setup Tensorboard logger
if rank == 0:
tb_logger = common.setup_tb_logging(
config.output_path.as_posix(),
trainer,
optimizer,
evaluators={"training": train_evaluator, "validation": evaluator},
)
# Log validation predictions as images
# We define a custom event filter to log less frequently the images (to reduce storage size)
# - we plot images with masks of the middle validation batch
# - once every 3 validations and
# - at the end of the training
def custom_event_filter(_, val_iteration):
c1 = val_iteration == len(val_loader) // 2
c2 = trainer.state.epoch % (config.get("val_interval", 1) * 3) == 0
c2 |= trainer.state.epoch == config.num_epochs
return c1 and c2
# Image denormalization function to plot predictions with images
mean = config.get("mean", (0.485, 0.456, 0.406))
std = config.get("std", (0.229, 0.224, 0.225))
img_denormalize = partial(data.denormalize, mean=mean, std=std)
tb_logger.attach(
evaluator,
log_handler=vis.predictions_gt_images_handler(
img_denormalize_fn=img_denormalize, n_images=15, another_engine=trainer, prefix_tag="validation",
),
event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter),
)
# Log confusion matrix to ClearML:
if with_clearml:
trainer.add_event_handler(Events.COMPLETED, compute_and_log_cm, cm_metric, trainer.state.iteration)
trainer.run(train_loader, max_epochs=config.num_epochs)
if idist.get_rank() == 0:
tb_logger.close()
def training(local_rank, config):
rank = idist.get_rank()
manual_seed(config["seed"] + rank)
device = idist.device()
logger = setup_logger(name="CIFAR10-Training", distributed_rank=local_rank)
log_basic_info(logger, config)
output_path = config["output_path"]
if rank == 0:
now = datetime.now().strftime("%Y%m%d-%H%M%S")
folder_name = f"{config['model']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}"
output_path = Path(output_path) / folder_name
if not output_path.exists():
output_path.mkdir(parents=True)
config["output_path"] = output_path.as_posix()
logger.info(f"Output path: {config['output_path']}")
if "cuda" in device.type:
config["cuda device name"] = torch.cuda.get_device_name(local_rank)
if config["with_clearml"]:
try:
from clearml import Task
except ImportError:
# Backwards-compatibility for legacy Trains SDK
from trains import Task
task = Task.init("CIFAR10-Training", task_name=output_path.stem)
task.connect_configuration(config)
task.connect(config)
# Setup dataflow, model, optimizer, criterion
train_loader, test_loader = get_dataflow(config)
config["num_iters_per_epoch"] = len(train_loader)
model, optimizer, criterion, lr_scheduler = initialize(config)
logger.info(
f"# model parameters (M): {sum([m.numel() for m in model.parameters()]) * 1e-6}"
)
# Create trainer for current task
trainer = create_trainer(model, optimizer, criterion, lr_scheduler,
train_loader.sampler, config, logger)
# Let's now setup evaluator engine to perform model's validation and
# compute metrics
metrics = {
"Accuracy": Accuracy(),
"Loss": Loss(criterion),
}
# We define two evaluators as they wont have exactly similar roles:
# - `evaluator` will save the best model based on validation score
evaluator = create_evaluator(model, metrics=metrics, config=config)
train_evaluator = create_evaluator(model, metrics=metrics, config=config)
if config["smoke_test"]:
logger.info(
"Reduce the size of training and test dataloader as smoke_test=True"
)
def get_batches(loader):
loader_iter = iter(loader)
return [next(loader_iter) for _ in range(5)]
train_loader = get_batches(train_loader)
test_loader = get_batches(test_loader)
if config["with_pbar"] and rank == 0:
ProgressBar(desc="Evaluation (train)",
persist=False).attach(train_evaluator)
ProgressBar(desc="Evaluation (val)", persist=False).attach(evaluator)
def run_validation(engine):
epoch = trainer.state.epoch
state = train_evaluator.run(train_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train",
state.metrics)
state = evaluator.run(test_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test",
state.metrics)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(every=config["validate_every"])
| Events.COMPLETED,
run_validation,
)
if rank == 0:
# Setup TensorBoard logging on trainer and evaluators. Logged values are:
# - Training metrics, e.g. running average loss values
# - Learning rate
# - Evaluation train/test metrics
evaluators = {"training": train_evaluator, "test": evaluator}
tb_logger = common.setup_tb_logging(output_path,
trainer,
optimizer,
evaluators=evaluators)
# Store 1 best models by validation accuracy starting from num_epochs / 2:
best_model_handler = Checkpoint(
{"model": model},
get_save_handler(config),
filename_prefix="best",
n_saved=1,
global_step_transform=global_step_from_engine(trainer),
score_name="test_accuracy",
score_function=Checkpoint.get_default_score_fn("Accuracy"),
)
evaluator.add_event_handler(
Events.COMPLETED(
lambda *_: trainer.state.epoch > config["num_epochs"] // 2),
best_model_handler,
)
try:
trainer.run(train_loader, max_epochs=config["num_epochs"])
except Exception as e:
logger.exception("")
raise e
if rank == 0:
tb_logger.close()
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--model", type=str, default='ffn', help="model's name")
parser.add_argument("--mode", type=int, choices=[0, 1, 2], default=None)
parser.add_argument("--SNRdb", type=float, default=None)
parser.add_argument("--pilot_version", type=int, choices=[1, 2], default=1)
parser.add_argument("--loss_type", type=str, default="BCELoss")
parser.add_argument("--train_batch_size", type=int, default=128)
parser.add_argument("--valid_batch_size", type=int, default=128)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--max_norm", type=float, default=-1)
parser.add_argument("--lr", type=float, default=1e-3)
parser.add_argument("--noise_lambda", type=float, default=1.0)
parser.add_argument("--lr_scheduler", type=str, choices=["linear", "cycle", "cosine"], default="linear")
parser.add_argument("--reset_lr_scheduler", type=str, choices=["linear", "cycle", "cosine"], default=None)
parser.add_argument("--reset_trainer", action='store_true')
parser.add_argument("--modify_model", action='store_true')
parser.add_argument("--wd", type=float, default=1e-4, help="weight decay")
parser.add_argument("--eval_iter", type=int, default=10)
parser.add_argument("--save_iter", type=int, default=10)
parser.add_argument("--n_epochs", type=int, default=10)
parser.add_argument("--flush_dataset", type=int, default=0)
parser.add_argument("--no_cache", action='store_true')
parser.add_argument("--with_pure_y", action='store_true')
parser.add_argument("--with_h", action='store_true')
parser.add_argument("--only_l1", action='store_true', help="Only loss 1")
parser.add_argument("--interpolation", action='store_true', help="if interpolate between pure and reconstruction.")
parser.add_argument("--data_dir", type=str, default="data")
parser.add_argument("--cache_dir", type=str, default="train_cache")
parser.add_argument("--output_path", type=str, default="runs", help="model save")
parser.add_argument("--resume_from", type=str, default=None, help="resume training.")
parser.add_argument("--first_cache_index", type=int, default=0)
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
help="Device (cuda or cpu)")
parser.add_argument("--local_rank", type=int, default=-1,
help="Local rank for distributed training (-1: not distributed)")
parser.add_argument("--seed", type=int, default=43)
parser.add_argument("--debug", action='store_true')
args = parser.parse_args()
args.output_path = os.path.join(args.output_path, f'pilot_{args.pilot_version}')
args.cache_dir = os.path.join(args.data_dir, args.cache_dir)
# Setup CUDA, GPU & distributed training
args.distributed = (args.local_rank != -1)
if not args.distributed:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method='env://')
args.n_gpu = torch.cuda.device_count() if not args.distributed else 1
args.device = device
# Set seed
set_seed(args)
logger = setup_logger("trainer", distributed_rank=args.local_rank)
# Model construction
model = getattr(models, args.model)(args)
model = model.to(device)
optimizer = AdamW(model.parameters(), lr = args.lr, weight_decay=args.wd)
if args.loss_type == "MSELoss":
criterion = nn.MSELoss(reduction='sum').to(device)
else:
criterion = getattr(nn, args.loss_type, getattr(auxiliary, args.loss_type, None))().to(device)
criterion2 = nn.MSELoss(reduction='sum').to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
)
train_dataset = SIGDataset(args, data_type="train")
valid_dataset = SIGDataset(args, data_type="valid")
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None
valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None
train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, pin_memory=True, shuffle=(not args.distributed))
valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.valid_batch_size, pin_memory=True, shuffle=False)
lr_scheduler = None
if args.lr_scheduler == "linear":
lr_scheduler = PiecewiseLinear(optimizer, "lr", [(0, args.lr), (args.n_epochs * len(train_loader), 0.0)])
elif args.lr_scheduler == "cycle":
lr_scheduler = LinearCyclicalScheduler(optimizer, 'lr', 0.0, args.lr, args.eval_iter * len(train_loader))
elif args.lr_scheduler == "cosine":
lr_scheduler = CosineAnnealingScheduler(optimizer, 'lr', args.lr, 0.0, args.eval_iter * len(train_loader))
# Training function and trainer
def update(engine, batch):
model.train()
y, x_label, y_pure, H = train_dataset.prepare_batch(batch, device=args.device)
if args.with_pure_y and args.with_h:
x_pred, y_pure_pred, H_pred = model(y, pure=y_pure, H=H, opp=True)
loss_1 = criterion(x_pred, x_label) / args.gradient_accumulation_steps
if args.loss_type == "MSELoss":
loss_1 = loss_1 / x_pred.size(0)
loss_noise = criterion2(y_pure_pred, y_pure) / y.size(0) / args.gradient_accumulation_steps
loss_noise_h = criterion2(H_pred, H) / H.size(0) / args.gradient_accumulation_steps
if args.only_l1:
loss = loss_1
else:
loss = loss_1 + loss_noise * args.noise_lambda + loss_noise_h
output = (loss.item(), loss_1.item(), loss_noise.item(), loss_noise_h.item())
elif args.with_pure_y:
x_pred, y_pure_pred = model(y, pure=y_pure if args.interpolation else None, opp=True)
loss_1 = criterion(x_pred, x_label) / args.gradient_accumulation_steps
loss_noise = criterion2(y_pure_pred, y_pure) / y.size(0) / args.gradient_accumulation_steps
loss = loss_1 + loss_noise * args.noise_lambda
output = (loss.item(), loss_1.item(), loss_noise.item())
elif args.with_h:
x_pred, H_pred = model(y, opp=True)
loss_1 = criterion(x_pred, x_label) / args.gradient_accumulation_steps
loss_noise = criterion2(H_pred, H) / H.size(0) / args.gradient_accumulation_steps
loss = loss_1 + loss_noise * args.noise_lambda
output = (loss.item(), loss_1.item(), loss_noise.item())
else:
x_pred = model(y)
loss_1 = criterion(x_pred, x_label) / args.gradient_accumulation_steps
loss = loss_1
output = (loss.item(), loss_1.item(), torch.zeros_like(loss_1).item())
loss.backward()
if args.max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
if engine.state.iteration % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
return output
trainer = Engine(update)
to_save = {"trainer": trainer, "model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler}
metric_names = ["loss", "l1", "ln"]
if args.with_pure_y and args.with_h:
metric_names.append("lnH")
common.setup_common_training_handlers(
trainer=trainer,
train_sampler=train_loader.sampler,
to_save=to_save,
save_every_iters=len(train_loader) * args.save_iter,
lr_scheduler=lr_scheduler,
output_names=metric_names,
with_pbars=False,
clear_cuda_cache=False,
output_path=args.output_path,
n_saved=2,
)
resume_from = args.resume_from
if resume_from is not None:
checkpoint_fp = Path(resume_from)
assert checkpoint_fp.exists(), "Checkpoint '{}' is not found".format(checkpoint_fp.as_posix())
logger.info("Resume from a checkpoint: {}".format(checkpoint_fp.as_posix()))
checkpoint = torch.load(checkpoint_fp.as_posix(), map_location="cpu")
if args.reset_trainer:
to_save.pop("trainer")
checkpoint_to_load = to_save if 'validation' not in resume_from else {"model": model}
Checkpoint.load_objects(to_load=checkpoint_to_load, checkpoint=checkpoint)
if args.reset_lr_scheduler is not None:
if args.reset_lr_scheduler == "linear":
lr_scheduler = PiecewiseLinear(optimizer, "lr", [(0, args.lr), (args.n_epochs * len(train_loader), 0.0)])
elif args.reset_lr_scheduler == "cycle":
lr_scheduler = LinearCyclicalScheduler(optimizer, 'lr', 0.0, args.lr, args.eval_iter * len(train_loader))
elif args.reset_lr_scheduler == "cosine":
lr_scheduler = CosineAnnealingScheduler(optimizer, 'lr', args.lr, 0.0, args.eval_iter * len(train_loader))
metrics = {
"accuracy": Accuracy(lambda output: (torch.round(output[0][0]), output[1][0])),
"loss_1": Loss(criterion, output_transform=lambda output: (output[0][0], output[1][0])),
"loss_noise": Loss(criterion2, output_transform=lambda output: (output[0][1], output[1][1]))
}
if args.with_pure_y and args.with_h:
metrics["loss_noise_h"] = Loss(criterion2, output_transform=lambda output: (output[0][2], output[1][2]))
def _inference(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]:
model.eval()
with torch.no_grad():
x, y, x_pure, H = valid_dataset.prepare_batch(batch, device=args.device, non_blocking=True)
if args.with_pure_y and args.with_h:
y_pred, x_pure_pred, h_pred = model(x, opp=True)
outputs = (y_pred, x_pure_pred, h_pred), (y, x_pure, H)
elif args.with_pure_y:
y_pred, x_pure_pred = model(x, opp=True)
outputs = (y_pred, x_pure_pred), (y, x_pure)
elif args.with_h:
y_pred, h_pred = model(x, opp=True)
outputs = (y_pred, h_pred), (y, H)
else:
y_pred = model(x)
x_pure_pred = x_pure
outputs = (y_pred, x_pure_pred), (y, x_pure)
return outputs
evaluator = Engine(_inference)
for name, metric in metrics.items():
metric.attach(evaluator, name)
trainer.add_event_handler(Events.EPOCH_COMPLETED(every=args.eval_iter), lambda _: evaluator.run(valid_loader))
if args.flush_dataset > 0:
trainer.add_event_handler(Events.EPOCH_COMPLETED(every=args.n_epochs//args.flush_dataset),
lambda _: train_loader.dataset.reset() if args.no_cache else train_loader.dataset.reload())
# On the main process: add progress bar, tensorboard, checkpoints and save model, configuration and tokenizer before we start to train
if args.local_rank in [-1, 0]:
pbar = ProgressBar(persist=True)
pbar.attach(trainer, metric_names=metric_names, output_transform=lambda _: {"lr": f"{optimizer.param_groups[0]['lr']:.2e}"})
evaluator.add_event_handler(Events.COMPLETED, lambda _: pbar.log_message("Validation: %s" % pformat(evaluator.state.metrics)))
tb_logger = common.setup_tb_logging(args.output_path, trainer, optimizer, evaluators={'validation': evaluator}, log_every_iters=1)
# Store 3 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=DiskSaver(args.output_path, require_empty=False),
evaluator=evaluator,
models={"model": model},
metric_name="accuracy",
n_saved=3,
trainer=trainer,
tag="validation"
)
# Run the training
trainer.run(train_loader, max_epochs=args.n_epochs)
if args.local_rank in [-1, 0]:
tb_logger.close()
def training(local_rank, config, logger=None):
#
# if not getattr(config, "use_fp16", True):
# raise RuntimeError("This training script uses by default fp16 AMP")
torch.backends.cudnn.benchmark = True
set_seed(config.seed + local_rank)
train_loader, val_loader, train_eval_loader = config.train_loader, config.val_loader, config.train_eval_loader
# Setup model, optimizer, criterion
model, optimizer, criterion = initialize(config)
# Setup trainer for this specific task
trainer = create_trainer(model, optimizer, criterion, train_loader.sampler,
config, logger)
# Setup evaluators
num_classes = config.num_classes
cm_metric = ConfusionMatrix(num_classes=num_classes)
val_metrics = {
"IoU": IoU(cm_metric),
"mIoU_bg": mIoU(cm_metric),
}
if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
evaluator, train_evaluator = create_evaluators(model, val_metrics, config)
val_interval = getattr(config, "val_interval", 1)
@trainer.on(Events.EPOCH_COMPLETED(every=val_interval))
def run_validation():
epoch = trainer.state.epoch
state = train_evaluator.run(train_eval_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train",
state.metrics)
state = evaluator.run(val_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test",
state.metrics)
if config.num_epochs % val_interval != 0:
trainer.add_event_handler(Events.COMPLETED, run_validation)
if getattr(config, "start_by_validation", False):
trainer.add_event_handler(Events.STARTED, run_validation)
score_metric_name = "mIoU_bg"
if hasattr(config, "es_patience"):
common.add_early_stopping_by_val_score(config.es_patience,
evaluator,
trainer,
metric_name=score_metric_name)
# Store 3 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=get_save_handler(config),
evaluator=evaluator,
models=model,
metric_name=score_metric_name,
n_saved=3,
trainer=trainer,
tag="val",
)
if idist.get_rank() == 0:
tb_logger = common.setup_tb_logging(
config.output_path.as_posix(),
trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
},
)
exp_tracking_logger = tracking.setup_logging(trainer,
optimizer,
evaluators={
"training":
train_evaluator,
"validation":
evaluator
})
# Log validation predictions as images
# We define a custom event filter to log less frequently the images (to reduce storage size)
# - we plot images with masks of the middle validation batch
# - once every 3 validations and
# - at the end of the training
def custom_event_filter(_, val_iteration):
c1 = val_iteration == len(val_loader) // 2
c2 = trainer.state.epoch % (getattr(config, "val_interval", 1) *
3) == 0
c2 |= trainer.state.epoch == config.num_epochs
return c1 and c2
tb_logger.attach(
evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(
event_filter=custom_event_filter),
)
# Log confusion matrix to Trains:
trainer.run(train_loader, max_epochs=config.num_epochs)
if idist.get_rank() == 0:
tb_logger.close()
exp_tracking_logger.close()
def training(local_rank, config):
rank = idist.get_rank()
manual_seed(config["seed"] + rank)
device = idist.device()
logger = setup_logger(name="CIFAR10-QAT-Training",
distributed_rank=local_rank)
log_basic_info(logger, config)
output_path = config["output_path"]
if rank == 0:
now = datetime.now().strftime("%Y%m%d-%H%M%S")
folder_name = "{}_backend-{}-{}_{}".format(config["model"],
idist.backend(),
idist.get_world_size(), now)
output_path = Path(output_path) / folder_name
if not output_path.exists():
output_path.mkdir(parents=True)
config["output_path"] = output_path.as_posix()
logger.info("Output path: {}".format(config["output_path"]))
if "cuda" in device.type:
config["cuda device name"] = torch.cuda.get_device_name(local_rank)
# Setup dataflow, model, optimizer, criterion
train_loader, test_loader = get_dataflow(config)
config["num_iters_per_epoch"] = len(train_loader)
model, optimizer, criterion, lr_scheduler = initialize(config)
# Create trainer for current task
trainer = create_trainer(model, optimizer, criterion, lr_scheduler,
train_loader.sampler, config, logger)
# Let's now setup evaluator engine to perform model's validation and compute metrics
metrics = {
"Accuracy": Accuracy(),
"Loss": Loss(criterion),
}
# We define two evaluators as they wont have exactly similar roles:
# - `evaluator` will save the best model based on validation score
evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
train_evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
def run_validation(engine):
epoch = trainer.state.epoch
state = train_evaluator.run(train_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train",
state.metrics)
state = evaluator.run(test_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test",
state.metrics)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(every=config["validate_every"])
| Events.COMPLETED, run_validation)
if rank == 0:
# Setup TensorBoard logging on trainer and evaluators. Logged values are:
# - Training metrics, e.g. running average loss values
# - Learning rate
# - Evaluation train/test metrics
evaluators = {"training": train_evaluator, "test": evaluator}
tb_logger = common.setup_tb_logging(output_path,
trainer,
optimizer,
evaluators=evaluators)
# Store 3 best models by validation accuracy:
common.save_best_model_by_val_score(
output_path=config["output_path"],
evaluator=evaluator,
model=model,
metric_name="Accuracy",
n_saved=1,
trainer=trainer,
tag="test",
)
trainer.run(train_loader, max_epochs=config["num_epochs"])
if rank == 0:
tb_logger.close()
def training(config, local_rank, with_pbar_on_iters=True):
if not getattr(config, "use_fp16", True):
raise RuntimeError("This training script uses by default fp16 AMP")
set_seed(config.seed + local_rank)
torch.cuda.set_device(local_rank)
device = 'cuda'
torch.backends.cudnn.benchmark = True
train_loader = config.train_loader
train_sampler = getattr(train_loader, "sampler", None)
assert train_sampler is not None, "Train loader of type '{}' " \
"should have attribute 'sampler'".format(type(train_loader))
assert hasattr(train_sampler, 'set_epoch') and callable(train_sampler.set_epoch), \
"Train sampler should have a callable method `set_epoch`"
train_eval_loader = config.train_eval_loader
val_loader = config.val_loader
model = config.model.to(device)
optimizer = config.optimizer
model, optimizer = amp.initialize(model,
optimizer,
opt_level=getattr(
config, "fp16_opt_level", "O2"),
num_losses=1)
model = DDP(model, delay_allreduce=True)
criterion = config.criterion.to(device)
# Setup trainer
prepare_batch = getattr(config, "prepare_batch")
non_blocking = getattr(config, "non_blocking", True)
accumulation_steps = getattr(config, "accumulation_steps", 1)
model_output_transform = getattr(config, "model_output_transform",
lambda x: x)
def train_update_function(engine, batch):
model.train()
x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
y_pred = model(x)
y_pred = model_output_transform(y_pred)
loss = criterion(y_pred, y)
if isinstance(loss, Mapping):
assert 'supervised batch loss' in loss
loss_dict = loss
output = {k: v.item() for k, v in loss_dict.items()}
loss = loss_dict['supervised batch loss'] / accumulation_steps
else:
output = {'supervised batch loss': loss.item()}
with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss:
scaled_loss.backward()
if engine.state.iteration % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
return output
output_names = getattr(config, "output_names", [
'supervised batch loss',
])
trainer = Engine(train_update_function)
common.setup_common_distrib_training_handlers(
trainer,
train_sampler,
to_save={
'model': model,
'optimizer': optimizer
},
save_every_iters=1000,
output_path=config.output_path.as_posix(),
lr_scheduler=config.lr_scheduler,
output_names=output_names,
with_pbars=True,
with_pbar_on_iters=with_pbar_on_iters,
log_every_iters=1)
def output_transform(output):
return output['y_pred'], output['y']
num_classes = config.num_classes
cm_metric = ConfusionMatrix(num_classes=num_classes,
output_transform=output_transform)
pr = cmPrecision(cm_metric, average=False)
re = cmRecall(cm_metric, average=False)
val_metrics = {
"IoU": IoU(cm_metric),
"mIoU_bg": mIoU(cm_metric),
"Accuracy": cmAccuracy(cm_metric),
"Precision": pr,
"Recall": re,
"F1": Fbeta(beta=1.0, output_transform=output_transform)
}
if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
evaluator_args = dict(model=model,
metrics=val_metrics,
device=device,
non_blocking=non_blocking,
prepare_batch=prepare_batch,
output_transform=lambda x, y, y_pred: {
'y_pred': model_output_transform(y_pred),
'y': y
})
train_evaluator = create_supervised_evaluator(**evaluator_args)
evaluator = create_supervised_evaluator(**evaluator_args)
if dist.get_rank() == 0 and with_pbar_on_iters:
ProgressBar(persist=False,
desc="Train Evaluation").attach(train_evaluator)
ProgressBar(persist=False, desc="Val Evaluation").attach(evaluator)
def run_validation(engine):
train_evaluator.run(train_eval_loader)
evaluator.run(val_loader)
if getattr(config, "start_by_validation", False):
trainer.add_event_handler(Events.STARTED, run_validation)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(every=getattr(config, "val_interval", 1)),
run_validation)
trainer.add_event_handler(Events.COMPLETED, run_validation)
score_metric_name = "mIoU_bg"
if hasattr(config, "es_patience"):
common.add_early_stopping_by_val_score(config.es_patience,
evaluator,
trainer,
metric_name=score_metric_name)
if dist.get_rank() == 0:
tb_logger = common.setup_tb_logging(config.output_path.as_posix(),
trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
})
common.setup_mlflow_logging(trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
})
common.save_best_model_by_val_score(config.output_path.as_posix(),
evaluator,
model,
metric_name=score_metric_name,
trainer=trainer)
# Log train/val predictions:
tb_logger.attach(
evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(once=len(val_loader) // 2))
log_train_predictions = getattr(config, "log_train_predictions", False)
if log_train_predictions:
tb_logger.attach(train_evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(
once=len(train_eval_loader) // 2))
trainer.run(train_loader, max_epochs=config.num_epochs)
def training(config, local_rank, with_pbar_on_iters=True):
if not getattr(config, "use_fp16", True):
raise RuntimeError("This training script uses by default fp16 AMP")
set_seed(config.seed + local_rank)
torch.cuda.set_device(local_rank)
device = 'cuda'
torch.backends.cudnn.benchmark = True
train_loader = config.train_loader
train_sampler = getattr(train_loader, "sampler", None)
assert train_sampler is not None, "Train loader of type '{}' " \
"should have attribute 'sampler'".format(type(train_loader))
assert hasattr(train_sampler, 'set_epoch') and callable(train_sampler.set_epoch), \
"Train sampler should have a callable method `set_epoch`"
unsup_train_loader = config.unsup_train_loader
unsup_train_sampler = getattr(unsup_train_loader, "sampler", None)
assert unsup_train_sampler is not None, "Train loader of type '{}' " \
"should have attribute 'sampler'".format(type(unsup_train_loader))
assert hasattr(unsup_train_sampler, 'set_epoch') and callable(unsup_train_sampler.set_epoch), \
"Unsupervised train sampler should have a callable method `set_epoch`"
train_eval_loader = config.train_eval_loader
val_loader = config.val_loader
model = config.model.to(device)
optimizer = config.optimizer
model, optimizer = amp.initialize(model, optimizer, opt_level=getattr(config, "fp16_opt_level", "O2"), num_losses=2)
model = DDP(model, delay_allreduce=True)
criterion = config.criterion.to(device)
unsup_criterion = config.unsup_criterion.to(device)
unsup_batch_num_repetitions = getattr(config, "unsup_batch_num_repetitions", 1)
# Setup trainer
prepare_batch = getattr(config, "prepare_batch")
non_blocking = getattr(config, "non_blocking", True)
accumulation_steps = getattr(config, "accumulation_steps", 1)
model_output_transform = getattr(config, "model_output_transform", lambda x: x)
def cycle(seq):
while True:
for i in seq:
yield i
unsup_train_loader_iter = cycle(unsup_train_loader)
def supervised_loss(batch):
x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
y_pred = model(x)
y_pred = model_output_transform(y_pred)
loss = criterion(y_pred, y)
return loss
def unsupervised_loss(x):
with torch.no_grad():
y_pred_orig = model(x)
# Data augmentation: geom only
k = random.randint(1, 3)
x_aug = torch.rot90(x, k=k, dims=(2, 3))
y_pred_orig_aug = torch.rot90(y_pred_orig, k=k, dims=(2, 3))
if random.random() < 0.5:
x_aug = torch.flip(x_aug, dims=(2, ))
y_pred_orig_aug = torch.flip(y_pred_orig_aug, dims=(2, ))
if random.random() < 0.5:
x_aug = torch.flip(x_aug, dims=(3, ))
y_pred_orig_aug = torch.flip(y_pred_orig_aug, dims=(3, ))
y_pred_orig_aug = y_pred_orig_aug.argmax(dim=1).long()
y_pred_aug = model(x_aug.detach())
loss = unsup_criterion(y_pred_aug, y_pred_orig_aug.detach())
return loss
def train_update_function(engine, batch):
model.train()
loss = supervised_loss(batch)
if isinstance(loss, Mapping):
assert 'supervised batch loss' in loss
loss_dict = loss
output = {k: v.item() for k, v in loss_dict.items()}
loss = loss_dict['supervised batch loss'] / accumulation_steps
else:
output = {'supervised batch loss': loss.item()}
# Difference with original UDA
# Apply separately grads from supervised/unsupervised parts
with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss:
scaled_loss.backward()
if engine.state.iteration % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
unsup_batch = next(unsup_train_loader_iter)
unsup_x = unsup_batch['image']
unsup_x = convert_tensor(unsup_x, device=device, non_blocking=non_blocking)
for _ in range(unsup_batch_num_repetitions):
unsup_loss = engine.state.unsup_lambda * unsupervised_loss(unsup_x)
assert isinstance(unsup_loss, torch.Tensor)
output['unsupervised batch loss'] = unsup_loss.item()
with amp.scale_loss(unsup_loss, optimizer, loss_id=1) as scaled_loss:
scaled_loss.backward()
if engine.state.iteration % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
unsup_batch = None
unsup_x = None
total_loss = loss + unsup_loss
output['total batch loss'] = total_loss.item()
return output
output_names = getattr(config, "output_names",
['supervised batch loss', 'unsupervised batch loss', 'total batch loss'])
trainer = Engine(train_update_function)
@trainer.on(Events.STARTED)
def init(engine):
if hasattr(config, "unsup_lambda_min"):
engine.state.unsup_lambda = config.unsup_lambda_min
else:
engine.state.unsup_lambda = getattr(config, "unsup_lambda", 0.001)
@trainer.on(Events.ITERATION_COMPLETED)
def update_unsup_params(engine):
engine.state.unsup_lambda += getattr(config, "unsup_lambda_delta", 0.00001)
if hasattr(config, "unsup_lambda_max"):
m = config.unsup_lambda_max
engine.state.unsup_lambda = engine.state.unsup_lambda if engine.state.unsup_lambda < m else m
common.setup_common_distrib_training_handlers(
trainer, train_sampler,
to_save={'model': model, 'optimizer': optimizer},
save_every_iters=1000, output_path=config.output_path.as_posix(),
lr_scheduler=config.lr_scheduler, output_names=output_names,
with_pbars=True, with_pbar_on_iters=with_pbar_on_iters, log_every_iters=1
)
def output_transform(output):
return output['y_pred'], output['y']
num_classes = config.num_classes
cm_metric = ConfusionMatrix(num_classes=num_classes, output_transform=output_transform)
pr = cmPrecision(cm_metric, average=False)
re = cmRecall(cm_metric, average=False)
val_metrics = {
"IoU": IoU(cm_metric),
"mIoU_bg": mIoU(cm_metric),
"Accuracy": cmAccuracy(cm_metric),
"Precision": pr,
"Recall": re,
"F1": Fbeta(beta=1.0, output_transform=output_transform)
}
if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
evaluator_args = dict(
model=model, metrics=val_metrics, device=device, non_blocking=non_blocking, prepare_batch=prepare_batch,
output_transform=lambda x, y, y_pred: {'y_pred': model_output_transform(y_pred), 'y': y}
)
train_evaluator = create_supervised_evaluator(**evaluator_args)
evaluator = create_supervised_evaluator(**evaluator_args)
if dist.get_rank() == 0 and with_pbar_on_iters:
ProgressBar(persist=False, desc="Train Evaluation").attach(train_evaluator)
ProgressBar(persist=False, desc="Val Evaluation").attach(evaluator)
def run_validation(engine):
train_evaluator.run(train_eval_loader)
evaluator.run(val_loader)
if getattr(config, "start_by_validation", False):
trainer.add_event_handler(Events.STARTED, run_validation)
trainer.add_event_handler(Events.EPOCH_COMPLETED(every=getattr(config, "val_interval", 1)), run_validation)
trainer.add_event_handler(Events.COMPLETED, run_validation)
score_metric_name = "mIoU_bg"
if hasattr(config, "es_patience"):
common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name)
if dist.get_rank() == 0:
tb_logger = common.setup_tb_logging(config.output_path.as_posix(), trainer, optimizer,
evaluators={"training": train_evaluator, "validation": evaluator})
common.setup_mlflow_logging(trainer, optimizer,
evaluators={"training": train_evaluator, "validation": evaluator})
common.save_best_model_by_val_score(config.output_path.as_posix(), evaluator, model,
metric_name=score_metric_name, trainer=trainer)
# Log unsup_lambda
@trainer.on(Events.ITERATION_COMPLETED(every=100))
def tblog_unsupervised_lambda(engine):
tb_logger.writer.add_scalar("training/unsupervised lambda", engine.state.unsup_lambda, engine.state.iteration)
mlflow.log_metric("training unsupervised lambda", engine.state.unsup_lambda, step=engine.state.iteration)
# Log train/val predictions:
tb_logger.attach(evaluator,
log_handler=predictions_gt_images_handler(img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(once=len(val_loader) // 2))
log_train_predictions = getattr(config, "log_train_predictions", False)
if log_train_predictions:
tb_logger.attach(train_evaluator,
log_handler=predictions_gt_images_handler(img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(once=len(train_eval_loader) // 2))
trainer.run(train_loader, max_epochs=config.num_epochs)
def run_training(local_rank: int, config: ConfigSchema) -> Dict[str, float]:
rank = idist.get_rank()
if config.seed is not None:
manual_seed(config.seed + rank)
logger = setup_logger(name=config.experiment_name, distributed_rank=local_rank)
log_basic_info(logger, config)
if rank == 0:
prepare_output_directory(config)
logger.info("Output path: {}".format(config.output_path))
weak_label_mgr = get_weak_label_manager(config)
# Setup dataflow, model, optimizer, criterion
data_loaders = get_dataflow(config, weak_label_mgr)
train_loader = data_loaders["train"]
config.num_iters_per_epoch = len(train_loader)
model, optimizer, criterion = initialize(config, weak_label_mgr)
metrics = get_metrics(criterion)
trainer, evaluators = create_trainer_and_evaluators(
model, optimizer, criterion, data_loaders, metrics, config, logger
)
if rank == 0:
tb_logger = common.setup_tb_logging(
config.output_path, trainer, optimizer, evaluators=evaluators
)
# Store 3 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=get_save_handler(config),
evaluator=evaluators["val"],
models={"model": model},
metric_name="accuracy",
n_saved=3,
trainer=trainer,
tag="test",
)
state_at_best_val = StateAtBestVal(
score_function=lambda: evaluators["val"].state.metrics["accuracy"],
state_function=lambda: dict(
{"val_" + key: val for key, val in evaluators["val"].state.metrics.items()},
**{
"test_" + key: val
for key, val in evaluators["test"].state.metrics.items()
},
epoch=trainer.state.epoch
),
)
trainer.add_event_handler(Events.EPOCH_COMPLETED, state_at_best_val)
try:
trainer.run(train_loader, max_epochs=config.num_epochs)
except Exception:
import traceback
print(traceback.format_exc())
else:
assert state_at_best_val.best_state is not None
tb_logger.writer.add_hparams( # type: ignore
get_hparams(config),
{"hparam/" + key: val for key, val in state_at_best_val.best_state.items()},
)
finally:
if rank == 0:
tb_logger.close() # type: ignore
return evaluators["val"].state.metrics
def run(
data_path="/tmp/MNIST",
seed=3321,
mode="xentropy",
noise_fraction=0.35,
batch_size=64,
val_batch_size=1000,
num_epochs=50,
lr=0.01,
momentum=0.5,
as_pseudo_label=None,
log_dir="/tmp/output-bootstraping-loss/mnist/",
with_trains=False,
):
"""Training on noisy labels with bootstrapping
Args:
data_path (str): Path to MNIST dataset. Default, "/tmp/MNIST"
seed (int): Random seed to setup. Default, 3321
mode (str): Loss function mode: cross-entropy or bootstrapping (soft, hard).
Choices 'xentropy', 'soft_bootstrap', 'hard_bootstrap'.
noise_fraction (float): Label noise fraction. Default, 0.35.
batch_size (int): Input batch size for training. Default, 64.
val_batch_size (int): input batch size for validation. Default, 1000.
num_epochs (int): Number of epochs to train. Default, 50.
lr (float): Learning rate. Default, 0.01.
momentum (float): SGD momentum. Default, 0.5.
log_dir (str): Log directory for Tensorboard log output. Default="/tmp/output-bootstraping-loss/mnist/".
with_trains (bool): if True, experiment Trains logger is setup. Default, False.
"""
assert torch.cuda.is_available(), "Training should running on GPU"
device = "cuda"
manual_seed(seed)
logger = setup_logger(name="MNIST-Training")
now = datetime.now().strftime("%Y%m%d-%H%M%S")
# Setup output path
suffix = ""
if mode == "soft_bootstrap" and (as_pseudo_label is not None
and not as_pseudo_label):
suffix = "as_xreg"
output_path = Path(log_dir) / "train_{}_{}_{}_{}__{}".format(
mode, noise_fraction, suffix, now, num_epochs)
if not output_path.exists():
output_path.mkdir(parents=True)
parameters = {
"seed": seed,
"mode": mode,
"noise_fraction": noise_fraction,
"batch_size": batch_size,
"num_epochs": num_epochs,
"lr": lr,
"momentum": momentum,
"as_pseudo_label": as_pseudo_label,
}
log_basic_info(logger, parameters)
if with_trains:
from trains import Task
task = Task.init("BootstrappingLoss - Experiments on MNIST",
task_name=output_path.name)
# Log hyper parameters
task.connect(parameters)
train_loader, test_loader = get_data_loaders(data_path, noise_fraction,
batch_size, val_batch_size)
model = Net().to(device)
optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
if mode == 'xentropy':
criterion = nn.CrossEntropyLoss()
elif mode == 'soft_bootstrap':
if as_pseudo_label is None:
as_pseudo_label = True
criterion = SoftBootstrappingLoss(beta=0.95,
as_pseudo_label=as_pseudo_label)
elif mode == 'hard_bootstrap':
criterion = HardBootstrappingLoss(beta=0.8)
else:
raise ValueError(
"Wrong mode {}, expected: xentropy, soft_bootstrap or hard_bootstrap"
.format(mode))
trainer = create_supervised_trainer(model,
optimizer,
criterion,
device=device,
non_blocking=True)
metrics = {
"Accuracy": Accuracy(),
"{} loss".format(mode): Loss(criterion),
}
if mode is not "xentropy":
metrics["xentropy loss"] = Loss(nn.CrossEntropyLoss())
evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
train_evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
def run_validation(engine):
epoch = trainer.state.epoch
state = train_evaluator.run(train_loader)
log_metrics(logger, epoch, "Train", state.metrics)
state = evaluator.run(test_loader)
log_metrics(logger, epoch, "Test", state.metrics)
trainer.add_event_handler(Events.EPOCH_COMPLETED | Events.COMPLETED,
run_validation)
evaluators = {"training": train_evaluator, "test": evaluator}
tb_logger = common.setup_tb_logging(output_path,
trainer,
optimizer,
evaluators=evaluators)
trainer.run(train_loader, max_epochs=num_epochs)
test_acc = evaluator.state.metrics["Accuracy"]
tb_logger.writer.add_hparams(parameters,
{"hparam/test_accuracy": test_acc})
tb_logger.close()
return (mode, noise_fraction, as_pseudo_label, test_acc)
def training(local_rank, config, logger=None):
if not getattr(config, "use_fp16", True):
raise RuntimeError("This training script uses by default fp16 AMP")
torch.backends.cudnn.benchmark = True
set_seed(config.seed + local_rank)
train_loader, val_loader, train_eval_loader = config.train_loader, config.val_loader, config.train_eval_loader
# Setup model, optimizer, criterion
model, optimizer, criterion = initialize(config)
# Setup trainer for this specific task
trainer = create_trainer(model, optimizer, criterion, train_loader.sampler,
config, logger)
# Setup evaluators
num_classes = config.num_classes
cm_metric = ConfusionMatrix(num_classes=num_classes)
val_metrics = {
"IoU": IoU(cm_metric),
"mIoU_bg": mIoU(cm_metric),
}
if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
evaluator, train_evaluator = create_evaluators(model, val_metrics, config)
val_interval = getattr(config, "val_interval", 1)
@trainer.on(Events.EPOCH_COMPLETED(every=val_interval))
def run_validation():
epoch = trainer.state.epoch
state = train_evaluator.run(train_eval_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train",
state.metrics)
state = evaluator.run(val_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test",
state.metrics)
if config.num_epochs % val_interval != 0:
trainer.add_event_handler(Events.COMPLETED, run_validation)
if getattr(config, "start_by_validation", False):
trainer.add_event_handler(Events.STARTED, run_validation)
score_metric_name = "mIoU_bg"
if hasattr(config, "es_patience"):
common.add_early_stopping_by_val_score(config.es_patience,
evaluator,
trainer,
metric_name=score_metric_name)
# Store 3 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=get_save_handler(config),
evaluator=evaluator,
models=model,
metric_name=score_metric_name,
n_saved=3,
trainer=trainer,
tag="val",
)
if idist.get_rank() == 0:
tb_logger = common.setup_tb_logging(
config.output_path.as_posix(),
trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
},
)
if not exp_tracking.has_clearml:
exp_tracking_logger = exp_tracking.setup_logging(
trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
})
# Log validation predictions as images
# We define a custom event filter to log less frequently the images (to reduce storage size)
# - we plot images with masks of the middle validation batch
# - once every 3 validations and
# - at the end of the training
def custom_event_filter(_, val_iteration):
c1 = val_iteration == len(val_loader) // 2
c2 = trainer.state.epoch % (getattr(config, "val_interval", 1) *
3) == 0
c2 |= trainer.state.epoch == config.num_epochs
return c1 and c2
tb_logger.attach(
evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(
event_filter=custom_event_filter),
)
# Log confusion matrix to ClearML:
if exp_tracking.has_clearml:
@trainer.on(Events.COMPLETED)
def compute_and_log_cm():
cm = cm_metric.compute()
# CM: values are normalized such that diagonal values represent class recalls
cm = ConfusionMatrix.normalize(cm, "recall").cpu().numpy()
if idist.get_rank() == 0:
try:
from clearml import Task
except ImportError:
# Backwards-compatibility for legacy Trains SDK
from trains import Task
clearml_logger = Task.current_task().get_logger()
clearml_logger.report_confusion_matrix(
title="Final Confusion Matrix",
series="cm-preds-gt",
matrix=cm,
iteration=trainer.state.iteration,
xlabels=VOCSegmentationOpencv.target_names,
ylabels=VOCSegmentationOpencv.target_names,
)
trainer.run(train_loader, max_epochs=config.num_epochs)
if idist.get_rank() == 0:
tb_logger.close()
if not exp_tracking.has_clearml:
exp_tracking_logger.close()
def training(local_rank, config, logger=None):
if not getattr(config, "use_fp16", True):
raise RuntimeError("This training script uses by default fp16 AMP")
torch.backends.cudnn.benchmark = True
set_seed(config.seed + local_rank)
train_loader, val_loader, train_eval_loader = config.train_loader, config.val_loader, config.train_eval_loader
# Setup model, optimizer, criterion
model, optimizer, criterion = initialize(config)
if not hasattr(config, "prepare_batch"):
config.prepare_batch = _prepare_batch
# Setup trainer for this specific task
trainer = create_trainer(model, optimizer, criterion, train_loader.sampler,
config, logger)
if getattr(config, "benchmark_dataflow", False):
benchmark_dataflow_num_iters = getattr(config,
"benchmark_dataflow_num_iters",
1000)
DataflowBenchmark(benchmark_dataflow_num_iters,
prepare_batch=config.prepare_batch).attach(
trainer, train_loader)
# Setup evaluators
val_metrics = {
"Accuracy": Accuracy(),
"Top-5 Accuracy": TopKCategoricalAccuracy(k=5),
}
if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict):
val_metrics.update(config.val_metrics)
evaluator, train_evaluator = create_evaluators(model, val_metrics, config)
@trainer.on(
Events.EPOCH_COMPLETED(every=getattr(config, "val_interval", 1))
| Events.COMPLETED)
def run_validation():
epoch = trainer.state.epoch
state = train_evaluator.run(train_eval_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train",
state.metrics)
state = evaluator.run(val_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test",
state.metrics)
if getattr(config, "start_by_validation", False):
trainer.add_event_handler(Events.STARTED, run_validation)
score_metric_name = "Accuracy"
if hasattr(config, "es_patience"):
common.add_early_stopping_by_val_score(config.es_patience,
evaluator,
trainer,
metric_name=score_metric_name)
# Store 3 best models by validation accuracy:
common.save_best_model_by_val_score(
config.output_path.as_posix(),
evaluator,
model=model,
metric_name=score_metric_name,
n_saved=3,
trainer=trainer,
tag="val",
)
if idist.get_rank() == 0:
tb_logger = common.setup_tb_logging(
config.output_path.as_posix(),
trainer,
optimizer,
evaluators={
"training": train_evaluator,
"validation": evaluator
},
)
exp_tracking_logger = exp_tracking.setup_logging(trainer,
optimizer,
evaluators={
"training":
train_evaluator,
"validation":
evaluator
})
# Log train/val predictions:
tb_logger.attach(
evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="validation"),
event_name=Events.ITERATION_COMPLETED(once=len(val_loader) // 2),
)
tb_logger.attach(
train_evaluator,
log_handler=predictions_gt_images_handler(
img_denormalize_fn=config.img_denormalize,
n_images=15,
another_engine=trainer,
prefix_tag="training"),
event_name=Events.ITERATION_COMPLETED(
once=len(train_eval_loader) // 2),
)
trainer.run(train_loader, max_epochs=config.num_epochs)
if idist.get_rank() == 0:
tb_logger.close()
exp_tracking_logger.close()
def training(local_rank, config):
config["device"] = "cuda" if config["active_gpu_ids"] else "cpu"
rank = idist.get_rank()
manual_seed(config["seed"] + rank)
device = idist.device()
logger = setup_logger(name="Carbon Black Semantic Segmentation Training",
distributed_rank=local_rank)
log_basic_info(logger, config)
output_path = config["output_path"]
if rank == 0:
if config["stop_iteration"] is None:
now = utils.get_time_stamp()
else:
now = f"stop-on-{config['stop_iteration']}"
folder_name = (
f"{config['architecture']}-{config['encoder']}-{config['encoder_weights']}_"
f"backend-{idist.backend()}-{idist.get_world_size()}_{now}")
output_path = Path(output_path) / folder_name
output_path.mkdir(parents=True, exist_ok=True)
config["output_path"] = output_path.as_posix()
config["task_name"] = output_path.stem
logger.info(f"Output path: {output_path}")
if "cuda" in idist.device().type:
config["cuda_device_name"] = torch.cuda.get_device_name(local_rank)
setup_trains_logging(config)
dataloader_train, dataloader_val = get_dataloaders(config)
config["num_iterations_per_epoch"] = len(dataloader_train)
config["num_epochs"] = round(config["num_iterations"] /
config["num_iterations_per_epoch"])
model = modeling.get_model(config)
optimizer = get_optimizer(model, config)
loss = get_loss()
lr_scheduler = get_lr_scheduler(optimizer, config)
trainer = create_trainer(model, optimizer, loss, lr_scheduler,
dataloader_train.sampler, config, logger)
metrics = get_metrics(loss)
# We define two evaluators as they wont have exactly similar roles:
# - `evaluator` will save the best model based on validation score
evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
evaluator_train = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
if rank == 0:
# Setup TensorBoard logging on trainer and evaluators. Logged values are:
# - Training metrics, e.g. running average loss values
# - Learning rate
# - Evaluation train/test metrics
evaluators = {"training": evaluator_train, "validation": evaluator}
tb_logger = common.setup_tb_logging(output_path,
trainer,
optimizer,
evaluators=evaluators)
example_prediction_logger = ExamplePredictionLogger(
tb_logger, model, device)
def run_validation(engine):
epoch = trainer.state.epoch
state = evaluator_train.run(dataloader_train)
data_subset = "Train"
log_metrics(logger, epoch, state.times["COMPLETED"], data_subset,
state.metrics)
log_confusion_matrix(tb_logger, epoch, data_subset, state.metrics)
state = evaluator.run(dataloader_val)
data_subset = "Val"
log_metrics(logger, epoch, state.times["COMPLETED"], data_subset,
state.metrics)
log_confusion_matrix(tb_logger, epoch, data_subset, state.metrics)
example_prediction_logger.log_visualization(dataloader_val.dataset,
epoch)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(every=config["validate_every"])
| Events.COMPLETED, run_validation)
# Store 3 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=get_save_handler(config),
evaluator=evaluator,
models={"model": model},
metric_name="accuracy",
n_saved=3,
trainer=trainer,
tag="validation",
)
# TODO: Add early stopping
# In order to check training resuming we can stop training on a given iteration
if config["stop_iteration"] is not None:
@trainer.on(Events.ITERATION_STARTED(once=config["stop_iteration"]))
def _():
logger.info(
f"Stop training on {trainer.state.iteration} iteration")
trainer.terminate()
# noinspection PyBroadException
try:
trainer.run(dataloader_train, max_epochs=config["num_epochs"])
except Exception:
import traceback
print(traceback.format_exc())
if rank == 0:
# noinspection PyUnboundLocalVariable
tb_logger.close()
def training(local_rank, config):
rank = idist.get_rank()
manual_seed(config["seed"] + rank)
device = idist.device()
logger = setup_logger(name="ImageNet-Training",
distributed_rank=local_rank)
log_basic_info(logger, config)
output_path = config["output_path"]
if rank == 0:
if config["stop_iteration"] is None:
now = datetime.now().strftime("%Y%m%d-%H%M%S")
else:
now = "stop-on-{}".format(config["stop_iteration"])
folder_name = "{}_backend-{}-{}_{}".format(config["model"],
idist.backend(),
idist.get_world_size(), now)
output_path = Path(output_path) / folder_name
if not output_path.exists():
output_path.mkdir(parents=True)
config["output_path"] = output_path.as_posix()
logger.info("Output path: {}".format(config["output_path"]))
if "cuda" in device.type:
config["cuda device name"] = torch.cuda.get_device_name(local_rank)
# Setup dataflow, model, optimizer, criterion
train_loader, test_loader = get_imagenet_dataloader(config)
config["num_iters_per_epoch"] = len(train_loader)
model, optimizer, criterion, lr_scheduler = initialize(config)
# Create trainer for current task
trainer = create_supervised_trainer(model, optimizer, criterion,
lr_scheduler, train_loader.sampler,
config, logger)
# Let's now setup evaluator engine to perform model's validation and compute metrics
metrics = {
"accuracy": Accuracy(),
"loss": Loss(criterion),
}
# We define two evaluators as they wont have exactly similar roles:
# - `evaluator` will save the best model based on validation score
evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
train_evaluator = create_supervised_evaluator(model,
metrics=metrics,
device=device,
non_blocking=True)
def run_validation(engine):
epoch = trainer.state.epoch
state = train_evaluator.run(train_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Train",
state.metrics)
state = evaluator.run(test_loader)
log_metrics(logger, epoch, state.times["COMPLETED"], "Test",
state.metrics)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(every=config["validate_every"])
| Events.COMPLETED, run_validation)
if rank == 0:
# Setup TensorBoard logging on trainer and evaluators. Logged values are:
# - Training metrics, e.g. running average loss values
# - Learning rate
# - Evaluation train/test metrics
evaluators = {"training": train_evaluator, "test": evaluator}
tb_logger = common.setup_tb_logging(output_path,
trainer,
optimizer,
evaluators=evaluators)
# Store 3 best models by validation accuracy:
common.gen_save_best_models_by_val_score(
save_handler=get_save_handler(config),
evaluator=evaluator,
models={"model": model},
metric_name="accuracy",
n_saved=3,
trainer=trainer,
tag="test",
)
# In order to check training resuming we can stop training on a given iteration
if config["stop_iteration"] is not None:
@trainer.on(Events.ITERATION_STARTED(once=config["stop_iteration"]))
def _():
logger.info("Stop training on {} iteration".format(
trainer.state.iteration))
trainer.terminate()
@trainer.on(Events.ITERATION_COMPLETED(every=20))
def print_acc(engine):
if rank == 0:
print("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}"\
.format(engine.state.epoch, engine.state.iteration, len(train_loader),
engine.state.saved_batch_loss
))
try:
trainer.run(train_loader, max_epochs=config["num_epochs"])
except Exception as e:
import traceback
print(traceback.format_exc())
if rank == 0:
tb_logger.close()
def create_trainer(
train_step,
output_names,
model,
ema_model,
optimizer,
lr_scheduler,
supervised_train_loader,
test_loader,
cfg,
logger,
cta=None,
unsup_train_loader=None,
cta_probe_loader=None,
):
trainer = Engine(train_step)
trainer.logger = logger
output_path = os.getcwd()
to_save = {
"model": model,
"ema_model": ema_model,
"optimizer": optimizer,
"trainer": trainer,
"lr_scheduler": lr_scheduler,
}
if cta is not None:
to_save["cta"] = cta
common.setup_common_training_handlers(
trainer,
train_sampler=supervised_train_loader.sampler,
to_save=to_save,
save_every_iters=cfg.solver.checkpoint_every,
output_path=output_path,
output_names=output_names,
lr_scheduler=lr_scheduler,
with_pbars=False,
clear_cuda_cache=False,
)
ProgressBar(persist=False).attach(
trainer, metric_names="all", event_name=Events.ITERATION_COMPLETED
)
unsupervised_train_loader_iter = None
if unsup_train_loader is not None:
unsupervised_train_loader_iter = cycle(unsup_train_loader)
cta_probe_loader_iter = None
if cta_probe_loader is not None:
cta_probe_loader_iter = cycle(cta_probe_loader)
# Setup handler to prepare data batches
@trainer.on(Events.ITERATION_STARTED)
def prepare_batch(e):
sup_batch = e.state.batch
e.state.batch = {
"sup_batch": sup_batch,
}
if unsupervised_train_loader_iter is not None:
unsup_batch = next(unsupervised_train_loader_iter)
e.state.batch["unsup_batch"] = unsup_batch
if cta_probe_loader_iter is not None:
cta_probe_batch = next(cta_probe_loader_iter)
cta_probe_batch["policy"] = [
deserialize(p) for p in cta_probe_batch["policy"]
]
e.state.batch["cta_probe_batch"] = cta_probe_batch
# Setup handler to update EMA model
@trainer.on(Events.ITERATION_COMPLETED, cfg.ema_decay)
def update_ema_model(ema_decay):
# EMA on parametes
for ema_param, param in zip(ema_model.parameters(), model.parameters()):
ema_param.data.mul_(ema_decay).add_(param.data, alpha=1.0 - ema_decay)
# Setup handlers for debugging
if cfg.debug:
@trainer.on(Events.STARTED | Events.ITERATION_COMPLETED(every=100))
@idist.one_rank_only()
def log_weights_norms():
wn = []
ema_wn = []
for ema_param, param in zip(ema_model.parameters(), model.parameters()):
wn.append(torch.mean(param.data))
ema_wn.append(torch.mean(ema_param.data))
msg = "\n\nWeights norms"
msg += "\n- Raw model: {}".format(
to_list_str(torch.tensor(wn[:10] + wn[-10:]))
)
msg += "\n- EMA model: {}\n".format(
to_list_str(torch.tensor(ema_wn[:10] + ema_wn[-10:]))
)
logger.info(msg)
rmn = []
rvar = []
ema_rmn = []
ema_rvar = []
for m1, m2 in zip(model.modules(), ema_model.modules()):
if isinstance(m1, nn.BatchNorm2d) and isinstance(m2, nn.BatchNorm2d):
rmn.append(torch.mean(m1.running_mean))
rvar.append(torch.mean(m1.running_var))
ema_rmn.append(torch.mean(m2.running_mean))
ema_rvar.append(torch.mean(m2.running_var))
msg = "\n\nBN buffers"
msg += "\n- Raw mean: {}".format(to_list_str(torch.tensor(rmn[:10])))
msg += "\n- Raw var: {}".format(to_list_str(torch.tensor(rvar[:10])))
msg += "\n- EMA mean: {}".format(to_list_str(torch.tensor(ema_rmn[:10])))
msg += "\n- EMA var: {}\n".format(to_list_str(torch.tensor(ema_rvar[:10])))
logger.info(msg)
# TODO: Need to inspect a bug
# if idist.get_rank() == 0:
# from ignite.contrib.handlers import ProgressBar
#
# profiler = BasicTimeProfiler()
# profiler.attach(trainer)
#
# @trainer.on(Events.ITERATION_COMPLETED(every=200))
# def log_profiling(_):
# results = profiler.get_results()
# profiler.print_results(results)
# Setup validation engine
metrics = {
"accuracy": Accuracy(),
}
if not (idist.has_xla_support and idist.backend() == idist.xla.XLA_TPU):
metrics.update({
"precision": Precision(average=False),
"recall": Recall(average=False),
})
eval_kwargs = dict(
metrics=metrics,
prepare_batch=sup_prepare_batch,
device=idist.device(),
non_blocking=True,
)
evaluator = create_supervised_evaluator(model, **eval_kwargs)
ema_evaluator = create_supervised_evaluator(ema_model, **eval_kwargs)
def log_results(epoch, max_epochs, metrics, ema_metrics):
msg1 = "\n".join(
["\t{:16s}: {}".format(k, to_list_str(v)) for k, v in metrics.items()]
)
msg2 = "\n".join(
["\t{:16s}: {}".format(k, to_list_str(v)) for k, v in ema_metrics.items()]
)
logger.info(
"\nEpoch {}/{}\nRaw:\n{}\nEMA:\n{}\n".format(epoch, max_epochs, msg1, msg2)
)
if cta is not None:
logger.info("\n" + stats(cta))
@trainer.on(
Events.EPOCH_COMPLETED(every=cfg.solver.validate_every)
| Events.STARTED
| Events.COMPLETED
)
def run_evaluation():
evaluator.run(test_loader)
ema_evaluator.run(test_loader)
log_results(
trainer.state.epoch,
trainer.state.max_epochs,
evaluator.state.metrics,
ema_evaluator.state.metrics,
)
# setup TB logging
if idist.get_rank() == 0:
tb_logger = common.setup_tb_logging(
output_path,
trainer,
optimizers=optimizer,
evaluators={"validation": evaluator, "ema validation": ema_evaluator},
log_every_iters=15,
)
if cfg.online_exp_tracking.wandb:
from ignite.contrib.handlers import WandBLogger
wb_dir = Path("/tmp/output-fixmatch-wandb")
if not wb_dir.exists():
wb_dir.mkdir()
_ = WandBLogger(
project="fixmatch-pytorch",
name=cfg.name,
config=cfg,
sync_tensorboard=True,
dir=wb_dir.as_posix(),
reinit=True,
)
resume_from = cfg.solver.resume_from
if resume_from is not None:
resume_from = list(Path(resume_from).rglob("training_checkpoint*.pt*"))
if len(resume_from) > 0:
# get latest
checkpoint_fp = max(resume_from, key=lambda p: p.stat().st_mtime)
assert checkpoint_fp.exists(), "Checkpoint '{}' is not found".format(
checkpoint_fp.as_posix()
)
logger.info("Resume from a checkpoint: {}".format(checkpoint_fp.as_posix()))
checkpoint = torch.load(checkpoint_fp.as_posix())
Checkpoint.load_objects(to_load=to_save, checkpoint=checkpoint)
@trainer.on(Events.COMPLETED)
def release_all_resources():
nonlocal unsupervised_train_loader_iter, cta_probe_loader_iter
if idist.get_rank() == 0:
tb_logger.close()
if unsupervised_train_loader_iter is not None:
unsupervised_train_loader_iter = None
if cta_probe_loader_iter is not None:
cta_probe_loader_iter = None
return trainer
def get_logger(
config: Any,
trainer: Engine,
evaluator: Optional[Union[Engine, Dict[str, Engine]]] = None,
optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
**kwargs: Any,
) -> Optional[BaseLogger]:
"""Get Ignite provided logger.
Parameters
----------
config
Config object for setting up loggers
`config` has to contain
- `filepath`: logging path to output file
- `logger_log_every_iters`: logging iteration interval for loggers
trainer
trainer engine
evaluator
evaluator engine
optimizers
optimizers to log optimizer parameters
kwargs
optional keyword arguments passed to the logger
Returns
-------
logger_handler
Ignite provided logger instance
"""
{% if logger_deps == 'clearml' %}
logger_handler = common.setup_clearml_logging(
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% elif logger_deps == 'mlflow' %}
logger_handler = common.setup_mlflow_logging(
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% elif logger_deps == 'neptune-client' %}
logger_handler = common.setup_neptune_logging(
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% elif logger_deps == 'polyaxon-client' %}
logger_handler = common.setup_plx_logging(
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% elif logger_deps == 'tensorboard' %}
logger_handler = common.setup_tb_logging(
output_path=config.output_dir,
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% elif logger_deps == 'visdom' %}
logger_handler = common.setup_visdom_logging(
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% elif logger_deps == 'wandb' %}
logger_handler = common.setup_wandb_logging(
trainer=trainer,
optimizers=optimizers,
evaluators=evaluator,
log_every_iters=config.logger_log_every_iters,
**kwargs,
)
{% else %}
logger_handler = None
{% endif %}
return logger_handler
