def run(*options, cfg=None, debug=False):
"""Run training and validation of model
Notes:
Options can be passed in via the options argument and loaded from the cfg file
Options from default.py will be overridden by options loaded from cfg file
Options from default.py will be overridden by options loaded from cfg file
Options passed in via options argument will override option loaded from cfg file
Args:
*options (str,int ,optional): Options used to overide what is loaded from the
config. To see what options are available consult
default.py
cfg (str, optional): Location of config file to load. Defaults to None.
debug (bool): Places scripts in debug/test mode and only executes a few iterations
"""
# Configuration:
update_config(config, options=options, config_file=cfg)
# The model will be saved under: outputs/<config_file_name>/<model_dir>
config_file_name = "default_config" if not cfg else cfg.split("/")[-1].split(".")[0]
try:
output_dir = generate_path(
config.OUTPUT_DIR, git_branch(), git_hash(), config_file_name, config.TRAIN.MODEL_DIR, current_datetime(),
)
except:
output_dir = generate_path(config.OUTPUT_DIR, config_file_name, config.TRAIN.MODEL_DIR, current_datetime(),)
# Logging:
load_log_configuration(config.LOG_CONFIG)
logger = logging.getLogger(__name__)
logger.debug(config.WORKERS)
# Set CUDNN benchmark mode:
torch.backends.cudnn.benchmark = config.CUDNN.BENCHMARK
# We will write the model under outputs / config_file_name / model_dir
config_file_name = "default_config" if not cfg else cfg.split("/")[-1].split(".")[0]
# Fix random seeds:
torch.manual_seed(config.SEED)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(seed=config.SEED)
# Augmentation:
basic_aug = Compose(
[
Normalize(mean=(config.TRAIN.MEAN,), std=(config.TRAIN.STD,), max_pixel_value=1),
PadIfNeeded(
min_height=config.TRAIN.PATCH_SIZE,
min_width=config.TRAIN.PATCH_SIZE,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=255,
value=0,
),
Resize(
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT, config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH, always_apply=True,
),
PadIfNeeded(
min_height=config.TRAIN.AUGMENTATIONS.PAD.HEIGHT,
min_width=config.TRAIN.AUGMENTATIONS.PAD.WIDTH,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=255,
),
]
)
if config.TRAIN.AUGMENTATION:
train_aug = Compose([basic_aug, HorizontalFlip(p=0.5)])
val_aug = basic_aug
else:
train_aug = val_aug = basic_aug
# Training and Validation Loaders:
TrainPatchLoader = get_patch_loader(config)
logging.info(f"Using {TrainPatchLoader}")
train_set = TrainPatchLoader(
config.DATASET.ROOT,
config.DATASET.NUM_CLASSES,
split="train",
is_transform=True,
stride=config.TRAIN.STRIDE,
patch_size=config.TRAIN.PATCH_SIZE,
augmentations=train_aug,
debug=debug,
)
logger.info(train_set)
n_classes = train_set.n_classes
val_set = TrainPatchLoader(
config.DATASET.ROOT,
config.DATASET.NUM_CLASSES,
split="val",
is_transform=True,
stride=config.TRAIN.STRIDE,
patch_size=config.TRAIN.PATCH_SIZE,
augmentations=val_aug,
debug=debug,
)
logger.info(val_set)
if debug:
logger.info("Running in debug mode..")
train_set = data.Subset(train_set, range(config.TRAIN.BATCH_SIZE_PER_GPU * config.NUM_DEBUG_BATCHES))
val_set = data.Subset(val_set, range(config.VALIDATION.BATCH_SIZE_PER_GPU))
train_loader = data.DataLoader(
train_set, batch_size=config.TRAIN.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS, shuffle=True
)
val_loader = data.DataLoader(
val_set, batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU, num_workers=1
) # config.WORKERS)
# Model:
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# Optimizer and LR Scheduler:
optimizer = torch.optim.SGD(
model.parameters(),
lr=config.TRAIN.MAX_LR,
momentum=config.TRAIN.MOMENTUM,
weight_decay=config.TRAIN.WEIGHT_DECAY,
)
epochs_per_cycle = config.TRAIN.END_EPOCH // config.TRAIN.SNAPSHOTS
snapshot_duration = epochs_per_cycle * len(train_loader) if not debug else 2 * len(train_loader)
scheduler = CosineAnnealingScheduler(
optimizer, "lr", config.TRAIN.MAX_LR, config.TRAIN.MIN_LR, cycle_size=snapshot_duration
)
# Tensorboard writer:
summary_writer = create_summary_writer(log_dir=path.join(output_dir, "logs"))
# class weights are inversely proportional to the frequency of the classes in the training set
class_weights = torch.tensor(config.DATASET.CLASS_WEIGHTS, device=device, requires_grad=False)
# Loss:
criterion = torch.nn.CrossEntropyLoss(weight=class_weights, ignore_index=255, reduction="mean")
# Ignite trainer and evaluator:
trainer = create_supervised_trainer(model, optimizer, criterion, prepare_batch, device=device)
transform_fn = lambda output_dict: (output_dict["y_pred"].squeeze(), output_dict["mask"].squeeze())
evaluator = create_supervised_evaluator(
model,
prepare_batch,
metrics={
"nll": Loss(criterion, output_transform=transform_fn),
"pixacc": pixelwise_accuracy(n_classes, output_transform=transform_fn, device=device),
"cacc": class_accuracy(n_classes, output_transform=transform_fn),
"mca": mean_class_accuracy(n_classes, output_transform=transform_fn),
"ciou": class_iou(n_classes, output_transform=transform_fn),
"mIoU": mean_iou(n_classes, output_transform=transform_fn),
},
device=device,
)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
# Logging:
trainer.add_event_handler(
Events.ITERATION_COMPLETED, logging_handlers.log_training_output(log_interval=config.PRINT_FREQ),
)
trainer.add_event_handler(Events.EPOCH_COMPLETED, logging_handlers.log_lr(optimizer))
# Tensorboard and Logging:
trainer.add_event_handler(Events.ITERATION_COMPLETED, tensorboard_handlers.log_training_output(summary_writer))
trainer.add_event_handler(Events.ITERATION_COMPLETED, tensorboard_handlers.log_validation_output(summary_writer))
# add specific logger which also triggers printed metrics on training set
@trainer.on(Events.EPOCH_COMPLETED)
def log_training_results(engine):
evaluator.run(train_loader)
tensorboard_handlers.log_results(engine, evaluator, summary_writer, n_classes, stage="Training")
logging_handlers.log_metrics(engine, evaluator, stage="Training")
# add specific logger which also triggers printed metrics on validation set
@trainer.on(Events.EPOCH_COMPLETED)
def log_validation_results(engine):
evaluator.run(val_loader)
tensorboard_handlers.log_results(engine, evaluator, summary_writer, n_classes, stage="Validation")
logging_handlers.log_metrics(engine, evaluator, stage="Validation")
# dump validation set metrics at the very end for debugging purposes
if engine.state.epoch == config.TRAIN.END_EPOCH and debug:
fname = f"metrics_{config_file_name}_{config.TRAIN.MODEL_DIR}.json"
metrics = evaluator.state.metrics
out_dict = {x: metrics[x] for x in ["nll", "pixacc", "mca", "mIoU"]}
with open(fname, "w") as fid:
json.dump(out_dict, fid)
log_msg = " ".join(f"{k}: {out_dict[k]}" for k in out_dict.keys())
logging.info(log_msg)
# Checkpointing: snapshotting trained models to disk
checkpoint_handler = SnapshotHandler(
output_dir,
config.MODEL.NAME,
extract_metric_from("mIoU"),
lambda: (trainer.state.iteration % snapshot_duration) == 0,
)
evaluator.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler, {"model": model})
logger.info("Starting training")
trainer.run(train_loader, max_epochs=config.TRAIN.END_EPOCH, epoch_length=len(train_loader), seed=config.SEED)
summary_writer.close()
def run(*options, cfg=None, debug=False):
"""Run training and validation of model
Notes:
Options can be passed in via the options argument and loaded from the cfg file
Options from default.py will be overridden by options loaded from cfg file
Options passed in via options argument will override option loaded from cfg file
Args:
*options (str,int ,optional): Options used to overide what is loaded from the
config. To see what options are available consult
default.py
cfg (str, optional): Location of config file to load. Defaults to None.
"""
update_config(config, options=options, config_file=cfg)
# Start logging
load_log_configuration(config.LOG_CONFIG)
logger = logging.getLogger(__name__)
logger.debug(config.WORKERS)
scheduler_step = config.TRAIN.END_EPOCH // config.TRAIN.SNAPSHOTS
torch.backends.cudnn.benchmark = config.CUDNN.BENCHMARK
torch.manual_seed(config.SEED)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(seed=config.SEED)
# Setup Augmentations
basic_aug = Compose([
Normalize(mean=(config.TRAIN.MEAN, ),
std=(config.TRAIN.STD, ),
max_pixel_value=1)
])
if config.TRAIN.AUGMENTATION:
train_aug = Compose([basic_aug, HorizontalFlip(p=0.5)])
val_aug = basic_aug
else:
train_aug = val_aug = basic_aug
TrainLoader = get_section_loader(config)
train_set = TrainLoader(
data_dir=config.DATASET.ROOT,
split="train",
is_transform=True,
augmentations=train_aug,
)
val_set = TrainLoader(
data_dir=config.DATASET.ROOT,
split="val",
is_transform=True,
augmentations=val_aug,
)
class CustomSampler(torch.utils.data.Sampler):
def __init__(self, data_source):
self.data_source = data_source
def __iter__(self):
char = ["i" if np.random.randint(2) == 1 else "x"]
self.indices = [
idx for (idx, name) in enumerate(self.data_source)
if char[0] in name
]
return (self.indices[i] for i in torch.randperm(len(self.indices)))
def __len__(self):
return len(self.data_source)
n_classes = train_set.n_classes
val_list = val_set.sections
train_list = val_set.sections
train_loader = data.DataLoader(
train_set,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
sampler=CustomSampler(train_list),
num_workers=config.WORKERS,
shuffle=False,
)
val_loader = data.DataLoader(
val_set,
batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU,
sampler=CustomSampler(val_list),
num_workers=config.WORKERS,
)
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
model = model.to(device) # Send to GPU
optimizer = torch.optim.SGD(
model.parameters(),
lr=config.TRAIN.MAX_LR,
momentum=config.TRAIN.MOMENTUM,
weight_decay=config.TRAIN.WEIGHT_DECAY,
)
try:
output_dir = generate_path(
config.OUTPUT_DIR,
git_branch(),
git_hash(),
config.MODEL.NAME,
current_datetime(),
)
except TypeError:
output_dir = generate_path(
config.OUTPUT_DIR,
config.MODEL.NAME,
current_datetime(),
)
summary_writer = create_summary_writer(
log_dir=path.join(output_dir, config.LOG_DIR))
snapshot_duration = scheduler_step * len(train_loader)
scheduler = CosineAnnealingScheduler(optimizer, "lr", config.TRAIN.MAX_LR,
config.TRAIN.MIN_LR,
snapshot_duration)
# weights are inversely proportional to the frequency of the classes in
# the training set
class_weights = torch.tensor(config.DATASET.CLASS_WEIGHTS,
device=device,
requires_grad=False)
criterion = torch.nn.CrossEntropyLoss(weight=class_weights,
ignore_index=255,
reduction="mean")
trainer = create_supervised_trainer(model,
optimizer,
criterion,
prepare_batch,
device=device)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
logging_handlers.log_training_output(log_interval=config.PRINT_FREQ),
)
trainer.add_event_handler(Events.EPOCH_STARTED,
logging_handlers.log_lr(optimizer))
trainer.add_event_handler(
Events.EPOCH_STARTED,
tensorboard_handlers.log_lr(summary_writer, optimizer, "epoch"),
)
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
tensorboard_handlers.log_training_output(summary_writer),
)
def _select_pred_and_mask(model_out_dict):
return (model_out_dict["y_pred"].squeeze(),
model_out_dict["mask"].squeeze())
evaluator = create_supervised_evaluator(
model,
prepare_batch,
metrics={
"nll":
Loss(criterion,
output_transform=_select_pred_and_mask,
device=device),
"pixacc":
pixelwise_accuracy(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"cacc":
class_accuracy(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"mca":
mean_class_accuracy(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"ciou":
class_iou(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"mIoU":
mean_iou(n_classes,
output_transform=_select_pred_and_mask,
device=device),
},
device=device,
)
if debug:
logger.info("Running Validation in Debug/Test mode")
val_loader = take(3, val_loader)
trainer.add_event_handler(Events.EPOCH_COMPLETED,
Evaluator(evaluator, val_loader))
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
logging_handlers.log_metrics(
"Validation results",
metrics_dict={
"nll": "Avg loss :",
"pixacc": "Pixelwise Accuracy :",
"mca": "Avg Class Accuracy :",
"mIoU": "Avg Class IoU :",
},
),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
logging_handlers.log_class_metrics(
"Per class validation results",
metrics_dict={
"ciou": "Class IoU :",
"cacc": "Class Accuracy :"
},
),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
tensorboard_handlers.log_metrics(
summary_writer,
trainer,
"epoch",
metrics_dict={
"mIoU": "Validation/mIoU",
"nll": "Validation/Loss",
"mca": "Validation/MCA",
"pixacc": "Validation/Pixel_Acc",
},
),
)
def _select_max(pred_tensor):
return pred_tensor.max(1)[1]
def _tensor_to_numpy(pred_tensor):
return pred_tensor.squeeze().cpu().numpy()
transform_func = compose(np_to_tb, decode_segmap(n_classes=n_classes),
_tensor_to_numpy)
transform_pred = compose(transform_func, _select_max)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer, "Validation/Image", "image"),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer,
"Validation/Mask",
"mask",
transform_func=transform_func),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer,
"Validation/Pred",
"y_pred",
transform_func=transform_pred),
)
def snapshot_function():
return (trainer.state.iteration % snapshot_duration) == 0
checkpoint_handler = SnapshotHandler(
path.join(output_dir, config.TRAIN.MODEL_DIR),
config.MODEL.NAME,
extract_metric_from("mIoU"),
snapshot_function,
)
evaluator.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler,
{"model": model})
logger.info("Starting training")
if debug:
logger.info("Running Validation in Debug/Test mode")
train_loader = take(3, train_loader)
trainer.run(train_loader, max_epochs=config.TRAIN.END_EPOCH)
def run(*options, cfg=None, local_rank=0, debug=False):
"""Run training and validation of model
Notes:
Options can be passed in via the options argument and loaded from the cfg file
Options from default.py will be overridden by options loaded from cfg file
Options passed in via options argument will override option loaded from cfg file
Args:
*options (str,int ,optional): Options used to overide what is loaded from the
config. To see what options are available consult
default.py
cfg (str, optional): Location of config file to load. Defaults to None.
"""
update_config(config, options=options, config_file=cfg)
# we will write the model under outputs / config_file_name / model_dir
config_file_name = "default_config" if not cfg else cfg.split(
"/")[-1].split(".")[0]
# Start logging
load_log_configuration(config.LOG_CONFIG)
logger = logging.getLogger(__name__)
logger.debug(config.WORKERS)
silence_other_ranks = True
world_size = int(os.environ.get("WORLD_SIZE", 1))
distributed = world_size > 1
if distributed:
# FOR DISTRIBUTED: Set the device according to local_rank.
torch.cuda.set_device(local_rank)
# FOR DISTRIBUTED: Initialize the backend. torch.distributed.launch will
# provide environment variables, and requires that you use init_method=`env://`.
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
epochs_per_cycle = config.TRAIN.END_EPOCH // config.TRAIN.SNAPSHOTS
torch.backends.cudnn.benchmark = config.CUDNN.BENCHMARK
torch.manual_seed(config.SEED)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(seed=config.SEED)
# Setup Augmentations
basic_aug = Compose([
Normalize(mean=(config.TRAIN.MEAN, ),
std=(config.TRAIN.STD, ),
max_pixel_value=1),
PadIfNeeded(
min_height=config.TRAIN.PATCH_SIZE,
min_width=config.TRAIN.PATCH_SIZE,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=255,
),
Resize(
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT,
config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH,
always_apply=True,
),
PadIfNeeded(
min_height=config.TRAIN.AUGMENTATIONS.PAD.HEIGHT,
min_width=config.TRAIN.AUGMENTATIONS.PAD.WIDTH,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=255,
),
])
if config.TRAIN.AUGMENTATION:
train_aug = Compose([basic_aug, HorizontalFlip(p=0.5)])
val_aug = basic_aug
else:
train_aug = val_aug = basic_aug
TrainPatchLoader = get_patch_loader(config)
train_set = TrainPatchLoader(
config.DATASET.ROOT,
split="train",
is_transform=True,
stride=config.TRAIN.STRIDE,
patch_size=config.TRAIN.PATCH_SIZE,
augmentations=train_aug,
)
val_set = TrainPatchLoader(
config.DATASET.ROOT,
split="val",
is_transform=True,
stride=config.TRAIN.STRIDE,
patch_size=config.TRAIN.PATCH_SIZE,
augmentations=val_aug,
)
logger.info(f"Validation examples {len(val_set)}")
n_classes = train_set.n_classes
if debug:
val_set = data.Subset(val_set,
range(config.VALIDATION.BATCH_SIZE_PER_GPU))
train_set = data.Subset(train_set,
range(config.TRAIN.BATCH_SIZE_PER_GPU * 2))
logger.info(f"Training examples {len(train_set)}")
logger.info(f"Validation examples {len(val_set)}")
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set, num_replicas=world_size, rank=local_rank)
train_loader = data.DataLoader(
train_set,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
num_workers=config.WORKERS,
sampler=train_sampler,
)
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_set, num_replicas=world_size, rank=local_rank)
val_loader = data.DataLoader(
val_set,
batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU,
num_workers=config.WORKERS,
sampler=val_sampler,
)
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
model = model.to(device) # Send to GPU
optimizer = torch.optim.SGD(
model.parameters(),
lr=config.TRAIN.MAX_LR,
momentum=config.TRAIN.MOMENTUM,
weight_decay=config.TRAIN.WEIGHT_DECAY,
)
# weights are inversely proportional to the frequency of the classes in
# the training set
class_weights = torch.tensor(config.DATASET.CLASS_WEIGHTS,
device=device,
requires_grad=False)
criterion = torch.nn.CrossEntropyLoss(weight=class_weights,
ignore_index=255,
reduction="mean")
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[device], find_unused_parameters=True)
snapshot_duration = epochs_per_cycle * len(
train_loader) if not debug else 2 * len(train_loader)
warmup_duration = 5 * len(train_loader)
warmup_scheduler = LinearCyclicalScheduler(
optimizer,
"lr",
start_value=config.TRAIN.MAX_LR,
end_value=config.TRAIN.MAX_LR * world_size,
cycle_size=10 * len(train_loader),
)
cosine_scheduler = CosineAnnealingScheduler(
optimizer,
"lr",
config.TRAIN.MAX_LR * world_size,
config.TRAIN.MIN_LR * world_size,
cycle_size=snapshot_duration,
)
scheduler = ConcatScheduler(
schedulers=[warmup_scheduler, cosine_scheduler],
durations=[warmup_duration])
trainer = create_supervised_trainer(model,
optimizer,
criterion,
prepare_batch,
device=device)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
# Set to update the epoch parameter of our distributed data sampler so that we get
# different shuffles
trainer.add_event_handler(Events.EPOCH_STARTED,
update_sampler_epoch(train_loader))
if silence_other_ranks & local_rank != 0:
logging.getLogger("ignite.engine.engine.Engine").setLevel(
logging.WARNING)
def _select_pred_and_mask(model_out_dict):
return (model_out_dict["y_pred"].squeeze(),
model_out_dict["mask"].squeeze())
evaluator = create_supervised_evaluator(
model,
prepare_batch,
metrics={
"nll":
Loss(criterion,
output_transform=_select_pred_and_mask,
device=device),
"pixa":
pixelwise_accuracy(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"cacc":
class_accuracy(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"mca":
mean_class_accuracy(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"ciou":
class_iou(n_classes,
output_transform=_select_pred_and_mask,
device=device),
"mIoU":
mean_iou(n_classes,
output_transform=_select_pred_and_mask,
device=device),
},
device=device,
)
# Set the validation run to start on the epoch completion of the training run
trainer.add_event_handler(Events.EPOCH_COMPLETED,
Evaluator(evaluator, val_loader))
if local_rank == 0: # Run only on master process
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
logging_handlers.log_training_output(
log_interval=config.TRAIN.BATCH_SIZE_PER_GPU),
)
trainer.add_event_handler(Events.EPOCH_STARTED,
logging_handlers.log_lr(optimizer))
try:
output_dir = generate_path(
config.OUTPUT_DIR,
git_branch(),
git_hash(),
config_file_name,
config.TRAIN.MODEL_DIR,
current_datetime(),
)
except TypeError:
output_dir = generate_path(
config.OUTPUT_DIR,
config_file_name,
config.TRAIN.MODEL_DIR,
current_datetime(),
)
summary_writer = create_summary_writer(
log_dir=path.join(output_dir, config.LOG_DIR))
logger.info(
f"Logging Tensorboard to {path.join(output_dir, config.LOG_DIR)}")
trainer.add_event_handler(
Events.EPOCH_STARTED,
tensorboard_handlers.log_lr(summary_writer, optimizer, "epoch"),
)
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
tensorboard_handlers.log_training_output(summary_writer),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
logging_handlers.log_metrics(
"Validation results",
metrics_dict={
"nll": "Avg loss :",
"mIoU": " Avg IoU :",
"pixa": "Pixelwise Accuracy :",
"mca": "Mean Class Accuracy :",
},
),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
tensorboard_handlers.log_metrics(
summary_writer,
trainer,
"epoch",
metrics_dict={
"mIoU": "Validation/IoU",
"nll": "Validation/Loss",
"mca": "Validation/MCA",
},
),
)
def _select_max(pred_tensor):
return pred_tensor.max(1)[1]
def _tensor_to_numpy(pred_tensor):
return pred_tensor.squeeze().cpu().numpy()
transform_func = compose(np_to_tb, decode_segmap(n_classes=n_classes),
_tensor_to_numpy)
transform_pred = compose(transform_func, _select_max)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer, "Validation/Image", "image"),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer,
"Validation/Mask",
"mask",
transform_func=transform_func),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(
summary_writer,
"Validation/Pred",
"y_pred",
transform_func=transform_pred,
),
)
def snapshot_function():
return (trainer.state.iteration % snapshot_duration) == 0
checkpoint_handler = SnapshotHandler(
output_dir,
config.MODEL.NAME,
extract_metric_from("mIoU"),
snapshot_function,
)
evaluator.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler,
{"model": model})
logger.info("Starting training")
if debug:
trainer.run(
train_loader,
max_epochs=config.TRAIN.END_EPOCH,
epoch_length=config.TRAIN.BATCH_SIZE_PER_GPU * 2,
seed=config.SEED,
)
else:
trainer.run(train_loader,
max_epochs=config.TRAIN.END_EPOCH,
epoch_length=len(train_loader),
seed=config.SEED)
def run(*options,
cfg=None,
local_rank=0,
debug=False,
input=None,
distributed=False):
"""Run training and validation of model
Notes:
Options can be passed in via the options argument and loaded from the cfg file
Options from default.py will be overridden by options loaded from cfg file
Options from default.py will be overridden by options loaded from cfg file
Options passed in via options argument will override option loaded from cfg file
Args:
*options (str,int ,optional): Options used to overide what is loaded from the
config. To see what options are available consult
default.py
cfg (str, optional): Location of config file to load. Defaults to None.
debug (bool): Places scripts in debug/test mode and only executes a few iterations
input (str, optional): Location of data if Azure ML run,
for local runs input is config.DATASET.ROOT
distributed (bool): This flag tells the training script to run in distributed mode
if more than one GPU exists.
"""
# if AML training pipeline supplies us with input
if input is not None:
data_dir = input
output_dir = data_dir + config.OUTPUT_DIR
# Start logging
load_log_configuration(config.LOG_CONFIG)
logger = logging.getLogger(__name__)
logger.debug(config.WORKERS)
# Configuration:
update_config(config, options=options, config_file=cfg)
silence_other_ranks = True
world_size = int(os.environ.get("WORLD_SIZE", 1))
distributed = world_size > 1
if distributed:
# FOR DISTRIBUTED: Set the device according to local_rank.
torch.cuda.set_device(local_rank)
# FOR DISTRIBUTED: Initialize the backend. torch.distributed.launch will
# provide environment variables, and requires that you use init_method=`env://`.
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
logging.info(f"Started train.py using distributed mode.")
else:
logging.info(f"Started train.py using local mode.")
# Set CUDNN benchmark mode:
torch.backends.cudnn.benchmark = config.CUDNN.BENCHMARK
# Fix random seeds:
torch.manual_seed(config.SEED)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(seed=config.SEED)
# Augmentation:
basic_aug = Compose([
Normalize(mean=(config.TRAIN.MEAN, ),
std=(config.TRAIN.STD, ),
max_pixel_value=1),
PadIfNeeded(
min_height=config.TRAIN.PATCH_SIZE,
min_width=config.TRAIN.PATCH_SIZE,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=255,
value=0,
),
Resize(
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT,
config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH,
always_apply=True,
),
PadIfNeeded(
min_height=config.TRAIN.AUGMENTATIONS.PAD.HEIGHT,
min_width=config.TRAIN.AUGMENTATIONS.PAD.WIDTH,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=255,
),
])
if config.TRAIN.AUGMENTATION:
train_aug = Compose([basic_aug, HorizontalFlip(p=0.5)])
val_aug = basic_aug
else:
train_aug = val_aug = basic_aug
# Training and Validation Loaders:
TrainPatchLoader = get_patch_loader(config)
logging.info(f"Using {TrainPatchLoader}")
train_set = TrainPatchLoader(
config,
split="train",
is_transform=True,
augmentations=train_aug,
debug=debug,
)
logger.info(train_set)
n_classes = train_set.n_classes
val_set = TrainPatchLoader(
config,
split="val",
is_transform=True,
augmentations=val_aug,
debug=debug,
)
logger.info(val_set)
if debug:
data_flow_dict = dict()
data_flow_dict["train_patch_loader_length"] = len(train_set)
data_flow_dict["validation_patch_loader_length"] = len(val_set)
data_flow_dict["train_input_shape"] = train_set.seismic.shape
data_flow_dict["train_label_shape"] = train_set.labels.shape
data_flow_dict["n_classes"] = n_classes
logger.info("Running in debug mode..")
train_range = min(
config.TRAIN.BATCH_SIZE_PER_GPU * config.NUM_DEBUG_BATCHES,
len(train_set))
logging.info(f"train range in debug mode {train_range}")
train_set = data.Subset(train_set, range(train_range))
valid_range = min(config.VALIDATION.BATCH_SIZE_PER_GPU, len(val_set))
val_set = data.Subset(val_set, range(valid_range))
data_flow_dict["train_length_subset"] = len(train_set)
data_flow_dict["validation_length_subset"] = len(val_set)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set, num_replicas=world_size, rank=local_rank)
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_set, num_replicas=world_size, rank=local_rank)
train_loader = data.DataLoader(
train_set,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
num_workers=config.WORKERS,
sampler=train_sampler,
)
val_loader = data.DataLoader(
val_set,
batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU,
num_workers=config.WORKERS,
sampler=val_sampler)
if debug:
data_flow_dict["train_loader_length"] = len(train_loader)
data_flow_dict["validation_loader_length"] = len(val_loader)
config_file_name = "default_config" if not cfg else cfg.split(
"/")[-1].split(".")[0]
fname = f"data_flow_train_{config_file_name}_{config.TRAIN.MODEL_DIR}.json"
with open(fname, "w") as f:
json.dump(data_flow_dict, f, indent=2)
# Model:
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# Optimizer and LR Scheduler:
optimizer = torch.optim.SGD(
model.parameters(),
lr=config.TRAIN.MAX_LR,
momentum=config.TRAIN.MOMENTUM,
weight_decay=config.TRAIN.WEIGHT_DECAY,
)
epochs_per_cycle = config.TRAIN.END_EPOCH // config.TRAIN.SNAPSHOTS
snapshot_duration = epochs_per_cycle * len(
train_loader) if not debug else 2 * len(train_loader)
cosine_scheduler = CosineAnnealingScheduler(
optimizer,
"lr",
config.TRAIN.MAX_LR * world_size,
config.TRAIN.MIN_LR * world_size,
cycle_size=snapshot_duration,
)
if distributed:
warmup_duration = 5 * len(train_loader)
warmup_scheduler = LinearCyclicalScheduler(
optimizer,
"lr",
start_value=config.TRAIN.MAX_LR,
end_value=config.TRAIN.MAX_LR * world_size,
cycle_size=10 * len(train_loader),
)
scheduler = ConcatScheduler(
schedulers=[warmup_scheduler, cosine_scheduler],
durations=[warmup_duration])
else:
scheduler = cosine_scheduler
# class weights are inversely proportional to the frequency of the classes in the training set
class_weights = torch.tensor(config.DATASET.CLASS_WEIGHTS,
device=device,
requires_grad=False)
# Loss:
criterion = torch.nn.CrossEntropyLoss(weight=class_weights,
ignore_index=255,
reduction="mean")
# Model:
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[device], find_unused_parameters=True)
if silence_other_ranks & local_rank != 0:
logging.getLogger("ignite.engine.engine.Engine").setLevel(
logging.WARNING)
# Ignite trainer and evaluator:
trainer = create_supervised_trainer(model,
optimizer,
criterion,
prepare_batch,
device=device)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
# Set to update the epoch parameter of our distributed data sampler so that we get
# different shuffles
trainer.add_event_handler(Events.EPOCH_STARTED,
update_sampler_epoch(train_loader))
transform_fn = lambda output_dict: (output_dict["y_pred"].squeeze(),
output_dict["mask"].squeeze())
evaluator = create_supervised_evaluator(
model,
prepare_batch,
metrics={
"nll":
Loss(criterion, output_transform=transform_fn, device=device),
"pixacc":
pixelwise_accuracy(n_classes,
output_transform=transform_fn,
device=device),
"cacc":
class_accuracy(n_classes,
output_transform=transform_fn,
device=device),
"mca":
mean_class_accuracy(n_classes,
output_transform=transform_fn,
device=device),
"ciou":
class_iou(n_classes, output_transform=transform_fn, device=device),
"mIoU":
mean_iou(n_classes, output_transform=transform_fn, device=device),
},
device=device,
)
# The model will be saved under: outputs/<config_file_name>/<model_dir>
config_file_name = "default_config" if not cfg else cfg.split(
"/")[-1].split(".")[0]
try:
output_dir = generate_path(
config.OUTPUT_DIR,
git_branch(),
git_hash(),
config_file_name,
config.TRAIN.MODEL_DIR,
current_datetime(),
)
except:
output_dir = generate_path(
config.OUTPUT_DIR,
config_file_name,
config.TRAIN.MODEL_DIR,
current_datetime(),
)
if local_rank == 0: # Run only on master process
# Logging:
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
logging_handlers.log_training_output(
log_interval=config.PRINT_FREQ),
)
trainer.add_event_handler(Events.EPOCH_STARTED,
logging_handlers.log_lr(optimizer))
# Checkpointing: snapshotting trained models to disk
checkpoint_handler = SnapshotHandler(
output_dir,
config.MODEL.NAME,
extract_metric_from("mIoU"),
lambda: (trainer.state.iteration % snapshot_duration) == 0,
)
evaluator.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler,
{"model": model})
# Tensorboard and Logging:
summary_writer = create_summary_writer(
log_dir=path.join(output_dir, "logs"))
trainer.add_event_handler(
Events.EPOCH_STARTED,
tensorboard_handlers.log_lr(summary_writer, optimizer, "epoch"))
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
tensorboard_handlers.log_training_output(summary_writer))
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
tensorboard_handlers.log_validation_output(summary_writer))
@trainer.on(Events.EPOCH_COMPLETED)
def log_training_results(engine):
evaluator.run(train_loader)
if local_rank == 0: # Run only on master process
tensorboard_handlers.log_results(engine,
evaluator,
summary_writer,
n_classes,
stage="Training")
logging_handlers.log_metrics(engine, evaluator, stage="Training")
logger.info("Logging training results..")
@trainer.on(Events.EPOCH_COMPLETED)
def log_validation_results(engine):
evaluator.run(val_loader)
if local_rank == 0: # Run only on master process
tensorboard_handlers.log_results(engine,
evaluator,
summary_writer,
n_classes,
stage="Validation")
logging_handlers.log_metrics(engine, evaluator, stage="Validation")
logger.info("Logging validation results..")
# dump validation set metrics at the very end for debugging purposes
if engine.state.epoch == config.TRAIN.END_EPOCH and debug:
fname = f"metrics_{config_file_name}_{config.TRAIN.MODEL_DIR}.json"
metrics = evaluator.state.metrics
out_dict = {
x: metrics[x]
for x in ["nll", "pixacc", "mca", "mIoU"]
}
with open(fname, "w") as fid:
json.dump(out_dict, fid)
log_msg = " ".join(f"{k}: {out_dict[k]}"
for k in out_dict.keys())
logging.info(log_msg)
logger.info("Starting training")
trainer.run(train_loader,
max_epochs=config.TRAIN.END_EPOCH,
epoch_length=len(train_loader),
seed=config.SEED)
if local_rank == 0:
summary_writer.close()