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")
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..")
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)
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, debug=False):
"""Run testing 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)
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
test_aug = Compose(
[
Normalize(mean=(config.TRAIN.MEAN,), std=(config.TRAIN.STD,), max_pixel_value=config.TRAIN.MAX,),
PadIfNeeded(
min_height=config.TRAIN.PATCH_SIZE,
min_width=config.TRAIN.PATCH_SIZE,
border_mode=cv2.BORDER_CONSTANT,
always_apply=True,
mask_value=mask_value,
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=cv2.BORDER_CONSTANT,
always_apply=True,
mask_value=mask_value,
value=0,
),
]
)
PenobscotDataset = get_patch_dataset(config)
test_set = PenobscotDataset(
config.DATASET.ROOT,
config.TRAIN.PATCH_SIZE,
config.TRAIN.STRIDE,
split="test",
transforms=test_aug,
n_channels=config.MODEL.IN_CHANNELS,
complete_patches_only=config.TEST.COMPLETE_PATCHES_ONLY,
)
logger.info(str(test_set))
n_classes = test_set.n_classes
test_loader = data.DataLoader(
test_set, batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS,
)
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
logger.info(f"Loading model {config.TEST.MODEL_PATH}")
model.load_state_dict(torch.load(config.TEST.MODEL_PATH), strict=False)
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
model = model.to(device) # Send to GPU
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))
# 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=mask_value, reduction="mean")
def _select_pred_and_mask(model_out_dict):
return (model_out_dict["y_pred"].squeeze(), model_out_dict["mask"].squeeze())
def _select_all(model_out_dict):
return (
model_out_dict["y_pred"].squeeze(),
model_out_dict["mask"].squeeze(),
model_out_dict["ids"],
model_out_dict["patch_locations"],
)
inline_mean_iou = InlineMeanIoU(
config.DATASET.INLINE_HEIGHT,
config.DATASET.INLINE_WIDTH,
config.TRAIN.PATCH_SIZE,
n_classes,
padding=_padding_from(config),
scale=_scale_from(config),
output_transform=_select_all,
)
evaluator = create_supervised_evaluator(
model,
_prepare_batch,
metrics={
"nll": Loss(criterion, output_transform=_select_pred_and_mask, device=device),
"inIoU": inline_mean_iou,
"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,
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
logging_handlers.log_metrics(
"Test results",
metrics_dict={
"nll": "Avg loss :",
"mIoU": "Avg IoU :",
"pixa": "Pixelwise Accuracy :",
"mca": "Mean Class Accuracy :",
"inIoU": "Mean Inline IoU :",
},
),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
tensorboard_handlers.log_metrics(
summary_writer,
evaluator,
"epoch",
metrics_dict={"mIoU": "Test/IoU", "nll": "Test/Loss", "mca": "Test/MCA", "inIoU": "Test/MeanInlineIoU",},
),
)
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, label_colours=_SEG_COLOURS), _tensor_to_numpy,
)
transform_pred = compose(transform_func, _select_max)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED, create_image_writer(summary_writer, "Test/Image", "image"),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED, create_image_writer(summary_writer, "Test/Mask", "mask", transform_func=transform_func),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer, "Test/Pred", "y_pred", transform_func=transform_pred),
)
logger.info("Starting training")
if debug:
logger.info("Running in Debug/Test mode")
test_loader = take(3, test_loader)
evaluator.run(test_loader, max_epochs=1)
# Log top N and bottom N inlines in terms of IoU to tensorboard
inline_ious = inline_mean_iou.iou_per_inline()
sorted_ious = sorted(inline_ious.items(), key=lambda x: x[1], reverse=True)
topk = ((inline_mean_iou.predictions[key], inline_mean_iou.masks[key]) for key, iou in take(_TOP_K, sorted_ious))
bottomk = (
(inline_mean_iou.predictions[key], inline_mean_iou.masks[key]) for key, iou in tail(_BOTTOM_K, sorted_ious)
)
stack_and_decode = compose(transform_func, torch.stack)
predictions, masks = unzip(chain(topk, bottomk))
predictions_tensor = stack_and_decode(list(predictions))
masks_tensor = stack_and_decode(list(masks))
_log_tensor_to_tensorboard(predictions_tensor, "Test/InlinePredictions", summary_writer, evaluator)
_log_tensor_to_tensorboard(masks_tensor, "Test/InlineMasks", summary_writer, evaluator)
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):
"""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)
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)
# load the data
TrainVoxelLoader = get_voxel_loader(config)
train_set = TrainVoxelLoader(
config.DATASET.ROOT,
config.DATASET.FILENAME,
split="train",
window_size=config.WINDOW_SIZE,
len=config.TRAIN.BATCH_SIZE_PER_GPU * config.TRAIN.BATCH_PER_EPOCH,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
)
val_set = TrainVoxelLoader(
config.DATASET.ROOT,
config.DATASET.FILENAME,
split="val",
window_size=config.WINDOW_SIZE,
len=config.TRAIN.BATCH_SIZE_PER_GPU * config.TRAIN.BATCH_PER_EPOCH,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
)
n_classes = train_set.n_classes
# set dataset length to batch size to be consistent with 5000 iterations
# each of size 32 in the original Waldeland implementation
train_loader = data.DataLoader(
train_set, batch_size=config.TRAIN.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS, shuffle=False,
)
val_loader = data.DataLoader(
val_set, batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS, shuffle=False,
)
# this is how we import model for CV - here we're importing a seismic
# segmentation model
model = TextureNet(n_classes=config.DATASET.NUM_CLASSES)
optimizer = torch.optim.Adam(
model.parameters(),
lr=config.TRAIN.LR,
# momentum=config.TRAIN.MOMENTUM,
weight_decay=config.TRAIN.WEIGHT_DECAY,
)
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
model = model.cuda()
loss = torch.nn.CrossEntropyLoss()
trainer = create_supervised_trainer(model, optimizer, loss, prepare_batch=_prepare_batch, device=device)
desc = "ITERATION - loss: {:.2f}"
pbar = tqdm(initial=0, leave=False, total=len(train_loader), desc=desc.format(0))
# add model checkpointing
output_dir = path.join(config.OUTPUT_DIR, config.TRAIN.MODEL_DIR)
checkpoint_handler = ModelCheckpoint(
output_dir, "model", save_interval=1, n_saved=3, create_dir=True, require_empty=False,
)
criterion = torch.nn.CrossEntropyLoss(reduction="mean")
# save model at each epoch
trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler, {config.MODEL.NAME: model})
def _select_pred_and_mask(model_out):
# receive a tuple of (x, y_pred), y
# so actually in line 51 of
# cv_lib/cv_lib/segmentation/dutch_f3/metrics/__init__.py
# we do the following line, so here we just select the model
# _, y_pred = torch.max(model_out[0].squeeze(), 1, keepdim=True)
y_pred = model_out[0].squeeze()
y = model_out[1].squeeze()
return (y_pred.squeeze(), y)
evaluator = create_supervised_evaluator(
model,
metrics={
"nll": Loss(criterion, 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,
prepare_batch=_prepare_batch,
)
# 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))
summary_writer = create_summary_writer(log_dir=path.join(output_dir, config.LOG_DIR))
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
logging_handlers.log_metrics(
"Validation results",
metrics_dict={
"mIoU": "Avg IoU :",
"nll": "Avg loss :",
"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",},
),
)
summary_writer = create_summary_writer(log_dir=path.join(output_dir, config.LOG_DIR))
snapshot_duration = 2
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")
trainer.run(train_loader, max_epochs=config.TRAIN.END_EPOCH // config.TRAIN.BATCH_PER_EPOCH)
pbar.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 loaded from default.py will be overridden by those loaded from cfg file
Options passed in via options argument will override those 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
"""
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)
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)
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
# Setup Augmentations
basic_aug = Compose(
[
Normalize(mean=(config.TRAIN.MEAN,), std=(config.TRAIN.STD,), max_pixel_value=config.TRAIN.MAX,),
PadIfNeeded(
min_height=config.TRAIN.PATCH_SIZE,
min_width=config.TRAIN.PATCH_SIZE,
border_mode=config.OPENCV_BORDER_CONSTANT,
always_apply=True,
mask_value=mask_value,
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=mask_value,
value=0,
),
]
)
if config.TRAIN.AUGMENTATION:
train_aug = Compose([basic_aug, HorizontalFlip(p=0.5)])
val_aug = basic_aug
else:
train_aug = val_aug = basic_aug
PenobscotDataset = get_patch_dataset(config)
train_set = PenobscotDataset(
config.DATASET.ROOT,
config.TRAIN.PATCH_SIZE,
config.TRAIN.STRIDE,
split="train",
transforms=train_aug,
n_channels=config.MODEL.IN_CHANNELS,
complete_patches_only=config.TRAIN.COMPLETE_PATCHES_ONLY,
)
val_set = PenobscotDataset(
config.DATASET.ROOT,
config.TRAIN.PATCH_SIZE,
config.TRAIN.STRIDE,
split="val",
transforms=val_aug,
n_channels=config.MODEL.IN_CHANNELS,
complete_patches_only=config.VALIDATION.COMPLETE_PATCHES_ONLY,
)
logger.info(train_set)
logger.info(val_set)
n_classes = train_set.n_classes
train_loader = data.DataLoader(
train_set, batch_size=config.TRAIN.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS, shuffle=True,
)
if debug:
val_set = data.Subset(val_set, range(3))
val_loader = data.DataLoader(val_set, batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS)
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
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_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))
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
)
# 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=mask_value, 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.TRAIN.BATCH_SIZE_PER_GPU),
)
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={
"pixacc": pixelwise_accuracy(n_classes, output_transform=_select_pred_and_mask),
"nll": Loss(criterion, output_transform=_select_pred_and_mask),
"cacc": class_accuracy(n_classes, output_transform=_select_pred_and_mask),
"mca": mean_class_accuracy(n_classes, output_transform=_select_pred_and_mask),
"ciou": class_iou(n_classes, output_transform=_select_pred_and_mask),
"mIoU": mean_iou(n_classes, output_transform=_select_pred_and_mask),
},
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))
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,
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, _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,
seed=config.SEED,
)
else:
trainer.run(train_loader, max_epochs=config.TRAIN.END_EPOCH, epoch_length=len(train_loader), seed=config.SEED)
