def run_cli():
from pl_bolts.datamodules import VOCDetectionDataModule
seed_everything(42)
parser = ArgumentParser()
parser = VOCDetectionDataModule.add_argparse_args(parser)
parser = Trainer.add_argparse_args(parser)
parser = FasterRCNN.add_model_specific_args(parser)
args = parser.parse_args()
datamodule = VOCDetectionDataModule.from_argparse_args(args)
args.num_classes = datamodule.num_classes
model = FasterRCNN(**vars(args))
trainer = Trainer.from_argparse_args(args)
trainer.fit(model, datamodule=datamodule)
def run_cli():
from pl_bolts.datamodules import VOCDetectionDataModule
pl.seed_everything(42)
parser = ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument("--data_dir", type=str, default=".")
parser.add_argument("--batch_size", type=int, default=1)
parser = FasterRCNN.add_model_specific_args(parser)
args = parser.parse_args()
datamodule = VOCDetectionDataModule.from_argparse_args(args)
args.num_classes = datamodule.num_classes
model = FasterRCNN(**vars(args))
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model, datamodule=datamodule)
def run_cli():
from argparse import ArgumentParser
from pytorch_lightning import Trainer, seed_everything
from pl_bolts.datamodules import VOCDetectionDataModule
from pl_bolts.datamodules.vocdetection_datamodule import Compose
from pl_bolts.models.detection.yolo.yolo_config import YOLOConfiguration
seed_everything(42)
parser = ArgumentParser()
parser.add_argument(
"--config",
type=str,
metavar="PATH",
required=True,
help="read model configuration from PATH",
)
parser.add_argument(
"--darknet-weights",
type=str,
metavar="PATH",
help="read the initial model weights from PATH in Darknet format",
)
parser.add_argument(
"--lr",
type=float,
metavar="LR",
default=0.0013,
help="learning rate after the warmup period",
)
parser.add_argument(
"--momentum",
type=float,
metavar="GAMMA",
default=0.9,
help="if nonzero, the optimizer uses momentum with factor GAMMA",
)
parser.add_argument(
"--weight-decay",
type=float,
metavar="LAMBDA",
default=0.0005,
help=
"if nonzero, the optimizer uses weight decay (L2 penalty) with factor LAMBDA",
)
parser.add_argument(
"--warmup-epochs",
type=int,
metavar="N",
default=1,
help="learning rate warmup period is N epochs",
)
parser.add_argument(
"--max-epochs",
type=int,
metavar="N",
default=300,
help="train at most N epochs",
)
parser.add_argument(
"--initial-lr",
type=float,
metavar="LR",
default=0.0,
help="learning rate before the warmup period",
)
parser.add_argument(
"--confidence-threshold",
type=float,
metavar="THRESHOLD",
default=0.001,
help="keep predictions only if the confidence is above THRESHOLD",
)
parser.add_argument(
"--nms-threshold",
type=float,
metavar="THRESHOLD",
default=0.45,
help=
"non-maximum suppression removes predicted boxes that have IoU greater than "
"THRESHOLD with a higher scoring box",
)
parser.add_argument(
"--max-predictions-per-image",
type=int,
metavar="N",
default=100,
help="keep at most N best predictions",
)
parser = VOCDetectionDataModule.add_argparse_args(parser)
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
config = YOLOConfiguration(args.config)
transforms = [
lambda image, target: (F.to_tensor(image), target),
Resize((config.height, config.width))
]
transforms = Compose(transforms)
datamodule = VOCDetectionDataModule.from_argparse_args(
args, train_transforms=transforms, val_transforms=transforms)
optimizer_params = {
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
}
lr_scheduler_params = {
"warmup_epochs": args.warmup_epochs,
"max_epochs": args.max_epochs,
"warmup_start_lr": args.initial_lr,
}
model = YOLO(
network=config.get_network(),
optimizer_params=optimizer_params,
lr_scheduler_params=lr_scheduler_params,
confidence_threshold=args.confidence_threshold,
nms_threshold=args.nms_threshold,
max_predictions_per_image=args.max_predictions_per_image,
)
if args.darknet_weights is not None:
with open(args.darknet_weights) as weight_file:
model.load_darknet_weights(weight_file)
trainer = Trainer.from_argparse_args(args)
trainer.fit(model, datamodule=datamodule)