def get_model(cfg):
cfg.merge_from_file('../configs/second/car.yaml')
anchors = AnchorGenerator(cfg).anchors
preprocessor = Preprocessor(cfg)
model = Second(cfg).cuda().eval()
ckpt = torch.load('../pvrcnn/ckpts/epoch_12.pth')['state_dict']
model.load_state_dict(ckpt, strict=True)
return model, preprocessor, anchors
return scheduler
def main():
"""TODO: Trainer class to manage objects."""
# model = Second(cfg).cuda()
model = PV_RCNN(cfg).cuda()
print("Number parameters: ", sum(p.numel() for p in model.parameters() if p.requires_grad))
parameters = model.parameters()
loss_fn = ProposalLoss(cfg)
# loss_fn = OverallLoss(cfg)
preprocessor = TrainPreprocessor(cfg)
dataloader = build_train_dataloader(cfg, preprocessor)
optimizer = torch.optim.Adam(parameters, lr=0.01)
start_epoch = load_ckpt('./ckpts/epoch_10.pth', model, optimizer)
scheduler = build_lr_scheduler(optimizer, cfg, start_epoch, len(dataloader))
train_model(model, dataloader, optimizer,
scheduler, loss_fn, cfg.TRAIN.EPOCHS, start_epoch)
if __name__ == '__main__':
try:
multiprocessing.set_start_method('spawn')
except RuntimeError:
pass
global plotter
plotter = VisdomLinePlotter(env='pvrcnn_testing')
# cfg.merge_from_file('../configs/second/car.yaml')
cfg.merge_from_file('../configs/pvrcnn/car.yaml')
main()
return model.parameters()
def main():
"""TODO: Trainer class to manage objects."""
model = Second(cfg).cuda()
parameters = model.parameters()
loss_fn = ProposalLoss(cfg)
preprocessor = TrainPreprocessor(cfg)
dataloader = build_train_dataloader(cfg, preprocessor)
optimizer = torch.optim.Adam(parameters, lr=0.01)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=0.01,
steps_per_epoch=len(dataloader),
epochs=cfg.TRAIN.EPOCHS)
start_epoch = load_ckpt('./ckpts/epoch_5.pth', model, optimizer)
train_model(model, dataloader, optimizer, scheduler, loss_fn,
cfg.TRAIN.EPOCHS, start_epoch)
if __name__ == '__main__':
try:
multiprocessing.set_start_method('spawn')
except RuntimeError:
pass
global plotter
plotter = VisdomLinePlotter(env='second')
cfg.merge_from_file('../configs/second/car.yaml')
main()
def main():
"""TODO: Trainer class to manage objects."""
model = PV_RCNN(cfg).cuda()
loss_fn = ProposalLoss(cfg)
preprocessor = TrainPreprocessor(cfg)
dataloader = build_train_dataloader(cfg, preprocessor)
parameters = get_proposal_parameters(model)
optimizer = torch.optim.Adam(parameters, lr=cfg.TRAIN.LR)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=3e-3,
steps_per_epoch=len(dataloader),
epochs=cfg.TRAIN.EPOCHS)
start_epoch = load_ckpt('./ckpts/epoch_8.pth', model, optimizer)
train_model(model, dataloader, optimizer, scheduler, loss_fn,
cfg.TRAIN.EPOCHS, start_epoch)
from multiprocessing import set_start_method
if __name__ == '__main__':
try:
set_start_method('spawn')
except RuntimeError:
pass
global plotter
plotter = VisdomLinePlotter(env='training')
cfg.merge_from_file('../configs/car_lite.yaml')
main()
steps_per_epoch=N,
epochs=cfg.TRAIN.EPOCHS,
last_epoch=last_epoch)
return scheduler
def main():
"""TODO: Trainer class to manage objects."""
model = Second(cfg).cuda()
parameters = model.parameters()
loss_fn = ProposalLoss(cfg)
preprocessor = TrainPreprocessor(cfg)
dataloader = build_train_dataloader(cfg, preprocessor)
optimizer = torch.optim.Adam(parameters, lr=0.01)
start_epoch = load_ckpt('./ckpts/carla/epoch_10.pth', model, optimizer)
scheduler = build_lr_scheduler(optimizer, cfg, start_epoch,
len(dataloader))
train_model(model, dataloader, optimizer, scheduler, loss_fn,
cfg.TRAIN.EPOCHS, start_epoch)
if __name__ == '__main__':
try:
multiprocessing.set_start_method('spawn')
except RuntimeError:
pass
global plotter
plotter = VisdomLinePlotter(env='carla')
cfg.merge_from_file('../configs/carla/car.yaml')
main()
for p in model.refinement_layer.parameters():
p.requires_grad = False
return model.parameters()
def main():
"""TODO: Trainer class to manage objects."""
model = PV_RCNN(cfg).cuda()
loss_fn = ProposalLoss(cfg)
preprocessor = TrainPreprocessor(cfg)
dataloader = build_train_dataloader(cfg, preprocessor)
parameters = get_proposal_parameters(model)
optimizer = torch.optim.Adam(parameters,
lr=cfg.TRAIN.LR,
weight_decay=1e-3)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=3e-3,
steps_per_epoch=len(dataloader),
epochs=cfg.TRAIN.EPOCHS)
start_epoch = load_ckpt('./ckpts/epoch_31.pth', model, optimizer)
train_model(model, dataloader, optimizer, scheduler, loss_fn,
cfg.TRAIN.EPOCHS, start_epoch)
if __name__ == '__main__':
global plotter
plotter = VisdomLinePlotter(env='training')
cfg.merge_from_file('../configs/all.yaml')
main()