def main():
loss_fn = ProposalLoss(cfg)
preprocessor = TrainPreprocessor(cfg)
model = PV_RCNN(cfg, preprocessor).cuda()
dataloader_train = build_train_dataloader(cfg)
parameters = get_proposal_parameters(model)
optimizer = torch.optim.Adam(parameters, lr=cfg.TRAIN.LR)
start_epoch = load_ckpt('./epoch_0.pth', model, optimizer)
train_model(model, dataloader_train, optimizer, loss_fn, cfg.TRAIN.EPOCHS,
start_epoch)
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/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)
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)
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)