def main():
args = parse_args()
cfg = Config.fromfile(args.config)
init_dist(**cfg.dist_params)
if "stages" in cfg:
cfg = cfg.stages[args.stage]
experiment = CIFARExperiment(cfg)
runner = Runner(
model=experiment.model,
optimizers=experiment.optimizers,
schedulers=experiment.schedulers,
batch_processor=CIFARBatchProcessor(cfg),
hooks=experiment.hooks,
work_dir=experiment.work_dir,
)
runner.run(
data_loaders={
"train": experiment.dataloader("train"),
"val": experiment.dataloader("val")
},
max_epochs=cfg.max_epochs,
)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
init_dist(**cfg.dist_params)
experiment = GANExperiment(cfg)
runner = Runner(model=experiment.model,
optimizers=experiment.optimizers,
batch_processor=GANBatchProcessor(cfg),
hooks=experiment.hooks,
work_dir=experiment.work_dir)
runner.run(data_loaders={'train': experiment.dataloader('train')},
max_epochs=cfg.total_epochs,
resume_from=cfg.resume_from,
load_from=cfg.load_from)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if cfg.get("cudnn_benchmark", False):
torch.backends.cudnn.benchmark = True
init_dist(**cfg.dist_params)
experiment = CIFARExperiment(cfg)
# build data loader
data_loader = experiment.dataloader("val")
# build model
model = experiment.model
load_checkpoint(model, args.checkpoint, map_location="cpu")
batch_processor = CIFARBatchProcessor(cfg)
outputs = multi_gpu_test(model, data_loader, batch_processor)
outputs = collect_results(outputs, len(data_loader.dataset))
io.dump(outputs, args.out)
if dist.is_initialized():
builder = TileClassifierDDPBuilder(config)
else:
builder = TileClassifierDPBuilder(config)
data_loaders = {x: builder.data_loader(x) for x in config.DATA.keys()}
batch_processor = TileClassifierBatchProcessor(builder)
runner = Runner(
model=builder.model,
optimizers=builder.optimizers,
schedulers=builder.schedulers,
hooks=builder.hooks,
work_dir=builder.config.WORK_DIR,
batch_processor=batch_processor,
)
runner.run(data_loaders=data_loaders, max_epochs=builder.config.MAX_EPOCHS)
if __name__ == "__main__":
logging.getLogger().addHandler(logging.StreamHandler())
args = parse_args()
config = Config.fromfile(args.config_path)
if args.is_distributed:
init_dist(**config.DIST_PARAMS)
train_func = locals()[config.TRAIN_FUNC]
train_func(config)