def run(max_lr: float = 1e-1, steps_per_epoch: int = 1413, device: str = None, check: bool = False) -> dict:
config = copy.deepcopy(experiment_config)
device = device or utils.get_device()
print(f"device: {device}")
utils.set_global_seed(SEED)
# convert parquet ot zip
parquet_to_images(TRAIN, ZIP_TRAIN_FILE, SIZE)
parquet_to_images(TEST, ZIP_TEST_FILE, SIZE)
config['monitoring_params']['name'] = EXPERIMENT_NAME
config['stages']['state_params']['checkpoint_data']['image_size'] = SIZE
# add scheduler to config
config["stages"]["scheduler_params"] = {
"scheduler": "OneCycleLR",
"max_lr": max_lr,
"epochs": config["stages"]["state_params"]["num_epochs"],
"steps_per_epoch": steps_per_epoch,
"div_factor": 200,
"final_div_factor": 1e5,
}
experiment = Experiment(config)
# run experiment
runner = SupervisedWandbRunner(
device=device,
input_key="images",
output_key=["logit_" + c for c in output_classes.keys()],
input_target_key=list(output_classes.keys()),)
runner.run_experiment(experiment, check=check)
return experiment, runner
return schedulers[scheduler_name](**scheduler_params, optimizer=optimizer_)
# @TODO: add metrics support
# (catalyst expects logits, rather than sigmoid outputs)
# metrics = [
# smp.utils.metrics.IoUMetric(eps=1.),
# smp.utils.metrics.FscoreMetric(eps=1.),
# ]
if __name__ == '__main__':
args = parse_args()
config = safitty.load(args.config_path)
runner = SupervisedWandbRunner()
model = get_model(
model_name=safitty.get(config, 'model', 'name', default='unet'),
model_params=safitty.get(config, 'model', 'params', default={}))
criterion = get_criterion(
criterion_name=safitty.get(config, 'criterion', 'name', default='bce_dice'),
criterion_params=safitty.get(config, 'criterion', 'params', default={}))
optimizer = get_optimizer(
optimizer_name=safitty.get(config, 'optimizer', 'name', default='adam'),
optimizer_params=safitty.get(config, 'optimizer', 'params', default={}),
model_=model)
scheduler = get_scheduler(