def run(name: str = None,
config: dict = None,
device: str = None,
check: bool = False) -> dict:
config = config or experiment_config
device = device or utils.get_device()
print(f"device: {device}")
utils.set_global_seed(SEED)
# inititalize weigths & biases
name = name or '_'.join(
filter(None,
[experiment_name, f"{datetime.datetime.now():%Y-%m-%d-%S}"]))
# convert parquet ot zip
parquet_to_images(TRAIN, ZIP_TRAIN_FILE, SIZE)
parquet_to_images(TEST, ZIP_TEST_FILE, SIZE)
# run experiment
runner = SupervisedRunner(
device=device,
input_key="images",
output_key=["logit_" + c for c in output_classes.keys()],
input_target_key=list(output_classes.keys()),
)
experiment = Experiment(config)
runner.run_experiment(experiment, check=check)
return {
'runner': runner,
'experiment': experiment,
'config': config,
}
def run(name: str = None,
config: dict = None,
device: str = None,
check: bool = False) -> dict:
config = config or experiment_config
device = device or utils.get_device()
print(f"device: {device}")
utils.set_global_seed(SEED)
config['monitoring_params']['name'] = EXPERIMENT_NAME
# convert parquet ot zip
parquet_to_images(TRAIN, ZIP_TRAIN_FILE, SIZE)
parquet_to_images(TEST, ZIP_TEST_FILE, SIZE)
# run experiment
runner = SupervisedRunner(
device=device,
input_key="images",
output_key=["logit_" + c for c in output_classes.keys()],
input_target_key=list(output_classes.keys()),
)
experiment = Experiment(config)
runner.run_experiment(experiment, check=check)
return {
'runner': runner,
'experiment': experiment,
'config': config,
}
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
def run(logdir_suffix: str = '',
device: str = None,
check: bool = False) -> dict:
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)
# run experiment
RunnerClass = SupervisedRunner if check else SupervisedWandbRunner
runner = RunnerClass(
device=device,
input_key="images",
output_key=["features"] +
["logit_" + c for c in output_classes.keys()],
input_target_key=list(output_classes.keys()),
)
experiment = Experiment(logdir='./logs' + logdir_suffix)
runner.run_experiment(experiment, check=check)
return {
'runner': runner,
'experiment': experiment,
}
def run(config: dict = None,
logdir_suffix: str = '',
device: str = None,
check: bool = False) -> dict:
config = config or experiment_config
device = device or utils.get_device()
print(f"device: {device}")
utils.set_global_seed(SEED)
config['monitoring_params']['name'] = EXPERIMENT_NAME
config['stages']['state_params']['checkpoint_data']['image_size'] = SIZE
config['args']['logdir'] += logdir_suffix
# convert parquet ot zip
parquet_to_images(TRAIN, ZIP_TRAIN_FILE, SIZE)
parquet_to_images(TEST, ZIP_TEST_FILE, SIZE)
# run experiment
RunnerClass = SupervisedRunner if check else SupervisedWandbRunner
runner = RunnerClass(
device=device,
input_key="images",
output_key=["logit_" + c for c in output_classes.keys()],
input_target_key=list(output_classes.keys()),
)
experiment = Experiment(config)
runner.run_experiment(experiment, check=check)
return {
'runner': runner,
'experiment': experiment,
'config': config,
}
def run(config: dict = None,
model_filepath: str = None,
logdir_suffix: str = '_' + EXPERIMENT_NAME,
max_lr: float = 1e-1,
steps_per_epoch: int = 1413,
device: str = None,
check: bool = False) -> dict:
config = config or experiment_config
device = device or utils.get_device()
print(f"device: {device}")
utils.set_global_seed(SEED)
config['monitoring_params']['name'] = EXPERIMENT_NAME
config['stages']['state_params']['checkpoint_data']['image_size'] = SIZE
config['args']['logdir'] += logdir_suffix
# convert parquet ot zip
parquet_to_images(TRAIN, ZIP_TRAIN_FILE, SIZE)
parquet_to_images(TEST, ZIP_TEST_FILE, 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": 500,
"final_div_factor": 1e5,
"max_momentum": 0.999
}
# run experiment
RunnerClass = SupervisedRunner if check else SupervisedWandbRunner
runner = RunnerClass(
device=device,
input_key="images",
output_key=["logit_" + c for c in output_classes.keys()],
input_target_key=list(output_classes.keys()),
)
experiment = Experiment(config, model_filepath)
runner.run_experiment(experiment, check=check)
return {
'runner': runner,
'experiment': experiment,
'config': config,
}
