def main():
"""
Driver function for the script
"""
_parser = NmArgumentParser(
dataclass_types=LRAnalysisArguments,
description="Utility script to Run a "
"learning rate sensitivity analysis "
"for a desired image classification architecture",
)
args_, _ = _parser.parse_args_into_dataclasses()
save_dir, loggers = helpers.get_save_dir_and_loggers(
args_,
task=CURRENT_TASK,
)
input_shape = ModelRegistry.input_shape(args_.arch_key)
# assume shape [C, S, S] where S is the image size
image_size = input_shape[1]
(
train_dataset,
train_loader,
val_dataset,
val_loader,
) = helpers.get_train_and_validation_loaders(
args_,
image_size,
task=CURRENT_TASK,
)
num_classes = helpers.infer_num_classes(args_, train_dataset, val_dataset)
model = helpers.create_model(args_, num_classes)
lr_sensitivity(args_, model, train_loader, save_dir)
def main():
"""
Driver function for the script
"""
_parser = NmArgumentParser(dataclass_types=TrainingArguments)
training_args, _ = _parser.parse_args_into_dataclasses()
save_dir, loggers = helpers.get_save_dir_and_loggers(training_args,
task=CURRENT_TASK)
input_shape = ModelRegistry.input_shape(training_args.arch_key)
image_size = input_shape[
1] # assume shape [C, S, S] where S is the image size
(
train_dataset,
train_loader,
val_dataset,
val_loader,
) = helpers.get_train_and_validation_loaders(training_args,
image_size,
task=CURRENT_TASK)
num_classes = helpers.infer_num_classes(training_args, train_dataset,
val_dataset)
# # model creation
model = helpers.create_model(training_args, num_classes)
train(training_args, model, train_loader, val_loader, input_shape,
save_dir, loggers)
def test_with_enum(self):
parser = NmArgumentParser(EnumExample)
expected = argparse.ArgumentParser()
expected.add_argument(
"--foo", default="toto", choices=["titi", "toto"], type=str
)
self.argparsersEqual(parser, expected)
args = parser.parse_args([])
self.assertEqual(args.foo, "toto")
enum_ex = parser.parse_args_into_dataclasses([])[0]
self.assertEqual(enum_ex.foo, BasicEnum.toto)
args = parser.parse_args(["--foo", "titi"])
self.assertEqual(args.foo, "titi")
enum_ex = parser.parse_args_into_dataclasses(["--foo", "titi"])[0]
self.assertEqual(enum_ex.foo, BasicEnum.titi)
def main():
"""
Driver function
"""
_parser = NmArgumentParser(
dataclass_types=ExportArgs,
description="Utility script to export a model to onnx "
"and also store sample inputs/outputs",
)
(args_, ) = _parser.parse_args_into_dataclasses()
model, save_dir, val_loader = export_setup(args_)
export(args_, model, val_loader, save_dir)
def test_basic(self):
parser = NmArgumentParser(BasicExample)
expected = argparse.ArgumentParser()
expected.add_argument("--foo", type=int, required=True)
expected.add_argument("--bar", type=float, required=True)
expected.add_argument("--baz", type=str, required=True)
expected.add_argument(
"--flag", type=string_to_bool, default=False, const=True, nargs="?"
)
self.argparsersEqual(parser, expected)
args = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
(example,) = parser.parse_args_into_dataclasses(args, look_for_args_file=False)
self.assertFalse(example.flag)