def test_get_arch_config(arch: str):
arch_configs = fastface.list_arch_configs(arch)
for arch_config in arch_configs:
config = fastface.get_arch_config(arch, arch_config)
assert isinstance(
config, Dict
), f"{arch}.{arch_config} must be dictionary but found: {type(config)}"
def test_list_arch_configs(arch: str):
arch_configs = fastface.list_arch_configs(arch)
assert isinstance(
arch_configs,
List), f"returned value must be list but found:{type(arch_configs)}"
for arch_config in arch_configs:
assert isinstance(
arch_config, str
), f"architecture config must contain string but found:{type(arch_config)}"
def parse_arguments():
ap = argparse.ArgumentParser()
ap.add_argument('--input-mx-model',
'-im',
type=str,
help='mxnet .params model to convert',
required=True)
ap.add_argument('--model-configuration',
'-mc',
type=str,
choices=fastface.list_arch_configs("lffd"),
default='560_25L_8S')
ap.add_argument('--output-path', '-o', type=str, default='./')
return ap.parse_args()
hparams = {
"learning_rate": 0.1,
"momentum": 0.9,
"weight_decay": 0.00001,
"milestones": [500000, 1000000, 1500000],
"gamma": 0.1,
"ratio": 10
}
# checkout available architectures to train
print(ff.list_archs())
# ["lffd"]
arch = "lffd"
# checkout available configs for the architecture
print(ff.list_arch_configs(arch))
# ["original", "slim"]
config = "slim"
# build pl.LightningModule with random weights
model = ff.FaceDetector.build(arch,
config=config,
preprocess=preprocess,
hparams=hparams)
# add average precision pl.metrics.Metric to the model
model.add_metric("average_precision",
ff.metric.AveragePrecision(iou_threshold=0.5))
model_save_name = "{}_{}_{}_best".format(arch, config, "fddb")
ckpt_save_path = "./checkpoints"
def build_module_args() -> Tuple:
for arch in ff.list_archs():
for config in ff.list_arch_configs(arch):
yield (arch, config)