def main():
args = docopt(__doc__)
cfg = dk.load_config()
tubs = args['--tubs']
model = args['--model']
model_type = args['--type']
train(cfg, tubs, model, model_type)
def auto_train(model_types, datasets):
cfg = dk.load_config()
for model_type in model_types:
for dataset in datasets:
model = f"models/model_{model_type}_{dataset}.h5"
train(cfg, f"datasets/{dataset}", model, model_type)
def run(self, args):
args = self.parse_args(args)
args.tub = ','.join(args.tub)
cfg = load_config(args.config)
framework = args.framework if args.framework else cfg.DEFAULT_AI_FRAMEWORK
if framework == 'tensorflow':
from donkeycar.pipeline.training import train
train(cfg, args.tub, args.model, args.type)
elif framework == 'pytorch':
from donkeycar.parts.pytorch.torch_train import train
train(cfg,
args.tub,
args.model,
args.type,
checkpoint_path=args.checkpoint)
else:
print(
"Unrecognized framework: {}. Please specify one of 'tensorflow' or 'pytorch'"
.format(framework))
def main():
args = docopt(__doc__)
cfg = dk.load_config()
tubs = args['--tubs']
model = args['--model']
model_type = args['--type']
framework = args.get('--framework', 'tf')
if framework == 'tf':
from donkeycar.pipeline.training import train
train(cfg, tubs, model, model_type)
elif framework == 'torch':
from donkeycar.parts.pytorch.torch_train import train
checkpoint_path = args.get('--checkpoint', None)
train(cfg, tubs, model, model_type, checkpoint_path=checkpoint_path)
else:
print(
"Unrecognized framework: {}. Please specify one of 'tf' or 'torch'"
.format(framework))
def run(self, args):
args = self.parse_args(args)
args.tub = ','.join(args.tub)
cfg = load_config(args.config)
framework = args.framework if args.framework \
else getattr(cfg, 'DEFAULT_AI_FRAMEWORK', 'tensorflow')
if framework == 'tensorflow':
from donkeycar.pipeline.training import train
train(cfg, args.tub, args.model, args.type, args.transfer,
args.comment)
elif framework == 'pytorch':
from donkeycar.parts.pytorch.torch_train import train
train(cfg,
args.tub,
args.model,
args.type,
checkpoint_path=args.checkpoint)
else:
print(
f"Unrecognized framework: {framework}. Please specify one of "
f"'tensorflow' or 'pytorch'")
def test_train(config: Config, data: Data) -> None:
"""
Testing convergence of the linear an categorical models
:param config: donkey config
:param data: test case data
:return: None
"""
def pilot_path(name):
pilot_name = f'pilot_{name}.h5'
return os.path.join(config.MODELS_PATH, pilot_name)
if data.pretrained:
config.LATENT_TRAINED = pilot_path(data.pretrained)
tub_dir = config.DATA_PATH
history = train(config, tub_dir, pilot_path(data.name), data.type)
loss = history.history['loss']
# check loss is converging
assert loss[-1] < loss[0] * data.convergence
def train_call(self, model_type, *args):
# remove car directory from path
tub_path = tub_screen().ids.tub_loader.tub.base_path
transfer = self.ids.transfer_spinner.text
if transfer != 'Choose transfer model':
transfer = os.path.join(self.config.MODELS_PATH, transfer + '.h5')
else:
transfer = None
try:
history = train(self.config, tub_paths=tub_path,
model_type=model_type,
transfer=transfer,
comment=self.ids.comment.text)
self.ids.status.text = f'Training completed.'
self.ids.train_button.state = 'normal'
self.ids.transfer_spinner.text = 'Choose transfer model'
self.reload_database()
except Exception as e:
self.ids.status.text = f'Train error {e}'
def test_train(config: Config, data: Data) -> None:
"""
Testing convergence of the linear an categorical models
:param config: donkey config
:param data: test case data
:return: None
"""
def pilot_path(name):
pilot_name = f'pilot_{name}.h5'
return os.path.join(config.MODELS_PATH, pilot_name)
if data.pretrained:
config.LATENT_TRAINED = pilot_path(data.pretrained)
tub_dir = config.DATA_PATH_ALL if data.type in full_tub else \
config.DATA_PATH
if data.preprocess == 'aug':
add_augmentation_to_config(config)
elif data.preprocess == 'trans':
add_transformation_to_config(config)
history = train(config, tub_dir, pilot_path(data.name), data.type)
loss = history.history['loss']
# check loss is converging
assert loss[-1] < loss[0] * data.convergence
