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Added config option to enable/disable dropout

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-09-13 12:25:13 +02:00
parent 53022bf1a9
commit dd2d0f407b
3 changed files with 35 additions and 42 deletions
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23
src/twomartens/masterthesis/cli.py
View File

@ -185,7 +185,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
conf_obj = conf.Config()
use_dropout = _ssd_is_dropout(args)
use_dropout = _ssd_is_bayesian(args)
paths = _ssd_train_prepare_paths(args, conf_obj)
ground_truth = _ssd_train_load_gt(conf_obj)
@ -240,19 +240,16 @@ def _ssd_test(args: argparse.Namespace) -> None:
_init_eager_mode()
conf_obj = conf.Config()
use_dropout = _ssd_is_dropout(args)
use_bayesian = _ssd_is_bayesian(args)
paths = _ssd_test_prepare_paths(args, conf_obj)
ground_truth = _ssd_test_load_gt(conf_obj)
ssd_model, predictor_sizes = ssd.get_model(use_dropout,
keras_ssd300_dropout.ssd_300_dropout,
keras_ssd300.ssd_300,
conf_obj.parameters.ssd_image_size,
conf_obj.parameters.nr_classes,
"training",
conf_obj.parameters.ssd_dropout_rate,
conf_obj.parameters.ssd_top_k,
paths.weights_file)
ssd_model, predictor_sizes = ssd.get_model(use_bayesian=use_bayesian,
bayesian_model=keras_ssd300_dropout.ssd_300_dropout,
vanilla_model=keras_ssd300.ssd_300,
conf_obj=conf_obj,
mode="training",
pre_trained_weights_file=paths.weights_file)
loss_func = ssd.get_loss_func()
ssd.compile_model(ssd_model, conf_obj.parameters.learning_rate, loss_func)
@ -270,7 +267,7 @@ def _ssd_test(args: argparse.Namespace) -> None:
model=ssd_model,
conf_obj=conf_obj,
steps_per_epoch=steps_per_epoch,
use_dropout=use_dropout,
use_bayesian=use_bayesian,
nr_digits=nr_digits,
output_path=paths.output_path)
@ -730,7 +727,7 @@ def _visualise_get_config_values(config_get: Callable[[str], Union[str, int, flo
return output_path, coco_path, ground_truth_path
def _ssd_is_dropout(args: argparse.Namespace) -> bool:
def _ssd_is_bayesian(args: argparse.Namespace) -> bool:
return False if args.network == "ssd" else True

1
src/twomartens/masterthesis/config.py
View File

@ -68,6 +68,7 @@ _CONFIG_PROPS = {
"ssd_entropy_threshold_max": (float, "2.5"),
"ssd_test_pretrained": (bool, "False"),
"ssd_use_coco": (bool, "False"),
"ssd_use_dropout": (bool, "True"),
"nr_trajectories": (int, "-1")
}
}

53
src/twomartens/masterthesis/ssd.py
View File

@ -50,53 +50,48 @@ K = tf.keras.backend
tfe = tf.contrib.eager
def get_model(use_dropout: bool,
dropout_model: callable, vanilla_model: callable,
image_size: int, nr_classes: int, mode: str,
dropout_rate: float, top_k: int,
pre_trained_weights_file: Optional[str] = None,
iou_threshold: Optional[float] = None) -> Tuple[tf.keras.models.Model, np.ndarray]:
def get_model(use_bayesian: bool,
bayesian_model: callable, vanilla_model: callable,
conf_obj: config.Config,
mode: str,
pre_trained_weights_file: Optional[str] = None) -> Tuple[tf.keras.models.Model, np.ndarray]:
"""
Returns the correct SSD model and the corresponding predictor sizes.
Args:
use_dropout: True if dropout variant should be used, False otherwise
dropout_model: function to build dropout SSD model
use_bayesian: True if Bayesian variant should be used, False otherwise
bayesian_model: function to build Bayesian SSD model
vanilla_model: function to build vanilla SSD model
image_size: size of the resized images
nr_classes: number of classes
conf_obj: configuration object
mode: one of "training", "inference", "inference_fast"
dropout_rate: rate for dropout layers (only applies if dropout is used)
top_k: number of highest scoring predictions kept for each batch item
pre_trained_weights_file: path to h5 file with pre-trained weights
iou_threshold: all boxes with higher iou to local maximum box are suppressed
(only relevant for inference modes)
Returns:
SSD model, predictor_sizes
"""
image_size = (image_size, image_size, 3)
image_size = (conf_obj.parameters.ssd_image_size, conf_obj.parameters.ssd_image_size, 3)
scales = [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]
if use_dropout:
model, predictor_sizes = dropout_model(
if use_bayesian:
model, predictor_sizes = bayesian_model(
image_size=image_size,
n_classes=nr_classes,
n_classes=conf_obj.parameters.nr_classes,
mode=mode,
iou_threshold=iou_threshold,
dropout_rate=dropout_rate,
top_k=top_k,
iou_threshold=conf_obj.parameters.ssd_iou_threshold,
dropout_rate=conf_obj.parameters.ssd_dropout_rate,
top_k=conf_obj.parameters.ssd_top_k,
scales=scales,
return_predictor_sizes=True,
coords="corners"
coords="corners",
use_dropout=conf_obj.parameters.ssd_use_dropout
)
else:
model, predictor_sizes = vanilla_model(
image_size=image_size,
n_classes=nr_classes,
n_classes=conf_obj.parameters.nr_classes,
mode=mode,
iou_threshold=iou_threshold,
top_k=top_k,
iou_threshold=conf_obj.parameters.ssd_iou_threshold,
top_k=conf_obj.parameters.ssd_top_k,
scales=scales,
return_predictor_sizes=True,
coords="corners"
@ -150,7 +145,7 @@ def predict(generator: callable,
model: tf.keras.models.Model,
conf_obj: config.Config,
steps_per_epoch: int,
use_dropout: bool,
use_bayesian: bool,
nr_digits: int,
output_path: str) -> None:
"""
@ -163,13 +158,13 @@ def predict(generator: callable,
model: compiled and trained Keras model
conf_obj: configuration object
steps_per_epoch: number of batches per epoch
use_dropout: if True, multiple forward passes and observations will be used
use_bayesian: if True, multiple forward passes and observations will be used
nr_digits: number of digits needed to print largest batch number
output_path: the path in which the results should be saved
"""
output_file, label_output_file = _predict_prepare_paths(output_path, use_dropout)
output_file, label_output_file = _predict_prepare_paths(output_path, use_bayesian)
_predict_loop(generator, use_dropout, steps_per_epoch,
_predict_loop(generator, use_bayesian, steps_per_epoch,
dropout_step=functools.partial(
_predict_dropout_step,
model=model,