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Changed paths to use config provided paths

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-07 13:55:01 +02:00
parent 3e9c90f201
commit 94a4c69896
1 changed files with 36 additions and 23 deletions
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src/twomartens/masterthesis/cli.py
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@ -181,6 +181,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
summary_path = conf.get_property("Paths.summaries")
weights_path = conf.get_property("Paths.weights")
coco_path = conf.get_property("Paths.coco")
pre_trained_weights_file = f"{weights_path}/{args.network}/VGG_coco_SSD_300x300_iter_400000.h5"
weights_path = f"{weights_path}/{args.network}/train/"
os.makedirs(weights_path, exist_ok=True)
@ -204,14 +205,14 @@ def _ssd_train(args: argparse.Namespace) -> None:
ssd_model = ssd.SSD(mode='training', weights_path=pre_trained_weights_file)
train_generator, train_length = \
data.load_scenenet_data(file_names_train, instances_train, conf.get_property("Paths.coco"),
data.load_scenenet_data(file_names_train, instances_train, coco_path,
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=(image_size, image_size),
training=True, evaluation=False, augment=False,
nr_trajectories=1)
val_generator, val_length = \
data.load_scenenet_data(file_names_val, instances_val, args.coco_path,
data.load_scenenet_data(file_names_val, instances_val, coco_path,
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=(image_size, image_size),
@ -226,7 +227,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
train_length -= batch_size
train_images = train_data[0]
train_labels = train_data[1]
output_path = f"{summary_path}/train/{args.network}/{args.iteration}"
output_path = f"{summary_path}/{args.network}/train/{args.iteration}"
debug.save_ssd_train_images(train_images, train_labels, output_path)
@ -235,7 +236,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
if args.debug and conf.get_property("Debug.summaries"):
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=f"{summary_path}/train/{args.network}/{args.iteration}"
log_dir=f"{summary_path}/{args.network}/train/{args.iteration}"
)
else:
tensorboard_callback = None
@ -254,7 +255,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
tensorboard_callback=tensorboard_callback
)
with open(f"{summary_path}/train/{args.network}/{args.iteration}/history", "wb") as file:
with open(f"{summary_path}/{args.network}/train/{args.iteration}/history", "wb") as file:
pickle.dump(history.history, file)
@ -271,18 +272,20 @@ def _auto_encoder_train(args: argparse.Namespace) -> None:
image_size = 256
coco_data = data.load_coco_train(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size,
resized_shape=(image_size, image_size))
summary_path = conf.get_property("Paths.summary")
train_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/train/category-{category}/{args.iteration}"
f"{summary_path}/{args.network}/train/category-{category}/{args.iteration}"
)
weights_path = conf.get_property("Paths.weights")
if args.debug:
with train_summary_writer.as_default():
train.train_simple(coco_data, iteration=args.iteration,
weights_prefix=f"{args.weights_path}/category-{category}",
weights_prefix=f"{weights_path}/{args.network}/category-{category}",
zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size,
channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
else:
train.train_simple(coco_data, iteration=args.iteration,
weights_prefix=f"{args.weights_path}/category-{category}",
weights_prefix=f"{weights_path}/{args.network}/category-{category}",
zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size,
channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
@ -301,30 +304,34 @@ def _ssd_test(args: argparse.Namespace) -> None:
config.gpu_options.allow_growth = False
tf.enable_eager_execution(config=config)
batch_size = 16
image_size = (300, 300)
forward_passes_per_image = 10
batch_size = conf.get_property("Parameters.batch_size")
image_size = conf.get_property("Parameters.ssd_image_size")
forward_passes_per_image = conf.get_property("Parameters.ssd_forward_passes_per_image")
use_dropout = False if args.network == "ssd" else True
checkpoint_path = f"{args.weights_path}/{args.network}/train/{args.train_iteration}"
weights_path = conf.get_property("Paths.weights")
output_path = conf.get_property("Paths.output")
coco_path = conf.get_property("Paths.coco")
checkpoint_path = f"{weights_path}/{args.network}/train/{args.train_iteration}"
model_file = f"{checkpoint_path}/ssd300.h5"
output_path = f"{args.output_path}/val/{args.network}/{args.iteration}/"
output_path = f"{output_path}/{args.network}/val/{args.iteration}/"
os.makedirs(output_path, exist_ok=True)
# load prepared ground truth
with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file:
ground_truth_path = conf.get_property("Paths.scenenet_gt_test")
with open(f"{ground_truth_path}/photo_paths.bin", "rb") as file:
file_names_photos = pickle.load(file)
with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
with open(f"{ground_truth_path}/instances.bin", "rb") as file:
instances = pickle.load(file)
# model
ssd_model = tf.keras.models.load_model(model_file)
test_generator, length_dataset = \
data.load_scenenet_data(file_names_photos, instances, args.coco_path,
data.load_scenenet_data(file_names_photos, instances, coco_path,
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=image_size,
resized_shape=(image_size, image_size),
training=False, evaluation=True, augment=False)
del file_names_photos, instances
@ -347,25 +354,29 @@ def _auto_encoder_test(args: argparse.Namespace) -> None:
from tensorflow.python.ops import summary_ops_v2
tf.enable_eager_execution()
coco_path = args.coco_path
coco_path = conf.get_property("Paths.coco")
category = args.category
category_trained = args.category_trained
batch_size = 16
image_size = 256
coco_data = data.load_coco_val(coco_path, category, num_epochs=1,
batch_size=batch_size, resized_shape=(image_size, image_size))
summary_path = conf.get_property("Paths.summary")
use_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/val/category-{category}/{args.iteration}"
f"{args.summary_path}/{args.network}/val/category-{category}/{args.iteration}"
)
weights_path = conf.get_property("Paths.weights")
if args.debug:
with use_summary_writer.as_default():
run.run_simple(coco_data, iteration=args.iteration_trained,
weights_prefix=f"{args.weights_path}/{args.network}/category-{category_trained}",
weights_prefix=f"{weights_path}/{args.network}/category-{category_trained}",
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
image_size=image_size)
else:
run.run_simple(coco_data, iteration=args.iteration_trained,
weights_prefix=f"{args.weights_path}/{args.network}/category-{category_trained}",
weights_prefix=f"{weights_path}/{args.network}/category-{category_trained}",
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
image_size=image_size)
@ -385,8 +396,10 @@ def _ssd_evaluate(args: argparse.Namespace) -> None:
batch_size = 16
use_dropout = False if args.network == "ssd" else True
output_path = f"{args.output_path}/val/{args.network}/{args.iteration}"
evaluation_path = f"{args.evaluation_path}/{args.network}"
output_path = conf.get_property("Paths.output")
evaluation_path = conf.get_property("Paths.evaluation")
output_path = f"{output_path}/{args.network}/val/{args.iteration}"
evaluation_path = f"{evaluation_path}/{args.network}"
result_file = f"{evaluation_path}/results-{args.iteration}.bin"
label_file = f"{output_path}/labels.bin"
predictions_file = f"{output_path}/predictions.bin"