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Updated the SSD training with new config

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-04 16:43:44 +02:00
parent 01f5de6d6c
commit eda3496f04
2 changed files with 16 additions and 19 deletions
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28
src/twomartens/masterthesis/cli.py
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@ -169,22 +169,25 @@ def _ssd_train(args: argparse.Namespace) -> None:
tf.enable_eager_execution()
batch_size = 32
image_size = 300
batch_size = conf.get_property("Parameters.batch_size")
image_size = conf.get_property("Parameters.ssd_image_size")
use_dropout = False if args.network == "ssd" else True
pre_trained_weights_file = f"{args.weights_path}/VGG_coco_SSD_300x300_iter_400000.h5"
weights_path = f"{args.weights_path}/train/{args.network}/"
weights_path = conf.get_property("Paths.weights")
pre_trained_weights_file = f"{weights_path}/VGG_coco_SSD_300x300_iter_400000.h5"
weights_path = f"{weights_path}/train/{args.network}/"
os.makedirs(weights_path, exist_ok=True)
# load prepared ground truth
with open(f"{args.ground_truth_path_train}/photo_paths.bin", "rb") as file:
train_gt_path = conf.get_property('Paths.scenenet_gt_train')
val_gt_path = conf.get_property('Paths.scenenet_gt_val')
with open(f"{train_gt_path}/photo_paths.bin", "rb") as file:
file_names_train = pickle.load(file)
with open(f"{args.ground_truth_path_train}/instances.bin", "rb") as file:
with open(f"{train_gt_path}/instances.bin", "rb") as file:
instances_train = pickle.load(file)
with open(f"{args.ground_truth_path_val}/photo_paths.bin", "rb") as file:
with open(f"{val_gt_path}/photo_paths.bin", "rb") as file:
file_names_val = pickle.load(file)
with open(f"{args.ground_truth_path_val}/instances.bin", "rb") as file:
with open(f"{val_gt_path}/instances.bin", "rb") as file:
instances_val = pickle.load(file)
# model
@ -194,7 +197,7 @@ 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, args.coco_path,
data.load_scenenet_data(file_names_train, instances_train, conf.get_property("Paths.coco"),
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=(image_size, image_size),
@ -209,7 +212,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
nr_trajectories=1)
del file_names_train, instances_train, file_names_val, instances_val
if args.debug:
if args.debug and conf.get_property("Debug.train_images"):
from matplotlib import pyplot
import numpy as np
from PIL import Image
@ -260,8 +263,9 @@ def _ssd_train(args: argparse.Namespace) -> None:
nr_batches_train = int(math.floor(train_length / batch_size))
nr_batches_val = int(math.floor(val_length / batch_size))
summary_path = conf.get_property("Paths.summaries")
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=f"{args.summary_path}/train/{args.network}/{args.iteration}"
log_dir=f"{summary_path}/train/{args.network}/{args.iteration}"
)
history = ssd.train_keras(
@ -278,7 +282,7 @@ def _ssd_train(args: argparse.Namespace) -> None:
tensorboard_callback=tensorboard_callback
)
with open(f"{args.summary_path}/train/{args.network}/{args.iteration}/history", "wb") as file:
with open(f"{summary_path}/train/{args.network}/{args.iteration}/history", "wb") as file:
pickle.dump(history.history, file)

7
src/twomartens/masterthesis/main.py
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@ -116,13 +116,6 @@ def _build_train(parser: argparse.ArgumentParser) -> None:
def _build_ssd_train(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--coco_path", type=str, help="the path to the COCO data set")
parser.add_argument("--weights_path", type=str, help="path to the weights directory")
parser.add_argument("--ground_truth_path_train", type=str,
help="path to the prepared ground truth directory for training")
parser.add_argument("--ground_truth_path_val", type=str,
help="path to the prepared ground truth directory for validation")
parser.add_argument("--summary_path", type=str, help="path to the summaries directory")
parser.add_argument("num_epochs", type=int, help="the number of epochs to train", default=80)
parser.add_argument("iteration", type=int, help="the training iteration")