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Updated predict functionality to use Keras

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-06-13 16:56:12 +02:00
parent d466e8e376
commit b2a1db6485
1 changed files with 20 additions and 16 deletions
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34
src/twomartens/masterthesis/cli.py
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@ -249,7 +249,6 @@ def _ssd_val(args: argparse.Namespace) -> None:
import os
import tensorflow as tf
from tensorflow.python.ops import summary_ops_v2
from twomartens.masterthesis import data
from twomartens.masterthesis import ssd
@ -260,12 +259,12 @@ def _ssd_val(args: argparse.Namespace) -> None:
tf.enable_eager_execution(config=config)
batch_size = 16
image_size = 300
image_size = (300, 300)
forward_passes_per_image = 10
use_dropout = False if args.network == "ssd" else True
weights_file = f"{args.weights_path}/VGG_coco_SSD_300x300_iter_400000.h5"
checkpoint_path = f"{args.weights_path}/train/{args.network}/{args.train_iteration}"
model_file = f"{checkpoint_path}/ssd300.h5"
output_path = f"{args.output_path}/val/{args.network}/{args.iteration}/"
os.makedirs(output_path, exist_ok=True)
@ -275,22 +274,27 @@ def _ssd_val(args: argparse.Namespace) -> None:
with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
instances = pickle.load(file)
scenenet_data, nr_digits, length_dataset = \
# 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,
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=(image_size, image_size))
resized_shape=image_size,
training=False, evaluation=True)
del file_names_photos, instances
use_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/val/{args.network}/{args.iteration}"
)
if args.debug:
with use_summary_writer.as_default():
ssd.predict(scenenet_data, use_dropout, output_path, weights_file, checkpoint_path,
nr_digits=nr_digits, forward_passes_per_image=forward_passes_per_image)
else:
ssd.predict(scenenet_data, use_dropout, output_path, weights_file, checkpoint_path,
nr_digits=nr_digits, forward_passes_per_image=forward_passes_per_image)
nr_digits = math.ceil(math.log10(math.ceil(length_dataset / batch_size)))
steps_per_epoch = int(math.ceil(length_dataset / batch_size))
ssd.predict_keras(test_generator,
steps_per_epoch,
ssd_model,
use_dropout,
forward_passes_per_image,
image_size,
output_path,
nr_digits)
def _auto_encoder_val(args: argparse.Namespace) -> None: