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test_nyu_depth.py
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test_nyu_depth.py
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import matplotlib
matplotlib.use('Agg')
import tensorflow as tf
import argparse
from libs.train_vae import train_vae
def test_depth():
parser = argparse.ArgumentParser(description='Parser added')
parser.add_argument(
'-c',
action="store_true",
dest="convolutional",
help='Whether use convolution or not')
parser.add_argument(
'-f',
action="store_true",
dest="fire",
help='Whether use fire module or not')
parser.add_argument(
'-v',
action="store_true",
dest="variational",
help='Wether use latent variance or not')
parser.add_argument(
'-m',
action="store_true",
dest="metric",
help='Whether use metric loss or not')
parser.add_argument(
'-o',
action="store",
dest="output_path",
default="result_vae",
help='Destination for storing results')
parser.print_help()
results = parser.parse_args()
# Train an autoencoder on Synthetic data rendered from ShapeNet.
train_vae(
files_train="./list_nyu_depth_train.csv",
files_valid="./list_nyu_depth_test.csv",
input_shape=[240, 320, 3],
output_shape=[240, 320, 1],
batch_size=16,
n_epochs=5000,
crop_shape=[240, 320],
crop_factor=1.0,
convolutional=results.convolutional,
fire=results.fire,
variational=results.variational,
metric=results.metric,
n_filters=[256, 128, 128, 128, 128, 128],
n_hidden=None,
n_code=128,
n_clusters=28,
dropout=True,
filter_sizes=[5, 3, 3, 3, 3, 3],
activation=tf.nn.relu,
ckpt_name='depth.ckpt',
output_path=results.output_path)
if __name__ == '__main__':
test_depth()