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authorJules Laplace <julescarbon@gmail.com>2019-12-10 22:44:49 +0100
committerJules Laplace <julescarbon@gmail.com>2019-12-10 22:44:49 +0100
commit3562a70cccb842becb9a33d867f83e6a00dc3635 (patch)
tree2d03339cc3b94d22a47549c7bf0bef6a4b2a1e44 /cli/app/commands
parent3852f8b30183f87633151aab017d7fbe4891bdd6 (diff)
write json params
Diffstat (limited to 'cli/app/commands')
-rw-r--r--cli/app/commands/biggan/search.py11
1 files changed, 8 insertions, 3 deletions
diff --git a/cli/app/commands/biggan/search.py b/cli/app/commands/biggan/search.py
index 0b31a0d..5844855 100644
--- a/cli/app/commands/biggan/search.py
+++ b/cli/app/commands/biggan/search.py
@@ -17,6 +17,7 @@ import tensorflow_hub as hub
import shutil
import h5py
+from app.search.json import save_params_latent, save_params_dense
from app.search.image import image_to_uint8, imconvert_uint8, imconvert_float32, \
imread, imwrite, imgrid
from app.search.vector import truncated_z_sample, truncated_z_single, create_labels
@@ -28,11 +29,13 @@ from app.search.vector import truncated_z_sample, truncated_z_single, create_lab
help='Dimensions of BigGAN network (128, 256, 512)')
@click.option('-v', '--video', 'opt_video', is_flag=True,
help='Export a video for each dataset')
+@click.option('-t', '--tag', 'opt_tag', default='inverse_' + int(time.time() * 1000),
+ help='Tag this dataset')
# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
@click.pass_context
def cli(ctx, opt_fp_in, opt_dims, opt_video):
"""
- Search for an image in BigGAN using gradient descent
+ Search for an image (class vector) in BigGAN using gradient descent
"""
generator = hub.Module('https://tfhub.dev/deepmind/biggan-' + str(opt_dims) + '/2')
@@ -56,9 +59,11 @@ def cli(ctx, opt_fp_in, opt_dims, opt_video):
else:
paths = [opt_fp_in]
- fp_inverses = os.path.join(app_cfg.INVERSES_DIR, 'inverse_' + int(time.time() * 1000))
+ fp_inverses = os.path.join(app_cfg.INVERSES_DIR, opt_tag)
os.makedirs(fp_inverses, exist_ok=True)
- out_file = h5py.File(fp_inverses, 'w')
+ save_params_latent(fp_inverses, opt_tag)
+ save_params_dense(fp_inverses, opt_tag)
+ out_file = h5py.File(join(fp_inverses, 'dataset.hdf5'), 'w')
out_images = out_file.create_dataset('xtrain', (len(paths), 3, 512, 512,), dtype='float32')
out_labels = out_file.create_dataset('ytrain', (len(paths), vocab_size,), dtype='float32')