def project_real_images(dataset_name, data_dir, num_images, num_snapshots):
proj = projector.Projector(num_steps)
proj.set_network(Gs)
print('Loading images from "%s"...' % dataset_name)
dataset_obj = training.dataset.load_dataset(data_dir=data_dir,
tfrecord_dir=dataset_name,
max_label_size=0,
verbose=True,
repeat=False,
shuffle_mb=0)
assert dataset_obj.shape == Gs.output_shape[1:]
latents = []
for row, image_idx in enumerate(range(num_images)):
print('Projecting image %d/%d ...' % ((image_idx + 1), num_images))
images, _labels = dataset_obj.get_minibatch_np(1)
images = training.misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
run_projector.project_image(proj,
targets=images,
png_prefix=dnnlib.make_run_dir_path(
'/tmp/%s-' % files[image_idx]),
num_snapshots=num_snapshots)
# Save tmp copy in Google Drive
save_latent(proj.get_dlatents(), '/latents/%s' % files[row])
# Add to array
latents.append(proj.get_dlatents())
# Returns all latents projected
return latents
def project_real_images(dataset_name, data_dir, num_images, num_snapshots):
proj = projector.Projector()
#proj.verbose = True
proj.set_network(Gs)
print('Loading images from "%s"...' % dataset_name)
dataset_obj = training.dataset.load_dataset(data_dir=data_dir,
tfrecord_dir=dataset_name,
max_label_size=0,
verbose=True,
repeat=False,
shuffle_mb=0)
assert dataset_obj.shape == Gs.output_shape[1:]
for image_idx in range(num_images):
print('Projecting image %d/%d ...' % (image_idx, num_images))
images, _labels = dataset_obj.get_minibatch_np(1)
images = training.misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
print("will run projector")
run_projector.project_image(
proj,
targets=images,
png_prefix=dnnlib.make_run_dir_path('projection/out/image%04d-' %
image_idx),
num_snapshots=num_snapshots)
print("done running projector")
def project_images(Gs,
images_dir,
tfrecord_dir,
data_dir,
num_snapshots,
pure_projector=False):
"""setup projector"""
print('Setting up projector')
proj = projector.Projector()
proj.set_network(Gs)
# generate tfrecords
nb_images = dataset_tool.create_from_images(str(tfrecord_dir),
str(images_dir), True)
# loading images from tfrecords
dataset_obj = training.dataset.load_dataset(tfrecord_dir=tfrecord_dir,
max_label_size=0,
verbose=True,
repeat=False,
shuffle_mb=0)
assert dataset_obj.shape == Gs.output_shape[1:]
# project all loaded images
print('=======================')
for image_idx in tqdm(range(nb_images)):
print(f'Projecting image {image_idx + 1}/{nb_images}')
images, _labels = dataset_obj.get_minibatch_np(1)
images = training.misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
run_path = data_dir / f'out_{image_idx}'
run_path.mkdir()
run_projector.project_image(proj,
targets=images,
png_prefix=dnnlib.make_run_dir_path(
str(run_path / 'image_')),
num_snapshots=num_snapshots)
def project_real_images(dataset_name, data_dir, num_images, num_snapshots, model_pkl, steps=1000):
stream = open(model_pkl, 'rb')
tflib.init_tf()
with stream:
G, D, Gs = pickle.load(stream, encoding='latin1')
proj = projector.Projector()
proj.set_network(Gs)
proj.num_steps = steps
print('Loading images from "%s"...' % dataset_name)
dataset_obj = training.dataset.load_dataset(data_dir=data_dir, tfrecord_dir=dataset_name, max_label_size=0, verbose=True, repeat=False, shuffle_mb=0)
print(dataset_obj.shape)
print(Gs.output_shape)
assert dataset_obj.shape == Gs.output_shape[1:]
for image_idx in range(num_images):
print('Projecting image %d/%d ...' % (image_idx, num_images))
images, _labels = dataset_obj.get_minibatch_np(1)
images = training.misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
run_projector.project_image(proj, targets=images, png_prefix=dnnlib.make_run_dir_path(f'{img_path}image%04d-' % image_idx), num_snapshots=num_snapshots)