def run(self):
super().run()
search_query = self.args['query']
num_of_images = self.args['num_of_images']
folder = '{}_{}'.format(num_of_images, orig_time)
if not os.path.exists('../data/raw/{}'.format(search_query)):
os.mkdir('../data/raw/{}'.format(search_query))
if not os.path.exists('../data/raw/{}/{}'.format(search_query,
folder)):
os.mkdir('../data/raw/{}/{}'.format(search_query, folder))
photos = flickr.walk(text=search_query,
tag_mode='all',
tags=search_query,
extras='url_c',
per_page=num_of_images,
sort='relevance')
progress_bar = ProgressBar(num_of_images, log=self.log)
for i, photo in enumerate(photos):
try:
url = photo.get('url_c')
img = download_image_from_url(url)
cv2.imwrite(
'../data/raw/{}/{}/img_{}_{}.png'.format(
search_query, folder, i, int(time.time())), img)
except:
pass
if progress_bar.increment(): break
def create_from_image_folders(datadir, jpg=False, shuffle=True, size=None):
assert os.path.isdir(datadir)
imgs = []
labels = []
for root, subdirs, files in os.walk(datadir):
for i, subdir in enumerate(subdirs):
tmp_list = img_list(os.path.join(root, subdir))
imgs = imgs + tmp_list
labels = labels + [i] * len(tmp_list)
labels = np.array(labels)
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.
assert len(imgs) > 0, ' No input images found!'
sample_img = np.asarray(PIL.Image.open(imgs[0]))
sample_shape = sample_img.shape
channels = sample_shape[2] if sample_img.ndim == 3 else 1
assert channels in [1, 3, 4], ' Weird color dim: %d' % channels
print(' Making dataset ..', datadir, sample_shape,
'%d labels' % (np.max(labels) + 1))
if jpg is True: print(' Loading JPG as is!')
with TFRecordExporter(datadir, len(imgs)) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(
len(imgs))
pbar = ProgressBar(order.size)
for idx in range(order.size):
img_path = imgs[order[idx]]
tfr.add_image(img_path, jpg=jpg, size=size)
pbar.upd()
tfr.add_labels(onehot[order])
return tfr.tfr_file, len(imgs)
def start(self):
# Inicializa a barra usada para medir o progresso do simulador
# Ela é contabilizada de acordo com o valor do menor intervalo de confiança encontrado a cada rodada,
# Chegando a 100% quando o intervalo chega a 10% da média do estimador
# Mostrada quando o simulador não está definido na forma de teste
if not(self.test):
prog = ProgressBar(0, 0.9, 77, mode='fixed', char='#')
print "Processando as rodadas:"
print prog, '\r',
sys.stdout.flush()
# Loop principal do simulador.
# Termina quando todos os intervalos de confiança forem menores que 10% da média do estimador.
while not(self.valid_confidence_interval()):
# Loop de cada rodada, processa um evento a cada iteração.
while len(self.clients) <= self.total_clients:
self.process_event()
self.discard_clients()
# Processa os dados gerados por uma rodada.
self.process_sample()
if self.samples > 1:
self.calc_results()
prog.update_amount(max(prog.amount, self.pb_amount()))
print prog, '\r',
self.samples += 1
sys.stdout.flush()
# Linha para forçar o teste a executar apenas 1 rodada.
if self.test:
break
print
def run(self):
super().run()
query = args['query']
options = os.listdir('../data/preprocessed/' + query)
ind = -1
if len(options) == 1:
ind = 0
else:
for i, option in enumerate(options):
print('({}) {}'.format(i, option))
ind = input('Enter your option index: ')
while not ind.isdigit() or int(ind) < 0 or int(ind) >= len(options):
ind = input('Please enter a valid option: ')
ind = int(ind)
img_dir = '../data/preprocessed/' + query + '/' + options[ind]
img_names = os.listdir(img_dir)
progress_bar = ProgressBar(len(img_names), log=self.log)
if not os.path.exists('../data/loaded/' + query):
os.mkdir('../data/loaded/{}'.format(query))
images = []
while True:
img = cv2.imread(img_dir + '/' + img_names[progress_bar.i])
images.append(img)
if progress_bar.increment(): break
timer = Timer(log=self.log)
images = timer.time(lambda:np.array(images), 'Converting to numpy array')
timer.time(lambda:np.save('../data/loaded/{}/{}'.format(query, options[ind] + '.npy'), images), 'Saving numpy array to file')
def make_transit(lat1, lat2, fcount, start_frame=0):
pbar = ProgressBar(fcount)
for i in range(fcount):
img = render_latent_mix(lat1, lat2, i/fcount)
fname = os.path.join(a.out_dir, 'ttt', "%05d.jpg" % (i+start_frame))
if i%2==0:
cv2.imshow('latent', img[:,:,::-1])
cv2.waitKey(10)
imsave(fname, img)
pbar.upd()
def make_loop(base_latent, direction, lrange, fcount, start_frame=0):
coeffs = get_coeffs_dir(lrange, fcount//2)
pbar = ProgressBar(fcount)
for i in range(fcount):
img = render_latent_dir(base_latent, direction, coeffs[i])
fname1 = os.path.join(a.out_dir, 'ttt', "%05d.jpg" % (i+start_frame))
if i%2==0:
cv2.imshow('latent', img[:,:,::-1])
cv2.waitKey(10)
imsave(fname1, img)
pbar.upd()
def copy_and_crop_or_pad_trainables(src_net, tgt_net) -> None:
source_trainables = src_net.trainables.keys()
target_trainables = tgt_net.trainables.keys()
names = [pair for pair in zip(source_trainables, target_trainables)]
skip = []
pbar = ProgressBar(len(names))
for pair in names:
source_name, target_name = pair
log = source_name
x = src_net.get_var(source_name)
y = tgt_net.get_var(target_name)
source_shape = x.shape
target_shape = y.shape
if source_shape != target_shape:
update = x
index = None
if 'Dense' in source_name:
if source_shape[0] > target_shape[0]:
gap = source_shape[0] - target_shape[0]
start = abs(gap) // 2
end = start + target_shape[0]
update = update[start:end, :]
else:
update = pad_symm_np(update, target_shape)
log = (log, source_shape, '=>', target_shape)
else:
try:
if source_shape[2] > target_shape[2]:
index = 2
gap = source_shape[index] - target_shape[index]
start = abs(gap) // 2
end = start + target_shape[index]
update = update[:, :, start:end, :]
if source_shape[3] > target_shape[3]:
index = 3
gap = source_shape[index] - target_shape[index]
start = abs(gap) // 2
end = start + target_shape[index]
update = update[:, :, :, start:end]
except:
print(' Wrong var pair?', source_name, source_shape,
target_name, target_shape)
exit(1)
if source_shape[2] < target_shape[2] or source_shape[
3] < target_shape[3]:
update = pad_symm_np(update, target_shape[2:])
log = (log, source_shape, '=>', target_shape)
# print(pair, source_shape, target_shape)
tgt_net.set_var(target_name, update)
skip.append(source_name)
pbar.upd(pair)
weights_to_copy = {
tgt_net.vars[pair[1]]: src_net.vars[pair[0]]
for pair in names if pair[0] not in skip
}
tfutil.set_vars(tfutil.run(weights_to_copy))
def create_from_images(dataset, jpg=False, shuffle=True, size=None):
assert os.path.isdir(dataset)
image_filenames = sorted(img_list(dataset))
assert len(image_filenames) > 0, ' No input images found!'
sample_img = np.asarray(PIL.Image.open(image_filenames[0]))
sample_shape = sample_img.shape
channels = sample_shape[2] if sample_img.ndim == 3 else 1
assert channels in [1,3,4], ' Weird color dim: %d' % channels
print(' Making dataset', dataset, sample_shape)
if jpg is True: print(' Loading JPG as is!')
with TFRecordExporter(dataset, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
pbar = ProgressBar(order.size)
for idx in range(order.size):
img_path = image_filenames[order[idx]]
tfr.add_image(img_path, jpg=jpg, size=size)
pbar.upd()
return tfr.tfr_file, len(image_filenames)
def project_image(proj, targets, work_dir, resolution, num_snapshots):
filename = osp.join(work_dir, basename(work_dir))
video_out = cv2.VideoWriter(filename + '.avi',
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 24,
resolution)
snapshot_steps = set(proj.num_steps - np.linspace(
0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
misc.save_image_grid(targets, filename + '.jpg', drange=[-1, 1])
proj.start(targets)
pbar = ProgressBar(proj.num_steps)
while proj.get_cur_step() < proj.num_steps:
proj.step()
write_video_frame(proj, video_out)
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(proj.get_images(),
filename + '-%04d.jpg' % proj.get_cur_step(),
drange=[-1, 1])
pbar.upd()
dlats = proj.get_dlatents()
np.save(filename + '-%04d.npy' % proj.get_cur_step(), dlats)
video_out.release()
def run(self):
super().run()
query = args['query']
options = os.listdir('../data/raw/' + query)
ind = -1
if len(options) == 1:
ind = 0
else:
for i, option in enumerate(options):
print('({}) {}'.format(i, option))
ind = input('Enter your option index: ')
while not ind.isdigit() or int(ind) < 0 or int(ind) >= len(options):
ind = input('Please enter a valid option: ')
ind = int(ind)
img_dir = '../data/raw/' + query + '/' + options[ind]
img_names = os.listdir(img_dir)
progress_bar = ProgressBar(len(img_names), self.log)
if not os.path.exists('../data/preprocessed/' + query):
os.mkdir('../data/preprocessed/' + query)
if not os.path.exists('../data/preprocessed/' + query + '/' + options[ind]):
os.mkdir('../data/preprocessed/' + query + '/' + options[ind])
while True:
img = cv2.imread(img_dir + '/' + img_names[progress_bar.i])
dim = min(img.shape[:2])
if img.shape[0] == dim:
lrg_dim = img.shape[1]
img = img[:, int((lrg_dim - dim) / 2): int((lrg_dim + dim) / 2)]
else:
lrg_dim = img.shape[0]
img = img[int((lrg_dim - dim) / 2): int((lrg_dim + dim) / 2), :]
img = cv2.resize(img, (200, 200))
cv2.imwrite('../data/preprocessed/' + query + '/' + options[ind] + '/' + img_names[progress_bar.i], img)
if progress_bar.increment(): break
def generate_quake_motion_path(args):
trace_file = args[0] #input trace file
output_filename = _get_option_or_default(args, 1, 'quake.txt')
xoffset = int(_get_option_or_default(args, 2, 0)) # uniform x translation
yoffset = int(_get_option_or_default(args, 3, 0)) # uniform y translation
trace = ObjectPathTrace(trace_file)
trace.fill_parents(report=True)
pb = ProgressBar(len(trace.roots()))
obj_sizes = trace.aggregate_sizes()
fout = open(output_filename, 'w')
trace_subsets = trace.clusters()
subtraces = trace.subset_traces(trace_subsets)
obj_count = 0
for subtrace in subtraces:
subtrace_roots = subtrace.roots()
for objid, mots in subtrace.sim_motions_iter(subtrace_roots):
# above the actual output to ensure it gets updated
obj_count += 1
pb.update(obj_count)
pb.report()
idx = 0
for mot in mots:
num_updates = sum([len(mot) for mot in mots])
mot.squeeze(fudge=.05)
#if num_updates <= 1: continue
for t, pos in mot:
# note that we flip y and z to go from SL coords -> meru coords
bbox = obj_sizes[objid]
bbox_rad = vec3.dist(bbox[0], bbox[1]) / 2.0
line = "%s: %f, %f, %f, %d, %f" % (
str(objid), xoffset + pos[0], yoffset + pos[1], pos[2],
int(t * 1000), bbox_rad)
print >> fout, line
idx += 1
fout.close()
pb.finish()
return 0
def generate_quake_motion_path(args):
trace_file = args[0] #input trace file
output_filename = _get_option_or_default(args, 1, 'quake.txt')
xoffset = int(_get_option_or_default(args, 2, 0)) # uniform x translation
yoffset = int(_get_option_or_default(args, 3, 0)) # uniform y translation
trace = ObjectPathTrace(trace_file)
trace.fill_parents(report=True)
pb = ProgressBar(len(trace.roots()))
obj_sizes = trace.aggregate_sizes()
fout = open(output_filename,'w')
trace_subsets = trace.clusters()
subtraces = trace.subset_traces(trace_subsets)
obj_count = 0
for subtrace in subtraces:
subtrace_roots = subtrace.roots()
for objid,mots in subtrace.sim_motions_iter(subtrace_roots):
# above the actual output to ensure it gets updated
obj_count += 1
pb.update(obj_count)
pb.report()
idx = 0
for mot in mots:
num_updates = sum( [ len(mot) for mot in mots ] )
mot.squeeze(fudge=.05)
#if num_updates <= 1: continue
for t,pos in mot:
# note that we flip y and z to go from SL coords -> meru coords
bbox = obj_sizes[objid]
bbox_rad = vec3.dist(bbox[0], bbox[1])/2.0
line = "%s: %f, %f, %f, %d, %f" % (str(objid), xoffset+pos[0], yoffset+pos[1], pos[2], int(t*1000), bbox_rad)
print >>fout, line
idx += 1
fout.close()
pb.finish()
return 0
def copy_and_fill_trainables(src_net, tgt_net) -> None: # model => conditional
train_vars = [
name for name in src_net.trainables.keys()
if name in tgt_net.trainables.keys()
]
skip = []
pbar = ProgressBar(len(train_vars))
for name in train_vars:
x = src_net.get_var(name)
y = tgt_net.get_var(name)
src_shape = x.shape
tgt_shape = y.shape
if src_shape != tgt_shape:
assert len(src_shape) == len(
tgt_shape), "Different shapes: %s %s" % (str(src_shape),
str(tgt_shape))
if np.less(
tgt_shape,
src_shape).any(): # kill labels: [1024,512] => [512,512]
try:
update = x[:tgt_shape[0], :tgt_shape[1],
...] # !!! corrects only first two dims
except:
update = x[:tgt_shape[0]]
elif np.greater(
tgt_shape,
src_shape).any(): # add labels: [512,512] => [1024,512]
tile_count = [
tgt_shape[i] // src_shape[i] for i in range(len(src_shape))
]
if a.verbose is True:
print(name, tile_count, src_shape, '=>', tgt_shape,
'\n\n') # G_mapping/Dense0, D/Output
update = np.tile(x, tile_count)
tgt_net.set_var(name, update)
skip.append(name)
pbar.upd(name)
weights_to_copy = {
tgt_net.vars[name]: src_net.vars[name]
for name in train_vars if name not in skip
}
tfutil.set_vars(tfutil.run(weights_to_copy))
def update_G(src_dict, tgt_net, size, n_mlp):
log_size = int(math.log(size, 2))
pbar = ProgressBar(n_mlp + log_size - 2 + log_size - 2 +
(log_size - 2) * 2 + 1 + 2)
for i in range(n_mlp):
convert_dense(tgt_net, src_dict, f"G_mapping/Dense{i}", f"style.{i+1}")
pbar.upd()
update(tgt_net, "G_synthesis/4x4/Const/const", src_dict["input.input"])
convert_torgb(tgt_net, src_dict, "G_synthesis/4x4/ToRGB", "to_rgb1")
pbar.upd()
for i in range(log_size - 2):
reso = 4 * 2**(i + 1)
convert_torgb(tgt_net, src_dict, f"G_synthesis/{reso}x{reso}/ToRGB",
f"to_rgbs.{i}")
pbar.upd()
convert_modconv(tgt_net, src_dict, "G_synthesis/4x4/Conv", "conv1")
pbar.upd()
conv_i = 0
for i in range(log_size - 2):
reso = 4 * 2**(i + 1)
convert_modconv(tgt_net,
src_dict,
f"G_synthesis/{reso}x{reso}/Conv0_up",
f"convs.{conv_i}",
flip=True)
convert_modconv(tgt_net, src_dict, f"G_synthesis/{reso}x{reso}/Conv1",
f"convs.{conv_i + 1}")
conv_i += 2
pbar.upd()
for i in range(0, (log_size - 2) * 2 + 1):
update(tgt_net, f"G_synthesis/noise{i}", src_dict[f"noises.noise_{i}"])
pbar.upd()
def main():
os.makedirs(a.out_dir, exist_ok=True)
# parse filename to model parameters
mparams = basename(a.model).split('-')
res = int(mparams[1])
cfg = mparams[2]
# setup generator
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_custom.G_main'
Gs_kwargs.verbose = False
Gs_kwargs.resolution = res
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.latent_size = a.latent_size
if cfg.lower() == 'f':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
elif cfg.lower() == 'e':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
Gs_kwargs.fmap_base = 8 << 10
else:
print(' old modes [A-D] not implemented')
exit()
# check initial model resolution
if len(mparams) > 3:
if 'x' in mparams[3].lower():
init_res = [int(x) for x in mparams[3].lower().split('x')]
Gs_kwargs.init_res = list(reversed(init_res)) # [H,W]
# load model, check channels
sess = tflib.init_tf({'allow_soft_placement': True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try:
_, _, Gs = network
except:
Gs = network
Gs_kwargs.num_channels = Gs.output_shape[1]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..')
else:
print(' .. Gs custom ..')
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
z_dim = Gs.input_shape[1]
dz_dim = a.dlatent_size # 512
dl_dim = 2 * (int(np.floor(np.log2(res))) - 1)
dlat_shape = (1, dl_dim, dz_dim) # [1,18,512]
# read saved latents
if a.dlatents is not None and osp.isfile(a.dlatents):
key_dlatents = load_latents(a.dlatents)
if len(key_dlatents.shape) == 2:
key_dlatents = np.expand_dims(key_dlatents, 0)
elif a.dlatents is not None and osp.isdir(a.dlatents):
# if a.dlatents.endswith('/') or a.dlatents.endswith('\\'): a.dlatents = a.dlatents[:-1]
key_dlatents = []
npy_list = file_list(a.dlatents, 'npy')
for npy in npy_list:
key_dlatent = load_latents(npy)
if len(key_dlatent.shape) == 2:
key_dlatent = np.expand_dims(key_dlatent, 0)
key_dlatents.append(key_dlatent)
key_dlatents = np.concatenate(key_dlatents) # [frm,18,512]
else:
print(' No input dlatents found')
exit()
key_dlatents = key_dlatents[:, np.newaxis] # [frm,1,18,512]
print(' key dlatents', key_dlatents.shape)
# replace higher layers with single (style) latent
if a.style_npy_file is not None:
print(' styling with latent', a.style_npy_file)
style_dlatent = load_latents(a.style_npy_file)
while len(style_dlatent.shape) < 4:
style_dlatent = np.expand_dims(style_dlatent, 0)
# try other values < dl_dim besides 5
key_dlatents[:, :,
range(5, dl_dim), :] = style_dlatent[:, :,
range(5, dl_dim), :]
frames = key_dlatents.shape[0] * a.fstep
dlatents = latent_anima(dlat_shape,
frames,
a.fstep,
key_latents=key_dlatents,
cubic=a.cubic,
verbose=True) # [frm,1,512]
print(' dlatents', dlatents.shape)
# truncation trick
dlatent_avg = Gs.get_var('dlatent_avg') # (512,)
tr_range = range(0, 8)
dlatents[:, :, tr_range, :] = dlatent_avg + (dlatents[:, :, tr_range, :] -
dlatent_avg) * a.trunc
# loop for graph frame by frame
frame_count = dlatents.shape[0]
pbar = ProgressBar(frame_count)
for i in range(frame_count):
dlatent = dlatents[i]
output = Gs.components.synthesis.run(dlatent,
randomize_noise=False,
output_transform=fmt,
minibatch_size=1)
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%05d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
def main():
if a.vector_dir is not None:
if a.vector_dir.endswith('/') or a.vector_dir.endswith('\\'):
a.vector_dir = a.vector_dir[:-1]
os.makedirs(a.out_dir, exist_ok=True)
global Gs, use_d
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_multi.G_main'
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.impl = a.ops
# load model with arguments
sess = tflib.init_tf({'allow_soft_placement': True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try:
_, _, network = network
except:
pass
for k in list(network.static_kwargs.keys()):
Gs_kwargs[k] = network.static_kwargs[k]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..', basename(a.model))
Gs = network
else: # reconstruct network
print(' .. Gs custom ..', basename(a.model))
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
# load directions
if a.vector_dir is not None:
directions = []
vector_list = file_list(a.vector_dir, 'npy')
for v in vector_list:
direction = load_latents(v)
if len(direction.shape) == 2:
direction = np.expand_dims(direction, 0)
directions.append(direction)
directions = np.concatenate(directions)[:,
np.newaxis] # [frm,1,18,512]
else:
print(' No vectors found')
exit()
if len(direction[0].shape) > 1 and direction[0].shape[0] > 1:
use_d = True
print(' directions', directions.shape, 'using d' if use_d else 'using w')
# latent direction range
lrange = [-0.5, 0.5]
# load saved latents
if a.base_lat is not None:
base_latent = load_latents(a.base_lat)
else:
print(' No NPY input given, making random')
z_dim = Gs.input_shape[1]
shape = (1, z_dim)
base_latent = np.random.randn(*shape)
if use_d:
base_latent = Gs.components.mapping.run(base_latent,
None) # [frm,18,512]
pbar = ProgressBar(len(directions))
for i, direction in enumerate(directions):
make_loop(base_latent, direction, lrange, a.fstep * 2, a.fstep * 2 * i)
pbar.upd()
def main():
os.makedirs(a.out_dir, exist_ok=True)
np.random.seed(seed=696)
# setup generator
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_multi.G_main'
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.impl = a.ops
# mask/blend latents with external latmask or by splitting the frame
if a.latmask is None:
nHW = [int(s) for s in a.nXY.split('-')][::-1]
assert len(nHW)==2, ' Wrong count nXY: %d (must be 2)' % len(nHW)
n_mult = nHW[0] * nHW[1]
if a.verbose is True and n_mult > 1: print(' Latent blending w/split frame %d x %d' % (nHW[1], nHW[0]))
lmask = np.tile(np.asarray([[[[None]]]]), (1,n_mult,1,1))
Gs_kwargs.countHW = nHW
Gs_kwargs.splitfine = a.splitfine
else:
if a.verbose is True: print(' Latent blending with mask', a.latmask)
n_mult = 2
if os.path.isfile(a.latmask): # single file
lmask = np.asarray([[img_read(a.latmask)[:,:,0] / 255.]]) # [h,w]
elif os.path.isdir(a.latmask): # directory with frame sequence
lmask = np.asarray([[img_read(f)[:,:,0] / 255. for f in img_list(a.latmask)]]) # [h,w]
else:
print(' !! Blending mask not found:', a.latmask); exit(1)
lmask = np.concatenate((lmask, 1 - lmask), 1) # [frm,2,h,w]
Gs_kwargs.latmask_res = lmask.shape[2:]
# load model with arguments
sess = tflib.init_tf({'allow_soft_placement':True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try: _, _, network = network
except: pass
for k in list(network.static_kwargs.keys()):
Gs_kwargs[k] = network.static_kwargs[k]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..', basename(a.model))
Gs = network
else: # reconstruct network
print(' .. Gs custom ..', basename(a.model))
# print(Gs_kwargs)
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
if a.verbose is True: print('kwargs:', ['%s: %s'%(kv[0],kv[1]) for kv in sorted(Gs.static_kwargs.items())])
if a.verbose is True: print(' out shape', Gs.output_shape[1:])
if a.size is None: a.size = Gs.output_shape[2:]
if a.verbose is True: print(' making timeline..')
lats = [] # list of [frm,1,512]
for i in range(n_mult):
lat_tmp = latent_anima((1, Gs.input_shape[1]), a.frames, a.fstep, cubic=a.cubic, gauss=a.gauss, verbose=False) # [frm,1,512]
lats.append(lat_tmp) # list of [frm,1,512]
latents = np.concatenate(lats, 1) # [frm,X,512]
print(' latents', latents.shape)
frame_count = latents.shape[0]
# distort image by tweaking initial const layer
if a.digress > 0:
try: latent_size = Gs.static_kwargs['latent_size']
except: latent_size = 512 # default latent size
try: init_res = Gs.static_kwargs['init_res']
except: init_res = (4,4) # default initial layer size
dconst = []
for i in range(n_mult):
dc_tmp = a.digress * latent_anima([1, latent_size, *init_res], a.frames, a.fstep, cubic=True, verbose=False)
dconst.append(dc_tmp)
dconst = np.concatenate(dconst, 1)
else:
dconst = np.zeros([frame_count, 1, 1, 1, 1])
# labels / conditions
try:
label_size = Gs_kwargs.label_size
except:
label_size = 0
if label_size > 0:
labels = np.zeros((frame_count, n_mult, label_size)) # [frm,X,lbl]
if a.labels is None:
label_ids = []
for i in range(n_mult):
label_ids.append(random.randint(0, label_size-1))
else:
label_ids = [int(x) for x in a.labels.split('-')]
label_ids = label_ids[:n_mult] # ensure we have enough labels
for i, l in enumerate(label_ids):
labels[:,i,l] = 1
else:
labels = [None]
# generate images from latent timeline
pbar = ProgressBar(frame_count)
for i in range(frame_count):
latent = latents[i] # [X,512]
label = labels[i % len(labels)]
latmask = lmask[i % len(lmask)] if lmask is not None else [None] # [X,h,w]
dc = dconst[i % len(dconst)] # [X,512,4,4]
# generate multi-latent result
if Gs.num_inputs == 2:
output = Gs.run(latent, label, truncation_psi=a.trunc, randomize_noise=False, output_transform=fmt)
else:
output = Gs.run(latent, label, latmask, dc, truncation_psi=a.trunc, randomize_noise=False, output_transform=fmt)
# save image
ext = 'png' if output.shape[3]==4 else 'jpg'
filename = osp.join(a.out_dir, "%06d.%s" % (i,ext))
imsave(filename, output[0])
pbar.upd()
# convert latents to dlatents, save them
if a.save_lat is True:
latents = latents.squeeze(1) # [frm,512]
dlatents = Gs.components.mapping.run(latents, label, dtype='float16') # [frm,18,512]
filename = '{}-{}-{}.npy'.format(basename(a.model), a.size[1], a.size[0])
filename = osp.join(osp.dirname(a.out_dir), filename)
np.save(filename, dlatents)
print('saved dlatents', dlatents.shape, 'to', filename)
def main():
os.makedirs(a.out_dir, exist_ok=True)
# setup generator
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_multi.G_main'
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.impl = a.ops
# load model with arguments
sess = tflib.init_tf({'allow_soft_placement': True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try:
_, _, network = network
except:
pass
for k in list(network.static_kwargs.keys()):
Gs_kwargs[k] = network.static_kwargs[k]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..', basename(a.model))
Gs = network
else: # reconstruct network
print(' .. Gs custom ..', basename(a.model))
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
z_dim = Gs.input_shape[1]
dz_dim = 512 # dlatent_size
try:
dl_dim = 2 * (int(np.floor(np.log2(Gs_kwargs.resolution))) - 1)
except:
print(' Resave model, no resolution kwarg found!')
exit(1)
dlat_shape = (1, dl_dim, dz_dim) # [1,18,512]
# read saved latents
if a.dlatents is not None and osp.isfile(a.dlatents):
key_dlatents = load_latents(a.dlatents)
if len(key_dlatents.shape) == 2:
key_dlatents = np.expand_dims(key_dlatents, 0)
elif a.dlatents is not None and osp.isdir(a.dlatents):
# if a.dlatents.endswith('/') or a.dlatents.endswith('\\'): a.dlatents = a.dlatents[:-1]
key_dlatents = []
npy_list = file_list(a.dlatents, 'npy')
for npy in npy_list:
key_dlatent = load_latents(npy)
if len(key_dlatent.shape) == 2:
key_dlatent = np.expand_dims(key_dlatent, 0)
key_dlatents.append(key_dlatent)
key_dlatents = np.concatenate(key_dlatents) # [frm,18,512]
else:
print(' No input dlatents found')
exit()
key_dlatents = key_dlatents[:, np.newaxis] # [frm,1,18,512]
print(' key dlatents', key_dlatents.shape)
# replace higher layers with single (style) latent
if a.style_npy_file is not None:
print(' styling with latent', a.style_npy_file)
style_dlatent = load_latents(a.style_npy_file)
while len(style_dlatent.shape) < 4:
style_dlatent = np.expand_dims(style_dlatent, 0)
# try replacing 5 by other value, less than dl_dim
key_dlatents[:, :,
range(5, dl_dim), :] = style_dlatent[:, :,
range(5, dl_dim), :]
frames = key_dlatents.shape[0] * a.fstep
dlatents = latent_anima(dlat_shape,
frames,
a.fstep,
key_latents=key_dlatents,
cubic=a.cubic,
verbose=True) # [frm,1,512]
print(' dlatents', dlatents.shape)
frame_count = dlatents.shape[0]
# truncation trick
dlatent_avg = Gs.get_var('dlatent_avg') # (512,)
tr_range = range(0, 8)
dlatents[:, :, tr_range, :] = dlatent_avg + (dlatents[:, :, tr_range, :] -
dlatent_avg) * a.trunc
# distort image by tweaking initial const layer
if a.digress > 0:
try:
latent_size = Gs.static_kwargs['latent_size']
except:
latent_size = 512 # default latent size
try:
init_res = Gs.static_kwargs['init_res']
except:
init_res = (4, 4) # default initial layer size
dconst = a.digress * latent_anima([1, latent_size, *init_res],
frames,
a.fstep,
cubic=True,
verbose=False)
else:
dconst = np.zeros([frame_count, 1, 1, 1, 1])
# generate images from latent timeline
pbar = ProgressBar(frame_count)
for i in range(frame_count):
if a.digress is True:
tf.get_default_session().run(tf.assign(wvars[0], wts[i]))
# generate multi-latent result
if Gs.num_inputs == 2:
output = Gs.components.synthesis.run(dlatents[i],
randomize_noise=False,
output_transform=fmt,
minibatch_size=1)
else:
output = Gs.components.synthesis.run(dlatents[i], [None],
dconst[i],
randomize_noise=False,
output_transform=fmt,
minibatch_size=1)
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%06d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
def main():
os.makedirs(a.out_dir, exist_ok=True)
np.random.seed(seed=696)
# parse filename to model parameters
mparams = basename(a.model).split('-')
res = int(mparams[1])
cfg = mparams[2]
# setup generator
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_custom.G_main'
Gs_kwargs.verbose = False
Gs_kwargs.resolution = res
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.latent_size = a.latent_size
Gs_kwargs.impl = a.ops
if cfg.lower() == 'f':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
elif cfg.lower() == 'e':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
Gs_kwargs.fmap_base = 8 << 10
else:
print(' old modes [A-D] not implemented'); exit()
# check initial model resolution
if len(mparams) > 3:
if 'x' in mparams[3].lower():
init_res = [int(x) for x in mparams[3].lower().split('x')]
Gs_kwargs.init_res = list(reversed(init_res)) # [H,W]
# load model, check channels
sess = tflib.init_tf({'allow_soft_placement':True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try: _, _, Gs = network
except: Gs = network
Gs_kwargs.num_channels = Gs.output_shape[1]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..')
else:
print(' .. Gs custom ..')
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
# Gs.print_layers()
print(' out shape', Gs.output_shape[1:])
if a.size is None: a.size = Gs.output_shape[2:]
z_dim = Gs.input_shape[1]
shape = (1, z_dim)
print(' making timeline..')
latents = latent_anima(shape, a.frames, a.fstep, cubic=a.cubic, gauss=a.gauss, verbose=True) # [frm,1,512]
print(' latents', latents.shape)
# generate images from latent timeline
frame_count = latents.shape[0]
pbar = ProgressBar(frame_count)
for i in range(frame_count):
output = Gs.run(latents[i], [None], truncation_psi=a.trunc, randomize_noise=False, output_transform=fmt)
ext = 'png' if output.shape[3]==4 else 'jpg'
filename = osp.join(a.out_dir, "%05d.%s" % (i,ext))
imsave(filename, output[0])
pbar.upd()
# convert latents to dlatents, save them
latents = latents.squeeze(1) # [frm,512]
dlatents = Gs.components.mapping.run(latents, None, latent_size=z_dim, dtype='float16') # [frm,18,512]
filename = '{}-{}-{}.npy'.format(basename(a.model), a.size[1], a.size[0])
filename = osp.join(osp.dirname(a.out_dir), filename)
np.save(filename, dlatents)
print('saved dlatents', dlatents.shape, 'to', filename)
def main():
os.makedirs(a.out_dir, exist_ok=True)
device = torch.device('cuda')
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
# load base or custom network
pkl_name = osp.splitext(a.model)[0]
if '.pkl' in a.model.lower():
custom = False
print(' .. Gs from pkl ..', basename(a.model))
else:
custom = True
print(' .. Gs custom ..', basename(a.model))
with dnnlib.util.open_url(pkl_name + '.pkl') as f:
Gs = legacy.load_network_pkl(f,
custom=custom, **Gs_kwargs)['G_ema'].to(
device) # type: ignore
dlat_shape = (1, Gs.num_ws, Gs.w_dim) # [1,18,512]
# read saved latents
if a.dlatents is not None and osp.isfile(a.dlatents):
key_dlatents = load_latents(a.dlatents)
if len(key_dlatents.shape) == 2:
key_dlatents = np.expand_dims(key_dlatents, 0)
elif a.dlatents is not None and osp.isdir(a.dlatents):
# if a.dlatents.endswith('/') or a.dlatents.endswith('\\'): a.dlatents = a.dlatents[:-1]
key_dlatents = []
npy_list = file_list(a.dlatents, 'npy')
for npy in npy_list:
key_dlatent = load_latents(npy)
if len(key_dlatent.shape) == 2:
key_dlatent = np.expand_dims(key_dlatent, 0)
key_dlatents.append(key_dlatent)
key_dlatents = np.concatenate(key_dlatents) # [frm,18,512]
else:
print(' No input dlatents found')
exit()
key_dlatents = key_dlatents[:, np.newaxis] # [frm,1,18,512]
print(' key dlatents', key_dlatents.shape)
# replace higher layers with single (style) latent
if a.style_dlat is not None:
print(' styling with dlatent', a.style_dlat)
style_dlatent = load_latents(a.style_dlat)
while len(style_dlatent.shape) < 4:
style_dlatent = np.expand_dims(style_dlatent, 0)
# try replacing 5 by other value, less than Gs.num_ws
key_dlatents[:, :, range(5, Gs.num_ws
), :] = style_dlatent[:, :,
range(5, Gs.num_ws), :]
frames = key_dlatents.shape[0] * a.fstep
dlatents = latent_anima(dlat_shape,
frames,
a.fstep,
key_latents=key_dlatents,
cubic=a.cubic,
verbose=True) # [frm,1,512]
print(' dlatents', dlatents.shape)
frame_count = dlatents.shape[0]
dlatents = torch.from_numpy(dlatents).to(device)
# distort image by tweaking initial const layer
if a.digress > 0:
try:
init_res = Gs.init_res
except Exception:
init_res = (4, 4) # default initial layer size
dconst = a.digress * latent_anima([1, Gs.z_dim, *init_res],
frame_count,
a.fstep,
cubic=True,
verbose=False)
else:
dconst = np.zeros([frame_count, 1, 1, 1, 1])
dconst = torch.from_numpy(dconst).to(device)
# generate images from latent timeline
pbar = ProgressBar(frame_count)
for i in range(frame_count):
# generate multi-latent result
if custom:
output = Gs.synthesis(dlatents[i],
None,
dconst[i],
noise_mode='const')
else:
output = Gs.synthesis(dlatents[i], noise_mode='const')
output = (output.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
torch.uint8).cpu().numpy()
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%06d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
def project(
G,
target: torch.
Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
*,
num_steps=1000,
w_avg_samples=10000,
initial_learning_rate=0.1,
initial_noise_factor=0.05,
lr_rampdown_length=0.25,
lr_rampup_length=0.05,
noise_ramp_length=0.75,
regularize_noise_weight=1e5,
verbose=False,
device: torch.device):
assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution)
# def logprint(*args):
# if verbose:
# print(*args)
G = copy.deepcopy(G).eval().requires_grad_(False).to(
device) # type: ignore
# Compute w stats.
# logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
w_samples = G.mapping(torch.from_numpy(z_samples).to(device),
None) # [N, L, C]
w_samples = w_samples[:, :1, :].cpu().numpy().astype(
np.float32) # [N, 1, C]
w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C]
w_std = (np.sum((w_samples - w_avg)**2) / w_avg_samples)**0.5
# Setup noise inputs.
noise_bufs = {
name: buf
for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name
}
# Load VGG16 feature detector.
vgg_file = 'models/vgg/vgg16.pt'
if os.path.isfile(vgg_file) and os.stat(vgg_file).st_size == 553469545:
with dnnlib.util.open_url(vgg_file) as file:
# network = pickle.load(file, encoding='latin1')
vgg16 = torch.jit.load(file).eval().to(device)
else:
with dnnlib.util.open_url(
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
) as file:
vgg16 = torch.jit.load(file).eval().to(device)
# Features for target image.
target_images = target.unsqueeze(0).to(device).to(torch.float32)
if target_images.shape[2] > 256:
target_images = F.interpolate(target_images,
size=(256, 256),
mode='area')
target_features = vgg16(target_images,
resize_images=False,
return_lpips=True)
w_opt = torch.tensor(w_avg,
dtype=torch.float32,
device=device,
requires_grad=True) # pylint: disable=not-callable
w_out = torch.zeros([num_steps] + list(w_opt.shape[1:]),
dtype=torch.float32,
device=device)
optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()),
betas=(0.9, 0.999),
lr=initial_learning_rate)
# Init noise.
for buf in noise_bufs.values():
buf[:] = torch.randn_like(buf)
buf.requires_grad = True
pbar = ProgressBar(num_steps)
for step in range(num_steps):
# Learning rate schedule.
t = step / num_steps
w_noise_scale = w_std * initial_noise_factor * max(
0.0, 1.0 - t / noise_ramp_length)**2
lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
lr = initial_learning_rate * lr_ramp
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# Synth images from opt_w.
w_noise = torch.randn_like(w_opt) * w_noise_scale
ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1])
synth_images = G.synthesis(ws, noise_mode='const')
# Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
synth_images = (synth_images + 1) * (255 / 2)
if synth_images.shape[2] > 256:
synth_images = F.interpolate(synth_images,
size=(256, 256),
mode='area')
# Features for synth images.
synth_features = vgg16(synth_images,
resize_images=False,
return_lpips=True)
dist = (target_features - synth_features).square().sum()
# Noise regularization.
reg_loss = 0.0
for v in noise_bufs.values():
noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d()
while True:
reg_loss += (noise *
torch.roll(noise, shifts=1, dims=3)).mean()**2
reg_loss += (noise *
torch.roll(noise, shifts=1, dims=2)).mean()**2
if noise.shape[2] <= 8:
break
noise = F.avg_pool2d(noise, kernel_size=2)
loss = dist + reg_loss * regularize_noise_weight
# Step
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
# logprint(f'step {step+1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
# Save projected W for each optimization step.
w_out[step] = w_opt.detach()[0]
# Normalize noise.
with torch.no_grad():
for buf in noise_bufs.values():
buf -= buf.mean()
buf *= buf.square().mean().rsqrt()
pbar.upd()
return w_out.repeat([1, G.mapping.num_ws, 1])
def generate():
os.makedirs(a.out_dir, exist_ok=True)
np.random.seed(seed=696)
device = torch.device('cuda')
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
# mask/blend latents with external latmask or by splitting the frame
if a.latmask is None:
nHW = [int(s) for s in a.nXY.split('-')][::-1]
assert len(nHW) == 2, ' Wrong count nXY: %d (must be 2)' % len(nHW)
n_mult = nHW[0] * nHW[1]
if a.verbose is True and n_mult > 1:
print(' Latent blending w/split frame %d x %d' % (nHW[1], nHW[0]))
lmask = np.tile(np.asarray([[[[1]]]]), (1, n_mult, 1, 1))
Gs_kwargs.countHW = nHW
Gs_kwargs.splitfine = a.splitfine
else:
if a.verbose is True: print(' Latent blending with mask', a.latmask)
n_mult = 2
if os.path.isfile(a.latmask): # single file
lmask = np.asarray([[img_read(a.latmask)[:, :, 0] / 255.]
]) # [h,w]
elif os.path.isdir(a.latmask): # directory with frame sequence
lmask = np.asarray([[
img_read(f)[:, :, 0] / 255. for f in img_list(a.latmask)
]]) # [h,w]
else:
print(' !! Blending mask not found:', a.latmask)
exit(1)
lmask = np.concatenate((lmask, 1 - lmask), 1) # [frm,2,h,w]
lmask = torch.from_numpy(lmask).to(device)
# load base or custom network
pkl_name = osp.splitext(a.model)[0]
if '.pkl' in a.model.lower():
custom = False
print(' .. Gs from pkl ..', basename(a.model))
else:
custom = True
print(' .. Gs custom ..', basename(a.model))
with dnnlib.util.open_url(pkl_name + '.pkl') as f:
Gs = legacy.load_network_pkl(f,
custom=custom, **Gs_kwargs)['G_ema'].to(
device) # type: ignore
if a.verbose is True: print(' out shape', Gs.output_shape[1:])
if a.verbose is True: print(' making timeline..')
lats = [] # list of [frm,1,512]
for i in range(n_mult):
lat_tmp = latent_anima((1, Gs.z_dim),
a.frames,
a.fstep,
cubic=a.cubic,
gauss=a.gauss,
verbose=False) # [frm,1,512]
lats.append(lat_tmp) # list of [frm,1,512]
latents = np.concatenate(lats, 1) # [frm,X,512]
print(' latents', latents.shape)
latents = torch.from_numpy(latents).to(device)
frame_count = latents.shape[0]
# distort image by tweaking initial const layer
if a.digress > 0:
try:
init_res = Gs.init_res
except:
init_res = (4, 4) # default initial layer size
dconst = []
for i in range(n_mult):
dc_tmp = a.digress * latent_anima([1, Gs.z_dim, *init_res],
a.frames,
a.fstep,
cubic=True,
verbose=False)
dconst.append(dc_tmp)
dconst = np.concatenate(dconst, 1)
else:
dconst = np.zeros([frame_count, 1, 1, 1, 1])
dconst = torch.from_numpy(dconst).to(device)
# labels / conditions
label_size = Gs.c_dim
if label_size > 0:
labels = torch.zeros((frame_count, n_mult, label_size),
device=device) # [frm,X,lbl]
if a.labels is None:
label_ids = []
for i in range(n_mult):
label_ids.append(random.randint(0, label_size - 1))
else:
label_ids = [int(x) for x in a.labels.split('-')]
label_ids = label_ids[:n_mult] # ensure we have enough labels
for i, l in enumerate(label_ids):
labels[:, i, l] = 1
else:
labels = [None]
# generate images from latent timeline
pbar = ProgressBar(frame_count)
for i in range(frame_count):
latent = latents[i] # [X,512]
label = labels[i % len(labels)]
latmask = lmask[i %
len(lmask)] if lmask is not None else [None] # [X,h,w]
dc = dconst[i % len(dconst)] # [X,512,4,4]
# generate multi-latent result
if custom:
output = Gs(latent,
label,
latmask,
dc,
truncation_psi=a.trunc,
noise_mode='const')
else:
output = Gs(latent,
label,
truncation_psi=a.trunc,
noise_mode='const')
output = (output.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
torch.uint8).cpu().numpy()
# save image
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%06d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
# convert latents to dlatents, save them
if a.save_lat is True:
latents = latents.squeeze(1) # [frm,512]
dlatents = Gs.mapping(latents, label) # [frm,18,512]
if a.size is None: a.size = [''] * 2
filename = '{}-{}-{}.npy'.format(basename(a.model), a.size[1],
a.size[0])
filename = osp.join(osp.dirname(a.out_dir), filename)
dlatents = dlatents.cpu().numpy()
np.save(filename, dlatents)
print('saved dlatents', dlatents.shape, 'to', filename)
def main():
if a.vector_dir is not None:
if a.vector_dir.endswith('/') or a.vector_dir.endswith('\\'):
a.vector_dir = a.vector_dir[:-1]
os.makedirs(a.out_dir, exist_ok=True)
device = torch.device('cuda')
global Gs, use_d, custom
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
# load base or custom network
pkl_name = osp.splitext(a.model)[0]
if '.pkl' in a.model.lower():
custom = False
print(' .. Gs from pkl ..', basename(a.model))
else:
custom = True
print(' .. Gs custom ..', basename(a.model))
with dnnlib.util.open_url(pkl_name + '.pkl') as f:
Gs = legacy.load_network_pkl(f,
custom=custom, **Gs_kwargs)['G_ema'].to(
device) # type: ignore
# load directions
if a.vector_dir is not None:
directions = []
vector_list = file_list(a.vector_dir, 'npy')
for v in vector_list:
direction = load_latents(v)
if len(direction.shape) == 2:
direction = np.expand_dims(direction, 0)
directions.append(direction)
directions = np.concatenate(directions)[:,
np.newaxis] # [frm,1,18,512]
else:
print(' No vectors found')
exit()
if len(direction[0].shape) > 1 and direction[0].shape[0] > 1:
use_d = True
print(' directions', directions.shape, 'using d' if use_d else 'using w')
directions = torch.from_numpy(directions).to(device)
# latent direction range
lrange = [-0.5, 0.5]
# load saved latents
if a.base_lat is not None:
base_latent = load_latents(a.base_lat)
base_latent = torch.from_numpy(base_latent).to(device)
else:
print(' No NPY input given, making random')
base_latent = np.random.randn(1, Gs.z_dim)
if use_d:
base_latent = Gs.mapping(base_latent, None) # [frm,18,512]
pbar = ProgressBar(len(directions))
for i, direction in enumerate(directions):
make_loop(base_latent, direction, lrange, a.fstep * 2, a.fstep * 2 * i)
pbar.upd()
