def Vis(bname, suffix, out, rownames=None, colnames=None):
num_images = out.shape[0]
step = out.shape[1]
if colnames is None:
colnames = [f'Step {i:02d}' for i in range(1, step + 1)]
if rownames is None:
rownames = [str(i) for i in range(num_images)]
visualizer = HtmlPageVisualizer(num_rows=num_images,
num_cols=step + 1,
viz_size=256)
visualizer.set_headers(['Name'] + colnames)
for i in range(num_images):
visualizer.set_cell(i, 0, text=rownames[i])
for i in range(num_images):
for k in range(step):
image = out[i, k, :, :, :]
visualizer.set_cell(i, 1 + k, image=image)
# Save results.
visualizer.save(f'./html/' + bname + '_' + suffix + '.html')
model_list = []
for model_name, model_setting in MODEL_POOL.items():
if model_setting['gan_type'] == 'pggan':
model_list.append(model_name)
else:
model_list = ['pggan_celebahq', 'pggan_bedroom']
for model_name in model_list:
logger = setup_logger(
work_dir=RESULT_DIR,
logfile_name=f'{model_name}_generator_test.log',
logger_name=f'{model_name}_generator_logger')
G = build_generator(model_name, logger=logger)
G.batch_size = TEST_BATCH_SIZE
z = G.easy_sample(args.test_num)
x = G.easy_synthesize(z)['image']
visualizer = HtmlPageVisualizer(grid_size=args.test_num)
for i in range(visualizer.num_rows):
for j in range(visualizer.num_cols):
visualizer.set_cell(i,
j,
image=x[i * visualizer.num_cols + j])
visualizer.save(f'{RESULT_DIR}/{model_name}_generator_test.html')
del G
print('Pass!')
TEST_FLAG = True
# StyleGAN Generator.
if args.stylegan or args.all:
print('==== StyleGAN Generator Test ====')
if args.verbose:
model_list = []
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
src_dir = args.src_dir
src_dir_name = os.path.basename(src_dir.rstrip('/'))
assert os.path.exists(src_dir)
assert os.path.exists(f'{src_dir}/image_list.txt')
assert os.path.exists(f'{src_dir}/inverted_codes.npy')
dst_dir = args.dst_dir
dst_dir_name = os.path.basename(dst_dir.rstrip('/'))
assert os.path.exists(dst_dir)
assert os.path.exists(f'{dst_dir}/image_list.txt')
assert os.path.exists(f'{dst_dir}/inverted_codes.npy')
output_dir = args.output_dir or 'results/interpolation'
job_name = f'{src_dir_name}_TO_{dst_dir_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
# Load model.
logger.info(f'Loading generator.')
tflib.init_tf({'rnd.np_random_seed': 1000})
with open(args.model_path, 'rb') as f:
_, _, _, Gs = pickle.load(f)
# Build graph.
logger.info(f'Building graph.')
sess = tf.get_default_session()
num_layers, latent_dim = Gs.components.synthesis.input_shape[1:3]
wp = tf.placeholder(tf.float32, [args.batch_size, num_layers, latent_dim],
name='latent_code')
x = Gs.components.synthesis.get_output_for(wp, randomize_noise=False)
# Load image and codes.
logger.info(f'Loading images and corresponding inverted latent codes.')
src_list = []
with open(f'{src_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{src_dir}/{name}_ori.png')
src_list.append(name)
src_codes = np.load(f'{src_dir}/inverted_codes.npy')
assert src_codes.shape[0] == len(src_list)
num_src = src_codes.shape[0]
dst_list = []
with open(f'{dst_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{dst_dir}/{name}_ori.png')
dst_list.append(name)
dst_codes = np.load(f'{dst_dir}/inverted_codes.npy')
assert dst_codes.shape[0] == len(dst_list)
num_dst = dst_codes.shape[0]
# Interpolate images.
logger.info(f'Start interpolation.')
step = args.step + 2
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=num_src * num_dst,
num_cols=step + 2,
viz_size=viz_size)
visualizer.set_headers(['Source', 'Source Inversion'] +
[f'Step {i:02d}' for i in range(1, step - 1)] +
['Target Inversion', 'Target'])
inputs = np.zeros((args.batch_size, num_layers, latent_dim), np.float32)
for src_idx in tqdm(range(num_src), leave=False):
src_code = src_codes[src_idx:src_idx + 1]
src_path = f'{src_dir}/{src_list[src_idx]}_ori.png'
codes = interpolate(src_codes=np.repeat(src_code, num_dst, axis=0),
dst_codes=dst_codes,
step=step)
for dst_idx in tqdm(range(num_dst), leave=False):
dst_path = f'{dst_dir}/{dst_list[dst_idx]}_ori.png'
output_images = []
for idx in range(0, step, args.batch_size):
batch = codes[dst_idx, idx:idx + args.batch_size]
inputs[0:len(batch)] = batch
images = sess.run(x, feed_dict={wp: inputs})
output_images.append(images[0:len(batch)])
output_images = adjust_pixel_range(
np.concatenate(output_images, axis=0))
row_idx = src_idx * num_dst + dst_idx
visualizer.set_cell(row_idx, 0, image=load_image(src_path))
visualizer.set_cell(row_idx, step + 1, image=load_image(dst_path))
for s, output_image in enumerate(output_images):
if s == 5 and row_idx == 2:
save_image(f'./results/interpolation/005_int.png',
output_image)
visualizer.set_cell(row_idx, s + 1, image=output_image)
# Save results.
visualizer.save(f'{output_dir}/{job_name}.html')
def main():
"""Main function."""
args = parse_args()
if args.num <= 0:
return
if not args.save_raw_synthesis and not args.generate_html:
return
# Parse model configuration.
if args.model_name not in MODEL_ZOO:
raise SystemExit(f'Model `{args.model_name}` is not registered in '
f'`models/model_zoo.py`!')
model_config = MODEL_ZOO[args.model_name].copy()
url = model_config.pop('url') # URL to download model if needed.
# Get work directory and job name.
if args.save_dir:
work_dir = args.save_dir
else:
work_dir = os.path.join('work_dirs', 'synthesis')
os.makedirs(work_dir, exist_ok=True)
job_name = f'{args.model_name}_{args.num}'
if args.save_raw_synthesis:
os.makedirs(os.path.join(work_dir, job_name), exist_ok=True)
# Build generation and get synthesis kwargs.
print(f'Building generator for model `{args.model_name}` ...')
generator = build_generator(**model_config)
synthesis_kwargs = dict(trunc_psi=args.trunc_psi,
trunc_layers=args.trunc_layers,
randomize_noise=args.randomize_noise)
print(f'Finish building generator.')
# Load pre-trained weights.
os.makedirs('checkpoints', exist_ok=True)
checkpoint_path = os.path.join('checkpoints', args.model_name + '.pth')
print(f'Loading checkpoint from `{checkpoint_path}` ...')
if not os.path.exists(checkpoint_path):
print(f' Downloading checkpoint from `{url}` ...')
subprocess.call(['wget', '--quiet', '-O', checkpoint_path, url])
print(f' Finish downloading checkpoint.')
checkpoint = torch.load(checkpoint_path, map_location='cpu')
if 'generator_smooth' in checkpoint:
generator.load_state_dict(checkpoint['generator_smooth'])
else:
generator.load_state_dict(checkpoint['generator'])
generator = generator.cuda()
generator.eval()
print(f'Finish loading checkpoint.')
# Set random seed.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# Sample and synthesize.
print(f'Synthesizing {args.num} samples ...')
indices = list(range(args.num))
if args.generate_html:
html = HtmlPageVisualizer(grid_size=args.num)
for batch_idx in tqdm(range(0, args.num, args.batch_size)):
sub_indices = indices[batch_idx:batch_idx + args.batch_size]
print(sub_indices)
code = torch.randn(len(sub_indices), generator.z_space_dim).cuda()
print(code.shape)
with torch.no_grad():
images = generator(code, **synthesis_kwargs)['image']
images = postprocess(images)
for sub_idx, image in zip(sub_indices, images):
if args.save_raw_synthesis:
save_path = os.path.join(work_dir, job_name,
f'{sub_idx:06d}.jpg')
save_image(save_path, image)
if args.generate_html:
row_idx, col_idx = divmod(sub_idx, html.num_cols)
html.set_cell(row_idx,
col_idx,
image=image,
text=f'Sample {sub_idx:06d}')
if args.generate_html:
html.save(os.path.join(work_dir, f'{job_name}.html'))
print(f'Finish synthesizing {args.num} samples.')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
image_dir = args.image_dir
image_dir_name = os.path.basename(image_dir.rstrip('/'))
assert os.path.exists(image_dir)
assert os.path.exists(f'{image_dir}/image_list.txt')
assert os.path.exists(f'{image_dir}/inverted_codes.npy')
boundary_path = args.boundary_path
assert os.path.exists(boundary_path)
boundary_name = os.path.splitext(os.path.basename(boundary_path))[0]
output_dir = args.output_dir or 'results/manipulation'
job_name = f'{boundary_name.upper()}_{image_dir_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
# Load model.
logger.info(f'Loading generator.')
generator = build_generator(args.model_name)
# Load image, codes, and boundary.
logger.info(f'Loading images and corresponding inverted latent codes.')
image_list = []
with open(f'{image_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{image_dir}/{name}_ori.png')
assert os.path.exists(f'{image_dir}/{name}_inv.png')
image_list.append(name)
latent_codes = np.load(f'{image_dir}/inverted_codes.npy')
assert latent_codes.shape[0] == len(image_list)
num_images = latent_codes.shape[0]
logger.info(f'Loading boundary.')
boundary_file = np.load(boundary_path, allow_pickle=True)[()]
if isinstance(boundary_file, dict):
boundary = boundary_file['boundary']
manipulate_layers = boundary_file['meta_data']['manipulate_layers']
else:
boundary = boundary_file
manipulate_layers = args.manipulate_layers
if manipulate_layers:
logger.info(f' Manipulating on layers `{manipulate_layers}`.')
else:
logger.info(f' Manipulating on ALL layers.')
# Manipulate images.
logger.info(f'Start manipulation.')
step = args.step
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=num_images,
num_cols=step + 3,
viz_size=viz_size)
visualizer.set_headers(['Name', 'Origin', 'Inverted'] +
[f'Step {i:02d}' for i in range(1, step + 1)])
for img_idx, img_name in enumerate(image_list):
ori_image = load_image(f'{image_dir}/{img_name}_ori.png')
inv_image = load_image(f'{image_dir}/{img_name}_inv.png')
visualizer.set_cell(img_idx, 0, text=img_name)
visualizer.set_cell(img_idx, 1, image=ori_image)
visualizer.set_cell(img_idx, 2, image=inv_image)
codes = manipulate(latent_codes=latent_codes,
boundary=boundary,
start_distance=args.start_distance,
end_distance=args.end_distance,
step=step,
layerwise_manipulation=True,
num_layers=generator.num_layers,
manipulate_layers=manipulate_layers,
is_code_layerwise=True,
is_boundary_layerwise=True)
for img_idx in tqdm(range(num_images), leave=False):
output_images = generator.easy_synthesize(
codes[img_idx], latent_space_type='wp')['image']
for s, output_image in enumerate(output_images):
visualizer.set_cell(img_idx, s + 3, image=output_image)
# Save results.
visualizer.save(f'{output_dir}/{job_name}.html')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
assert os.path.exists(args.target_list)
target_list_name = os.path.splitext(os.path.basename(args.target_list))[0]
assert os.path.exists(args.context_list)
context_list_name = os.path.splitext(os.path.basename(
args.context_list))[0]
output_dir = args.output_dir or f'results/diffusion'
job_name = f'{target_list_name}_TO_{context_list_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
logger.info(f'Loading model.')
tflib.init_tf({'rnd.np_random_seed': 1000})
with open(args.model_path, 'rb') as f:
E, _, _, Gs = pickle.load(f)
# Get input size.
image_size = E.input_shape[2]
assert image_size == E.input_shape[3]
crop_size = args.crop_size
crop_x = args.center_x - crop_size // 2
crop_y = args.center_y - crop_size // 2
mask = np.zeros((1, image_size, image_size, 3), dtype=np.float32)
mask[:, crop_y:crop_y + crop_size, crop_x:crop_x + crop_size, :] = 1.0
# Build graph.
logger.info(f'Building graph.')
sess = tf.get_default_session()
input_shape = E.input_shape
input_shape[0] = args.batch_size
x = tf.placeholder(tf.float32, shape=input_shape, name='real_image')
x_mask = (tf.transpose(x, [0, 2, 3, 1]) + 1) * mask - 1
x_mask_255 = (x_mask + 1) / 2 * 255
latent_shape = Gs.components.synthesis.input_shape
latent_shape[0] = args.batch_size
wp = tf.get_variable(shape=latent_shape, name='latent_code')
x_rec = Gs.components.synthesis.get_output_for(wp, randomize_noise=False)
x_rec_mask = (tf.transpose(x_rec, [0, 2, 3, 1]) + 1) * mask - 1
x_rec_mask_255 = (x_rec_mask + 1) / 2 * 255
w_enc = E.get_output_for(x, phase=False)
wp_enc = tf.reshape(w_enc, latent_shape)
setter = tf.assign(wp, wp_enc)
# Settings for optimization.
logger.info(f'Setting configuration for optimization.')
perceptual_model = PerceptualModel([image_size, image_size], False)
x_feat = perceptual_model(x_mask_255)
x_rec_feat = perceptual_model(x_rec_mask_255)
loss_feat = tf.reduce_mean(tf.square(x_feat - x_rec_feat), axis=[1])
loss_pix = tf.reduce_mean(tf.square(x_mask - x_rec_mask), axis=[1, 2, 3])
loss = loss_pix + args.loss_weight_feat * loss_feat
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
train_op = optimizer.minimize(loss, var_list=[wp])
tflib.init_uninitialized_vars()
# Load image list.
logger.info(f'Loading target images and context images.')
target_list = []
with open(args.target_list, 'r') as f:
for line in f:
target_list.append(line.strip())
num_targets = len(target_list)
context_list = []
with open(args.context_list, 'r') as f:
for line in f:
context_list.append(line.strip())
num_contexts = len(context_list)
num_pairs = num_targets * num_contexts
# Invert images.
logger.info(f'Start diffusion.')
save_interval = args.num_iterations // args.num_results
headers = [
'Target Image', 'Context Image', 'Stitched Image', 'Encoder Output'
]
for step in range(1, args.num_iterations + 1):
if step == args.num_iterations or step % save_interval == 0:
headers.append(f'Step {step:06d}')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=num_pairs,
num_cols=len(headers),
viz_size=viz_size)
visualizer.set_headers(headers)
images = np.zeros(input_shape, np.uint8)
latent_codes_enc = []
latent_codes = []
for target_idx in tqdm(range(num_targets), desc='Target ID', leave=False):
# Load target.
target_image = resize_image(load_image(target_list[target_idx]),
(image_size, image_size))
visualizer.set_cell(target_idx * num_contexts, 0, image=target_image)
for context_idx in tqdm(range(0, num_contexts, args.batch_size),
desc='Context ID',
leave=False):
row_idx = target_idx * num_contexts + context_idx
batch = context_list[context_idx:context_idx + args.batch_size]
for i, context_image_path in enumerate(batch):
context_image = resize_image(load_image(context_image_path),
(image_size, image_size))
visualizer.set_cell(row_idx + i, 1, image=context_image)
context_image[crop_y:crop_y + crop_size, crop_x:crop_x +
crop_size] = (target_image[crop_y:crop_y +
crop_size,
crop_x:crop_x +
crop_size])
visualizer.set_cell(row_idx + i, 2, image=context_image)
images[i] = np.transpose(context_image, [2, 0, 1])
inputs = images.astype(np.float32) / 255 * 2.0 - 1.0
# Run encoder.
sess.run([setter], {x: inputs})
outputs = sess.run([wp, x_rec])
latent_codes_enc.append(outputs[0][0:len(batch)])
outputs[1] = adjust_pixel_range(outputs[1])
for i, _ in enumerate(batch):
visualizer.set_cell(row_idx + i, 3, image=outputs[1][i])
# Optimize latent codes.
col_idx = 4
for step in tqdm(range(1, args.num_iterations + 1), leave=False):
sess.run(train_op, {x: inputs})
if step == args.num_iterations or step % save_interval == 0:
outputs = sess.run([wp, x_rec])
outputs[1] = adjust_pixel_range(outputs[1])
for i, _ in enumerate(batch):
visualizer.set_cell(row_idx + i,
col_idx,
image=outputs[1][i])
col_idx += 1
latent_codes.append(outputs[0][0:len(batch)])
# Save results.
code_shape = [num_targets, num_contexts] + list(latent_shape[1:])
np.save(f'{output_dir}/{job_name}_encoded_codes.npy',
np.concatenate(latent_codes_enc, axis=0).reshape(code_shape))
np.save(f'{output_dir}/{job_name}_inverted_codes.npy',
np.concatenate(latent_codes, axis=0).reshape(code_shape))
visualizer.save(f'{output_dir}/{job_name}.html')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
assert os.path.exists(args.image_list)
image_list_name = os.path.splitext(os.path.basename(args.image_list))[0]
output_dir = args.output_dir or f'results/ghfeat/{image_list_name}'
logger = setup_logger(output_dir, 'extract_feature.log',
'inversion_logger')
logger.info(f'Loading model.')
tflib.init_tf({'rnd.np_random_seed': 1000})
with open(args.model_path, 'rb') as f:
E, _, _, Gs = pickle.load(f)
# Get input size.
image_size = E.input_shape[2]
assert image_size == E.input_shape[3]
G_args = EasyDict(func_name='training.networks_stylegan.G_synthesis')
G_style_mod = tflib.Network('G_StyleMod',
resolution=image_size,
label_size=0,
**G_args)
Gs_vars_pairs = {
name: tflib.run(val)
for name, val in Gs.components.synthesis.vars.items()
}
for g_name, g_val in G_style_mod.vars.items():
tflib.set_vars({g_val: Gs_vars_pairs[g_name]})
# Build graph.
logger.info(f'Building graph.')
sess = tf.get_default_session()
input_shape = E.input_shape
input_shape[0] = args.batch_size
x = tf.placeholder(tf.float32, shape=input_shape, name='real_image')
ghfeat = E.get_output_for(x, is_training=False)
x_rec = G_style_mod.get_output_for(ghfeat, randomize_noise=False)
# Load image list.
logger.info(f'Loading image list.')
image_list = []
with open(args.image_list, 'r') as f:
for line in f:
image_list.append(line.strip())
# Extract GH-Feat from images.
logger.info(f'Start feature extraction.')
headers = ['Name', 'Original Image', 'Encoder Output']
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=len(image_list),
num_cols=len(headers),
viz_size=viz_size)
visualizer.set_headers(headers)
images = np.zeros(input_shape, np.uint8)
names = ['' for _ in range(args.batch_size)]
features = []
for img_idx in tqdm(range(0, len(image_list), args.batch_size),
leave=False):
# Load inputs.
batch = image_list[img_idx:img_idx + args.batch_size]
for i, image_path in enumerate(batch):
image = resize_image(load_image(image_path),
(image_size, image_size))
images[i] = np.transpose(image, [2, 0, 1])
names[i] = os.path.splitext(os.path.basename(image_path))[0]
inputs = images.astype(np.float32) / 255 * 2.0 - 1.0
# Run encoder.
outputs = sess.run([ghfeat, x_rec], {x: inputs})
features.append(outputs[0][0:len(batch)])
outputs[1] = adjust_pixel_range(outputs[1])
for i, _ in enumerate(batch):
image = np.transpose(images[i], [1, 2, 0])
save_image(f'{output_dir}/{names[i]}_ori.png', image)
save_image(f'{output_dir}/{names[i]}_enc.png', outputs[1][i])
visualizer.set_cell(i + img_idx, 0, text=names[i])
visualizer.set_cell(i + img_idx, 1, image=image)
visualizer.set_cell(i + img_idx, 2, image=outputs[1][i])
# Save results.
os.system(f'cp {args.image_list} {output_dir}/image_list.txt')
np.save(f'{output_dir}/ghfeat.npy', np.concatenate(features, axis=0))
visualizer.save(f'{output_dir}/reconstruction.html')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
assert os.path.exists(args.image_list)
image_list_name = os.path.splitext(os.path.basename(args.image_list))[0]
output_dir = args.output_dir or f'results/inversion/{image_list_name}'
logger = setup_logger(output_dir, 'inversion.log', 'inversion_logger')
logger.info(f'Loading model.')
tflib.init_tf({'rnd.np_random_seed': 1000})
with open(args.model_path, 'rb') as f:
E, _, _, Gs = pickle.load(f)
# Get input size.
image_size = E.input_shape[2]
assert image_size == E.input_shape[3]
# Build graph.
logger.info(f'Building graph.')
sess = tf.get_default_session()
input_shape = E.input_shape
input_shape[0] = args.batch_size
x = tf.placeholder(tf.float32, shape=input_shape, name='real_image')
x_255 = (tf.transpose(x, [0, 2, 3, 1]) + 1) / 2 * 255
latent_shape = Gs.components.synthesis.input_shape
latent_shape[0] = args.batch_size
wp = tf.get_variable(shape=latent_shape, name='latent_code')
x_rec = Gs.components.synthesis.get_output_for(wp, randomize_noise=False)
x_rec_255 = (tf.transpose(x_rec, [0, 2, 3, 1]) + 1) / 2 * 255
if args.random_init:
logger.info(f' Use random initialization for optimization.')
wp_rnd = tf.random.normal(shape=latent_shape, name='latent_code_init')
setter = tf.assign(wp, wp_rnd)
else:
logger.info(
f' Use encoder output as the initialization for optimization.')
w_enc = E.get_output_for(x, is_training=False)
wp_enc = tf.reshape(w_enc, latent_shape)
setter = tf.assign(wp, wp_enc)
# Settings for optimization.
logger.info(f'Setting configuration for optimization.')
perceptual_model = PerceptualModel([image_size, image_size], False)
x_feat = perceptual_model(x_255)
x_rec_feat = perceptual_model(x_rec_255)
loss_feat = tf.reduce_mean(tf.square(x_feat - x_rec_feat), axis=[1])
loss_pix = tf.reduce_mean(tf.square(x - x_rec), axis=[1, 2, 3])
if args.domain_regularizer:
logger.info(f' Involve encoder for optimization.')
w_enc_new = E.get_output_for(x_rec, is_training=False)
wp_enc_new = tf.reshape(w_enc_new, latent_shape)
loss_enc = tf.reduce_mean(tf.square(wp - wp_enc_new), axis=[1, 2])
else:
logger.info(f' Do NOT involve encoder for optimization.')
loss_enc = 0
loss = (loss_pix + args.loss_weight_feat * loss_feat +
args.loss_weight_enc * loss_enc)
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
train_op = optimizer.minimize(loss, var_list=[wp])
tflib.init_uninitialized_vars()
# Load image list.
logger.info(f'Loading image list.')
image_list = []
with open(args.image_list, 'r') as f:
for line in f:
image_list.append(line.strip())
# Invert images.
logger.info(f'Start inversion.')
save_interval = args.num_iterations // args.num_results
headers = ['Name', 'Original Image', 'Encoder Output']
for step in range(1, args.num_iterations + 1):
if step == args.num_iterations or step % save_interval == 0:
headers.append(f'Step {step:06d}')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=len(image_list),
num_cols=len(headers),
viz_size=viz_size)
visualizer.set_headers(headers)
images = np.zeros(input_shape, np.uint8)
names = ['' for _ in range(args.batch_size)]
latent_codes_enc = []
latent_codes = []
for img_idx in tqdm(range(0, len(image_list), args.batch_size),
leave=False):
# Load inputs.
batch = image_list[img_idx:img_idx + args.batch_size]
for i, image_path in enumerate(batch):
image = resize_image(load_image(image_path),
(image_size, image_size))
images[i] = np.transpose(image, [2, 0, 1])
names[i] = os.path.splitext(os.path.basename(image_path))[0]
inputs = images.astype(np.float32) / 255 * 2.0 - 1.0
# Run encoder.
sess.run([setter], {x: inputs})
outputs = sess.run([wp, x_rec])
latent_codes_enc.append(outputs[0][0:len(batch)])
outputs[1] = adjust_pixel_range(outputs[1])
for i, _ in enumerate(batch):
image = np.transpose(images[i], [1, 2, 0])
save_image(f'{output_dir}/{names[i]}_ori.png', image)
save_image(f'{output_dir}/{names[i]}_enc.png', outputs[1][i])
visualizer.set_cell(i + img_idx, 0, text=names[i])
visualizer.set_cell(i + img_idx, 1, image=image)
visualizer.set_cell(i + img_idx, 2, image=outputs[1][i])
# Optimize latent codes.
col_idx = 3
for step in tqdm(range(1, args.num_iterations + 1), leave=False):
sess.run(train_op, {x: inputs})
if step == args.num_iterations or step % save_interval == 0:
outputs = sess.run([wp, x_rec])
outputs[1] = adjust_pixel_range(outputs[1])
for i, _ in enumerate(batch):
if step == args.num_iterations:
save_image(f'{output_dir}/{names[i]}_inv.png',
outputs[1][i])
visualizer.set_cell(i + img_idx,
col_idx,
image=outputs[1][i])
col_idx += 1
latent_codes.append(outputs[0][0:len(batch)])
# Save results.
os.system(f'cp {args.image_list} {output_dir}/image_list.txt')
np.save(f'{output_dir}/encoded_codes.npy',
np.concatenate(latent_codes_enc, axis=0))
np.save(f'{output_dir}/inverted_codes.npy',
np.concatenate(latent_codes, axis=0))
visualizer.save(f'{output_dir}/inversion.html')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
style_dir = args.style_dir
style_dir_name = os.path.basename(style_dir.rstrip('/'))
assert os.path.exists(style_dir)
assert os.path.exists(f'{style_dir}/image_list.txt')
assert os.path.exists(f'{style_dir}/inverted_codes.npy')
content_dir = args.content_dir
content_dir_name = os.path.basename(content_dir.rstrip('/'))
assert os.path.exists(content_dir)
assert os.path.exists(f'{content_dir}/image_list.txt')
assert os.path.exists(f'{content_dir}/inverted_codes.npy')
output_dir = args.output_dir or 'results/style_mixing'
job_name = f'{style_dir_name}_STYLIZE_{content_dir_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
# Load model.
logger.info(f'Loading generator.')
generator = build_generator(args.model_name)
mix_layers = list(range(args.mix_layer_start_idx, generator.num_layers))
# Load image and codes.
logger.info(f'Loading images and corresponding inverted latent codes.')
style_list = []
with open(f'{style_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{style_dir}/{name}_ori.png')
style_list.append(name)
logger.info(f'Loading inverted latent codes.')
style_codes = np.load(f'{style_dir}/inverted_codes.npy')
assert style_codes.shape[0] == len(style_list)
num_styles = style_codes.shape[0]
content_list = []
with open(f'{content_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{content_dir}/{name}_ori.png')
content_list.append(name)
logger.info(f'Loading inverted latent codes.')
content_codes = np.load(f'{content_dir}/inverted_codes.npy')
assert content_codes.shape[0] == len(content_list)
num_contents = content_codes.shape[0]
# Mix styles.
logger.info(f'Start style mixing.')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=num_styles + 1,
num_cols=num_contents + 1,
viz_size=viz_size)
visualizer.set_headers(['Style'] +
[f'Content {i:03d}' for i in range(num_contents)])
for style_idx, style_name in enumerate(style_list):
style_image = load_image(f'{style_dir}/{style_name}_ori.png')
visualizer.set_cell(style_idx + 1, 0, image=style_image)
for content_idx, content_name in enumerate(content_list):
content_image = load_image(f'{content_dir}/{content_name}_ori.png')
visualizer.set_cell(0, content_idx + 1, image=content_image)
codes = mix_style(style_codes=style_codes,
content_codes=content_codes,
num_layers=generator.num_layers,
mix_layers=mix_layers)
for style_idx in tqdm(range(num_styles), leave=False):
output_images = generator.easy_synthesize(
codes[style_idx], latent_space_type='wp')['image']
for content_idx, output_image in enumerate(output_images):
visualizer.set_cell(style_idx + 1,
content_idx + 1,
image=output_image)
# Save results.
visualizer.save(f'{output_dir}/{job_name}.html')
def main(image_list=None):
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
assert os.path.exists(image_list)
image_list_name = os.path.splitext(os.path.basename(image_list))[0]
output_dir = f'/raid/celong/lele/tmp/inversion/{image_list_name}'
# output_dir = args.output_dir or f'results/inversion/{image_list_name}'
logger = setup_logger(output_dir, 'inversion.log', 'inversion_logger')
logger.info(f'Loading model.')
inverter = StyleGANInverter(
args.model_name,
learning_rate=args.learning_rate,
iteration=args.num_iterations,
reconstruction_loss_weight=1.0,
perceptual_loss_weight=args.loss_weight_feat,
regularization_loss_weight=args.loss_weight_enc,
logger=logger)
image_size = inverter.G.resolution
# Load image list.
logger.info(f'Loading image list.')
img_list = []
with open(image_list, 'r') as f:
for line in f:
img_list.append(line.strip())
# Initialize visualizer.
save_interval = args.num_iterations // args.num_results
headers = ['Name', 'Original Image', 'Encoder Output']
for step in range(1, args.num_iterations + 1):
if step == args.num_iterations or step % save_interval == 0:
headers.append(f'Step {step:06d}')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=len(img_list),
num_cols=len(headers),
viz_size=viz_size)
visualizer.set_headers(headers)
# Invert images.
logger.info(f'Start inversion.')
latent_codes = []
for img_idx in tqdm(range(50), leave=False):
# for img_idx in tqdm(range(len(img_list)), leave=False):
image_path = img_list[img_idx]
image_name = image_path.split('/')[-3] + '__' + image_path.split(
'/')[-2] + '__' + image_path.split('/')[-1][:-4]
mask_path = image_path[:-4] + '_mask.png'
# if os.path.exists (image_path[:-3] + 'npy'):
# print (image_path[:-3] + 'npy')
# print ('!!!')
# continue
# try:
image = load_image(image_path)
mask = cv2.imread(mask_path)
print(image_path, mask_path)
print(mask.max(), mask.min())
image = image * mask
image = resize_image(image, (image_size, image_size))
code, viz_results = inverter.easy_invert(image,
num_viz=args.num_results)
latent_codes.append(code)
np.save(image_path[:-3] + 'npy', code)
save_image(f'{output_dir}/{image_name}__ori.png', image)
save_image(f'{output_dir}/{image_name}__enc.png', viz_results[1])
save_image(f'{output_dir}/{image_name}__inv.png', viz_results[-1])
visualizer.set_cell(img_idx, 0, text=image_name)
visualizer.set_cell(img_idx, 1, image=image)
for viz_idx, viz_img in enumerate(viz_results[1:]):
visualizer.set_cell(img_idx, viz_idx + 2, image=viz_img)
# except:
# continue
# Save results.
# os.system(f'cp {args.image_list} {output_dir}/image_list.txt')
visualizer.save(f'{output_dir}/inversion.html')
def main():
"""Main function."""
args = parse_args()
if not args.save_raw_synthesis and not args.generate_html:
return
# Parse model configuration.
if args.model_name not in MODEL_ZOO:
raise SystemExit(f'Model `{args.model_name}` is not registered in '
f'`models/model_zoo.py`!')
model_config = MODEL_ZOO[args.model_name].copy()
url = model_config.pop('url') # URL to download model if needed.
# Get work directory and job name.
if args.save_dir:
work_dir = args.save_dir
else:
work_dir = os.path.join('work_dirs', 'synthesis')
os.makedirs(work_dir, exist_ok=True)
job_name = f'{args.model_name}_{args.num}'
if args.save_raw_synthesis:
os.makedirs(os.path.join(work_dir, job_name), exist_ok=True)
# Build generation and get synthesis kwargs.
print(f'Building generator for model `{args.model_name}` ...')
generator = build_generator(**model_config)
synthesis_kwargs = dict(trunc_psi=args.trunc_psi,
trunc_layers=args.trunc_layers,
randomize_noise=args.randomize_noise)
print(f'Finish building generator.')
# Load pre-trained weights.
os.makedirs('checkpoints', exist_ok=True)
checkpoint_path = os.path.join('checkpoints', args.model_name + '.pth')
print(f'Loading checkpoint from `{checkpoint_path}` ...')
if not os.path.exists(checkpoint_path):
print(f' Downloading checkpoint from `{url}` ...')
subprocess.call(['wget', '--quiet', '-O', checkpoint_path, url])
print(f' Finish downloading checkpoint.')
checkpoint = torch.load(checkpoint_path, map_location='cpu')
if 'generator_smooth' in checkpoint:
generator.load_state_dict(checkpoint['generator_smooth'])
else:
generator.load_state_dict(checkpoint['generator'])
code_dim = generator.z_space_dim
# generator = generator.cuda()
device_ids = [int(i) for i in args.device_ids.split(',')]
generator = torch.nn.DataParallel(generator, device_ids=device_ids).cuda()
generator.eval()
print(f'Finish loading checkpoint.')
# Set random seed.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# load gt images:
gt_imgs = load_data().cuda()
print (gt_imgs.shape)
total_num = gt_imgs.shape[0]
# define the code
code = np.zeros( ( total_num, code_dim ), dtype = np.float32)
l1_loss = torch.nn.L1Loss().cuda()
for batch_idx in tqdm(range(0, total_num, args.batch_size)):
frm_idx_start = batch_idx
frm_idx_end = np.min( [ total_num, frm_idx_start + args.batch_size ] )
sample_num = frm_idx_end - frm_idx_start
print( 'Optim lightingcode based on pca batch [%d/%d], frame [%d/%d]'
% (batch_idx/args.batch_size + 1, total_num, frm_idx_start, frm_idx_end) )
# get code batch
batch_code = [ ]
for sample_id in range( sample_num ):
frm_idx = frm_idx_start + sample_id
batch_code.append( code[ frm_idx ] )
batch_code = np.asarray(batch_code)
batch_code = torch.tensor( batch_code, dtype = torch.float32 ).cuda()
batch_code.requires_grad = True
# get gt image batch
batch_gt_img =gt_imgs[frm_idx_start : frm_idx_end]
# Define the optimizer
code_optimizer = torch.optim.Adam( [
{ 'params': [batch_code], 'lr': args.lr }
] )
# optimize
for iter_id in range( args. max_iter_num ):
images = generator(batch_code, **synthesis_kwargs)['image']
# SIZE: (BATCH_SIZE , 3,1024,1024)
# calculate loss:
print (batch_gt_img.max(),batch_gt_img.min(),'========')
print (images.max(),images.min())
global_pix_loss = (batch_gt_img - images).abs().mean()
code_reg_loss = (batch_code ** 2).mean()
loss = global_pix_loss + code_reg_loss * 0.01
# Optimize
code_optimizer.zero_grad()
loss.backward()
code_optimizer.step()
# Print errors
if iter_id == 0 or ( iter_id + 1 ) % 100 == 0:
print( ' iter [%d/%d]: global %f, code_reg %f'
% ( iter_id + 1, args.max_iter_num , global_pix_loss.item(),
code_reg_loss.item()) )# , end = '\r' )
# Reduce the lr
code_optimizer.param_groups[ 0 ][ 'lr' ] *= 0.5
print( ' Reduce learning rate to %f' % (code_optimizer.param_groups[ 0 ][ 'lr' ] ) )
# Set the data
n_code = batch_code.detach().cpu().numpy()
for sample_id in range( sample_num ):
frm_idx = frm_idx_start + sample_id
code[ frm_idx ] = n_code[ sample_id ]
# Sample and synthesize.
print(f'Synthesizing {total_num} samples ...')
indices = list(range(total_num))
if args.generate_html:
html = HtmlPageVisualizer(grid_size=total_num)
for batch_idx in tqdm(range(0, total_num, args.batch_size)):
sub_indices = indices[batch_idx:batch_idx + args.batch_size]
print (code.shape)
t_code = torch.FloatTensor(code[sub_indices]).cuda()
with torch.no_grad():
images = generator(t_code, **synthesis_kwargs)['image']
# images shape [1,3,1024,1024]
print (images.shape)
images = postprocess(images)
for sub_idx, image in zip(sub_indices, images):
if args.save_raw_synthesis:
save_path = os.path.join(
work_dir, job_name, f'{sub_idx:06d}.jpg')
save_image(save_path, image)
if args.generate_html:
row_idx, col_idx = divmod(sub_idx, html.num_cols)
html.set_cell(row_idx, col_idx, image=image,
text=f'Sample {sub_idx:06d}')
if args.generate_html:
html.save(os.path.join(work_dir, f'{job_name}.html'))
print(f'Finish synthesizing {total_num} samples.')
# Sample and ground truth image.
print(f'Synthesizing {total_num} samples ...')
indices = list(range(total_num))
if args.generate_html:
html = HtmlPageVisualizer(grid_size=total_num)
for batch_idx in tqdm(range(0, total_num, args.batch_size)):
sub_indices = indices[batch_idx:batch_idx + args.batch_size]
with torch.no_grad():
images = gt_imgs[sub_indices]
# images shape [1,3,1024,1024]
print (images.shape)
images = postprocess(images)
for sub_idx, image in zip(sub_indices, images):
if args.save_raw_synthesis:
save_path = os.path.join(
work_dir, job_name, f'{sub_idx:06d}_gt.jpg')
save_image(save_path, image)
if args.generate_html:
row_idx, col_idx = divmod(sub_idx, html.num_cols)
html.set_cell(row_idx, col_idx, image=image,
text=f'Sample {sub_idx:06d}')
if args.generate_html:
html.save(os.path.join(work_dir, f'{job_name}_gt.html'))
print(f'Finish synthesizing {total_num} samples.')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
assert os.path.exists(args.target_list)
target_list_name = os.path.splitext(os.path.basename(args.target_list))[0]
assert os.path.exists(args.context_list)
context_list_name = os.path.splitext(os.path.basename(args.context_list))[0]
output_dir = args.output_dir or f'results/diffusion'
job_name = f'{target_list_name}_TO_{context_list_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
logger.info(f'Loading model.')
inverter = StyleGANInverter(
args.model_name,
learning_rate=args.learning_rate,
iteration=args.num_iterations,
reconstruction_loss_weight=1.0,
perceptual_loss_weight=args.loss_weight_feat,
regularization_loss_weight=0.0,
logger=logger)
image_size = inverter.G.resolution
# Load image list.
logger.info(f'Loading target images and context images.')
target_list = []
with open(args.target_list, 'r') as f:
for line in f:
target_list.append(line.strip())
num_targets = len(target_list)
context_list = []
with open(args.context_list, 'r') as f:
for line in f:
context_list.append(line.strip())
num_contexts = len(context_list)
num_pairs = num_targets * num_contexts
# Initialize visualizer.
save_interval = args.num_iterations // args.num_results
headers = ['Target Image', 'Context Image', 'Stitched Image',
'Encoder Output']
for step in range(1, args.num_iterations + 1):
if step == args.num_iterations or step % save_interval == 0:
headers.append(f'Step {step:06d}')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(
num_rows=num_pairs, num_cols=len(headers), viz_size=viz_size)
visualizer.set_headers(headers)
# Diffuse images.
logger.info(f'Start diffusion.')
latent_codes = []
for target_idx in tqdm(range(num_targets), desc='Target ID', leave=False):
# Load target.
target_image = resize_image(load_image(target_list[target_idx]),
(image_size, image_size))
visualizer.set_cell(target_idx * num_contexts, 0, image=target_image)
for context_idx in tqdm(range(num_contexts), desc='Context ID',
leave=False):
row_idx = target_idx * num_contexts + context_idx
context_image = resize_image(load_image(context_list[context_idx]),
(image_size, image_size))
visualizer.set_cell(row_idx, 1, image=context_image)
code, viz_results = inverter.easy_diffuse(target=target_image,
context=context_image,
center_x=args.center_x,
center_y=args.center_y,
crop_x=args.crop_size,
crop_y=args.crop_size,
num_viz=args.num_results)
for viz_idx, viz_img in enumerate(viz_results):
visualizer.set_cell(row_idx, viz_idx + 2, image=viz_img)
latent_codes.append(code)
# Save results.
os.system(f'cp {args.target_list} {output_dir}/target_list.txt')
os.system(f'cp {args.context_list} {output_dir}/context_list.txt')
np.save(f'{output_dir}/{job_name}_inverted_codes.npy',
np.concatenate(latent_codes, axis=0))
visualizer.save(f'{output_dir}/{job_name}.html')
def main():
"""Main function."""
args = parse_args()
file_name = args.output_file
work_dir = args.output_dir or f'{args.model_name}_synthesis'
logger_name = f'{args.model_name}_synthesis_logger'
logger = setup_logger(work_dir, args.logfile_name, logger_name)
logger.info(f'Initializing generator.')
model = build_generator(args.model_name, logger=logger)
logger.info(f'Preparing latent codes.')
if os.path.isfile(args.latent_codes_path):
logger.info(f' Load latent codes from `{args.latent_codes_path}`.')
latent_codes = np.load(args.latent_codes_path)
latent_codes = model.preprocess(
latent_codes=latent_codes,
latent_space_type=args.latent_space_type)
else:
if args.num <= 0:
raise ValueError(
f'Argument `num` should be specified as a positive '
f'number since the latent code path '
f'`{args.latent_codes_path}` does not exist!')
logger.info(f' Sample latent codes randomly.')
latent_codes = model.easy_sample(
num=args.num, latent_space_type=args.latent_space_type)
total_num = latent_codes.shape[0]
if args.generate_prediction:
logger.info(f'Initializing predictor.')
predictor = build_predictor(args.predictor_name)
if args.generate_html:
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=args.html_row,
num_cols=args.html_col,
grid_size=total_num,
viz_size=viz_size)
logger.info(f'Generating {total_num} samples.')
results = defaultdict(list)
predictions = defaultdict(list)
pbar = tqdm(total=total_num, leave=False)
for inputs in model.get_batch_inputs(latent_codes):
outputs = model.easy_synthesize(
latent_codes=inputs,
latent_space_type=args.latent_space_type,
generate_style=args.generate_style,
generate_image=not args.skip_image)
for key, val in outputs.items():
if key == 'image':
if args.generate_prediction:
pred_outputs = predictor.easy_predict(val)
for pred_key, pred_val in pred_outputs.items():
predictions[pred_key].append(pred_val)
for image in val:
if args.save_raw_synthesis:
dest = os.path.join(work_dir, f'{pbar.n:06d}.jpg')
if file_name != "":
dest = os.path.join(work_dir, file_name)
print('saving image to ', dest)
save_image(dest, image)
if args.generate_html:
row_idx = pbar.n // visualizer.num_cols
col_idx = pbar.n % visualizer.num_cols
visualizer.set_cell(row_idx, col_idx, image=image)
pbar.update(1)
else:
results[key].append(val)
if 'image' not in outputs:
pbar.update(inputs.shape[0])
pbar.close()
logger.info(f'Saving results.')
if args.generate_html:
visualizer.save(os.path.join(work_dir, args.html_name))
for key, val in results.items():
np.save(os.path.join(work_dir, f'{key}.npy'),
np.concatenate(val, axis=0))
if predictions:
if args.predictor_name == 'scene':
# Categories
categories = np.concatenate(predictions['category'], axis=0)
detailed_categories = {
'score': categories,
'name_to_idx': predictor.category_name_to_idx,
'idx_to_name': predictor.category_idx_to_name,
}
np.save(os.path.join(work_dir, 'category.npy'),
detailed_categories)
# Attributes
attributes = np.concatenate(predictions['attribute'], axis=0)
detailed_attributes = {
'score': attributes,
'name_to_idx': predictor.attribute_name_to_idx,
'idx_to_name': predictor.attribute_idx_to_name,
}
np.save(os.path.join(work_dir, 'attribute.npy'),
detailed_attributes)
else:
for key, val in predictions.items():
np.save(os.path.join(work_dir, f'{key}.npy'),
np.concatenate(val, axis=0))
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
assert os.path.exists(args.image_list)
image_list_name = os.path.splitext(os.path.basename(args.image_list))[0]
output_dir = args.output_dir or f'results/inversion/{image_list_name}'
logger = setup_logger(output_dir, 'inversion.log', 'inversion_logger')
logger.info(f'Loading model.')
inverter = StyleGANInverter(
args.model_name,
learning_rate=args.learning_rate,
iteration=args.num_iterations,
reconstruction_loss_weight=1.0,
perceptual_loss_weight=args.loss_weight_feat,
regularization_loss_weight=args.loss_weight_enc,
clip_loss_weight=args.loss_weight_clip,
description=args.description,
logger=logger)
image_size = inverter.G.resolution
# Load image list.
logger.info(f'Loading image list.')
image_list = []
with open(args.image_list, 'r') as f:
for line in f:
image_list.append(line.strip())
# Initialize visualizer.
save_interval = args.num_iterations // args.num_results
headers = ['Name', 'Original Image', 'Encoder Output']
for step in range(1, args.num_iterations + 1):
if step == args.num_iterations or step % save_interval == 0:
headers.append(f'Step {step:06d}')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=len(image_list),
num_cols=len(headers),
viz_size=viz_size)
visualizer.set_headers(headers)
# Invert images.
logger.info(f'Start inversion.')
latent_codes = []
for img_idx in tqdm(range(len(image_list)), leave=False):
image_path = image_list[img_idx]
image_name = os.path.splitext(os.path.basename(image_path))[0]
image = resize_image(load_image(image_path), (image_size, image_size))
code, viz_results = inverter.easy_invert(image,
num_viz=args.num_results)
latent_codes.append(code)
np.save(f'{output_dir}/{image_name}.npy', code)
print(np.shape(code))
save_image(f'{output_dir}/{image_name}_ori.png', image)
save_image(f'{output_dir}/{image_name}_enc.png', viz_results[1])
save_image(f'{output_dir}/{image_name}_inv.png', viz_results[-1])
print(f'save {image_name} in {output_dir}')
visualizer.set_cell(img_idx, 0, text=image_name)
visualizer.set_cell(img_idx, 1, image=image)
for viz_idx, viz_img in enumerate(viz_results[1:]):
visualizer.set_cell(img_idx, viz_idx + 2, image=viz_img)
# Save results.
os.system(f'cp {args.image_list} {output_dir}/image_list.txt')
np.save(f'{output_dir}/inverted_codes.npy',
np.concatenate(latent_codes, axis=0))
visualizer.save(f'{output_dir}/inversion.html')
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
image_dir = args.image_dir
image_dir_name = os.path.basename(image_dir.rstrip('/'))
assert os.path.exists(image_dir)
assert os.path.exists(f'{image_dir}/image_list.txt')
assert os.path.exists(f'{image_dir}/inverted_codes.npy')
boundary_path = args.boundary_path
assert os.path.exists(boundary_path)
boundary_name = os.path.splitext(os.path.basename(boundary_path))[0]
output_dir = args.output_dir or 'results/manipulation'
job_name = f'{boundary_name.upper()}_{image_dir_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
# Load model.
logger.info(f'Loading generator.')
tflib.init_tf({'rnd.np_random_seed': 1000})
with open(args.model_path, 'rb') as f:
_, _, _, Gs = pickle.load(f)
# Build graph.
logger.info(f'Building graph.')
sess = tf.get_default_session()
num_layers, latent_dim = Gs.components.synthesis.input_shape[1:3]
wp = tf.placeholder(tf.float32, [args.batch_size, num_layers, latent_dim],
name='latent_code')
x = Gs.components.synthesis.get_output_for(wp, randomize_noise=False)
# Load image, codes, and boundary.
logger.info(f'Loading images and corresponding inverted latent codes.')
image_list = []
with open(f'{image_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{image_dir}/{name}_ori.png')
assert os.path.exists(f'{image_dir}/{name}_inv.png')
image_list.append(name)
latent_codes = np.load(f'{image_dir}/inverted_codes.npy')
assert latent_codes.shape[0] == len(image_list)
num_images = latent_codes.shape[0]
logger.info(f'Loading boundary.')
boundary_file = np.load(boundary_path, allow_pickle=True)[()]
if isinstance(boundary_file, dict):
boundary = boundary_file['boundary']
manipulate_layers = boundary_file['meta_data']['manipulate_layers']
else:
boundary = boundary_file
manipulate_layers = args.manipulate_layers
if manipulate_layers:
logger.info(f' Manipulating on layers `{manipulate_layers}`.')
else:
logger.info(f' Manipulating on ALL layers.')
# Manipulate images.
logger.info(f'Start manipulation.')
step = args.step
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=num_images,
num_cols=step + 3,
viz_size=viz_size)
visualizer.set_headers(['Name', 'Origin', 'Inverted'] +
[f'Step {i:02d}' for i in range(1, step + 1)])
for img_idx, img_name in enumerate(image_list):
ori_image = load_image(f'{image_dir}/{img_name}_ori.png')
inv_image = load_image(f'{image_dir}/{img_name}_inv.png')
visualizer.set_cell(img_idx, 0, text=img_name)
visualizer.set_cell(img_idx, 1, image=ori_image)
visualizer.set_cell(img_idx, 2, image=inv_image)
codes = manipulate(latent_codes=latent_codes,
boundary=boundary,
start_distance=args.start_distance,
end_distance=args.end_distance,
step=step,
layerwise_manipulation=True,
num_layers=num_layers,
manipulate_layers=manipulate_layers,
is_code_layerwise=True,
is_boundary_layerwise=True)
inputs = np.zeros((args.batch_size, num_layers, latent_dim), np.float32)
for img_idx in tqdm(range(num_images), leave=False):
output_images = []
for idx in range(0, step, args.batch_size):
batch = codes[img_idx, idx:idx + args.batch_size]
inputs[0:len(batch)] = batch
images = sess.run(x, feed_dict={wp: inputs})
output_images.append(images[0:len(batch)])
output_images = adjust_pixel_range(
np.concatenate(output_images, axis=0))
for s, output_image in enumerate(output_images):
visualizer.set_cell(img_idx, s + 3, image=output_image)
# Save results.
visualizer.save(f'{output_dir}/{job_name}.html')
logger.info(f'Loading boundary.')
path = f'boundaries/{args.model_name}/{args.boundary_name}_boundary.npy'
try:
boundary_file = np.load(path, allow_pickle=True).item()
boundary = boundary_file['boundary']
manipulate_layers = boundary_file['meta_data']['manipulate_layers']
except ValueError:
boundary = np.load(path)
manipulate_layers = args.manipulate_layers
logger.info(f' Manipulating on layers `{manipulate_layers}`.')
np.save(os.path.join(work_dir, f'{prefix}_boundary.npy'), boundary)
step = args.step + int(args.step % 2
== 0) # Make sure it is an odd number.
visualizer = HtmlPageVisualizer(num_rows=total_num, num_cols=step + 1)
visualizer.set_headers([''] +
[f'Step {i - step // 2}'
for i in range(step // 2)] + ['Origin'] +
[f'Step {i + 1}' for i in range(step // 2)])
for n in range(total_num):
visualizer.set_cell(n, 0, text=f'Sample {n:05d}')
strength = get_layerwise_manipulation_strength(model.num_layers,
model.truncation_psi,
model.truncation_layers)
codes = manipulate(latent_codes=latent_codes['wp'],
boundary=boundary,
start_distance=args.start_distance,
end_distance=args.end_distance,
step=step,
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
src_dir = args.src_dir
src_dir_name = os.path.basename(src_dir.rstrip('/'))
assert os.path.exists(src_dir)
assert os.path.exists(f'{src_dir}/image_list.txt')
assert os.path.exists(f'{src_dir}/inverted_codes.npy')
dst_dir = args.dst_dir
dst_dir_name = os.path.basename(dst_dir.rstrip('/'))
assert os.path.exists(dst_dir)
assert os.path.exists(f'{dst_dir}/image_list.txt')
assert os.path.exists(f'{dst_dir}/inverted_codes.npy')
output_dir = args.output_dir or 'results/interpolation'
job_name = f'{src_dir_name}_TO_{dst_dir_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
# Load model.
logger.info(f'Loading generator.')
generator = build_generator(args.model_name)
# Load image and codes.
logger.info(f'Loading images and corresponding inverted latent codes.')
src_list = []
with open(f'{src_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{src_dir}/{name}_ori.png')
src_list.append(name)
src_codes = np.load(f'{src_dir}/inverted_codes.npy')
assert src_codes.shape[0] == len(src_list)
num_src = src_codes.shape[0]
dst_list = []
with open(f'{dst_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{dst_dir}/{name}_ori.png')
dst_list.append(name)
dst_codes = np.load(f'{dst_dir}/inverted_codes.npy')
assert dst_codes.shape[0] == len(dst_list)
num_dst = dst_codes.shape[0]
# Interpolate images.
logger.info(f'Start interpolation.')
step = args.step + 2
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=num_src * num_dst,
num_cols=step + 2,
viz_size=viz_size)
visualizer.set_headers(['Source', 'Source Inversion'] +
[f'Step {i:02d}' for i in range(1, step - 1)] +
['Target Inversion', 'Target'])
for src_idx in tqdm(range(num_src), leave=False):
src_code = src_codes[src_idx:src_idx + 1]
src_path = f'{src_dir}/{src_list[src_idx]}_ori.png'
codes = interpolate(src_codes=np.repeat(src_code, num_dst, axis=0),
dst_codes=dst_codes,
step=step)
for dst_idx in tqdm(range(num_dst), leave=False):
dst_path = f'{dst_dir}/{dst_list[dst_idx]}_ori.png'
output_images = generator.easy_synthesize(
codes[dst_idx], latent_space_type='wp')['image']
row_idx = src_idx * num_dst + dst_idx
visualizer.set_cell(row_idx, 0, image=load_image(src_path))
visualizer.set_cell(row_idx, step + 1, image=load_image(dst_path))
for s, output_image in enumerate(output_images):
visualizer.set_cell(row_idx, s + 1, image=output_image)
# Save results.
visualizer.save(f'{output_dir}/{job_name}.html')
def synthesize(self,
num,
z=None,
html_name=None,
save_raw_synthesis=False):
"""Synthesizes images.
Args:
num: Number of images to synthesize.
z: Latent codes used for generation. If not specified, this function
will sample latent codes randomly. (default: None)
html_name: Name of the output html page for visualization. If not
specified, no visualization page will be saved. (default: None)
save_raw_synthesis: Whether to save raw synthesis on the disk.
(default: False)
"""
if not html_name and not save_raw_synthesis:
return
self.set_mode('val')
temp_dir = os.path.join(self.work_dir, 'synthesize_results')
os.makedirs(temp_dir, exist_ok=True)
if z is not None:
assert isinstance(z, np.ndarray)
assert z.ndim == 2 and z.shape[1] == self.z_space_dim
num = min(num, z.shape[0])
z = torch.from_numpy(z).type(torch.FloatTensor)
if not num:
return
# TODO: Use same z during the entire training process.
self.logger.init_pbar()
task1 = self.logger.add_pbar_task('Synthesize', total=num)
indices = list(range(self.rank, num, self.world_size))
for batch_idx in range(0, len(indices), self.val_batch_size):
sub_indices = indices[batch_idx:batch_idx + self.val_batch_size]
batch_size = len(sub_indices)
if z is None:
code = torch.randn(batch_size, self.z_space_dim).cuda()
else:
code = z[sub_indices].cuda()
with torch.no_grad():
if 'generator_smooth' in self.models:
G = self.models['generator_smooth']
else:
G = self.models['generator']
images = G(code, **self.G_kwargs_val)['image']
images = self.postprocess(images)
for sub_idx, image in zip(sub_indices, images):
save_image(os.path.join(temp_dir, f'{sub_idx:06d}.jpg'), image)
self.logger.update_pbar(task1, batch_size * self.world_size)
dist.barrier()
if self.rank != 0:
return
if html_name:
task2 = self.logger.add_pbar_task('Visualize', total=num)
html = HtmlPageVisualizer(grid_size=num)
for image_idx in range(num):
image = load_image(
os.path.join(temp_dir, f'{image_idx:06d}.jpg'))
row_idx, col_idx = divmod(image_idx, html.num_cols)
html.set_cell(row_idx,
col_idx,
image=image,
text=f'Sample {image_idx:06d}')
self.logger.update_pbar(task2, 1)
html.save(os.path.join(self.work_dir, html_name))
if not save_raw_synthesis:
shutil.rmtree(temp_dir)
self.logger.close_pbar()
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
style_dir = args.style_dir
style_dir_name = os.path.basename(style_dir.rstrip('/'))
assert os.path.exists(style_dir)
assert os.path.exists(f'{style_dir}/image_list.txt')
assert os.path.exists(f'{style_dir}/inverted_codes.npy')
content_dir = args.content_dir
content_dir_name = os.path.basename(content_dir.rstrip('/'))
assert os.path.exists(content_dir)
assert os.path.exists(f'{content_dir}/image_list.txt')
assert os.path.exists(f'{content_dir}/inverted_codes.npy')
output_dir = args.output_dir or 'results/style_mixing'
job_name = f'{style_dir_name}_STYLIZE_{content_dir_name}'
logger = setup_logger(output_dir, f'{job_name}.log', f'{job_name}_logger')
# Load model.
logger.info(f'Loading generator.')
tflib.init_tf({'rnd.np_random_seed': 1000})
with open(args.model_path, 'rb') as f:
_, _, _, Gs = pickle.load(f)
# Build graph.
logger.info(f'Building graph.')
sess = tf.get_default_session()
num_layers, latent_dim = Gs.components.synthesis.input_shape[1:3]
wp = tf.placeholder(
tf.float32, [args.batch_size, num_layers, latent_dim], name='latent_code')
x = Gs.components.synthesis.get_output_for(wp, randomize_noise=False)
mix_layers = list(range(args.mix_layer_start_idx, num_layers))
# Load image and codes.
logger.info(f'Loading images and corresponding inverted latent codes.')
style_list = []
with open(f'{style_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{style_dir}/{name}_ori.png')
style_list.append(name)
logger.info(f'Loading inverted latent codes.')
style_codes = np.load(f'{style_dir}/inverted_codes.npy')
assert style_codes.shape[0] == len(style_list)
num_styles = style_codes.shape[0]
content_list = []
with open(f'{content_dir}/image_list.txt', 'r') as f:
for line in f:
name = os.path.splitext(os.path.basename(line.strip()))[0]
assert os.path.exists(f'{content_dir}/{name}_ori.png')
content_list.append(name)
logger.info(f'Loading inverted latent codes.')
content_codes = np.load(f'{content_dir}/inverted_codes.npy')
assert content_codes.shape[0] == len(content_list)
num_contents = content_codes.shape[0]
# Mix styles.
logger.info(f'Start style mixing.')
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(
num_rows=num_styles + 1, num_cols=num_contents + 1, viz_size=viz_size)
visualizer.set_headers(
['Style'] +
[f'Content {i:03d}' for i in range(num_contents)]
)
for style_idx, style_name in enumerate(style_list):
style_image = load_image(f'{style_dir}/{style_name}_ori.png')
visualizer.set_cell(style_idx + 1, 0, image=style_image)
for content_idx, content_name in enumerate(content_list):
content_image = load_image(f'{content_dir}/{content_name}_ori.png')
visualizer.set_cell(0, content_idx + 1, image=content_image)
codes = mix_style(style_codes=style_codes,
content_codes=content_codes,
num_layers=num_layers,
mix_layers=mix_layers)
inputs = np.zeros((args.batch_size, num_layers, latent_dim), np.float32)
for style_idx in tqdm(range(num_styles), leave=False):
output_images = []
for idx in range(0, num_contents, args.batch_size):
batch = codes[style_idx, idx:idx + args.batch_size]
inputs[0:len(batch)] = batch
images = sess.run(x, feed_dict={wp: inputs})
output_images.append(images[0:len(batch)])
output_images = adjust_pixel_range(np.concatenate(output_images, axis=0))
for content_idx, output_image in enumerate(output_images):
visualizer.set_cell(style_idx + 1, content_idx + 1, image=output_image)
# Save results.
visualizer.save(f'{output_dir}/{job_name}.html')
def main():
"""Main function."""
args = parse_args()
work_dir = args.output_dir or f'{args.model_name}_manipulation'
logger_name = f'{args.model_name}_manipulation_logger'
logger = setup_logger(work_dir, args.logfile_name, logger_name)
logger.info(f'Initializing generator.')
model = build_generator(args.model_name, logger=logger)
logger.info(f'Preparing latent codes.')
if os.path.isfile(args.latent_codes_path):
logger.info(f' Load latent codes from `{args.latent_codes_path}`.')
latent_codes = np.load(args.latent_codes_path)
latent_codes = model.preprocess(
latent_codes=latent_codes,
latent_space_type=args.latent_space_type)
else:
if args.num <= 0:
raise ValueError(
f'Argument `num` should be specified as a positive '
f'number since the latent code path '
f'`{args.latent_codes_path}` does not exist!')
logger.info(f' Sample latent codes randomly.')
latent_codes = model.easy_sample(
num=args.num, latent_space_type=args.latent_space_type)
total_num = latent_codes.shape[0]
latent_codes = model.easy_synthesize(
latent_codes=latent_codes,
latent_space_type=args.latent_space_type,
generate_style=False,
generate_image=False)
for key, val in latent_codes.items():
np.save(os.path.join(work_dir, f'{key}.npy'), val)
boundaries = parse_boundary_list(args.boundary_list_path)
step = args.step + int(args.step % 2
== 0) # Make sure it is an odd number.
for boundary_info, boundary_path in boundaries.items():
boundary_name, space_type = boundary_info
logger.info(
f'Boundary `{boundary_name}` from {space_type.upper()} space.')
prefix = f'{boundary_name}_{space_type}'
if args.generate_html:
viz_size = None if args.viz_size == 0 else args.viz_size
visualizer = HtmlPageVisualizer(num_rows=total_num,
num_cols=step + 1,
viz_size=viz_size)
visualizer.set_headers(
[''] + [f'Step {i - step // 2}' for i in range(step // 2)] +
['Origin'] + [f'Step {i + 1}' for i in range(step // 2)])
if args.generate_video:
setup_images = model.easy_synthesize(
latent_codes=latent_codes[args.latent_space_type],
latent_space_type=args.latent_space_type)['image']
fusion_kwargs = {
'row': args.row,
'col': args.col,
'row_spacing': args.row_spacing,
'col_spacing': args.col_spacing,
'border_left': args.border_left,
'border_right': args.border_right,
'border_top': args.border_top,
'border_bottom': args.border_bottom,
'black_background': not args.white_background,
'image_size': None if args.viz_size == 0 else args.viz_size,
}
setup_image = fuse_images(setup_images, **fusion_kwargs)
video_writer = VideoWriter(os.path.join(
work_dir, f'{prefix}_{args.video_name}'),
frame_height=setup_image.shape[0],
frame_width=setup_image.shape[1],
fps=args.fps)
logger.info(f' Loading boundary.')
try:
boundary_file = np.load(boundary_path, allow_pickle=True).item()
boundary = boundary_file['boundary']
manipulate_layers = boundary_file['meta_data']['manipulate_layers']
except ValueError:
boundary = np.load(boundary_path)
manipulate_layers = args.manipulate_layers
logger.info(f' Manipulating on layers `{manipulate_layers}`.')
np.save(os.path.join(work_dir, f'{prefix}_boundary.npy'), boundary)
if args.layerwise_manipulation and space_type != 'z':
layerwise_manipulation = True
is_code_layerwise = True
is_boundary_layerwise = (space_type == 'wp')
if (not args.disable_manipulation_truncation
) and space_type == 'w':
strength = get_layerwise_manipulation_strength(
model.num_layers, model.truncation_psi,
model.truncation_layers)
else:
strength = 1.0
space_type = 'wp'
else:
if args.layerwise_manipulation:
logger.warning(f' Skip layer-wise manipulation for boundary '
f'`{boundary_name}` from Z space. Traditional '
f'manipulation is used instead.')
layerwise_manipulation = False
is_code_layerwise = False
is_boundary_layerwise = False
strength = 1.0
codes = manipulate(latent_codes=latent_codes[space_type],
boundary=boundary,
start_distance=args.start_distance,
end_distance=args.end_distance,
step=step,
layerwise_manipulation=layerwise_manipulation,
num_layers=model.num_layers,
manipulate_layers=manipulate_layers,
is_code_layerwise=is_code_layerwise,
is_boundary_layerwise=is_boundary_layerwise,
layerwise_manipulation_strength=strength)
np.save(
os.path.join(work_dir, f'{prefix}_manipulated_{space_type}.npy'),
codes)
logger.info(f' Start manipulating.')
for s in tqdm(range(step), leave=False):
images = model.easy_synthesize(
latent_codes=codes[:,
s], latent_space_type=space_type)['image']
if args.generate_video:
video_writer.write(fuse_images(images, **fusion_kwargs))
for n, image in enumerate(images):
if args.save_raw_synthesis:
save_image(
os.path.join(work_dir,
f'{prefix}_{n:05d}_{s:03d}.jpg'), image)
if args.generate_html:
visualizer.set_cell(n, s + 1, image=image)
if s == 0:
visualizer.set_cell(n, 0, text=f'Sample {n:05d}')
if args.generate_html:
visualizer.save(
os.path.join(work_dir, f'{prefix}_{args.html_name}'))
