def generate_fake_images(run_id=None,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8,
path=None,
latent=None,
Gs=None,
labels=None):
if path and latent is not None and Gs is not None:
# latents = misc.random_latents(np.prod([1, 1]), Gs, random_state=random_state)
if labels is None:
labels = np.zeros([latent.shape[0], 0], np.float32)
images = Gs.run(latent,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
misc.save_image_grid(images, os.path.join(path), [0, 255], [1, 1])
return
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
misc.save_image_grid(
images,
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_interpolate_midle_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8,
middle_img=10):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
latents = misc.random_latents(middle_img + 2,
Gs,
random_state=random_state)
from_to_tensor = latents[middle_img + 1] - latents[0]
from_z = latents[0]
#between_x_list = [from_x]
counter = 0
for alpha in np.linspace(-0.5, 0.5, middle_img +
2): #np.linspace(0, 1, middle_img + 1):
print('alpha: ', alpha, 'counter= ', counter)
between_z = from_z + alpha * from_to_tensor
latents[counter] = between_z
counter += 1
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
#grid_size_1=[middle_img+2,1]
grid_size_1 = [middle_img + 1, 1]
#png_prefix=0
misc.save_image_grid(
images[1:, :, :, :],
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size_1)
'''
def find_latent_with_query_image(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=4123, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
# Create query image - tensorflow constant
query_image = cv2.imread('../../data/ACDC/training/patient001/cardiac_cycles/0/0.png')
query_image = cv2.resize(query_image, (256, 256))
print('Saving query image to "%s"...' % result_subdir)
cv2.imwrite(result_subdir+'/query_image.png', query_image)
query_image = query_image.transpose(2,0,1)
query_image = query_image[np.newaxis]
x = tf.constant(query_image, dtype=tf.float32, name='query_image')
# Create G(z) - tensorflow variable and label
latent = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
initial = tf.constant(latent, dtype=tf.float32)
z = tf.Variable(initial_value=initial, dtype=tf.float32, name='latent_space')
label = np.zeros([latent.shape[0], 5], np.float32)
label[:,4] = 1 # | 0 -> NOR | 1 -> DCM | 2 -> HCM | 3 -> MINF | 4 -> RV |
gz = Gs.run(latent, label, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.float32)
gz = tf.Variable(gz, dtype=tf.float32)
# Define a loss function
residual_loss = tf.losses.absolute_difference(x, gz)
# Define an optimizer
train_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(residual_loss)
zs, gzs, step = [], [], 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_, loss_value = sess.run([train_op, residual_loss])
while (loss_value > 2e-04 and step <= 50000):
_, loss_value = sess.run([train_op, residual_loss])
step += 1
if step % 10000 == 0:
print('Step {}, Loss value: {}'.format(step, loss_value))
gzs.append(sess.run(gz))
zs.append(sess.run(z))
for png_idx, image in enumerate(gzs):
misc.save_image_grid(image, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
np.save(result_subdir+'/zs.npy', np.asarray(zs))
def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images_glob(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
latents = random_state.randn(num_pngs,
*G.input_shape[1:]).astype(np.float32)
dist = cdist(latents, latents)
np.fill_diagonal(dist, 100)
result_subdir = misc.create_result_subdir(config_test.result_dir,
config_test.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config_test.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
misc.save_image_grid(
images,
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(Gs,
D,
random_state,
race,
gender,
num_pngs=1,
grid_size=[1, 1],
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
trans_label, thr = parse_label(race, gender)
labels = np.zeros([latents.shape[0], 8], np.float32)
labels[:, trans_label] = 1.0
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
score, _ = D.run(images)
if score >= thr:
# output image
img = images[0].transpose(1, 2, 0)
scipy.misc.imsave('test%d.jpg' % png_idx, img)
print('save image %d' % png_idx)
def evaluate_metrics(run_id, log, metrics, num_images, real_passes, minibatch_size=None):
metric_class_names = {
'swd': 'metrics.sliced_wasserstein.API',
'fid': 'metrics.frechet_inception_distance.API',
'is': 'metrics.inception_score.API',
'msssim': 'metrics.ms_ssim.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
assert len(snapshot_pkls) >= 1
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(result_subdir, verbose=True, shuffle_mb=0)
if minibatch_size is None:
minibatch_size = np.clip(8192 // dataset_obj.shape[1], 4, 256)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(num_images=num_images, image_shape=image_shape, image_dtype=np.uint8, minibatch_size=minibatch_size)
tfutil.init_uninited_vars()
mode = 'warmup'
obj.begin(mode)
for idx in range(10):
obj.feed(mode, np.random.randint(0, 256, size=[minibatch_size]+image_shape, dtype=np.uint8))
obj.end(mode)
metric_objs.append(obj)
# Print table header.
print()
print('%-10s%-12s' % ('Snapshot', 'Time_eval'), end='')
for obj in metric_objs:
for name, fmt in zip(obj.get_metric_names(), obj.get_metric_formatting()):
print('%-*s' % (len(fmt % 0), name), end='')
print()
print('%-10s%-12s' % ('---', '---'), end='')
for obj in metric_objs:
for fmt in obj.get_metric_formatting():
print('%-*s' % (len(fmt % 0), '---'), end='')
print()
# Feed in reals.
for title, mode in [('Reals', 'reals'), ('Reals2', 'fakes')][:real_passes]:
print('%-10s' % title, end='')
time_begin = time.time()
labels = np.zeros([num_images, dataset_obj.label_size], dtype=np.float32)
[obj.begin(mode) for obj in metric_objs]
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
images, labels[begin:end] = dataset_obj.get_minibatch_np(end - begin)
if mirror_augment:
images = misc.apply_mirror_augment(images)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
# Evaluate each network snapshot.
for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
prefix = 'network-snapshot-'; postfix = '.pkl'
snapshot_name = os.path.basename(snapshot_pkl)
assert snapshot_name.startswith(prefix) and snapshot_name.endswith(postfix)
snapshot_kimg = int(snapshot_name[len(prefix) : -len(postfix)])
print('%-10d' % snapshot_kimg, end='')
mode ='fakes'
[obj.begin(mode) for obj in metric_objs]
time_begin = time.time()
with tf.Graph().as_default(), tfutil.create_session(config.tf_config).as_default():
G, D, Gs = misc.load_pkl(snapshot_pkl)
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
latents = misc.random_latents(end - begin, Gs)
images = Gs.run(latents, labels[begin:end], num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_dtype=np.uint8)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
print()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
batch_size=8,
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
lsfm_model = m3io.import_lsfm_model(
'/home/baris/Projects/faceganhd/models/all_all_all.mat')
lsfm_tcoords = \
mio.import_pickle('/home/baris/Projects/team members/stelios/UV_spaces_V2/UV_dicts/full_face/512_UV_dict.pkl')[
'tcoords']
lsfm_params = []
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(int(num_pngs / batch_size)):
start = time.time()
print('Generating png %d-%d / %d... in ' %
(png_idx * batch_size, (png_idx + 1) * batch_size, num_pngs),
end='')
latents = misc.random_latents(np.prod(grid_size) * batch_size,
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_shrink=image_shrink)
for i in range(batch_size):
if images.shape[1] == 3:
mio.export_pickle(
images[i],
os.path.join(
result_subdir,
'%s%06d.pkl' % (png_prefix, png_idx * batch_size + i)))
# misc.save_image(images[i], os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx*batch_size+i)), [0,255], grid_size)
elif images.shape[1] == 6:
mio.export_pickle(images[i][3:6],
os.path.join(
result_subdir, '%s%06d.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
misc.save_image(
images[i][0:3],
os.path.join(
result_subdir,
'%s%06d.png' % (png_prefix, png_idx * batch_size + i)),
[-1, 1], grid_size)
elif images.shape[1] == 9:
texture = Image(np.clip(images[i, 0:3] / 2 + 0.5, 0, 1))
mesh_raw = from_UV_2_3D(Image(images[i, 3:6]))
normals = images[i, 6:9]
normals_norm = (normals - normals.min()) / (normals.max() -
normals.min())
mesh = lsfm_model.reconstruct(mesh_raw)
lsfm_params.append(lsfm_model.project(mesh_raw))
t_mesh = TexturedTriMesh(mesh.points, lsfm_tcoords.points,
texture, mesh.trilist)
m3io.export_textured_mesh(
t_mesh,
os.path.join(result_subdir,
'%06d.obj' % (png_idx * minibatch_size + i)),
texture_extension='.png')
mio.export_image(
Image(normals_norm),
os.path.join(
result_subdir,
'%06d_nor.png' % (png_idx * minibatch_size + i)))
shape = images[i, 3:6]
shape_norm = (shape - shape.min()) / (shape.max() -
shape.min())
mio.export_image(
Image(shape_norm),
os.path.join(
result_subdir,
'%06d_shp.png' % (png_idx * minibatch_size + i)))
mio.export_pickle(
t_mesh,
os.path.join(result_subdir,
'%06d.pkl' % (png_idx * minibatch_size + i)))
print('%0.2f seconds' % (time.time() - start))
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def compute_fid(Gs,
minibatch_size,
dataset_obj,
iter_number,
lod=0,
num_images=10000,
printing=True):
# Initialize metrics.
from metrics.frechet_inception_distance import API as class_def
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(num_images=num_images,
image_shape=image_shape,
image_dtype=np.uint8,
minibatch_size=minibatch_size)
mode = 'warmup'
obj.begin(mode)
for idx in range(10):
obj.feed(
mode,
np.random.randint(0,
256,
size=[minibatch_size] + image_shape,
dtype=np.uint8))
obj.end(mode)
# Print table header
if printing:
print(flush=True)
print('%-10s%-12s' % ('KIMG', 'Time_eval'), end='', flush=True)
print('%-12s' % ('FID'), end='', flush=True)
print(flush=True)
print('%-10s%-12s%-12s' % ('---', '---', '---'), end='', flush=True)
print(flush=True)
# Feed in reals.
print('%-10s' % "Reals", end='', flush=True)
time_begin = time.time()
labels = np.zeros([num_images, dataset_obj.label_size], dtype=np.float32)
obj.begin(mode)
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
images, labels[begin:end] = dataset_obj.get_minibatch_np(end - begin,
lod=lod)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
obj.feed(mode, images)
results = obj.end(mode)
if printing:
print('%-12s' % misc.format_time(time.time() - time_begin),
end='',
flush=True)
print(results[0], end='', flush=True)
print(flush=True)
# Evaluate each network snapshot.
if printing:
print('%-10d' % iter_number, end='', flush=True)
mode = 'fakes'
obj.begin(mode)
time_begin = time.time()
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
latents = misc.random_latents(end - begin, Gs)
images = Gs.run(latents,
labels[begin:end],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
obj.feed(mode, images)
results = obj.end(mode)
if printing:
print('%-12s' % misc.format_time(time.time() - time_begin),
end='',
flush=True)
print(results[0], end='', flush=True)
print(flush=True)
return results[0]
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
embeddings_contant = False
labels_constant = False
latents_constant = False
idx = random.randint(0, 56880)
df = pandas.read_csv('datasets/50k_sorted_tf/50k_index_sorted.csv')
print('embeddings_contant : ' + str(embeddings_contant))
print('labels_constant : ' + str(labels_constant))
print('latents_constant : ' + str(latents_constant))
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir + '/' + run_id,
config.desc)
if latents_constant:
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=None)
#embeddings = np.zeros([1, 300], dtype=np.float32)
#labels = np.zeros([1, 32], dtype=np.float32)
embeddings = np.load(
'datasets/50k_sorted_tf/sum_embedding_title.embeddings')
embeddings = embeddings.astype('float32')
labels = np.load(
'datasets/50k_sorted_tf/sum_embedding_category_average.labels')
labels = labels.astype('float32')
name1 = ''
if labels_constant:
label = labels[idx]
name1 = name1 + ' ' + df.at[idx, 'category1']
label = label.reshape(1, label.shape[0])
if embeddings_contant:
embedding = embeddings[idx]
title = df.at[idx, 'title']
name1 = name1 + ' ' + title[:10]
embedding = embedding.reshape(1, embedding.shape[0])
#print(latents.shape)
for png_idx in range(num_pngs):
name = ''
name = name + name1
print('Generating png %d / %d...' % (png_idx, num_pngs))
rand = random.randint(0, 56880)
#rand = png_idx * 1810
#labels = sess.run(classes[0])
if not latents_constant:
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
if not labels_constant:
label = labels[rand]
label = label.reshape(1, label.shape[0])
name = name + ' ' + df.at[rand, 'category1']
if not embeddings_contant:
embedding = embeddings[rand]
title = df.at[rand, 'title']
name = name + ' ' + title[:10]
embedding = embedding.reshape(1, embedding.shape[0])
#print(labels.shape)
images = Gs.run(latents,
label,
embedding,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
misc.save_image_grid(
images, os.path.join(result_subdir,
'%s%06d.png' % (name, png_idx)), [0, 255],
grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
batch_size=8,
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config_test.result_dir,
config_test.desc)
for png_idx in range(int(num_pngs / batch_size)):
start = time.time()
print('Generating png %d-%d / %d... in ' %
(png_idx * batch_size, (png_idx + 1) * batch_size, num_pngs),
end='')
latents = misc.random_latents(np.prod(grid_size) * batch_size,
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 7], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config_test.num_gpus,
out_shrink=image_shrink)
for i in range(batch_size):
if images.shape[1] == 3:
mio.export_pickle(
images[i],
os.path.join(
result_subdir,
'%s%06d.pkl' % (png_prefix, png_idx * batch_size + i)))
# misc.save_image(images[i], os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx*batch_size+i)), [0,255], grid_size)
elif images.shape[1] == 6:
mio.export_pickle(images[i][3:6],
os.path.join(
result_subdir, '%s%06d.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
misc.save_image(
images[i][0:3],
os.path.join(
result_subdir,
'%s%06d.png' % (png_prefix, png_idx * batch_size + i)),
[-1, 1], grid_size)
elif images.shape[1] == 9:
mio.export_pickle(images[i][3:6],
os.path.join(
result_subdir, '%s%06d_shp.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
mio.export_pickle(images[i][6:9],
os.path.join(
result_subdir, '%s%06d_nor.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
misc.save_image(
images[i][0:3],
os.path.join(
result_subdir,
'%s%06d.png' % (png_prefix, png_idx * batch_size + i)),
[-1, 1], grid_size)
print('%0.2f seconds' % (time.time() - start))
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def evaluate_metrics_swd_distributions_training_trad_prog(
run_id,
network_dir_conv,
network_dir_prog,
log,
metrics,
num_images_per_group,
num_groups,
real_passes,
minibatch_size=None):
metric_class_names = {
'swd_distri_training_trad_prog':
'metrics.swd_distributions_training_trad_prog.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(image_shape=image_shape, image_dtype=np.uint8)
tfutil.init_uninited_vars()
metric_objs.append(obj)
mode = 'fakes'
[obj.begin(mode) for obj in metric_objs]
images_real, labels = dataset_obj.get_minibatch_np(
num_groups * num_images_per_group)
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(network_dir_conv)
#G, D, Gs = pickle.load(file)
latents = misc.random_latents(num_groups * num_images_per_group,
Gs)
images = images_real
for k in range(
10
): # because Gs can not generate lots of (>3000 around) images at one time. Make sure /10 = int
nn = int(num_groups * num_images_per_group / 10)
images_fake = Gs.run(latents[k * nn:(k + 1) * nn],
labels[k * nn:(k + 1) * nn],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
images = np.concatenate((images, images_fake), axis=0)
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(network_dir_prog)
# G, D, Gs = pickle.load(file)
latents = misc.random_latents(num_groups * num_images_per_group,
Gs)
for k in range(
10
): # because Gs can not generate lots of (>3000 around) images at one time. Make sure /10 = int
nn = int(num_groups * num_images_per_group / 10)
images_fake = Gs.run(latents[k * nn:(k + 1) * nn],
labels[k * nn:(k + 1) * nn],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
images = np.concatenate((images, images_fake), axis=0)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[
obj.feed(mode, images, num_images_per_group, num_groups,
result_subdir) for obj in metric_objs
]
def evaluate_metrics_swd_distributions(run_id,
log,
metrics,
num_images_per_group,
num_groups,
real_passes,
minibatch_size=None):
metric_class_names = {
'swd_distri': 'metrics.swd_distributions.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
assert len(snapshot_pkls) >= 1
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(image_shape=image_shape, image_dtype=np.uint8)
tfutil.init_uninited_vars()
metric_objs.append(obj)
# Evaluate each network snapshot.
for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
prefix = 'network-snapshot-'
postfix = '.pkl'
snapshot_name = os.path.basename(snapshot_pkl)
assert snapshot_name.startswith(prefix) and snapshot_name.endswith(
postfix)
snapshot_kimg = int(snapshot_name[len(prefix):-len(postfix)])
print('%-10d' % snapshot_kimg, end='')
mode = 'fakes'
[obj.begin(mode) for obj in metric_objs]
images_real, labels = dataset_obj.get_minibatch_np(
num_groups * num_images_per_group)
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(snapshot_pkl)
latents = misc.random_latents(num_groups * num_images_per_group,
Gs)
images = images_real
for k in range(
10
): # because Gs can not generate lots of (>3000 around) images at one time. Make sure /10 = int
nn = int(num_groups * num_images_per_group / 10)
images_fake = Gs.run(latents[k * nn:(k + 1) * nn],
labels[k * nn:(k + 1) * nn],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
images = np.concatenate((images, images_fake), axis=0)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[
obj.feed(mode, images, num_images_per_group, num_groups,
snapshot_kimg, result_subdir) for obj in metric_objs
]
def find_dir_latent_with_query_image(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=4123, minibatch_size=8, dir_path='../../data/ACDC/latents/cleaned_testing/'):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
replicate_folder_structure(dir_path, result_subdir+'/')
train_patients = sorted_nicely(glob.glob(dir_path+'*'))
for patient in train_patients:
cardiac_cycles = sorted_nicely(glob.glob(patient+'/*/*/*.png'))
cfg = open(patient+'/Info.cfg')
label = condition_to_onehot(cfg.readlines()[2][7:])
cont = 0
for cycle in cardiac_cycles:
# Get folder containing the image
supfolder = sup_folder(cycle)
latent_subir = result_subdir + '/' + supfolder
# Create query image - tensorflow constant
query_image = cv2.imread(cycle) # read frame
query_image = cv2.resize(query_image, (256, 256))
query_image = query_image.transpose(2,0,1)
query_image = query_image[np.newaxis]
x = tf.constant(query_image, dtype=tf.float32, name='query_image')
# Create G(z) - tensorflow variable and label
latent = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
initial = tf.constant(latent, dtype=tf.float32)
z = tf.Variable(initial_value=initial, dtype=tf.float32, name='latent_space')
gz = Gs.run(latent, label, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.float32)
gz = tf.Variable(gz, dtype=tf.float32)
# Define a loss function
residual_loss = tf.losses.absolute_difference(x, gz)
# Define an optimizer
train_op = tf.train.AdamOptimizer(learning_rate=0.1).minimize(residual_loss)
zs, gzs, step = [], [], 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_, loss_value = sess.run([train_op, residual_loss])
while (loss_value > 2e-04 and step <= 5000):
_, loss_value = sess.run([train_op, residual_loss])
step += 1
if step % 1000 == 0:
print('Step {}, Loss value: {}'.format(step, loss_value))
gzs.append(sess.run(gz))
zs.append(sess.run(z))
# save last image
print('Image saved at {}'.format(os.path.join(latent_subir, '%s.png' % (cont))))
misc.save_image_grid(gzs[-1], os.path.join(latent_subir, '%02d.png' % (cont)), [0,255], grid_size)
print('Latent vectors saved at {}'.format(os.path.join(latent_subir, 'latent_%02d.npy' % (cont))))
np.save(os.path.join(latent_subir, 'latent_%02d.npy' % (cont)), zs[-1])
print('Labels saved at {}'.format(os.path.join(latent_subir, 'label_%02d.npy' % (cont))))
np.save(os.path.join(latent_subir, 'label_%02d.npy' % (cont)), label)
cont+=1
cfg.close()
cont = 0
def evaluate_metrics(run_id, log, metrics, num_images, real_passes, minibatch_size=None):
metric_class_names = {
'swd': 'metrics.sliced_wasserstein.API',
'fid': 'metrics.frechet_inception_distance.API',
'is': 'metrics.inception_score.API',
'msssim': 'metrics.ms_ssim.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
assert len(snapshot_pkls) >= 1
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(result_subdir, verbose=True, shuffle_mb=0)
if minibatch_size is None:
minibatch_size = np.clip(8192 // dataset_obj.shape[1], 4, 256)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(num_images=num_images, image_shape=image_shape, image_dtype=np.uint8, minibatch_size=minibatch_size)
tfutil.init_uninited_vars()
mode = 'warmup'
obj.begin(mode)
for idx in range(10):
obj.feed(mode, np.random.randint(0, 256, size=[minibatch_size]+image_shape, dtype=np.uint8))
obj.end(mode)
metric_objs.append(obj)
# Print table header.
print()
print('%-10s%-12s' % ('Snapshot', 'Time_eval'), end='')
for obj in metric_objs:
for name, fmt in zip(obj.get_metric_names(), obj.get_metric_formatting()):
print('%-*s' % (len(fmt % 0), name), end='')
print()
print('%-10s%-12s' % ('---', '---'), end='')
for obj in metric_objs:
for fmt in obj.get_metric_formatting():
print('%-*s' % (len(fmt % 0), '---'), end='')
print()
# Feed in reals.
for title, mode in [('Reals', 'reals'), ('Reals2', 'fakes')][:real_passes]:
print('%-10s' % title, end='')
time_begin = time.time()
labels = np.zeros([num_images, dataset_obj.label_size], dtype=np.float32)
[obj.begin(mode) for obj in metric_objs]
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
images, labels[begin:end] = dataset_obj.get_minibatch_np(end - begin)
if mirror_augment:
images = misc.apply_mirror_augment(images)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
# Evaluate each network snapshot.
for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
prefix = 'network-snapshot-'; postfix = '.pkl'
snapshot_name = os.path.basename(snapshot_pkl)
assert snapshot_name.startswith(prefix) and snapshot_name.endswith(postfix)
snapshot_kimg = int(snapshot_name[len(prefix) : -len(postfix)])
print('%-10d' % snapshot_kimg, end='')
mode ='fakes'
[obj.begin(mode) for obj in metric_objs]
time_begin = time.time()
with tf.Graph().as_default(), tfutil.create_session(config.tf_config).as_default():
G, D, Gs = misc.load_pkl(snapshot_pkl)
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
latents = misc.random_latents(end - begin, Gs)
images = Gs.run(latents, labels[begin:end], num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_dtype=np.uint8)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
print()
