def compute_real_dist_stats(num_samples,
sess,
batch_size,
split,
dataset=None,
stats_file=None,
seed=0,
verbose=True,
log_dir='./log'):
"""
Reads the image data and compute the FID mean and cov statistics
for real images.
Args:
num_samples (int): Number of real images to compute statistics.
sess (Session): TensorFlow session to use.
dataset (str/Dataset): Dataset to load.
batch_size (int): The batch size to feedforward for inference.
stats_file (str): The statistics file to load from if there is already one.
verbose (bool): If True, prints progress of computation.
log_dir (str): Directory where feature statistics can be stored.
Returns:
ndarray: Mean features stored as np array.
ndarray: Covariance of features stored as np array.
"""
# Create custom stats file name
if stats_file is None:
stats_dir = os.path.join(log_dir, 'metrics', 'fid', 'statistics')
if not os.path.exists(stats_dir):
os.makedirs(stats_dir)
stats_file = os.path.join(
stats_dir,
"fid_stats_{}_{}k_run_{}.npz".format(dataset, num_samples // 1000,
seed))
if stats_file and os.path.exists(stats_file):
print("INFO: Loading existing statistics for real images...")
f = np.load(stats_file)
m_real, s_real = f['mu'][:], f['sigma'][:]
f.close()
else:
# Obtain the numpy format data
print("INFO: Obtaining images...")
images = get_dataset_images(dataset,
split=split,
num_samples=num_samples)
# Compute the mean and cov
print("INFO: Computing statistics for real images...")
m_real, s_real = fid_utils.calculate_activation_statistics(
images=images, sess=sess, batch_size=batch_size, verbose=verbose)
if not os.path.exists(stats_file):
print("INFO: Saving statistics for real images...")
np.savez(stats_file, mu=m_real, sigma=s_real)
return m_real, s_real
def test_calculate_activation_statistics(self):
inception_path = './metrics/inception_model'
inception_utils.create_inception_graph(inception_path)
mu, sigma = fid_utils.calculate_activation_statistics(
images=self.images, sess=self.sess)
assert mu.shape == (2048, )
assert sigma.shape == (2048, 2048)
def compute_gen_dist_stats(netG,
num_samples,
sess,
device,
seed,
batch_size,
print_every=20,
verbose=True):
"""
Directly produces the images and convert them into numpy format without
saving the images on disk.
Args:
netG (Module): Torch Module object representing the generator model.
num_samples (int): The number of fake images for computing statistics.
sess (Session): TensorFlow session to use.
device (str): Device identifier to use for computation.
seed (int): The random seed to use.
batch_size (int): The number of samples per batch for inference.
print_every (int): Interval for printing log.
verbose (bool): If True, prints progress.
Returns:
ndarray: Mean features stored as np array.
ndarray: Covariance of features stored as np array.
"""
with torch.no_grad():
# Set model to evaluation mode
netG.eval()
# Inference variables
batch_size = min(num_samples, batch_size)
# Collect all samples()
images = []
start_time = time.time()
for idx in range(num_samples // batch_size):
# Collect fake image
fake_images = netG.generate_images(num_images=batch_size,
device=device).detach().cpu()
images.append(fake_images)
# Print some statistics
if (idx + 1) % print_every == 0:
end_time = time.time()
print(
"INFO: Generated image {}/{} [Random Seed {}] ({:.4f} sec/idx)"
.format(
(idx + 1) * batch_size, num_samples, seed,
(end_time - start_time) / (print_every * batch_size)))
start_time = end_time
# Produce images in the required (N, H, W, 3) format for FID computation
images = torch.cat(images, 0) # Gives (N, 3, H, W)
images = _normalize_images(images) # Gives (N, H, W, 3)
# Compute the FID
print("INFO: Computing statistics for fake images...")
m_fake, s_fake = fid_utils.calculate_activation_statistics(
images=images, sess=sess, batch_size=batch_size, verbose=verbose)
return m_fake, s_fake
def compute_real_dist_stats_with_attr(attr,
sess,
batch_size,
dataset=None,
stats_file=None,
seed=0,
verbose=True,
log_dir='./log',
name=None):
"""
Reads the image data and compute the FID mean and cov statistics
for real images.
Args:
index (ndarray): The index array of real images to use.
sess (Session): TensorFlow session to use.
dataset (str/Dataset): Dataset to load.
batch_size (int): The batch size to feedforward for inference.
stats_file (str): The statistics file to load from if there is already one.
verbose (bool): If True, prints progress of computation.
log_dir (str): Directory where feature statistics can be stored.
Returns:
ndarray: Mean features stored as np array.
ndarray: Covariance of features stored as np array.
"""
# Create custom stats file name
if stats_file is None:
stats_dir = os.path.join(log_dir, 'metrics', 'fid', 'statistics')
if not os.path.exists(stats_dir):
os.makedirs(stats_dir)
stats_file = os.path.join(
stats_dir,
"fid_stats_{}_{}_{}_run_{}.npz".format(name, dataset, attr, seed))
if stats_file and os.path.exists(stats_file):
print("INFO: Loading existing statistics for real images...")
f = np.load(stats_file)
attr_m_real, attr_s_real = f['attr_mu'][:], f['attr_sigma'][:]
not_attr_m_real, not_attr_s_real = f['not_attr_mu'][:], f[
'not_attr_sigma'][:]
f.close()
else:
# Obtain the numpy format data
print("INFO: Obtaining images...")
attr_images, not_attr_images = get_dataset_images_with_attr(dataset,
attr=attr)
# Compute the mean and cov
print("INFO: Computing statistics for real images with attribute...")
attr_m_real, attr_s_real = fid_utils.calculate_activation_statistics(
images=attr_images,
sess=sess,
batch_size=batch_size,
verbose=verbose)
not_attr_m_real, not_attr_s_real = fid_utils.calculate_activation_statistics(
images=not_attr_images,
sess=sess,
batch_size=batch_size,
verbose=verbose)
if not os.path.exists(stats_file):
print("INFO: Saving statistics for real images...")
np.savez(stats_file,
attr_mu=attr_m_real,
attr_sigma=attr_s_real,
not_attr_mu=not_attr_m_real,
not_attr_sigma=not_attr_s_real)
return attr_m_real, attr_s_real, not_attr_m_real, not_attr_s_real