def load_fid(self, dataname):
# if dataname == 'cifar10':
# stats_path = 'metrics/stats/CIFAR10_inception_moments.npz'
# elif dataname == 'lsun-bedroom':
# stats_path = 'metrics/stats/LSUN-bedroom_inception_moments.npz'
# elif dataname == 'MNIST':
stats_path = 'metrics/stats/%s_inception_moments.npz' % dataname
print('Load stats of %s' % dataname)
f = np.load(stats_path)
self.mu_real, self.sigma_real = f['mu'][:], f['sigma'][:]
self.mu_real = torch.tensor(self.mu_real).float().cuda()
self.sigma_real = torch.tensor(self.sigma_real).float().cuda()
f.close()
self.net = load_inception_net(parallel=False)
def __init__(self,
G,
z_dim,
model_dir,
log_path,
device,
batchsize=100,
dim=1):
self.is_flag = False
self.fid_flag = False
self.log_path = log_path
self.device = device
self.G = G
self.z_dim = z_dim
self.dim = dim
self.batchsize = batchsize
self.model_dir = model_dir
self.init_writer()
self.net = load_inception_net(parallel=False)
def run(config):
# Get loader
config['drop_last'] = False
loader = get_data(dataname=config['dataset'], path=config['data_path'])
# Load inception net
net = load_inception_net(parallel=config['parallel'])
pool, logits = [], []
device = torch.device('cuda:0')
print(device)
for i, (x, y) in enumerate(tqdm(loader)):
x = x.to(device)
with torch.no_grad():
pool_val, logits_val = net(x)
pool += [np.asarray(pool_val.cpu())]
logits += [np.asarray(F.softmax(logits_val, 1).cpu())]
pool, logits = [np.concatenate(item, 0) for item in [pool, logits]]
# uncomment to save pool, logits, and labels to disk
# print('Saving pool, logits, and labels to disk...')
# np.savez(config['dataset']+'_inception_activations.npz',
# {'pool': pool, 'logits': logits, 'labels': labels})
# Calculate inception metrics and report them
print('Calculating inception metrics...')
IS_mean, IS_std = calculate_inception_score(logits)
print('Training data from dataset %s has IS of %5.5f +/- %5.5f' %
(config['dataset'], IS_mean, IS_std))
# Prepare mu and sigma, save to disk. Remove "hdf5" by default
# (the FID code also knows to strip "hdf5")
print('Calculating means and covariances...')
mu, sigma = np.mean(pool, axis=0), np.cov(pool, rowvar=False)
print('Saving calculated means and covariances to disk...')
np.savez('metrics/stats/' + config['dataset'] + '_inception_moments.npz',
**{
'mu': mu,
'sigma': sigma
})