def compute_inception_score(self):
self.generator.eval()
imgs = []
while(len(imgs) < self.inception_nsamples):
ztest = self.zdist.sample((self.batch_size,))
ytest = self.ydist.sample((self.batch_size,))
samples, _ = self.generator(ztest, ytest)
samples = [s.data.cpu().numpy() for s in samples]
imgs.extend(samples)
inception_imgs = imgs[:self.inception_nsamples]
score, score_std = inception_score(
inception_imgs, device=self.device, resize=True, splits=10,
batch_size=self.batch_size)
fid_imgs = np.array(imgs[:self.fid_sample_size])
if self.fid_real_samples is not None:
fid = calculate_fid_given_images(
self.fid_real_samples,
fid_imgs,
batch_size=self.batch_size,
cuda=True)
return score, score_std, fid
def compute_inception_score(self):
imgs, _ = self.get_fake_real_samples(self.inception_nsamples)
imgs = [img.numpy() for img in imgs]
score, score_std = inception_score(imgs,
device=self.device,
resize=True,
splits=1)
return score, score_std
def compute_inception_score(self, inception_nsamples):
imgs = []
while(len(imgs) < inception_nsamples):
ztest = self.zdist.sample((self.cfg.train.batch_size,)).to(self.device)
ytest = self.ydist.sample((self.cfg.train.batch_size,)).to(self.device)
samples = self.generator(ztest, ytest)
samples = [s.data.cpu().numpy() for s in samples]
imgs.extend(samples)
imgs = imgs[:inception_nsamples]
score, score_std = inception_score(
imgs, device=self.device, resize=True, splits=10
)
return score, score_std
def compute_inception_score(self, inception_model=None):
self.generator.eval()
imgs = []
while (len(imgs) < self.inception_nsamples):
ztest = self.zdist.sample((self.batch_size, ))
ytest = self.ydist.sample((self.batch_size, ))
samples = self.generator(ztest, ytest)
samples = [s.data.cpu().numpy() for s in samples]
imgs.extend(samples)
imgs = imgs[:self.inception_nsamples]
score, score_std = inception_score(
imgs,
device=self.device,
resize=True,
splits=10,
inception_model=inception_model,
)
return score, score_std
def compute_inception_score_MeRGAN(self,
task_id=-1,
is_FID=True,
exit_real_ms=False,
real_m=0,
real_s=0):
self.generator.eval()
imgs = []
while (len(imgs) < self.inception_nsamples):
ztest = self.zdist.sample((self.batch_size, ))
ytest = self.ydist.sample((self.batch_size, ))
y = ytest * 0 + task_id + 1
# samples,_ = self.generator(ztest, ytest)
samples, _ = self.generator(ztest,
y,
task_id=task_id,
is_FID=is_FID)
samples = [s.data.cpu().numpy() for s in samples]
imgs.extend(samples)
inception_imgs = imgs[:self.inception_nsamples]
score, score_std = inception_score(inception_imgs,
device=self.device,
resize=True,
splits=10,
batch_size=self.batch_size)
fid_imgs = np.array(imgs[:self.fid_sample_size])
if exit_real_ms:
fid = 0
else:
if self.fid_real_samples is not None:
fid = calculate_fid_given_images(self.fid_real_samples,
fid_imgs,
batch_size=self.batch_size,
cuda=True)
return score, score_std, fid