def inception_score(path_to_generated_imgs_dir,
path_to_generated_imgs_dir_cache, downsample_scale,
path_to_classifier, classifier_dimensionality,
cuda_enabled, batch_size, splits):
# Set up data
generated_brainpedia = Brainpedia(
data_dirs=[path_to_generated_imgs_dir],
cache_dir=path_to_generated_imgs_dir_cache,
scale=downsample_scale,
multi_tag_label_encoding=MULTI_TAG_LABEL_ENCODING)
generated_brain_data_shape, generated_brain_data_tag_shape = generated_brainpedia.sample_shapes(
)
all_generated_brain_data, all_generated_brain_data_tags = generated_brainpedia.all_data(
)
all_generated_brain_data = Variable(torch.Tensor(all_generated_brain_data))
if cuda_enabled:
all_generated_brain_data = all_generated_brain_data.cuda()
# Load classifier model
classifier = Classifier(dimensionality=classifier_dimensionality,
num_classes=generated_brain_data_tag_shape[0],
cudaEnabled=cuda_enabled)
classifier.load_state_dict(torch.load(path_to_classifier))
# Compute predictions
predictions = classifier.forward(
all_generated_brain_data).data.cpu().numpy()
# Now compute the mean kl-div
N = len(all_generated_brain_data)
split_scores = []
for k in range(splits):
part = predictions[k * (N // splits):(k + 1) * (N // splits), :]
py = np.mean(part, axis=0)
scores = []
for i in range(part.shape[0]):
pyx = part[i, :]
scores.append(entropy(pyx, py))
split_scores.append(np.exp(np.mean(scores)))
return np.mean(split_scores), np.std(split_scores)
