Chainer implementation of the inception score module presented in Improved Techniques for Training GANs. The code is derived from the official OpenAI implementation at https://github.com/openai/improved-gan.
A method proposed by Tim Salimans et al. (2016) in the above mentioned paper to evaluate generative models such as VAEs and GANs using a pre-trained classifier network and sampled images.
It is based on the fact that good samples (images that look like images from the true data distribution) are expected to yield
- low entropy p(y|x), i.e. high prediction confidence
- high entropy p(y), i.e. highly varied predictions
where x is an image, p(y|x) is the inferred class label probabilities given x by the pre-trained Inception network and p(y) is the marginal distribution over all images.
The Inception score is defined as exp(E_x[KL(p(y|x) || p(y))])
Download the pre-trained TensorFlow model and create a Chainer copy named inception_score.model.
python download.py --outfile inception_score.modelLoad the pretrained Chainer model and compute the inception score for the CIFAR-10 dataset including both train and test images. To limit the number of images, use the --samples option.
python example.py --model inception_score.model
...
Batch size: 100
Total number of images: 60000
Total number of batches: 600
Running batch 1 / 600 ...
Running batch 2 / 600 ...
...
Running batch 600 / 600 ...
Inception score mean: 12.003619194030762
Inception score std: 0.10357429087162018
import numpy as np
from chainer import serializers, datasets
from inception_score import Inception, inception_score
model = Inception()
serializers.load_hdf5('inception_score.model', model)
train, test = datasets.get_cifar10(ndim=3, withlabel=False, scale=255.0)
mean, std = inception_score(model, np.concatenate(train, test))
print('Inception score mean:', mean)
print('Inception score std:', std)This implementation seems to yield slightly higher scores than the original implementation looking at the inception scores based on CIFAR-10, upsampled from (32, 32) to (299, 299) using bilinear interpolation.
| Ours | Original | |
|---|---|---|
| Mean | 12.00 | 11.24 |
| Std | 0.10 | 0.12 |