def image_sampler(self): iters = int( math.ceil(float(self.opt.sample_num) / float(self.opt.batch_size))) self.ims = [] for iter in range(iters): sample_z = np.random.normal(0, 1, size=(64, self.opt.z_dimension)) samples = self.sess.run(self.samples, feed_dict={self.z: sample_z}) self.ims.append(samples) # Load trained model ims = np.reshape(self.ims, (-1, 32, 32, 3)) ims = ims[:self.opt.sample_num].transpose(0, 3, 1, 2) ims = ((ims + 1.0) * 127.5).astype(np.uint8) ims = ims.astype('f') model = Inception() serializers.load_hdf5('inception_score.model', model) cuda.get_device(0).use() model.to_gpu() with chainer.no_backprop_mode(), chainer.using_config('train', False): mean, std = inception_score(model, ims) self.mean.append(mean) print('Inception score mean: %4.4f / %4.4f' % (mean, max(self.mean))) print('Inception score std:', std) with open('data.txt', 'a', encoding='ascii') as f: f.write(str(self.counter)) f.write(':') f.write(str(mean)) f.write(', ') f.write(str(std)) f.write('\n') return mean, std
def main(args): # Load trained model model = Inception() serializers.load_hdf5(args.model, model) if args.gpu >= 0: cuda.get_device(args.gpu).use() model.to_gpu() # Load images if 0: train, test = datasets.get_cifar10(ndim=3, withlabel=False, scale=255.0) else: train, test = datasets.get_mnist(ndim=3, rgb_format=True, scale=255.0, withlabel=False) # Use all 60000 images, unless the number of samples are specified ims = np.concatenate((train, test)) if args.samples > 0: ims = ims[:args.samples] mean, std = inception_score(model, ims) print('Inception score mean:', mean) print('Inception score std:', std)
def main(args): # Load trained model model = Inception() serializers.load_hdf5(args.model, model) if args.gpu >= 0: cuda.get_device(args.gpu).use() model.to_gpu() # Load images train, test = datasets.get_cifar10(ndim=3, withlabel=False, scale=255.0) # Use all 60000 images, unless the number of samples are specified ims = np.concatenate((train, test)) if args.samples > 0: ims = ims[:args.samples] with chainer.no_backprop_mode(), chainer.using_config('train', False): mean, std = inception_score(model, ims) print('Inception score mean:', mean) print('Inception score std:', std)
import numpy as np from evaluation import * def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--data', type=str, default='CIFAR') return parser.parse_args() if __name__ == '__main__': args = parse_args() model = Inception() serializers.load_hdf5('metric/inception_score.model', model) if args.gpu >= 0: cuda.get_device(args.gpu).use() model.to_gpu() datapath = 'training_data/{}.npy'.format(args.data) mean_savepath = 'metric/{}_inception_mean.npy'.format(args.data) cov_savepath = 'metric/{}_inception_cov.npy'.format(args.data) img = 255 * xp.load(datapath).astype(xp.float32) with chainer.using_config('train', False), chainer.using_config( 'enable_backprop', False): mean, cov = get_mean_cov(model, img) np.save(mean_savepath, mean) np.save(cov_savepath, cov)
def load_inception_model(): infile = "%s/inception_score.model"%os.path.dirname(__file__) model = Inception() serializers.load_hdf5(infile, model) model.to_gpu() return model
def load_inception_model(): infile = "inception_score.model" model = Inception() serializers.load_hdf5(infile, model) model.to_gpu() return model
def load_inception_model(): model = Inception() serializers.load_hdf5('metric/inception_score.model', model) model.to_gpu() return model
if __name__ == '__main__': args = parse_args() if not os.path.exists("scores"): os.mkdir("scores") evmodel = Inception() serializers.load_hdf5('metric/inception_score.model', evmodel) G = ResNetGenerator(n_classes=1000) D = SNResNetProjectionDiscriminator(n_classes=1000) # available on https://drive.google.com/drive/folders/1m04Db3HbN10Tz5XHqiPpIg8kw23fkbSi serializers.load_npz("trained_models/" + args.G, G) serializers.load_npz("trained_models/" + args.D, D) if args.gpu >= 0: cuda.get_device(args.gpu).use() evmodel.to_gpu() G.to_gpu() D.to_gpu() G, D = DOT_cond.thermalize_spectral_norm(G, D) if args.k == None: k = L.EmbedID(1000, 1, initialW=DOT_cond.return_ks(G, D, nlabels=1000)) k.to_gpu() else: k = args.k * xp.ones([1]) main(args, G, D, args.data, evmodel, k, transport=args.transport,