def model(config: ml_collections.ConfigDict, **kwargs): return pixelcnn.PixelCNNPP(depth=config.n_resnet, features=config.n_feature, logistic_components=config.n_logistic_mix, **kwargs)
def test_pcnn_shape(self): x = random.normal(self.rng, (2, 4, 4, 3)) model = pixelcnn.PixelCNNPP(depth=0, features=2, dropout_p=0) out, _ = model.init_with_output(self.rng, x) self.assertEqual(out.shape, (2, 4, 4, 100))
def model(**kwargs): return pixelcnn.PixelCNNPP( depth=FLAGS.n_resnet, features=FLAGS.n_feature, logistic_components=FLAGS.n_logistic_mix, **kwargs)
def model(**kwargs): return pixelcnn.PixelCNNPP(depth=FLAGS.n_resnet, features=FLAGS.n_feature, **kwargs)