def _iterate_reals(self, minibatch_size): dataset_obj = self._get_dataset_obj() while True: images, _labels = dataset_obj.get_minibatch_np(minibatch_size) if self._mirror_augment: images = misc.apply_mirror_augment(images) yield images
def _iterate_reals(self, minibatch_size): dataset_obj = dataset.load_dataset(data_dir=config.data_dir, **self._dataset_args) while True: images, _labels = dataset_obj.get_minibatch_np(minibatch_size) if self._mirror_augment: images = misc.apply_mirror_augment(images) yield images
def _iterate_reals(self, minibatch_size, return_label=False, iterate_once=False, num_samples=None, mirror_augment=False): dataset_obj = self._get_dataset_obj() if iterate_once: assert dataset_obj.num_samples num_samples = dataset_obj.num_samples begin = 0 while True: end = begin + minibatch_size if num_samples: end = min(end, num_samples) images, labels = dataset_obj.get_minibatch_np(end - begin) if mirror_augment: images = misc.apply_mirror_augment(images) if return_label: yield (images, labels) else: yield images if end == num_samples: break begin = end
def _iterate_reals(self, minibatch_size, return_label=False, num_samples=None, mirror_augment=False): dataset_obj = self._get_dataset_obj() begin = 0 while True: end = begin + minibatch_size if num_samples: end = min(end, num_samples) try: images, labels = dataset_obj.get_minibatch_np(end - begin) except tf.errors.OutOfRangeError: if num_samples is None: break else: raise if mirror_augment: images = misc.apply_mirror_augment(images) if return_label: yield (images, labels) else: yield images if num_samples and end == num_samples: break begin = end
def _iterate_reals(self, minibatch_size): dataset_obj = self._get_dataset_obj() while True: images, _labels = dataset_obj.get_minibatch_np(minibatch_size) #print('images.shape', images.shape) if images.shape[1] == 1: images = np.repeat(images, 3, axis=1) #print('images.shape', images.shape) if self._mirror_augment: images = misc.apply_mirror_augment(images) yield images