def _example_parser(example: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: """Preprocesses Places-365 image Tensors using inception_preprocessing.""" # `preprocess_image` returns images in [-1, 1]. image = inception_preprocessing.preprocess_image( example['image'], height=224, width=224, is_training=self._is_training) # Rescale to [0, 1]. image = (image + 1.0) / 2.0 label = tf.cast(example['label'], tf.int32) return {'features': image, 'labels': label}
def _example_parser( example: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: """Preprocesses ImageNet image Tensors.""" per_example_step_seed = tf.random.experimental.stateless_fold_in( self._seed, example[self._enumerate_id_key]) if self._preprocessing_type == 'inception': # `inception_preprocessing.preprocess_image` returns images in [-1, 1]. image = inception_preprocessing.preprocess_image( example['image'], height=self._image_size, width=self._image_size, seed=per_example_step_seed, is_training=self._is_training) # Rescale to [0, 1]. image = (image + 1.0) / 2.0 elif self._preprocessing_type == 'resnet': # `resnet_preprocessing.preprocess_image` returns images in [0, 1]. image = resnet_preprocessing.preprocess_image( image_bytes=example['image'], is_training=self._is_training, use_bfloat16=self._use_bfloat16, image_size=self._image_size, seed=per_example_step_seed, resize_method=self._resnet_preprocessing_resize_method) else: raise ValueError( 'Invalid preprocessing type, must be one of "inception" or ' '"resnet", received {}.'.format(self._preprocessing_type)) if self._normalize_input: image = (tf.cast(image, tf.float32) - IMAGENET_MEAN) / IMAGENET_STDDEV if self._use_bfloat16: image = tf.cast(image, tf.bfloat16) # Note that labels are always float32, even when images are bfloat16. if self._one_hot: label = tf.one_hot(example['label'], 1000, dtype=tf.float32) else: label = tf.cast(example['label'], tf.float32) parsed_example = { 'features': image, 'labels': label, } if 'file_name' in example: parsed_example['file_name'] = example['file_name'] return parsed_example
def _example_parser( example: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: """Preprocesses Places-365 image Tensors using inception_preprocessing.""" per_example_step_seed = tf.random.experimental.stateless_fold_in( self._seed, example[self._enumerate_id_key]) # `preprocess_image` returns images in [-1, 1]. image = inception_preprocessing.preprocess_image( example['image'], height=224, width=224, seed=per_example_step_seed, is_training=self._is_training) # Rescale to [0, 1]. image = (image + 1.0) / 2.0 label = tf.cast(example['label'], tf.int32) return {'features': image, 'labels': label}
def _example_parser(example: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]: """Preprocesses ImageNet image Tensors using inception_preprocessing.""" # `preprocess_image` returns images in [-1, 1]. image = inception_preprocessing.preprocess_image( example['image'], height=224, width=224, is_training=self._is_training(split)) # Rescale to [0, 1]. image = (image + 1.0) / 2.0 label = tf.cast(example['label'], tf.int32) parsed_example = { 'features': image, 'labels': label, } if 'file_name' in example: parsed_example['file_name'] = example['file_name'] return parsed_example