def preprocess_fn(inputs):
image_tensor = export_utils.parse_image(inputs, input_type,
input_image_size, num_channels)
# If input_type is `tflite`, do not apply image preprocessing.
if input_type == 'tflite':
return image_tensor
def preprocess_image_fn(inputs):
image = tf.cast(inputs, dtype=tf.float32)
image = image / 255.
(image, image_info) = yolo_model_fn.letterbox(
image,
input_image_size,
letter_box=params.task.validation_data.parser.letter_box)
return image, image_info
images_spec = tf.TensorSpec(shape=input_image_size + [3],
dtype=tf.float32)
image_info_spec = tf.TensorSpec(shape=[4, 2], dtype=tf.float32)
images, image_info = tf.nest.map_structure(
tf.identity,
tf.map_fn(preprocess_image_fn,
elems=image_tensor,
fn_output_signature=(images_spec, image_info_spec),
parallel_iterations=32))
return images, image_info
def preprocess_fn(inputs):
image_tensor = export_utils.parse_image(inputs, input_type,
input_image_size, num_channels)
# If input_type is `tflite`, do not apply image preprocessing.
if input_type == 'tflite':
return image_tensor
def preprocess_image_fn(inputs):
return classification_input.Parser.inference_fn(
inputs, input_image_size, num_channels)
images = tf.map_fn(preprocess_image_fn,
elems=image_tensor,
fn_output_signature=tf.TensorSpec(
shape=input_image_size + [num_channels],
dtype=tf.float32))
return images