Ndtyjky

How can I implement the resize function in this construction?
For example, in_image has shape = (845, 594, 3), but I want to resize this image to shape = (299, 299, 3)

def main(_):

    with tf.Graph().as_default() as graph:

        input_graph = FLAGS.input_graph

        saved_model_dir = FLAGS.saved_model_dir

        # Read in the export graph

        with tf.gfile.FastGFile(input_graph, 'rb') as f:

            graph_def = tf.GraphDef()

            graph_def.ParseFromString(f.read())

            tf.import_graph_def(graph_def, name='')



        # Define SavedModel Signature (inputs and outputs)

        in_image = graph.get_tensor_by_name('input:0')

        inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}



        out_classes = graph.get_tensor_by_name('InceptionV3/Predictions/Reshape_1:0')

        outputs = {'prediction': 

                    tf.saved_model.utils.build_tensor_info(out_classes)}



        signature = tf.saved_model.signature_def_utils.build_signature_def(

            inputs=inputs,

            outputs=outputs,

            method_name='tensorflow/serving/predict'

        ) 



        with tf.Session(graph=graph) as sess:

            # Save out the SavedModel.

            b = saved_model_builder.SavedModelBuilder(saved_model_dir)

            b.add_meta_graph_and_variables(sess,

                                    [tf.saved_model.tag_constants.SERVING],

                                    signature_def_map={'serving_default': 

                                                        signature})

           b.save()



if __name__ == '__main__':

  tf.app.run()

I figured it out:

def main(_):

    with tf.Graph().as_default() as graph:

        input_graph = FLAGS.input_graph

        saved_model_dir = FLAGS.saved_model_dir

        # Read in the export graph

        with tf.gfile.FastGFile(input_graph, 'rb') as f:

            graph_def = tf.GraphDef()

            graph_def.ParseFromString(f.read())

            tf.import_graph_def(graph_def, name='')



        serialized_tf_example = tf.placeholder(tf.string, name='b64')

        jpeg = preprocess_image(serialized_tf_example)



        out, = tf.import_graph_def(graph.as_graph_def(),

                         input_map={'input:0': jpeg},

                         return_elements = ['InceptionV3/Predictions/Reshape_1:0'])



        inputs = {'inputs': tf.saved_model.utils.build_tensor_info(serialized_tf_example)}

        outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out)}



        signature = tf.saved_model.signature_def_utils.build_signature_def(

            inputs=inputs,

            outputs=outputs,

            method_name='tensorflow/serving/predict'

        )

        with tf.Session(graph=graph) as sess:

            # Save out the SavedModel.

            b = saved_model_builder.SavedModelBuilder(saved_model_dir)

            b.add_meta_graph_and_variables(sess,

                                    [tf.saved_model.tag_constants.SERVING],

                                    signature_def_map={'serving_default': signature})

            b.save()