在服务器上预处理图像

Question

如何在此构造中实现调整大小功能？例如，in_image有shape = (845, 594, 3)，但我想将此图像调整为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()

Answer 1

我想到了：

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()