tensorflow作为b64输出最高结果的预测

tensorflow作为b64输出最高结果的预测

发布我自己的解决方案，以防其他人遇到此问题。基本上，您只需执行输入函数的反函数即可。

def postprocess_image(img, in_shape):    class_image = tf.argmax(img, axis=2)    colored_class_image = utils.class_image_to_image_tensor(class_image, [HEIGHT,WIDTH])    image_expand = tf.expand_dims(colored_class_image, 0)    image_r = tf.image.resize_bilinear(image_expand, in_shape, align_corners=False)    casted_data = tf.bitcast(tf.cast(image_r[0], tf.int8), tf.uint8)    out_image = tf.image.enpre_png(casted_data)    return out_imagesess = K.get_session()g = sess.graphg_def = graph_util.convert_variables_to_constants(sess, g.as_graph_def(),[model.output.name.replace(':0','')])with tf.Graph().as_default() as g_input:    input_b64 = tf.placeholder(shape=(1,),         dtype=tf.string,         name='b64')    tf.logging.info('input b64 {}'.format(input_b64))    image = tf.image.depre_image(input_b64[0])    image_f = tf.image.convert_image_dtype(image, dtype=tf.uint8)    input_image = tf.expand_dims(image_f, 0)    image_r = tf.image.resize_bilinear(input_image, [HEIGHT, WIDTH], align_corners=False)    input_data = preprocess_image(image_r[0])    output = tf.identity(input_data, name='input_image')with tf.Graph().as_default() as g_output:    first = tf.placeholder(shape=[1,473,473,150],         dtype=tf.float32,         name='activation_58/div')    i_shape = tf.placeholder(dtype=tf.int32, shape=[2], name='in_shape')    post_image = postprocess_image(first[0], i_shape)    output_data = tf.identity(post_image, name='out')g_input_def = g_input.as_graph_def()g_output_def = g_output.as_graph_def()with tf.Graph().as_default() as g_combined:    x = tf.placeholder(tf.string, name="b64")    in_shape = tf.placeholder(tf.int32, shape=[1,2],name="original_shape")    im, = tf.import_graph_def(g_input_def,        input_map={'b64:0': x},        return_elements=["input_image:0"])    pred, = tf.import_graph_def(g_def,         input_map={model.input.name: im},         return_elements=[model.output.name])    y, = tf.import_graph_def(g_output_def,  input_map={model.output.name: pred,  'in_shape:0':in_shape[0]},  return_elements=["out:0"])    with tf.Session() as session:        inputs = {"image_bytes": tf.saved_model.utils.build_tensor_info(x),     "original_shape":tf.saved_model.utils.build_tensor_info(in_shape)}        outputs = {"output_bytes":tf.saved_model.utils.build_tensor_info(y)}        signature =tf.saved_model.signature_def_utils.build_signature_def(     inputs=inputs,     outputs=outputs,     method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME )        """Convert the Keras HDF5 model into TensorFlow SavedModel."""        if os.path.exists(export_path): shutil.rmtree(export_path)        legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')        builder = saved_model_builder.SavedModelBuilder(export_path)        builder.add_meta_graph_and_variables( sess=session, tags=[tag_constants.SERVING], signature_def_map={ signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature },        )        builder.save()