tensorflow 将预测作为 b64 输出最高结果 [英] tensorflow serving prediction as b64 output top result
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问题描述
我有一个 Keras 模型，我将其转换为 tensorflow 服务模型.我可以成功地将我的预训练 keras 模型转换为接受 b64 输入、预处理该输入并将其提供给我的模型.我的问题是我不知道如何获取我得到的预测数据(这是巨大的)并且只导出最高结果.我正在做图像分割，所以我的输出预测是形状 (?, 473, 473, 3)，我想获得最高结果并以 b64 编码格式返回它.我目前所拥有的只是返回整个预测:
I have a Keras model I converting to a tensorflow serving model. I can successfully convert my pretrained keras model to take b64 input, preprocess that input, and feed it to my model. My problem is that I don't know how to take the prediction data I am getting (which is enormous) and only export the top result. I am doing image segmentation so my output prediction is of shape (?, 473, 473, 3) and I'd like to get the top result and return it in b64 encoded format. What I have currently that just returns the entire prediction:
sess = K.get_session()
g = sess.graph
g_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.decode_image(input_b64[0])#input_bytes)
image_f = tf.image.convert_image_dtype(image, dtype=tf.float16)
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)
output = tf.identity(input_data, name='input_image')
# Convert to GraphDef
g_input_def = g_input.as_graph_def()
with tf.Graph().as_default() as g_combined:
x = tf.placeholder(tf.string, name="b64")
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])
with tf.Session() as session:
inputs = {"image_bytes": tf.saved_model.utils.build_tensor_info(x)}
outputs = {"output_bytes":tf.saved_model.utils.build_tensor_info(pred)}
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()
I took a lot of what I have working from https://medium.com/google-cloud/serverless-transfer-learning-with-cloud-ml-engine-and-keras-335435f31e15 for reference. Thanks!
推荐答案
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逆操作.
Posting my own solution I made in case someone else runs into this issue. Basically, you just do the inverse of the input function.
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.encode_png(casted_data)
return out_image
sess = K.get_session()
g = sess.graph
g_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.decode_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()
这篇关于tensorflow 将预测作为 b64 输出最高结果的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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