我尝试将我的keras模型导出到tensorflow服务，并且一切正常。我想做的是从客户端接受b64编码的输入图像字符串并输出True / False值。我的keras模型输出3个值，第一个值表示从模型预测的程度，我将其与另一个固定值进行比较，然后将整个算法从获取图像字符串导出到使用RESTful API将True / False值输出到Tensorflow服务。但是，我没有从客户端程序获得正确的输出。长话短说，让我显示代码
我的程序用于导出保存的模型：
df['Date'].dt.strftime('%d/%m/%Y')
这是我的客户程序：
import tensorflow as tf
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl
from keras.models import load_model
from keras.layers import Input
import os
tf.app.flags.DEFINE_string('model_dir', './keras_models',
'''Directory which contains keras models''')
tf.app.flags.DEFINE_string('output_dir', './model_output',
'''Directory where to export the model''')
tf.app.flags.DEFINE_string('model_version', '1',
'''version number of the model''')
tf.app.flags.DEFINE_string('model_file', 'pointer_model.json',
'''json file which contains model architecture''')
tf.app.flags.DEFINE_string('weights_file', 'pointer_model.h5',
'''h5 file that contains model weights''')
FLAGS = tf.app.flags.FLAGS
def preprocess_image(image_buffer):
'''
Preprocess JPEG encoded bytes to 3D floate tensor
:param image_buffer:
:return: 4D image tensor (1, width, height, channels)
'''
image = tf.image.decode_jpeg(image_buffer, channels=3)
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
return image
def main(_):
with tf.Graph().as_default():
serialized_tf_example = tf.placeholder(tf.string, name='input_image')
feature_configs = {
'image/encoded': tf.FixedLenFeature(
shape=[], dtype=tf.string),
}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
jpegs = tf_example['image/encoded']
images = tf.map_fn(preprocess_image, jpegs, dtype=tf.float32)
images = tf.squeeze(images, [0])
images = tf.expand_dims(images, axis=0)
# now the image shape is [1, ?, ?, 3]
images = tf.image.resize_images(images, tf.constant([224, 224]))
model = load_model('./keras_models/my_model.h5')
x = Input(tensor=images)
y = model(x)
model.summary()
compare_value = tf.Variable(100.0)
bool_out = tf.math.greater(y, compare_value)
bool_out = bool_out[:,0]
bool_out = tf.cast(bool_out, tf.float32)
bool_out = tf.expand_dims(bool_out, axis=0)
final_out = tf.concat([tf.transpose(y), bool_out], axis=0)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
# predict_tensor_input_info = tf.saved_model.utils.build_tensor_info(jpegs)
# predict_tensor_score_info = tf.saved_model.utils.build_tensor_info(bool_out)
prediction_signature = \
(tf.saved_model.signature_def_utils.predict_signature_def(
inputs={'images': jpegs},
outputs={'scores': final_out}
)
)
export_path = os.path.join(
tf.compat.as_bytes(FLAGS.output_dir),
tf.compat.as_bytes(FLAGS.model_version)
)
builder = saved_model_builder.SavedModelBuilder(export_path)
legacy_init_op = tf.group(tf.tables_initializer(),
name = 'legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature,
},
legacy_init_op = legacy_init_op
)
builder.save()
if __name__ =="__main__":
tf.app.run()
json_response.text的输出如下：
import base64
import requests
import json
import argparse
import time
from glob import glob
image_path = glob('./segmented_image/*.jpg')
for i in range(len(image_path)):
input_image = open(image_path[i], 'rb').read()
encoded_input_string = base64.b64encode(input_image)
input_string = encoded_input_string.decode('utf-8')
# input_image_recover = base64.b64decode(input_string)
# with open('recovered_image.jpg', 'wb') as output_file:
# output_file.write(input_image_recover)
#
# print('Base64 encoded string: ' + input_string[:10] + '...' + input_string[-10:])
instance = [{"b64": input_string}]
data = json.dumps({"instances": instance})
print(data[:30]+ '...' + data[-10:])
json_response = requests.post('http://localhost:8501/v1/models/pointer_model:predict',
data=data)
print(json_response.text)
end_time = time.time()
......
预测键中的前三个值应该是度，图像中的x，y坐标应该是几百个值...最后一个值是与100.0相比强制转换为float32的True / False值
好..最后，我还使用model.predict测试了我的模型，它给出了正确的答案...
现在，我完全感到困惑。有人可以告诉我我的代码哪里出问题了吗？
答案 0 :(得分：0)
使用我的脚本以张量流服务格式导出
import sys
from keras.models import load_model
import tensorflow as tf
from keras import backend as K
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
K.set_learning_phase(0)
K.set_image_data_format('channels_last')
INPUT_MODEL = sys.argv[1]
NUMBER_OF_OUTPUTS = 1
OUTPUT_NODE_PREFIX = 'output_node'
OUTPUT_FOLDER= 'frozen'
OUTPUT_GRAPH = 'frozen_model.pb'
OUTPUT_SERVABLE_FOLDER = sys.argv[2]
INPUT_TENSOR = sys.argv[3]
try:
model = load_model(INPUT_MODEL)
except ValueError as err:
print('Please check the input saved model file')
raise err
output = [None]*NUMBER_OF_OUTPUTS
output_node_names = [None]*NUMBER_OF_OUTPUTS
for i in range(NUMBER_OF_OUTPUTS):
output_node_names[i] = OUTPUT_NODE_PREFIX+str(i)
output[i] = tf.identity(model.outputs[i], name=output_node_names[i])
print('Output Tensor names: ', output_node_names)
sess = K.get_session()
try:
frozen_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), output_node_names)
graph_io.write_graph(frozen_graph, OUTPUT_FOLDER, OUTPUT_GRAPH, as_text=False)
print(f'Frozen graph ready for inference/serving at {OUTPUT_FOLDER}/{OUTPUT_GRAPH}')
except:
print('Error Occured')
builder = tf.saved_model.builder.SavedModelBuilder(OUTPUT_SERVABLE_FOLDER)
with tf.gfile.GFile(f'{OUTPUT_FOLDER}/{OUTPUT_GRAPH}', "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
OUTPUT_TENSOR = output_node_names
with tf.Session(graph=tf.Graph()) as sess:
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
inp = g.get_tensor_by_name(INPUT_TENSOR)
out = g.get_tensor_by_name(OUTPUT_TENSOR[0] + ':0')
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"input": inp}, {"outout": out})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
try:
builder.save()
print(f'Model ready for deployment at {OUTPUT_SERVABLE_FOLDER}/saved_model.pb')
print('Prediction signature : ')
print(sigs['serving_default'])
except:
print('Error Occured, please checked frozen graph')