将图形原型 (pb/pbtxt) 转换为 SavedModel 以便在 TensorFlow Serving 或 Cloud ML Engine 中使用
Convert a graph proto (pb/pbtxt) to a SavedModel for use in TensorFlow Serving or Cloud ML Engine
我一直在关注我训练过的模型的 TensorFlow for Poets 2 代码实验室,并创建了一个带有嵌入式权重的冻结量化图。它被捕获在一个文件中 - 比如 my_quant_graph.pb
.
因为我可以使用该图来推断 TensorFlow Android inference library 就好了,我想我可以用 Cloud ML Engine 做同样的事情,但它似乎只适用于 SavedModel 模型。
如何简单地转换单个 pb 文件中的 frozen/quantized 图以在 ML 引擎上使用?
事实证明,SavedModel 提供了一些关于已保存图形的额外信息。假设冻结图不需要资产,那么它只需要指定一个服务签名。
这是我 运行 将我的图形转换为 Cloud ML 引擎接受的格式的 python 代码。注意我只有一对 input/output 张量。
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
export_dir = './saved'
graph_pb = 'my_quant_graph.pb'
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# name="" is important to ensure we don't get spurious prefixing
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
inp = g.get_tensor_by_name("real_A_and_B_images:0")
out = g.get_tensor_by_name("generator/Tanh:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"in": inp}, {"out": out})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()
有多个输出节点的示例:
# Convert PtotoBuf model to saved_model, format for TF Serving
# https://cloud.google.com/ai-platform/prediction/docs/exporting-savedmodel-for-prediction
import shutil
import tensorflow.compat.v1 as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
export_dir = './1' # TF Serving supports run different versions of same model. So we put current model to '1' folder.
graph_pb = 'frozen_inference_graph.pb'
# Clear out folder
shutil.rmtree(export_dir, ignore_errors=True)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.io.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# Prepare input and outputs of model
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
image_tensor = g.get_tensor_by_name("image_tensor:0")
num_detections = g.get_tensor_by_name("num_detections:0")
detection_scores = g.get_tensor_by_name("detection_scores:0")
detection_boxes = g.get_tensor_by_name("detection_boxes:0")
detection_classes = g.get_tensor_by_name("detection_classes:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"input_image": image_tensor},
{ "num_detections": num_detections,
"detection_scores": detection_scores,
"detection_boxes": detection_boxes,
"detection_classes": detection_classes})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()
我一直在关注我训练过的模型的 TensorFlow for Poets 2 代码实验室,并创建了一个带有嵌入式权重的冻结量化图。它被捕获在一个文件中 - 比如 my_quant_graph.pb
.
因为我可以使用该图来推断 TensorFlow Android inference library 就好了,我想我可以用 Cloud ML Engine 做同样的事情,但它似乎只适用于 SavedModel 模型。
如何简单地转换单个 pb 文件中的 frozen/quantized 图以在 ML 引擎上使用?
事实证明,SavedModel 提供了一些关于已保存图形的额外信息。假设冻结图不需要资产,那么它只需要指定一个服务签名。
这是我 运行 将我的图形转换为 Cloud ML 引擎接受的格式的 python 代码。注意我只有一对 input/output 张量。
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
export_dir = './saved'
graph_pb = 'my_quant_graph.pb'
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# name="" is important to ensure we don't get spurious prefixing
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
inp = g.get_tensor_by_name("real_A_and_B_images:0")
out = g.get_tensor_by_name("generator/Tanh:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"in": inp}, {"out": out})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()
有多个输出节点的示例:
# Convert PtotoBuf model to saved_model, format for TF Serving
# https://cloud.google.com/ai-platform/prediction/docs/exporting-savedmodel-for-prediction
import shutil
import tensorflow.compat.v1 as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
export_dir = './1' # TF Serving supports run different versions of same model. So we put current model to '1' folder.
graph_pb = 'frozen_inference_graph.pb'
# Clear out folder
shutil.rmtree(export_dir, ignore_errors=True)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.io.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# Prepare input and outputs of model
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
image_tensor = g.get_tensor_by_name("image_tensor:0")
num_detections = g.get_tensor_by_name("num_detections:0")
detection_scores = g.get_tensor_by_name("detection_scores:0")
detection_boxes = g.get_tensor_by_name("detection_boxes:0")
detection_classes = g.get_tensor_by_name("detection_classes:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"input_image": image_tensor},
{ "num_detections": num_detections,
"detection_scores": detection_scores,
"detection_boxes": detection_boxes,
"detection_classes": detection_classes})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()