本文整理汇总了Python中tensorflow.python.framework.graph_io.write_graph方法的典型用法代码示例。如果您正苦于以下问题:Python graph_io.write_graph方法的具体用法?Python graph_io.write_graph怎么用?Python graph_io.write_graph使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.framework.graph_io
的用法示例。
在下文中一共展示了graph_io.write_graph方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: export_cnn
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def export_cnn() -> None:
input = tf.placeholder(tf.float32, shape=(1, 1, 3, 3))
filter = tf.constant(np.ones((3, 3, 1, 1)), dtype=tf.float32)
x = tf.nn.conv2d(input, filter, (1, 1, 1, 1), "SAME", data_format="NCHW")
x = tf.nn.sigmoid(x)
x = tf.nn.relu(x)
pred_node_names = ["output"]
tf.identity(x, name=pred_node_names[0])
with tf.Session() as sess:
constant_graph = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(), pred_node_names
)
frozen = graph_util.remove_training_nodes(constant_graph)
output = "cnn.pb"
graph_io.write_graph(frozen, ".", output, as_text=False)
示例2: export
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def export(x: tf.Tensor, filename: str, sess=None):
should_close = False
if sess is None:
should_close = True
sess = tf.Session()
pred_node_names = ["output"]
tf.identity(x, name=pred_node_names[0])
graph = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(), pred_node_names
)
graph = graph_util.remove_training_nodes(graph)
path = graph_io.write_graph(graph, ".", filename, as_text=False)
if should_close:
sess.close()
return path
示例3: keras_to_tensorflow
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def keras_to_tensorflow(keras_model, output_dir, model_name, out_prefix="output_", log_tensorboard=True):
if os.path.exists(output_dir) == False:
os.mkdir(output_dir)
out_nodes = []
for i in range(len(keras_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(keras_model.output[i], out_prefix + str(i + 1))
sess = K.get_session()
init_graph = sess.graph.as_graph_def()
main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes)
graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)
if log_tensorboard:
import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir)
示例4: convertMetaModelToPbModel
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def convertMetaModelToPbModel(meta_model, pb_model):
# Step 1
# import the model metagraph
saver = tf.train.import_meta_graph(meta_model + '.meta', clear_devices=True)
# make that as the default graph
graph = tf.get_default_graph()
sess = tf.Session()
# now restore the variables
saver.restore(sess, meta_model)
# Step 2
# Find the output name
for op in graph.get_operations():
print(op.name)
# Step 3
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session
sess.graph_def, # input_graph_def is useful for retrieving the nodes
["Placeholder", "output/Sigmoid"])
# Step 4
# output folder
output_fld = './'
# output pb file name
output_model_file = 'model.pb'
# write the graph
graph_io.write_graph(output_graph_def, pb_model + output_fld, output_model_file, as_text=False)
示例5: main
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def main(unused_args):
if not gfile.Exists(FLAGS.input):
print("Input graph file '" + FLAGS.input + "' does not exist!")
return -1
input_graph_def = graph_pb2.GraphDef()
with gfile.Open(FLAGS.input, "rb") as f:
data = f.read()
if FLAGS.frozen_graph:
input_graph_def.ParseFromString(data)
else:
text_format.Merge(data.decode("utf-8"), input_graph_def)
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def,
FLAGS.input_names.split(","),
FLAGS.output_names.split(","), FLAGS.placeholder_type_enum)
if FLAGS.frozen_graph:
f = gfile.FastGFile(FLAGS.output, "w")
f.write(output_graph_def.SerializeToString())
else:
graph_io.write_graph(output_graph_def,
os.path.dirname(FLAGS.output),
os.path.basename(FLAGS.output))
return 0
示例6: main
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def main(unused_args):
if not gfile.Exists(FLAGS.input):
print("Input graph file '" + FLAGS.input + "' does not exist!")
return -1
input_graph_def = graph_pb2.GraphDef()
with gfile.Open(FLAGS.input, "r") as f:
data = f.read()
if FLAGS.frozen_graph:
input_graph_def.ParseFromString(data)
else:
text_format.Merge(data.decode("utf-8"), input_graph_def)
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def,
FLAGS.input_names.split(","),
FLAGS.output_names.split(","), FLAGS.placeholder_type_enum)
if FLAGS.frozen_graph:
f = gfile.FastGFile(FLAGS.output, "w")
f.write(output_graph_def.SerializeToString())
else:
graph_io.write_graph(output_graph_def,
os.path.dirname(FLAGS.output),
os.path.basename(FLAGS.output))
return 0
示例7: save_model_to_tensorflow
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def save_model_to_tensorflow(self, new_model_folder, new_model_name=""):
"""
'save_model_to_tensorflow' function allows you to save your loaded Keras (.h5) model and save it to the Tensorflow (.pb) model format.
- new_model_folder (required), the path to the folder you want the converted Tensorflow model to be saved
- new_model_name (required), the desired filename for your converted Tensorflow model e.g 'my_new_model.pb'
:param new_model_folder:
:param new_model_name:
:return:
"""
if(self.__modelLoaded == True):
out_prefix = "output_"
output_dir = new_model_folder
if os.path.exists(output_dir) == False:
os.mkdir(output_dir)
model_name = os.path.join(output_dir, new_model_name)
keras_model = self.__model_collection[0]
out_nodes = []
for i in range(len(keras_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(keras_model.output[i], out_prefix + str(i + 1))
sess = K.get_session()
from tensorflow.python.framework import graph_util, graph_io
init_graph = sess.graph.as_graph_def()
main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes)
graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)
print("Tensorflow Model Saved")
示例8: build_model
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def build_model(self, model_fn, params):
"""Build the TPU model and infeed enqueue ops."""
tf.logging.info("TrainLowLevelRunner: build_model method")
def tpu_train_step(loss):
"""Generate the TPU graph."""
del loss
values = self.infeed_queue[0].generate_dequeue_op(tpu_device=0)
unflattened_inputs = data_nest.pack_sequence_as(self.feature_structure,
values)
features = unflattened_inputs["features"]
labels = unflattened_inputs["labels"]
estimator_spec = model_fn(features, labels, tf.estimator.ModeKeys.TRAIN,
params)
loss, train_op = estimator_spec.loss, estimator_spec.train_op
with tf.control_dependencies([train_op]):
return tf.identity(loss)
def train_loop():
return tpu.repeat(self.iterations, tpu_train_step, [_INITIAL_LOSS])
with self.graph.as_default():
(self.loss,) = tpu.shard(
train_loop,
inputs=[],
num_shards=self.hparams.num_shards,
outputs_from_all_shards=False,
)
global_initializer = tf.global_variables_initializer()
local_initializer = tf.local_variables_initializer()
graph_io.write_graph(
self.graph.as_graph_def(add_shapes=True), self.hparams.out_dir,
"graph.pbtxt")
self.saver = tf.train.Saver()
self.sess.run(global_initializer)
self.sess.run(local_initializer)
示例9: export_to_pb
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def export_to_pb(sess, x, filename):
pred_names = ["output"]
tf.identity(x, name=pred_names[0])
graph = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(), pred_names
)
graph = graph_util.remove_training_nodes(graph)
path = graph_io.write_graph(graph, ".", filename, as_text=False)
print("saved the frozen graph (ready for inference) at: ", path)
示例10: export_to_pb
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def export_to_pb(sess, x, filename):
pred_names = ["output"]
tf.identity(x, name=pred_names[0])
graph = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(), pred_names
)
graph = graph_util.remove_training_nodes(graph)
path = graph_io.write_graph(graph, ".", filename, as_text=False)
print("saved the frozen graph (ready for inference) at: ", filename)
return path
示例11: convertMetaModelToPbModel
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def convertMetaModelToPbModel(meta_model, pb_model):
# Step 1
# import the model metagraph
saver = tf.train.import_meta_graph(meta_model + '.meta', clear_devices=True)
# make that as the default graph
graph = tf.get_default_graph()
sess = tf.Session()
# now restore the variables
saver.restore(sess, meta_model)
# Step 2
# Find the output name
for op in graph.get_operations():
print(op.name)
# Step 3
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session
sess.graph_def, # input_graph_def is useful for retrieving the nodes
["Input", "output/Sigmoid"])
# Step 4
# output folder
output_fld = './'
# output pb file name
output_model_file = 'model.pb'
# write the graph
graph_io.write_graph(output_graph_def, pb_model + output_fld, output_model_file, as_text=False)
示例12: main
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def main(unused_args):
if not gfile.Exists(FLAGS.input):
print("Input graph file '" + FLAGS.input + "' does not exist!")
return -1
input_graph_def = graph_pb2.GraphDef()
with gfile.Open(FLAGS.input, "rb") as f:
data = f.read()
if FLAGS.frozen_graph:
input_graph_def.ParseFromString(data)
else:
text_format.Merge(data.decode("utf-8"), input_graph_def)
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def,
FLAGS.input_names.split(","),
FLAGS.output_names.split(","),
FLAGS.placeholder_type_enum,
FLAGS.toco_compatible)
if FLAGS.frozen_graph:
f = gfile.FastGFile(FLAGS.output, "w")
f.write(output_graph_def.SerializeToString())
else:
graph_io.write_graph(output_graph_def,
os.path.dirname(FLAGS.output),
os.path.basename(FLAGS.output))
return 0
示例13: convertGraph
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def convertGraph(modelPath, output, outPath):
'''
Converts an HD5F file to a .pb file for use with Tensorflow.
Args:
modelPath (str): path to the .h5 file
output (str): name of the referenced output
outPath (str): path to the output .pb file
Returns:
None
'''
dir = os.path.dirname(os.path.realpath(__file__))
outdir = os.path.join(dir, os.path.dirname(outPath))
name = os.path.basename(outPath)
basename, ext = os.path.splitext(name)
#NOTE: If using Python > 3.2, this could be replaced with os.makedirs( name, exist_ok=True )
if not os.path.isdir(outdir):
os.mkdir(outdir)
K.set_learning_phase(0)
net_model = load_model(modelPath)
# Alias the outputs in the model - this sometimes makes them easier to access in TF
tf.identity(net_model.output, name=output)
sess = K.get_session()
net_model.summary()
# Write the graph in human readable
f = '{}.reference.pb.ascii'.format(basename)
tf.train.write_graph(sess.graph.as_graph_def(), outdir, f, as_text=True)
print('Saved the graph definition in ascii format at: ', osp.join(outdir, f))
# Write the graph in binary .pb file
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), [output])
graph_io.write_graph(constant_graph, outdir, name, as_text=False)
print('Saved the constant graph (ready for inference) at: ', outPath)
示例14: testStripUnused
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def testStripUnused(self):
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"
# We'll create an input graph that has a single constant containing 1.0,
# and that then multiplies it by 2.
with ops.Graph().as_default():
constant_node = constant_op.constant(1.0, name="constant_node")
wanted_input_node = math_ops.subtract(constant_node,
3.0,
name="wanted_input_node")
output_node = math_ops.multiply(
wanted_input_node, 2.0, name="output_node")
math_ops.add(output_node, 2.0, name="later_node")
sess = session.Session()
output = sess.run(output_node)
self.assertNear(-4.0, output, 0.00001)
graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
# We save out the graph to disk, and then call the const conversion
# routine.
input_graph_path = os.path.join(self.get_temp_dir(), input_graph_name)
input_binary = False
input_node_names = "wanted_input_node"
output_binary = True
output_node_names = "output_node"
output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
strip_unused_lib.strip_unused_from_files(input_graph_path, input_binary,
output_graph_path, output_binary,
input_node_names,
output_node_names,
dtypes.float32.as_datatype_enum)
# Now we make sure the variable is now a constant, and that the graph still
# produces the expected result.
with ops.Graph().as_default():
output_graph_def = graph_pb2.GraphDef()
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
_ = importer.import_graph_def(output_graph_def, name="")
self.assertEqual(3, len(output_graph_def.node))
for node in output_graph_def.node:
self.assertNotEqual("Add", node.op)
self.assertNotEqual("Sub", node.op)
if node.name == input_node_names:
self.assertTrue("shape" in node.attr)
with session.Session() as sess:
input_node = sess.graph.get_tensor_by_name("wanted_input_node:0")
output_node = sess.graph.get_tensor_by_name("output_node:0")
output = sess.run(output_node, feed_dict={input_node: [10.0]})
self.assertNear(20.0, output, 0.00001)
示例15: testStripUnusedMultipleInputs
# 需要导入模块: from tensorflow.python.framework import graph_io [as 别名]
# 或者: from tensorflow.python.framework.graph_io import write_graph [as 别名]
def testStripUnusedMultipleInputs(self):
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"
# We'll create an input graph that multiplies two input nodes.
with ops.Graph().as_default():
constant_node1 = constant_op.constant(1.0, name="constant_node1")
constant_node2 = constant_op.constant(2.0, name="constant_node2")
input_node1 = math_ops.subtract(constant_node1, 3.0, name="input_node1")
input_node2 = math_ops.subtract(constant_node2, 5.0, name="input_node2")
output_node = math_ops.multiply(
input_node1, input_node2, name="output_node")
math_ops.add(output_node, 2.0, name="later_node")
sess = session.Session()
output = sess.run(output_node)
self.assertNear(6.0, output, 0.00001)
graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
# We save out the graph to disk, and then call the const conversion
# routine.
input_graph_path = os.path.join(self.get_temp_dir(), input_graph_name)
input_binary = False
input_node_names = "input_node1,input_node2"
input_node_types = [
dtypes.float32.as_datatype_enum, dtypes.float32.as_datatype_enum
]
output_binary = True
output_node_names = "output_node"
output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
strip_unused_lib.strip_unused_from_files(input_graph_path, input_binary,
output_graph_path, output_binary,
input_node_names,
output_node_names,
input_node_types)
# Now we make sure the variable is now a constant, and that the graph still
# produces the expected result.
with ops.Graph().as_default():
output_graph_def = graph_pb2.GraphDef()
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
_ = importer.import_graph_def(output_graph_def, name="")
self.assertEqual(3, len(output_graph_def.node))
for node in output_graph_def.node:
self.assertNotEqual("Add", node.op)
self.assertNotEqual("Sub", node.op)
if node.name == input_node_names:
self.assertTrue("shape" in node.attr)
with session.Session() as sess:
input_node1 = sess.graph.get_tensor_by_name("input_node1:0")
input_node2 = sess.graph.get_tensor_by_name("input_node2:0")
output_node = sess.graph.get_tensor_by_name("output_node:0")
output = sess.run(output_node,
feed_dict={input_node1: [10.0],
input_node2: [-5.0]})
self.assertNear(-50.0, output, 0.00001)