def call_impl(self):
"""
Returns:
Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs.
"""
graph = tf_util.load_graph(self.path)
return graph, tf_util.get_graph_output_names(graph)
def __call__(self):
"""
Loads a TensorFlow frozen model.
Returns:
Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs.
"""
graph = tf_util.load_graph(self.path)
return graph, tf_util.get_graph_output_names(graph)
def call_impl(self):
"""
Returns:
Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs.
"""
from tensorflow.python import keras
from tensorflow.python.keras import backend
model = keras.models.load_model(self.path)
graph = backend.get_session().graph
return graph, tf_util.get_graph_output_names(graph)
def call_impl(self):
"""
Returns:
Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs.
"""
from tensorflow.contrib import tensorrt as tf_trt
(graph, output_names), _ = util.invoke_if_callable(self._graph)
precision_mode = "FP16" if self.fp16 else "FP32"
precision_mode = "INT8" if self.int8 else precision_mode
G_LOGGER.info(
"For TF-TRT, using outputs={:}, max_workspace_size_bytes={:}, max_batch_size={:}, "
"minimum_segment_size={:}, is_dynamic_op={:}, precision_mode={:}".
format(
output_names,
self.max_workspace_size,
self.max_batch_size,
self.minimum_segment_size,
self.is_dynamic_op,
precision_mode,
))
graphdef = tf_trt.create_inference_graph(
graph.as_graph_def(),
outputs=output_names,
max_workspace_size_bytes=self.max_workspace_size,
max_batch_size=self.max_batch_size,
minimum_segment_size=self.minimum_segment_size,
is_dynamic_op=self.is_dynamic_op,
precision_mode=precision_mode,
)
segment_number = 0
for node in graphdef.node:
if node.op == "TRTEngineOp":
engine = node.attr["serialized_segment"].s
segment_number += 1
G_LOGGER.info(
"Found {:} engines in TFTRT graph".format(segment_number))
with tf.Graph().as_default() as graph:
tf.import_graph_def(graphdef, name="")
return graph, tf_util.get_graph_output_names(graph)
def call_impl(self):
"""
Returns:
Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs.
"""
# If `name` is not provided, this expects that the directory contains a `checkpoint` file with the contents:
#
# model_checkpoint_path: "model"
# all_model_checkpoint_paths: "model"
#
# where "model" is the checkpoint name
if not os.path.isdir(self.dir):
G_LOGGER.warning(
"Specified checkpoint directory: {:} does not look like a directory."
.format(self.dir))
if self.name is None:
G_LOGGER.verbose(
"Checkpoint name was not explicitly provided, searching for `checkpoint` file"
)
checkpoint = tf.train.get_checkpoint_state(self.dir)
if checkpoint is None:
ckpt_file_contents = '\nmodel_checkpoint_path: "model"\nall_model_checkpoint_paths: "model"\n'
G_LOGGER.critical(
"Checkpoint directory: {:} does not contain a `checkpoint` file, and the checkpoint name was "
"not provided. Please either create a checkpoint file with the contents:\n{:} "
"\nWhere `model` is the name of the checkpoint, or explicitly provide the name with "
"--ckpt, not including file extensions".format(
self.dir, ckpt_file_contents))
input_checkpoint = checkpoint.model_checkpoint_path
else:
input_checkpoint = os.path.join(self.dir, self.name)
meta_file = input_checkpoint + ".meta"
with tf.Graph().as_default() as graph, tf.compat.v1.Session(
graph=graph).as_default() as sess:
saver = tf.compat.v1.train.import_meta_graph(meta_file,
clear_devices=True)
saver.restore(sess, input_checkpoint)
return graph, tf_util.get_graph_output_names(graph)
