def main(_):
if not(os.path.exists(output_directory)):
os.mkdir(output_directory)
input_shape = None
additional_output_tensor_names = None
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(config_override, pipeline_config)
if input_shape:
input_shape = [int(dim) if dim != '-1' else None for dim in input_shape.split(',')]
else:
input_shape = None
if use_side_inputs:
side_input_shapes, side_input_names, side_input_types = (exporter.parse_side_inputs(side_input_shapes, side_input_names, side_input_types))
else:
side_input_shapes = None
side_input_names = None
side_input_types = None
if additional_output_tensor_names:
additional_output_tensor_names = list(additional_output_tensor_names.split(','))
else:
additional_output_tensor_names = None
exporter.export_inference_graph(input_type, pipeline_config, trained_checkpoint_prefix,
output_directory, input_shape=input_shape,
write_inference_graph=write_inference_graph,
additional_output_tensor_names=additional_output_tensor_names,
use_side_inputs=use_side_inputs,
side_input_shapes=side_input_shapes,
side_input_names=side_input_names,
side_input_types=side_input_types)
def main(_):
with open('system_dict.json') as json_file:
args = json.load(json_file)
if (os.path.isdir(args["output_directory"])):
os.system("rm -r " + args["output_directory"])
os.mkdir(args["output_directory"])
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(args["pipeline_config_path"], 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(args["config_override"], pipeline_config)
if args["input_shape"]:
input_shape = [
int(dim) if dim != '-1' else None
for dim in args["input_shape"].split(',')
]
else:
input_shape = None
if args["input_shape_flops"]:
input_shape_flops = [
int(dim) if dim != '-1' else None
for dim in args["input_shape_flops"].split(',')
]
else:
input_shape_flops = None
if args["use_side_inputs"]:
side_input_shapes, side_input_names, side_input_types = (
exporter.parse_side_inputs(args["side_input_shapes"],
args["side_input_names"],
args["side_input_types"]))
else:
side_input_shapes = None
side_input_names = None
side_input_types = None
if args["additional_output_tensor_names"]:
additional_output_tensor_names = list(
args["additional_output_tensor_names"].split(','))
else:
additional_output_tensor_names = None
exporter.export_inference_graph(
args["input_type"],
pipeline_config,
args["trained_checkpoint_prefix"],
args["output_directory"],
input_shape=input_shape,
write_inference_graph=args["write_inference_graph"],
additional_output_tensor_names=additional_output_tensor_names,
use_side_inputs=args["use_side_inputs"],
side_input_shapes=side_input_shapes,
side_input_names=side_input_names,
side_input_types=side_input_types)
'''
def main(_):
if FLAGS.gpu_device:
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu_device)
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(FLAGS.config_override, pipeline_config)
if FLAGS.input_shape:
input_shape = [
int(dim) if dim != '-1' else None
for dim in FLAGS.input_shape.split(',')
]
else:
input_shape = None
if FLAGS.use_side_inputs:
side_input_shapes, side_input_names, side_input_types = (
exporter.parse_side_inputs(FLAGS.side_input_shapes,
FLAGS.side_input_names,
FLAGS.side_input_types))
else:
side_input_shapes = None
side_input_names = None
side_input_types = None
if FLAGS.additional_output_tensor_names:
additional_output_tensor_names = list(
FLAGS.additional_output_tensor_names.split(','))
else:
additional_output_tensor_names = None
checkpoint_path = FLAGS.trained_checkpoint_prefix
if not checkpoint_path:
checkpoint_path = tf.train.latest_checkpoint(FLAGS.model_dir)
exporter.export_inference_graph(
FLAGS.input_type,
pipeline_config,
checkpoint_path,
FLAGS.output_directory,
input_shape=input_shape,
write_inference_graph=FLAGS.write_inference_graph,
additional_output_tensor_names=additional_output_tensor_names,
use_side_inputs=FLAGS.use_side_inputs,
side_input_shapes=side_input_shapes,
side_input_names=side_input_names,
side_input_types=side_input_types)
def main(_):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(FLAGS.pipeline_config_path, "r") as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(FLAGS.config_override, pipeline_config)
if FLAGS.input_shape:
input_shape = [
int(dim) if dim != "-1" else None
for dim in FLAGS.input_shape.split(",")
]
else:
input_shape = None
if FLAGS.use_side_inputs:
(
side_input_shapes,
side_input_names,
side_input_types,
) = exporter.parse_side_inputs(FLAGS.side_input_shapes,
FLAGS.side_input_names,
FLAGS.side_input_types)
else:
side_input_shapes = None
side_input_names = None
side_input_types = None
if FLAGS.additional_output_tensor_names:
additional_output_tensor_names = list(
FLAGS.additional_output_tensor_names.split(","))
else:
additional_output_tensor_names = None
exporter.export_inference_graph(
FLAGS.input_type,
pipeline_config,
FLAGS.trained_checkpoint_prefix,
FLAGS.output_directory,
input_shape=input_shape,
write_inference_graph=FLAGS.write_inference_graph,
additional_output_tensor_names=additional_output_tensor_names,
use_side_inputs=FLAGS.use_side_inputs,
side_input_shapes=side_input_shapes,
side_input_names=side_input_names,
side_input_types=side_input_types,
)
