def convert_tr(self, graph_def, fetches):
"""Convert to TensorRT."""
from tensorflow.python.compiler.tensorrt import trt # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
converter = trt.TrtGraphConverter(
nodes_blacklist=[t.split(':')[0] for t in fetches],
input_graph_def=graph_def,
precision_mode=self.tensorrt)
infer_graph = converter.convert()
goutput = tf.import_graph_def(infer_graph, return_elements=fetches)
return goutput
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='tf-trt model.', required=True)
args = parser.parse_args()
checkpoint_path, num_classes = download_classification_checkpoint(
args.model, 'data')
frozen_graph, input_names, output_names = build_classification_graph(
model=args.model,
checkpoint=checkpoint_path,
num_classes=num_classes,
is_remove_relu6=True)
print(input_names, output_names, num_classes)
converter = trt.TrtGraphConverter(
input_graph_def=frozen_graph,
nodes_blacklist=output_names, #output nodes
max_batch_size=1,
is_dynamic_op=False,
max_workspace_size_bytes=1 << 25,
precision_mode=trt.TrtPrecisionMode.
FP16, # trt.DEFAULT_TRT_MAX_WORKSPACE_SIZE_BYTES
minimum_segment_size=50)
trt_graph = converter.convert()
# trt_graph = trt.create_inference_graph(
# input_graph_def=frozen_graph,
# outputs=output_names,
# max_batch_size=1,
# max_workspace_size_bytes=1 << 25,
# precision_mode='FP16',
# minimum_segment_size=3
# )
trt_engine_opts = len(
[1 for n in trt_graph.node if str(n.op) == 'TRTEngineOp'])
print("trt_engine_opts = {}".format(trt_engine_opts))
model_dir = os.path.join('.', 'model')
if tf.gfile.Exists(model_dir) == False:
tf.gfile.MkDir(model_dir)
base_name = os.path.splitext(os.path.basename(checkpoint_path))[0]
save_model_file_name = base_name + '_frozen_fp16.pb'
with open(os.path.join(model_dir, save_model_file_name), 'wb') as f:
f.write(trt_graph.SerializeToString())
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='tf-trt model.')
parser.add_argument('--path', help='path to checkpoint dir.')
parser.add_argument('--output', help='Output dir.', default='model')
parser.add_argument('--force_nms_cpu', help='Force NMS CPU', action='store_true')
parser.add_argument('--threshold', help='Score threshold', default=0.5, type=float)
args = parser.parse_args()
model_dir = args.output
if tf.gfile.Exists(model_dir) == False:
tf.gfile.MkDir(model_dir)
if args.model:
config_path, checkpoint_path = download_detection_model(args.model, 'data')
elif args.path:
if tf.gfile.Exists(args.path) == False:
print('Error: Checkpoint dir dose note exist!')
return
config_path = os.path.join(args.path, 'pipeline.config')
checkpoint_path = os.path.join(args.path,'model.ckpt')
else:
print('Error: Either model or path is not specified in the argument.')
return
frozen_graph, input_names, output_names = build_detection_graph(
config=config_path,
force_nms_cpu=args.force_nms_cpu,
checkpoint=checkpoint_path,
batch_size=1
)
print(input_names, output_names)
base_name = os.path.splitext(os.path.basename(checkpoint_path))[0]
save_model_file_name = base_name + '_frozen.pb'
with open(os.path.join(model_dir, save_model_file_name), 'wb') as f:
f.write(frozen_graph.SerializeToString())
# base_name = os.path.splitext(os.path.basename(checkpoint_path))[0]
# save_model_file_name = base_name + '_frozen.pb'
# with open(os.path.join(model_dir, save_model_file_name), 'wb') as f:
# f.write(frozen_graph.SerializeToString())
converter = trt.TrtGraphConverter(
input_graph_def=frozen_graph,
nodes_blacklist=output_names, #output nodes
max_batch_size=1,
is_dynamic_op=False,
max_workspace_size_bytes = 1 << 25,
precision_mode=trt.TrtPrecisionMode.FP16, # trt.DEFAULT_TRT_MAX_WORKSPACE_SIZE_BYTES
minimum_segment_size=50)
trt_graph = converter.convert()
# trt_graph = trt.create_inference_graph(
# input_graph_def=frozen_graph,
# outputs=output_names,
# max_batch_size=1,
# max_workspace_size_bytes=1 << 25,
# precision_mode='FP16',
# minimum_segment_size=3
# )
trt_engine_opts = len([1 for n in trt_graph.node if str(n.op) == 'TRTEngineOp'])
print("trt_engine_opts = {}".format(trt_engine_opts))
base_name = os.path.splitext(os.path.basename(checkpoint_path))[0]
save_model_file_name = base_name + '_frozen_fp16.pb'
with open(os.path.join(model_dir, save_model_file_name), 'wb') as f:
f.write(trt_graph.SerializeToString())