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onnx_to_trt.py
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onnx_to_trt.py
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# onnx_to_tensorrt.py
#
# Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
#
# NOTICE TO LICENSEE:
#
# This source code and/or documentation ("Licensed Deliverables") are
# subject to NVIDIA intellectual property rights under U.S. and
# international Copyright laws.
#
# These Licensed Deliverables contained herein is PROPRIETARY and
# CONFIDENTIAL to NVIDIA and is being provided under the terms and
# conditions of a form of NVIDIA software license agreement by and
# between NVIDIA and Licensee ("License Agreement") or electronically
# accepted by Licensee. Notwithstanding any terms or conditions to
# the contrary in the License Agreement, reproduction or disclosure
# of the Licensed Deliverables to any third party without the express
# written consent of NVIDIA is prohibited.
#
# NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
# LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
# SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
# PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
# NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
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# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
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# OF THESE LICENSED DELIVERABLES.
#
# U.S. Government End Users. These Licensed Deliverables are a
# "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
# 1995), consisting of "commercial computer software" and "commercial
# computer software documentation" as such terms are used in 48
# C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
# only as a commercial end item. Consistent with 48 C.F.R.12.212 and
# 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
# U.S. Government End Users acquire the Licensed Deliverables with
# only those rights set forth herein.
#
# Any use of the Licensed Deliverables in individual and commercial
# software must include, in the user documentation and internal
# comments to the code, the above Disclaimer and U.S. Government End
# Users Notice.
#
from __future__ import print_function
import argparse
import traceback
import sys
import tensorrt as trt
MAX_BATCH_SIZE = 1
def build_engine_from_onnx(model_name,
dtype,
verbose=False,
int8_calib=False,
calib_loader=None,
calib_cache=None,
fp32_layer_names=[],
fp16_layer_names=[],
):
"""Initialization routine."""
if dtype == "int8":
t_dtype = trt.DataType.INT8
elif dtype == "fp16":
t_dtype = trt.DataType.HALF
elif dtype == "fp32":
t_dtype = trt.DataType.FLOAT
else:
raise ValueError("Unsupported data type: %s" % dtype)
if trt.__version__[0] < '8':
print('Exit, trt.version should be >=8. Now your trt version is ', trt.__version__[0])
network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
if dtype == "int8" and calib_loader is None:
print('QAT enabled!')
network_flags = network_flags | (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION))
"""Build a TensorRT engine from ONNX"""
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger()
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(flags=network_flags) as network, \
trt.OnnxParser(network, TRT_LOGGER) as parser:
with open(model_name, 'rb') as model:
if not parser.parse(model.read()):
print('ERROR: ONNX Parse Failed')
for error in range(parser.num_errors):
print(parser.get_error(error))
return None
print('Building an engine. This would take a while...')
print('(Use "--verbose" or "-v" to enable verbose logging.)')
config = builder.create_builder_config()
config.max_workspace_size = 2 << 30
if t_dtype == trt.DataType.HALF:
config.flags |= 1 << int(trt.BuilderFlag.FP16)
if t_dtype == trt.DataType.INT8:
print('trt.DataType.INT8')
config.flags |= 1 << int(trt.BuilderFlag.INT8)
config.flags |= 1 << int(trt.BuilderFlag.FP16)
if int8_calib:
from calibrator import Calibrator
config.int8_calibrator = Calibrator(calib_loader, calib_cache)
print('Int8 calibation is enabled.')
engine = builder.build_engine(network, config)
try:
assert engine
except AssertionError:
_, _, tb = sys.exc_info()
traceback.print_tb(tb) # Fixed format
tb_info = traceback.extract_tb(tb)
_, line, _, text = tb_info[-1]
raise AssertionError(
"Parsing failed on line {} in statement {}".format(line, text)
)
return engine
def main():
"""Create a TensorRT engine for ONNX-based YOLO."""
parser = argparse.ArgumentParser()
parser.add_argument(
'-v', '--verbose', action='store_true',
help='enable verbose output (for debugging)')
parser.add_argument(
'-m', '--model', type=str, required=True,
help=('onnx model path'))
parser.add_argument(
'-d', '--dtype', type=str, required=True,
help='one type of int8, fp16, fp32')
parser.add_argument(
'--qat', action='store_true',
help='whether the onnx model is qat; if it is, the int8 calibrator is not needed')
# If enable int8(not post-QAT model), then set the following
parser.add_argument('--img-size', type=int,
default=640, help='image size of model input')
parser.add_argument('--batch-size', type=int,
default=128, help='batch size for training: default 64')
parser.add_argument('--num-calib-batch', default=6, type=int,
help='Number of batches for calibration')
parser.add_argument('--calib-img-dir', default='../coco/images/train2017', type=str,
help='Number of batches for calibration')
parser.add_argument('--calib-cache', default='./yolov6s_calibration.cache', type=str,
help='Path of calibration cache')
args = parser.parse_args()
if args.dtype == "int8" and not args.qat:
from calibrator import DataLoader, Calibrator
calib_loader = DataLoader(args.batch_size, args.num_calib_batch, args.calib_img_dir,
args.img_size, args.img_size)
engine = build_engine_from_onnx(args.model, args.dtype, args.verbose,
int8_calib=True, calib_loader=calib_loader, calib_cache=args.calib_cache)
else:
engine = build_engine_from_onnx(args.model, args.dtype, args.verbose)
if engine is None:
raise SystemExit('ERROR: failed to build the TensorRT engine!')
engine_path = args.model.replace('.onnx', '.trt')
if args.dtype == "int8" and not args.qat:
engine_path = args.model.replace('.onnx', '-int8-{}-{}-minmax.trt'.format(args.batch_size, args.num_calib_batch))
with open(engine_path, 'wb') as f:
f.write(engine.serialize())
print('Serialized the TensorRT engine to file: %s' % engine_path)
if __name__ == '__main__':
main()