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| #!/usr/bin/env python
# coding=utf-8
import sys
import os
import argparse
import tensorrt as trt
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
parser.add_argument("--onnx_path", default="", dest="onnx_path", help="path to onnx file", type=str)
parser.add_argument("--trt_path", default="", dest="trt_path", help="path to trt file", type=str)
args = parser.parse_args()
onnx_file_path = args.onnx_path
engine_file_path = args.trt_path
print('get start')
TRT_LOGGER = trt.Logger()
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
config = builder.create_builder_config()
#builder.max_workspace_size =( 1 << 30 ) * 2
config.max_workspace_size =( 1 << 20 ) * 3 * 1024 # 3GB，可以根据需求改的更大
builder.max_batch_size = 128
config.set_flag(trt.BuilderFlag.FP16)
#builder.fp16_mode = True
# Parse model file
if not os.path.exists(onnx_file_path):
print('ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.'.format(onnx_file_path))
exit(0)
print('Loading ONNX file from path {}...'.format(onnx_file_path))
with open(onnx_file_path, 'rb') as model:
print('Beginning ONNX file parsing')
if not parser.parse(model.read()):
print ('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
print(f"raw shape of {network.get_input(0).name} is: ", network.get_input(0).shape)
# network.get_input(0).shape = [-1, 3, 32, -1] #dynamic model example
for i in range(1):
profile = builder.create_optimization_profile()
# 最小值 常规值 最大值
profile.set_shape(network.get_input(0).name, (1, 3, 32, 32), (16, 3, 32, 320), (128, 3, 32, 960))
config.add_optimization_profile(profile)
print('Completed parsing of ONNX file')
print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))
engine = builder.build_engine(network,config)
print("Completed creating Engine")
with open(engine_file_path, "wb") as f:
f.write(engine.serialize())
|