Thank you for replying, I am using Jetson Tx2 with jetpack 4.3. Below are additional details.
Linux distro and version:
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=18.04
DISTRIB_CODENAME=bionic
DISTRIB_DESCRIPTION=“Ubuntu 18.04.4 LTS”
NAME=“Ubuntu”
VERSION=“18.04.4 LTS (Bionic Beaver)”
Nvidia driver version
CUDA version:
10.0.326
cudNN version:
7.6.3
Python 3.6.9
TensorRT 6.0.1
Mxnet: 1.6.0
Gluoncv: 0.7.0
Code (to create onnx file):
‘’’
import mxnet as mx
import numpy as np
from mxnet.contrib import onnx as onnx_mxnet
import logging
import gluoncv as gcv
from gluoncv.utils import export_block
logging.basicConfig(level=logging.INFO)
net = gcv.model_zoo.get_model(‘cifar_resnet110_v1’, classes=10, pretrained=True)
export_block(‘cifar_resnet110_v1’, net, preprocess=True)
import glob
print(glob.glob(‘.json’) + glob.glob('.params’))
We will use the downloaded pre-trained model files (sym, params) and define input variables.
Downloaded input symbol and params files
sym = ‘./cifar_resnet110_v1-symbol.json’
params = ‘./cifar_resnet110_v1-0000.params’
input_shape = (1, 32, 32, 3)
Path of the output file
onnx_file = ‘./cifar_resnet110_v1.onnx’
Invoke export model API. It returns path of the converted onnx model
converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
from onnx import checker
import onnx
Load onnx model
model_proto = onnx.load_model(converted_model_path)
Check if converted ONNX protobuf is valid
checker.check_graph(model_proto.graph)
‘’’
Code (to create tensorrt engine file):
‘’’
import tensorrt.legacy as trt
import numpy as np
import time
from mxnet import gluon, nd, image
from mxnet.gluon.data.vision import transforms
from gluoncv import utils
import glob
model_path = ‘/home/sapphire/Desktop/cifar_resnet110_v1.onnx’
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
def build_engine(model_path):
with trt.Builder(TRT_LOGGER) as builder,
builder.create_network() as network,
trt.OnnxParser(network, TRT_LOGGER) as parser:
builder.max_workspace_size = 1<<25
builder.max_batch_size = 1
with open(model_path, “rb”) as f:
parser.parse(f.read())
engine = builder.build_cuda_engine(network)
return engine
engine = build_engine(model_path)
trt.utils.write_engine_to_file(‘cifar_resnet110_v1.engine’, engine.serialize())
glob.glob(“*.engine”)
print(‘done’)
‘’’
Code (to make inference with engine):
‘’’
from tensorrt.legacy.lite import Engine
from tensorrt.legacy.infer import LogSeverity
import numpy as np
from mxnet import gluon, nd, image
from mxnet.gluon.data.vision import transforms
from gluoncv import utils
import tensorrt.legacy as trt
import pycuda.autoinit
TRT_LOGGER =trt.Logger(trt.Logger.WARNING)
url = ‘https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/classification/plane-draw.jpeg’
im_fname = utils.download(url)
img = image.imread(im_fname)
def transform(img):
transform_fn = transforms.Compose([
transforms.Resize(32),
transforms.CenterCrop(32),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
return transform_fn(img)
engine = Engine(PLAN=‘cifar_resnet110_v1.engine’)
img = transform(img)
pred = engine.infer(img.expand_dims(axis=0))
print(pred)
class_names = [‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,
‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’]
ind = nd.argmax(pred, axis=1).astype(‘int’)
print(‘The input picture is classified as [%s], with probability %.3f.’% (class_names[ind.asscalar()], nd.softmax(pred)[0][ind].asscalar()))
‘’’
I am still trying to understand how to utilise tensorrt, so pls I would greatly appreciate any feedback and advice if I am using it wrongly. Thank you
