infer_onnx.py

#!/usr/bin/env python3
# -*- coding:utf-8 -*-

# Copyright 2023 Advanced Micro Devices, Inc. on behalf of itself and its subsidiaries and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Copyright (c) Megvii, Inc. and its affiliates.

import onnxruntime
import argparse
from PIL import Image 
import torchvision.transforms as transforms 

parser = argparse.ArgumentParser()
parser.add_argument('--onnx_path', type=str, default="EfficientNet_int.onnx", required=False)
parser.add_argument('--image_path', type=str, required=True)
parser.add_argument(
    "--ipu",
    action="store_true",
    help="Use IPU for inference.",
)
parser.add_argument(
    "--provider_config",
    type=str,
    default="vaip_config.json",
    help="Path of the config file for seting provider_options.",
)
parser.add_argument('--data_format', type=str, choices=["nchw", "nhwc"], default="nchw")
args = parser.parse_args()


def read_image():
  # Read a PIL image 
  image = Image.open(args.image_path)
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  
  transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((224, 224)),
    normalize,
  ])
  img_tensor = transform(image).unsqueeze(0)
  if args.data_format == "nhwc":
    img_tensor = transform(image).unsqueeze(0).permute((0, 2, 3, 1))
  return img_tensor.numpy()


def main():
  if args.ipu:
    providers = ["VitisAIExecutionProvider"]
    provider_options = [{"config_file": args.provider_config}]
  else:
    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
    provider_options = None
  ort_session = onnxruntime.InferenceSession(
    args.onnx_path, providers=providers, provider_options=provider_options)
  ort_inputs = {
    ort_session.get_inputs()[0].name: read_image()
  }
  output = ort_session.run(None, ort_inputs)[0]
  print("class id =", output[0].argmax())


if __name__ == "__main__":
  main()