inference.py

import requests
from PIL import Image

from optimum.amd.ryzenai import RyzenAIModelForImageClassification
from transformers import PretrainedConfig, pipeline

import timm
import torch

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

quantized_model_path = "mohitsha/timm-resnet18-onnx-quantized-ryzen"

# The path and name of the runtime configuration file. A default version of this file can be
# found in the voe-4.0-win_​amd64 folder of the Ryzen AI software installation package under
# the name vaip_​config.json
vaip_config = ".\\vaip_config.json"

model = RyzenAIModelForImageClassification.from_pretrained(quantized_model_path, vaip_config=vaip_config)

config = PretrainedConfig.from_pretrained(quantized_model_path)

# preprocess config
data_config = timm.data.resolve_data_config(pretrained_cfg=config.pretrained_cfg)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(image).unsqueeze(0)).logits  # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(torch.softmax(output, dim=1) * 100, k=5)

print(top5_class_indices)