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timm-resnet18-onnx-quantized-ryzen / inference.py

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Update inference.py

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	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)