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inference.py

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+import requests
+from PIL import Image
+
+from optimum.amd.ryzenai import RyzenAIModelForImageClassification
+from transformers import AutoFeatureExtractor, pipeline
+
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+quantized_model_path = "mohitsha/transformers-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)
+feature_extractor = AutoFeatureExtractor.from_pretrained(quantized_model_path)
+
+cls_pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
+outputs = cls_pipe(image)
+print(outputs)

quantize.py

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+from functools import partial
+
+from optimum.amd.ryzenai import (
+    AutoQuantizationConfig,
+    RyzenAIOnnxQuantizer,
+)
+from optimum.exporters.onnx import main_export
+from transformers import AutoFeatureExtractor
+
+
+# Define paths for exporting ONNX model and saving quantized model
+export_dir = "resnet_onnx"
+quantization_dir = "resnet_onnx_quantized"
+
+# Specify the model ID from Transformers
+model_id = "microsoft/resnet-18"
+
+# Step 1: Export the model to ONNX format using Optimum Exporters
+main_export(
+    model_name_or_path=model_id,
+    output=export_dir,
+    task="image-classification",
+    opset=13,
+    batch_size=1,
+    height=224,
+    width=224,
+    no_dynamic_axes=True,
+)
+
+# Step 2: Preprocess configuration and data transformations
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+
+def preprocess_fn(ex, feature_extractor):
+    image = ex["image"]
+    if image.mode == "L":
+        image = image.convert("RGB")
+    pixel_values = feature_extractor(image).pixel_values[0]
+    return {"pixel_values": pixel_values}
+
+
+# Step 3: Initialize the RyzenAIOnnxQuantizer with the exported model
+quantizer = RyzenAIOnnxQuantizer.from_pretrained(export_dir)
+
+# Step 4: Load recommended quantization config for model
+quantization_config = AutoQuantizationConfig.ipu_cnn_config()
+
+# Step 5: Obtain a calibration dataset for computing quantization parameters
+train_calibration_dataset = quantizer.get_calibration_dataset(
+    "imagenet-1k",
+    preprocess_function=partial(preprocess_fn, feature_extractor=feature_extractor),
+    num_samples=100,
+    dataset_split="train",
+    preprocess_batch=False,
+    streaming=True,
+)
+
+# Step 6: Run the quantizer with the specified configuration and calibration data
+quantizer.quantize(
+    quantization_config=quantization_config, dataset=train_calibration_dataset, save_dir=quantization_dir
+)