Quark ONNX Quantization Tutorial For Image Classification

Quark ONNX Quantization Tutorial For Image Classification#

In this tutorial, you will learn how to use AMD Quark, a lightweight and high-performance quantization framework, to optimize a resnet50-v1-12 model with crypto model. It’s designed for the quantization of highly confidential models. In this mode, all model exchanges with the quantizer are performed entirely via ONNX model protobuf that is stored in memory, instead of relying on path-based files in disk. No model-related data is saved to disk during processing (If any temporary storage is absolutely necessary, the data will be encrypted using AES-256). Furthermore, no model details are logged or printed to the screen, ensuring strict confidentiality of all model information throughout the workflow.

The example has the following parts:

  • Install requirements

  • Prepare model

  • Prepare data

  • Quantizatize Model

  • Evaluate Model

1) Install The Necessary Python Packages:#

In addition to Quark that must be installed as documented at here, extra packages are require for this tutorial.

%pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
%pip install "cmake<4.0" amd-quark
%pip install -r ./requirements.txt

2) Download resnet50-v1-12 Model#

The model is publicly available and can be downloaded from this link: onnx/models

!mkdir -p models
!wget -O models/resnet50-v1-12.onnx https://github.com/onnx/models/raw/refs/heads/new-models/vision/classification/resnet/model/resnet50-v1-12.onnx

3) Prepare data#

ILSVRC 2012, commonly known as ‘ImageNet’. This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 50,000 validation images.

If you already have an ImageNet datasets, you can directly use your dataset path.

To prepare the test data, please check the download section of the main website: https://huggingface.co/datasets/imagenet-1k/tree/main/data. You need to register and download val_images.tar.gz to the current directory.

Then, create a val_data folder and decompress the .gz file to the folder.

!mkdir -p val_data && tar -xzf val_images.tar.gz -C val_data

If you have a local cache to store the dataset, you can use and environment variable like LOCAL_DATA_CACHE to specify its path. This is useful to organize and store all your datasets for different experiments in a central place. Otherwise, the current folder is used, and validation dataset and calibration dataset will be created under current directory.

import os
import shutil
import sys

source_folder = "val_data"
calib_data_path = "calib_data"

if os.environ.get("LOCAL_DATA_CACHE") is not None:
    data_path = os.environ["LOCAL_DATA_CACHE"]
    source_folder = os.path.join(data_path, "Imagenet/val")
    calib_data_path = os.path.join(data_path, "Imagenet/calib_100")
else:
    files = os.listdir(source_folder)

    for filename in files:
        if not filename.startswith("ILSVRC2012_val_") or not filename.endswith(".JPEG"):
            continue

        n_identifier = filename.split("_")[-1].split(".")[0]
        folder_name = n_identifier
        folder_path = os.path.join(source_folder, folder_name)
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)
        file_path = os.path.join(source_folder, filename)
        destination = os.path.join(folder_path, filename)
        shutil.move(file_path, destination)

    print("File organization complete.")

    if not os.path.exists(calib_data_path):
        os.makedirs(calib_data_path)

    destination_folder = calib_data_path

    subfolders = os.listdir(source_folder)

    for subfolder in subfolders:
        source_subfolder = os.path.join(source_folder, subfolder)
        destination_subfolder = os.path.join(destination_folder, subfolder)
        os.makedirs(destination_subfolder, exist_ok=True)
        files = os.listdir(source_subfolder)

        if files:
            file_to_copy = files[0]
            source_file = os.path.join(source_subfolder, file_to_copy)
            destination_file = os.path.join(destination_subfolder, file_to_copy)

            shutil.copy(source_file, destination_file)

    print("Creating calibration dataset complete.")

if not os.path.exists(source_folder):
    print("The provided data path does not exist.")
    sys.exit(1)

The storage format of the val_data of the ImageNet dataset organized as follows:

  • val_data

    • n01440764

      • ILSVRC2012_val_00000293.JPEG

      • ILSVRC2012_val_00002138.JPEG

      • …

    • n01443537

      • ILSVRC2012_val_00000236.JPEG

      • ILSVRC2012_val_00000262.JPEG

      • …

    • …

The storage format of the calib_data of the ImageNet dataset organized as follows:

  • calib_data

    • n01440764

      • ILSVRC2012_val_00000293.JPEG

    • n01443537

      • ILSVRC2012_val_00000236.JPEG

    • …

4) Quantization Procedure#

First, create a data reader that gathers calibration statistics from the target dataset. Next, inside quantize_model, constructing the quantized model and passing in your configuration. This example uses XINT8 config to demonstrate the workflow. For more configurations, please go to https://quark.docs.amd.com/latest/onnx/user_guide_config_description.html

import os
import time

import numpy as np
import onnxruntime
import torch
import torchvision
from onnxruntime.quantization.calibrate import CalibrationDataReader
from PIL import Image
from torchvision import transforms

from quark.onnx import ModelQuantizer, QConfig
from quark.onnx.operators.custom_ops import get_library_path


def _preprocess_images(images_folder: str, height: int, width: int, size_limit=0, batch_size=100):
    """
    Loads a batch of images and preprocess them
    parameter images_folder: path to folder storing images
    parameter height: image height in pixels
    parameter width: image width in pixels
    parameter size_limit: number of images to load. Default is 0 which means all images are picked.
    return: list of matrices characterizing multiple images
    """
    image_path = os.listdir(images_folder)
    image_names = []
    for image_dir in image_path:
        image_name = os.listdir(os.path.join(images_folder, image_dir))
        image_names.append(os.path.join(image_dir, image_name[0]))
    if size_limit > 0 and len(image_names) >= size_limit:
        batch_filenames = [image_names[i] for i in range(size_limit)]
    else:
        batch_filenames = image_names
    unconcatenated_batch_data = []

    batch_data = []
    for index, image_name in enumerate(batch_filenames):
        image_filepath = images_folder + "/" + image_name
        pillow_img = Image.new("RGB", (width, height))
        pillow_img.paste(Image.open(image_filepath).resize((width, height)))
        image_array = np.array(pillow_img) / 255.0
        mean = np.array([0.485, 0.456, 0.406])
        image_array = image_array - mean
        std = np.array([0.229, 0.224, 0.225])
        nchw_data = image_array / std
        nchw_data = nchw_data.transpose((2, 0, 1))
        nchw_data = np.expand_dims(nchw_data, axis=0)
        nchw_data = nchw_data.astype(np.float32)
        unconcatenated_batch_data.append(nchw_data)

        if (index + 1) % batch_size == 0:
            one_batch_data = np.concatenate(unconcatenated_batch_data, axis=0)
            unconcatenated_batch_data.clear()
            batch_data.append(one_batch_data)

    return batch_data


class ImageDataReader(CalibrationDataReader):
    def __init__(self, calibration_image_folder: str, model_path: str, data_size: int, batch_size: int):
        self.enum_data = None

        # Use inference session to get input shape.
        session = onnxruntime.InferenceSession(model_path, providers=["CPUExecutionProvider"])
        (_, _, height, width) = session.get_inputs()[0].shape

        # Convert image to input data
        self.nhwc_data_list = _preprocess_images(calibration_image_folder, height, width, data_size, batch_size)
        self.input_name = session.get_inputs()[0].name
        self.datasize = len(self.nhwc_data_list)

    def get_next(self):
        if self.enum_data is None:
            self.enum_data = iter([{self.input_name: nhwc_data} for nhwc_data in self.nhwc_data_list])
        return next(self.enum_data, None)

    def rewind(self):
        self.enum_data = None

    def reset(self):
        self.enum_data = None

def quantize_model(args):
    dr = ImageDataReader(
        args["calibration_dataset_path"], args["input_model_path"], args["num_calib_data"], args["batch_size"]
    )

    # Get quantization configuration
    config = QConfig.get_default_config(args["config"])
    print(f"The configuration for quantization is {config}")

    # Create an ONNX quantizer
    quantizer = ModelQuantizer(config)

    # Quantize the ONNX model
    quantizer.quantize_model(args["input_model_path"], args["output_model_path"], dr)

Define your configuration, then perform quantization.

quant_config = {
    "input_model_path": "models/resnet50-v1-12.onnx",
    "output_model_path": "models/resnet50-v1-12_quantized.onnx",
    "calibration_dataset_path": calib_data_path,
    "config": "XINT8",
    "num_calib_data": 1000,
    "batch_size": 1,
    "device": "cpu",
}

quantize_model(quant_config)

5) Evaluation and Expected Results#

Evaluation is performed on the ImageNet validation set. We compare two models — (1) full-precision model and (2) quantized model — to assess quantized model’s effectiveness. The full-precision model serves as the baseline for measuring any accuracy change caused by quantization.

ImageNet has 1,000 classes, so we report both Prec@1 and Prec@5 to capture strict and relaxed accuracy. Both metrics are reported as percentages (higher is better). Prec@1 shows exact single-label correctness; Prec@5 is useful on large, fine-grained label spaces because it captures near-misses where the correct class is among the model’s top candidates.

class AverageMeter:
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def load_loader(data_dir, batch_size, workers):
    data_transform = transforms.Compose(
        [
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )
    dataset = torchvision.datasets.ImageFolder(data_dir, data_transform)
    data_loader = torch.utils.data.DataLoader(
        dataset, batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True
    )
    return data_loader


def accuracy_np(output, target):
    max_indices = np.argsort(output, axis=1)[:, ::-1]
    top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(axis=1).mean()
    top1 = 100 * np.equal(max_indices[:, 0], target).mean()
    return top1, top5


def metrics(onnx_model_path, sess_options, providers, data_loader, print_freq):
    session = onnxruntime.InferenceSession(onnx_model_path, sess_options, providers=providers)
    input_name = session.get_inputs()[0].name

    batch_time = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    end = time.time()
    for i, (input, target) in enumerate(data_loader):
        # run the net and return prediction
        output = session.run([], {input_name: input.data.numpy()})
        output = output[0]

        # measure accuracy and record loss
        prec1, prec5 = accuracy_np(output, target.numpy())
        top1.update(prec1.item(), input.size(0))
        top5.update(prec5.item(), input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % print_freq == 0:
            print(
                f"Test: [{i}/{len(data_loader)}]\t"
                f"Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {input.size(0) / batch_time.avg:.3f}/s, "
                f"{100 * batch_time.avg / input.size(0):.3f} ms/sample) \t"
                f"Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t"
                f"Prec@5 {top5.val:.3f} ({top5.avg:.3f})"
            )

    return top1, top5


def evaluate(args: dict):
    args["gpu_id"] = 0

    # Set graph optimization level
    sess_options = onnxruntime.SessionOptions()
    sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
    if args.get("profile"):
        sess_options.enable_profiling = True
    if args.get("onnx_output_opt"):
        sess_options.optimized_model_filepath = args["onnx_output_opt"]
    if args.get("gpu"):
        if "ROCMExecutionProvider" in onnxruntime.get_available_providers():
            device = "ROCM"
            providers = ["ROCMExecutionProvider"]
        elif "CUDAExecutionProvider" in onnxruntime.get_available_providers():
            device = "CUDA"
            providers = ["CUDAExecutionProvider"]
        else:
            device = "CPU"
            providers = ["CPUExecutionProvider"]
            print("Warning: GPU is not available, use CPU instead.")
    else:
        device = "CPU"
        providers = ["CPUExecutionProvider"]
    sess_options.register_custom_ops_library(get_library_path(device))

    if args.get("onnx_input"):
        val_loader = load_loader(args["data"], args["batch_size"], args["workers"])
        f_top1, f_top5 = metrics(args["onnx_input"], sess_options, providers, val_loader, args["print_freq"])
        print(f" * Prec@1 {f_top1.avg:.3f} ({100 - f_top1.avg:.3f}) Prec@5 {f_top5.avg:.3f} ({100.0 - f_top5.avg:.3f})")
    elif args.get("onnx_float") and args.get("onnx_quant"):
        val_loader = load_loader(args["data"], args["batch_size"], args["workers"])
        f_top1, f_top5 = metrics(args["onnx_float"], sess_options, providers, val_loader, args["print_freq"])
        f_top1 = format(f_top1.avg, ".2f")
        f_top5 = format(f_top5.avg, ".2f")

        q_top1, q_top5 = metrics(args["onnx_quant"], sess_options, providers, val_loader, args["print_freq"])
        q_top1 = format(q_top1.avg, ".2f")
        q_top5 = format(q_top5.avg, ".2f")

        f_size = format(os.path.getsize(args["onnx_float"]) / (1024 * 1024), ".2f")
        q_size = format(os.path.getsize(args["onnx_quant"]) / (1024 * 1024), ".2f")
        """
        --------------------------------------------------------
        |             | float model    | quantized model |
        --------------------------------------------------------
        | ****        | ****           | ****             |
        --------------------------------------------------------
        | Model Size  | ****           | ****             |
        --------------------------------------------------------
        """
        from rich.console import Console
        from rich.table import Table

        console = Console()

        table = Table()
        table.add_column("")
        table.add_column("Float Model")
        table.add_column("Quantized Model", style="bold green1")

        table.add_row("Model", args["onnx_float"], args["onnx_quant"])
        table.add_row("Model Size", str(f_size) + " MB", str(q_size) + " MB")
        table.add_row("Prec@1", str(f_top1) + " %", str(q_top1) + " %")
        table.add_row("Prec@5", str(f_top5) + " %", str(q_top5) + " %")

        console.print(table)

    else:
        print("Please specify both model-float and model-quant or model-input for evaluation.")

First, define an evaluation config, and record accuracy of the Full Precision model on ImageNet val dataset

import copy

eval_config = {
    "data": source_folder,
    "batch_size": 100,
    "workers": 1,
    "gpu": False,
    "print_freq": 1000,
}

full_precision_eval_config = copy.deepcopy(eval_config)
full_precision_eval_config["onnx_input"] = "models/resnet50-v1-12.onnx"

evaluate(full_precision_eval_config)

Then, specify the path to the quantized model and record its accuracy on ImageNet val dataset

quant_eval_config = copy.deepcopy(eval_config)
quant_eval_config["onnx_input"] = "models/resnet50-v1-12_quantized.onnx"

evaluate(quant_eval_config)

The following table contains the expected results, but please note that different machines can lead to minor variations in the accuracy of quantized model.

Float Model

Quantized Model

Model Size

97.82 MB

25.62 MB

Prec@1

74.114 %

73.562 %

Prec@5

91.716 %

91.420 %