INT8 Quantization with Post-training Optimization Tool (POT) in Simplified Mode tutorial¶

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS. To run without installing anything, click the launch binder button.

This tutorial shows how to quantize a ResNet20 image classification model, trained on CIFAR10 dataset, using the Post-Training Optimization Tool (POT) in Simplified Mode.

Simplified Mode is designed to make the data preparation step easier, before model optimization. The mode is represented by an implementation of the engine interface in the POT API in OpenVINO™. It enables reading data from an arbitrary folder specified by the user. Currently, Simplified Mode is available only for image data in PNG or JPEG formats, stored in a single folder.

NOTE: This mode cannot be used with the accuracy-aware method. It is not possible to control accuracy after optimization using this mode. However, Simplified Mode can be useful for estimating performance improvements when optimizing models.

This tutorial includes the following steps:

Downloading and saving the CIFAR10 dataset.
Preparing the model for quantization.
Compressing the prepared model.
Measuring and comparing the performance of the original and quantized models.
Demonstrating the use of the quantized model for image classification.

import os
from pathlib import Path
import warnings

import torch
from torchvision import transforms as T
from torchvision.datasets import CIFAR10

import matplotlib.pyplot as plt
import numpy as np

from openvino.runtime import Core, Tensor

warnings.filterwarnings("ignore")

# Set the data and model directories
MODEL_DIR = 'model'
CALIB_DIR = 'calib'
CIFAR_DIR = '../data/datasets/cifar10'
CALIB_SET_SIZE = 300
MODEL_NAME = 'resnet20'

os.makedirs(MODEL_DIR, exist_ok=True)
os.makedirs(CALIB_DIR, exist_ok=True)

Prepare the calibration dataset¶

The following steps are required to prepare the calibration dataset: - Download the CIFAR10 dataset from Torchvision.datasets repository. - Save the selected number of elements from this dataset as .png images in a separate folder.

transform = T.Compose([T.ToTensor()])
dataset = CIFAR10(root=CIFAR_DIR, train=False, transform=transform, download=True)

Files already downloaded and verified

pil_converter = T.ToPILImage(mode="RGB")

for idx, info in enumerate(dataset):
    im = info[0]
    if idx >= CALIB_SET_SIZE:
        break
    label = info[1]
    pil_converter(im.squeeze(0)).save(Path(CALIB_DIR) / f'{label}_{idx}.png')

Prepare the Model¶

Model preparation includes the following steps: - Download PyTorch model from Torchvision repository. - Convert the model to ONNX format. - Run Model Optimizer to convert ONNX to OpenVINO Intermediate Representation (OpenVINO IR).

model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar10_resnet20", pretrained=True, skip_validation=True)
dummy_input = torch.randn(1, 3, 32, 32)

onnx_model_path = Path(MODEL_DIR) / '{}.onnx'.format(MODEL_NAME)
ir_model_xml = onnx_model_path.with_suffix('.xml')
ir_model_bin = onnx_model_path.with_suffix('.bin')

torch.onnx.export(model, dummy_input, onnx_model_path)

Using cache found in /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master

Now, convert this model into the OpenVINO IR using Model Optimizer:

!mo -m $onnx_model_path  --output_dir $MODEL_DIR

Check for a new version of Intel(R) Distribution of OpenVINO(TM) toolkit here https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html?cid=other&source=prod&campid=ww_2023_bu_IOTG_OpenVINO-2022-3&content=upg_all&medium=organic or on https://github.com/openvinotoolkit/openvino
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/latest/openvino_2_0_transition_guide.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-408/.workspace/scm/ov-notebook/notebooks/114-quantization-simplified-mode/model/resnet20.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-408/.workspace/scm/ov-notebook/notebooks/114-quantization-simplified-mode/model/resnet20.bin

Compression stage¶

Compress the model with the following command:

pot -q default -m <path_to_xml> -w <path_to_bin> --engine simplified --data-source <path_to_data>

!pot -q default -m $ir_model_xml -w $ir_model_bin --engine simplified --data-source $CALIB_DIR --output-dir compressed --direct-dump --name $MODEL_NAME

/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-408/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/offline_transformations/__init__.py:10: FutureWarning: The module is private and following namespace offline_transformations will be removed in the future, use openvino.runtime.passes instead!
  warnings.warn(
INFO:openvino.tools.pot.app.run:Output log dir: compressed
INFO:openvino.tools.pot.app.run:Creating pipeline:
 Algorithm: DefaultQuantization
 Parameters:
    preset                     : performance
    stat_subset_size           : 300
    target_device              : ANY
    model_type                 : None
    dump_intermediate_model    : False
    inplace_statistics         : True
    exec_log_dir               : compressed
 ===========================================================================
INFO:openvino.tools.pot.data_loaders.image_loader:Layout value is set [N,C,H,W]
INFO:openvino.tools.pot.pipeline.pipeline:Inference Engine version:                2022.3.0-9052-9752fafe8eb-releases/2022/3
INFO:openvino.tools.pot.pipeline.pipeline:Model Optimizer version:                 2022.3.0-9052-9752fafe8eb-releases/2022/3
INFO:openvino.tools.pot.pipeline.pipeline:Post-Training Optimization Tool version: 2022.3.0-9052-9752fafe8eb-releases/2022/3
INFO:openvino.tools.pot.statistics.collector:Start computing statistics for algorithms : DefaultQuantization
INFO:openvino.tools.pot.statistics.collector:Computing statistics finished
INFO:openvino.tools.pot.pipeline.pipeline:Start algorithm: DefaultQuantization
INFO:openvino.tools.pot.algorithms.quantization.default.algorithm:Start computing statistics for algorithm : ActivationChannelAlignment
INFO:openvino.tools.pot.algorithms.quantization.default.algorithm:Computing statistics finished
INFO:openvino.tools.pot.algorithms.quantization.default.algorithm:Start computing statistics for algorithms : MinMaxQuantization,FastBiasCorrection
INFO:openvino.tools.pot.algorithms.quantization.default.algorithm:Computing statistics finished
INFO:openvino.tools.pot.pipeline.pipeline:Finished: DefaultQuantization
 ===========================================================================

Compare Performance of the Original and Quantized Models¶

Finally, measure the inference performance of the FP32 and INT8 models, using Benchmark Tool - an inference performance measurement tool in OpenVINO.

NOTE: For more accurate performance, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications. Run benchmark_app -m model.xml -d CPU to benchmark async inference on CPU for one minute. Change CPU to GPU to benchmark on GPU. Run benchmark_app --help to see an overview of all command-line options.

optimized_model_path = Path('compressed/optimized')
optimized_model_xml = optimized_model_path / '{}.xml'.format(MODEL_NAME)
optimized_model_bin = optimized_model_path / '{}.bin'.format(MODEL_NAME)

# Inference FP32 model (OpenVINO IR)
!benchmark_app -m $ir_model_xml -d CPU -api async

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 5.99 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     input.1 (node: input.1) : f32 / [...] / [1,3,32,32]
[ INFO ] Model outputs:
[ INFO ]     208 (node: 208) : f32 / [...] / [1,10]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     input.1 (node: input.1) : u8 / [N,C,H,W] / [1,3,32,32]
[ INFO ] Model outputs:
[ INFO ]     208 (node: 208) : f32 / [...] / [1,10]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 77.50 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: torch_jit
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]   NUM_STREAMS: 12
[ INFO ]   AFFINITY: Affinity.CORE
[ INFO ]   INFERENCE_NUM_THREADS: 24
[ INFO ]   PERF_COUNT: False
[ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'input.1'!. This input will be filled with random values!
[ INFO ] Fill input 'input.1' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 60000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 1.06 ms
[Step 11/11] Dumping statistics report
[ INFO ] Count:            967416 iterations
[ INFO ] Duration:         60000.59 ms
[ INFO ] Latency:
[ INFO ]    Median:        0.69 ms
[ INFO ]    Average:       0.71 ms
[ INFO ]    Min:           0.41 ms
[ INFO ]    Max:           4.74 ms
[ INFO ] Throughput:   16123.44 FPS

# Inference INT8 model (OpenVINO IR)
!benchmark_app -m $optimized_model_xml -d CPU -api async

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 21.56 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     input.1 (node: input.1) : f32 / [...] / [1,3,32,32]
[ INFO ] Model outputs:
[ INFO ]     208 (node: 208) : f32 / [...] / [1,10]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     input.1 (node: input.1) : u8 / [N,C,H,W] / [1,3,32,32]
[ INFO ] Model outputs:
[ INFO ]     208 (node: 208) : f32 / [...] / [1,10]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 121.78 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: torch_jit
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]   NUM_STREAMS: 12
[ INFO ]   AFFINITY: Affinity.CORE
[ INFO ]   INFERENCE_NUM_THREADS: 24
[ INFO ]   PERF_COUNT: False
[ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'input.1'!. This input will be filled with random values!
[ INFO ] Fill input 'input.1' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 60000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 0.71 ms
[Step 11/11] Dumping statistics report
[ INFO ] Count:            1590900 iterations
[ INFO ] Duration:         60000.64 ms
[ INFO ] Latency:
[ INFO ]    Median:        0.35 ms
[ INFO ]    Average:       0.36 ms
[ INFO ]    Min:           0.22 ms
[ INFO ]    Max:           9.05 ms
[ INFO ] Throughput:   26514.72 FPS

Demonstration of the results¶

This section demonstrates how to use the compressed model by running the optimized model on a subset of images from the CIFAR10 dataset and shows predictions, using the model.

The first step is to load the model:

ie = Core()

compiled_model = ie.compile_model(str(optimized_model_xml))

# Define all possible labels from the CIFAR10 dataset.
labels_names = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
all_images = []
all_labels = []

# Get all images and their labels.
for batch in dataset:
    all_images.append(torch.unsqueeze(batch[0], 0))
    all_labels.append(batch[1])

The code below defines the function that shows the images and their labels, using the indexes and two lists created in the previous step:

def plot_pictures(indexes: list, images=all_images, labels=all_labels):
    """Plot images with the specified indexes.
    :param indexes: a list of indexes of images to be displayed.
    :param images: a list of images from the dataset.
    :param labels: a list of labels for each image.
    """
    num_pics = len(indexes)
    _, axarr = plt.subplots(1, num_pics)
    for idx, im_idx in enumerate(indexes):
        assert idx < 10000, 'Cannot get such index, there are only 10000'
        pic = np.rollaxis(images[im_idx].squeeze().numpy(), 0, 3)
        axarr[idx].imshow(pic)
        axarr[idx].set_title(labels_names[labels[im_idx]])

Use the code below, to define a function that uses the optimized model to obtain predictions for the selected images:

def infer_on_images(net, indexes: list, images=all_images):
    """ Inference model on a set of images.
    :param net: model on which do inference
    :param indexes: a list of indexes of images to infer on.
    :param images: a list of images from the dataset.
    """
    predicted_labels = []
    infer_request = net.create_infer_request()
    for idx in indexes:
        assert idx < 10000, 'Cannot get such index, there are only 10000'
        input_tensor = Tensor(array=images[idx].detach().numpy(), shared_memory=True)
        infer_request.set_input_tensor(input_tensor)
        infer_request.start_async()
        infer_request.wait()
        output = infer_request.get_output_tensor()
        result = list(output.data)
        result = labels_names[np.argmax(result[0])]
        predicted_labels.append(result)
    return predicted_labels

indexes_to_infer = [0, 1, 2]  # to plot specify indexes

plot_pictures(indexes_to_infer)

results_quanized = infer_on_images(compiled_model, indexes_to_infer)

print(f"Image labels using the quantized model : {results_quanized}.")

Image labels using the quantized model : ['cat', 'ship', 'ship'].

../_images/114-quantization-simplified-mode-with-output_22_1.png