Quantization of Image Classification Models¶
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 demonstrates how to apply INT8 quantization to Image
Classification model using Post-training Optimization Tool
API. It also assumes that OpenVINO™
is already installed and it uses the Mobilenet V2 model, trained on
Cifar10 dataset. The code is designed to be extendable to custom models
and datasets.
This tutorial consists of the following steps: - Prepare the model for quantization. - Define a data loading and an accuracy validation functionality. - Run optimization pipeline. - Compare accuracy of the original and quantized models. - Compare performance of the original and quantized models. - Compare results on one picture.
import os
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from addict import Dict
from openvino.tools.pot.api import DataLoader, Metric
from openvino.tools.pot.engines.ie_engine import IEEngine
from openvino.tools.pot.graph import load_model, save_model
from openvino.tools.pot.graph.model_utils import compress_model_weights
from openvino.tools.pot.pipeline.initializer import create_pipeline
from openvino.runtime import Core
from torchvision import transforms
from torchvision.datasets import CIFAR10
# Set the data and model directories
DATA_DIR = 'data'
MODEL_DIR = 'model'
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(MODEL_DIR, exist_ok=True)
Prepare the Model¶
Model preparation stage has the following steps: - Download a PyTorch model from Torchvision repository - Convert it to ONNX format - Run Model Optimizer to convert ONNX to OpenVINO Intermediate Representation (OpenVINO IR)
model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar10_mobilenetv2_x1_0", pretrained=True)
model.eval()
dummy_input = torch.randn(1, 3, 32, 32)
onnx_model_path = Path(MODEL_DIR) / 'mobilenet_v2.onnx'
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, verbose=True)
# Run Model Optimizer to convert ONNX to OpenVINO IR.
!mo --framework=onnx --data_type=FP16 --input_shape=[1,3,32,32] -m $onnx_model_path --output_dir $MODEL_DIR
Using cache found in /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master
graph(%input.1 : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu),
%classifier.1.weight : Float(10, 1280, strides=[1280, 1], requires_grad=1, device=cpu),
%classifier.1.bias : Float(10, strides=[1], requires_grad=1, device=cpu),
%468 : Float(32, 3, 3, 3, strides=[27, 9, 3, 1], requires_grad=0, device=cpu),
%469 : Float(32, strides=[1], requires_grad=0, device=cpu),
%471 : Float(32, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%472 : Float(32, strides=[1], requires_grad=0, device=cpu),
%474 : Float(16, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=0, device=cpu),
%475 : Float(16, strides=[1], requires_grad=0, device=cpu),
%477 : Float(96, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=0, device=cpu),
%478 : Float(96, strides=[1], requires_grad=0, device=cpu),
%480 : Float(96, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%481 : Float(96, strides=[1], requires_grad=0, device=cpu),
%483 : Float(24, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=0, device=cpu),
%484 : Float(24, strides=[1], requires_grad=0, device=cpu),
%486 : Float(144, 24, 1, 1, strides=[24, 1, 1, 1], requires_grad=0, device=cpu),
%487 : Float(144, strides=[1], requires_grad=0, device=cpu),
%489 : Float(144, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%490 : Float(144, strides=[1], requires_grad=0, device=cpu),
%492 : Float(24, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=0, device=cpu),
%493 : Float(24, strides=[1], requires_grad=0, device=cpu),
%495 : Float(144, 24, 1, 1, strides=[24, 1, 1, 1], requires_grad=0, device=cpu),
%496 : Float(144, strides=[1], requires_grad=0, device=cpu),
%498 : Float(144, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%499 : Float(144, strides=[1], requires_grad=0, device=cpu),
%501 : Float(32, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=0, device=cpu),
%502 : Float(32, strides=[1], requires_grad=0, device=cpu),
%504 : Float(192, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=0, device=cpu),
%505 : Float(192, strides=[1], requires_grad=0, device=cpu),
%507 : Float(192, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%508 : Float(192, strides=[1], requires_grad=0, device=cpu),
%510 : Float(32, 192, 1, 1, strides=[192, 1, 1, 1], requires_grad=0, device=cpu),
%511 : Float(32, strides=[1], requires_grad=0, device=cpu),
%513 : Float(192, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=0, device=cpu),
%514 : Float(192, strides=[1], requires_grad=0, device=cpu),
%516 : Float(192, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%517 : Float(192, strides=[1], requires_grad=0, device=cpu),
%519 : Float(32, 192, 1, 1, strides=[192, 1, 1, 1], requires_grad=0, device=cpu),
%520 : Float(32, strides=[1], requires_grad=0, device=cpu),
%522 : Float(192, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=0, device=cpu),
%523 : Float(192, strides=[1], requires_grad=0, device=cpu),
%525 : Float(192, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%526 : Float(192, strides=[1], requires_grad=0, device=cpu),
%528 : Float(64, 192, 1, 1, strides=[192, 1, 1, 1], requires_grad=0, device=cpu),
%529 : Float(64, strides=[1], requires_grad=0, device=cpu),
%531 : Float(384, 64, 1, 1, strides=[64, 1, 1, 1], requires_grad=0, device=cpu),
%532 : Float(384, strides=[1], requires_grad=0, device=cpu),
%534 : Float(384, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%535 : Float(384, strides=[1], requires_grad=0, device=cpu),
%537 : Float(64, 384, 1, 1, strides=[384, 1, 1, 1], requires_grad=0, device=cpu),
%538 : Float(64, strides=[1], requires_grad=0, device=cpu),
%540 : Float(384, 64, 1, 1, strides=[64, 1, 1, 1], requires_grad=0, device=cpu),
%541 : Float(384, strides=[1], requires_grad=0, device=cpu),
%543 : Float(384, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%544 : Float(384, strides=[1], requires_grad=0, device=cpu),
%546 : Float(64, 384, 1, 1, strides=[384, 1, 1, 1], requires_grad=0, device=cpu),
%547 : Float(64, strides=[1], requires_grad=0, device=cpu),
%549 : Float(384, 64, 1, 1, strides=[64, 1, 1, 1], requires_grad=0, device=cpu),
%550 : Float(384, strides=[1], requires_grad=0, device=cpu),
%552 : Float(384, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%553 : Float(384, strides=[1], requires_grad=0, device=cpu),
%555 : Float(64, 384, 1, 1, strides=[384, 1, 1, 1], requires_grad=0, device=cpu),
%556 : Float(64, strides=[1], requires_grad=0, device=cpu),
%558 : Float(384, 64, 1, 1, strides=[64, 1, 1, 1], requires_grad=0, device=cpu),
%559 : Float(384, strides=[1], requires_grad=0, device=cpu),
%561 : Float(384, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%562 : Float(384, strides=[1], requires_grad=0, device=cpu),
%564 : Float(96, 384, 1, 1, strides=[384, 1, 1, 1], requires_grad=0, device=cpu),
%565 : Float(96, strides=[1], requires_grad=0, device=cpu),
%567 : Float(576, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=0, device=cpu),
%568 : Float(576, strides=[1], requires_grad=0, device=cpu),
%570 : Float(576, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%571 : Float(576, strides=[1], requires_grad=0, device=cpu),
%573 : Float(96, 576, 1, 1, strides=[576, 1, 1, 1], requires_grad=0, device=cpu),
%574 : Float(96, strides=[1], requires_grad=0, device=cpu),
%576 : Float(576, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=0, device=cpu),
%577 : Float(576, strides=[1], requires_grad=0, device=cpu),
%579 : Float(576, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%580 : Float(576, strides=[1], requires_grad=0, device=cpu),
%582 : Float(96, 576, 1, 1, strides=[576, 1, 1, 1], requires_grad=0, device=cpu),
%583 : Float(96, strides=[1], requires_grad=0, device=cpu),
%585 : Float(576, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=0, device=cpu),
%586 : Float(576, strides=[1], requires_grad=0, device=cpu),
%588 : Float(576, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%589 : Float(576, strides=[1], requires_grad=0, device=cpu),
%591 : Float(160, 576, 1, 1, strides=[576, 1, 1, 1], requires_grad=0, device=cpu),
%592 : Float(160, strides=[1], requires_grad=0, device=cpu),
%594 : Float(960, 160, 1, 1, strides=[160, 1, 1, 1], requires_grad=0, device=cpu),
%595 : Float(960, strides=[1], requires_grad=0, device=cpu),
%597 : Float(960, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%598 : Float(960, strides=[1], requires_grad=0, device=cpu),
%600 : Float(160, 960, 1, 1, strides=[960, 1, 1, 1], requires_grad=0, device=cpu),
%601 : Float(160, strides=[1], requires_grad=0, device=cpu),
%603 : Float(960, 160, 1, 1, strides=[160, 1, 1, 1], requires_grad=0, device=cpu),
%604 : Float(960, strides=[1], requires_grad=0, device=cpu),
%606 : Float(960, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%607 : Float(960, strides=[1], requires_grad=0, device=cpu),
%609 : Float(160, 960, 1, 1, strides=[960, 1, 1, 1], requires_grad=0, device=cpu),
%610 : Float(160, strides=[1], requires_grad=0, device=cpu),
%612 : Float(960, 160, 1, 1, strides=[160, 1, 1, 1], requires_grad=0, device=cpu),
%613 : Float(960, strides=[1], requires_grad=0, device=cpu),
%615 : Float(960, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cpu),
%616 : Float(960, strides=[1], requires_grad=0, device=cpu),
%618 : Float(320, 960, 1, 1, strides=[960, 1, 1, 1], requires_grad=0, device=cpu),
%619 : Float(320, strides=[1], requires_grad=0, device=cpu),
%621 : Float(1280, 320, 1, 1, strides=[320, 1, 1, 1], requires_grad=0, device=cpu),
%622 : Float(1280, strides=[1], requires_grad=0, device=cpu)):
%467 : Float(1, 32, 32, 32, strides=[32768, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%input.1, %468, %469)
%317 : Float(1, 32, 32, 32, strides=[32768, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%467) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%470 : Float(1, 32, 32, 32, strides=[32768, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=32, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%317, %471, %472)
%320 : Float(1, 32, 32, 32, strides=[32768, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%470) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%473 : Float(1, 16, 32, 32, strides=[16384, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%320, %474, %475)
%476 : Float(1, 96, 32, 32, strides=[98304, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%473, %477, %478)
%325 : Float(1, 96, 32, 32, strides=[98304, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%476) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%479 : Float(1, 96, 32, 32, strides=[98304, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=96, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%325, %480, %481)
%328 : Float(1, 96, 32, 32, strides=[98304, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%479) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%482 : Float(1, 24, 32, 32, strides=[24576, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%328, %483, %484)
%485 : Float(1, 144, 32, 32, strides=[147456, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%482, %486, %487)
%333 : Float(1, 144, 32, 32, strides=[147456, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%485) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%488 : Float(1, 144, 32, 32, strides=[147456, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=144, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%333, %489, %490)
%336 : Float(1, 144, 32, 32, strides=[147456, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%488) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%491 : Float(1, 24, 32, 32, strides=[24576, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%336, %492, %493)
%339 : Float(1, 24, 32, 32, strides=[24576, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Add(%482, %491) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%494 : Float(1, 144, 32, 32, strides=[147456, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%339, %495, %496)
%342 : Float(1, 144, 32, 32, strides=[147456, 1024, 32, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%494) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%497 : Float(1, 144, 16, 16, strides=[36864, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=144, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%342, %498, %499)
%345 : Float(1, 144, 16, 16, strides=[36864, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%497) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%500 : Float(1, 32, 16, 16, strides=[8192, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%345, %501, %502)
%503 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%500, %504, %505)
%350 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%503) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%506 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=192, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%350, %507, %508)
%353 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%506) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%509 : Float(1, 32, 16, 16, strides=[8192, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%353, %510, %511)
%356 : Float(1, 32, 16, 16, strides=[8192, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Add(%500, %509) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%512 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%356, %513, %514)
%359 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%512) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%515 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=192, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%359, %516, %517)
%362 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%515) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%518 : Float(1, 32, 16, 16, strides=[8192, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%362, %519, %520)
%365 : Float(1, 32, 16, 16, strides=[8192, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Add(%356, %518) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%521 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%365, %522, %523)
%368 : Float(1, 192, 16, 16, strides=[49152, 256, 16, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%521) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%524 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=192, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%368, %525, %526)
%371 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%524) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%527 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%371, %528, %529)
%530 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%527, %531, %532)
%376 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%530) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%533 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=384, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%376, %534, %535)
%379 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%533) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%536 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%379, %537, %538)
%382 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Add(%527, %536) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%539 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%382, %540, %541)
%385 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%539) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%542 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=384, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%385, %543, %544)
%388 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%542) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%545 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%388, %546, %547)
%391 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Add(%382, %545) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%548 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%391, %549, %550)
%394 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%548) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%551 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=384, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%394, %552, %553)
%397 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%551) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%554 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%397, %555, %556)
%400 : Float(1, 64, 8, 8, strides=[4096, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Add(%391, %554) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%557 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%400, %558, %559)
%403 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%557) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%560 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=384, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%403, %561, %562)
%406 : Float(1, 384, 8, 8, strides=[24576, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%560) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%563 : Float(1, 96, 8, 8, strides=[6144, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%406, %564, %565)
%566 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%563, %567, %568)
%411 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%566) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%569 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=576, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%411, %570, %571)
%414 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%569) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%572 : Float(1, 96, 8, 8, strides=[6144, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%414, %573, %574)
%417 : Float(1, 96, 8, 8, strides=[6144, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Add(%563, %572) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%575 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%417, %576, %577)
%420 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%575) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%578 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=576, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%420, %579, %580)
%423 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%578) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%581 : Float(1, 96, 8, 8, strides=[6144, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%423, %582, %583)
%426 : Float(1, 96, 8, 8, strides=[6144, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Add(%417, %581) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%584 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%426, %585, %586)
%429 : Float(1, 576, 8, 8, strides=[36864, 64, 8, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%584) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%587 : Float(1, 576, 4, 4, strides=[9216, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=576, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%429, %588, %589)
%432 : Float(1, 576, 4, 4, strides=[9216, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%587) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%590 : Float(1, 160, 4, 4, strides=[2560, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%432, %591, %592)
%593 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%590, %594, %595)
%437 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%593) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%596 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=960, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%437, %597, %598)
%440 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%596) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%599 : Float(1, 160, 4, 4, strides=[2560, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%440, %600, %601)
%443 : Float(1, 160, 4, 4, strides=[2560, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Add(%590, %599) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%602 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%443, %603, %604)
%446 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%602) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%605 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=960, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%446, %606, %607)
%449 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%605) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%608 : Float(1, 160, 4, 4, strides=[2560, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%449, %609, %610)
%452 : Float(1, 160, 4, 4, strides=[2560, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Add(%443, %608) # /opt/home/k8sworker/.cache/torch/hub/chenyaofo_pytorch-cifar-models_master/pytorch_cifar_models/mobilenetv2.py:144:0
%611 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%452, %612, %613)
%455 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%611) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%614 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=960, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%455, %615, %616)
%458 : Float(1, 960, 4, 4, strides=[15360, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%614) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%617 : Float(1, 320, 4, 4, strides=[5120, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%458, %618, %619)
%620 : Float(1, 1280, 4, 4, strides=[20480, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%617, %621, %622)
%463 : Float(1, 1280, 4, 4, strides=[20480, 16, 4, 1], requires_grad=1, device=cpu) = onnx::Clip[max=6., min=0.](%620) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1255:0
%464 : Float(1, 1280, 1, 1, strides=[1280, 1, 1, 1], requires_grad=1, device=cpu) = onnx::GlobalAveragePool(%463) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1037:0
%465 : Float(1, 1280, strides=[1280, 1], requires_grad=1, device=cpu) = onnx::Flatten[axis=1](%464) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1076:0
%466 : Float(1, 10, strides=[10, 1], requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%465, %classifier.1.weight, %classifier.1.bias) # /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:1753:0
return (%466)
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/notebooks/113-image-classification-quantization/model/mobilenet_v2.onnx
- Path for generated IR: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/notebooks/113-image-classification-quantization/model
- IR output name: mobilenet_v2
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: [1,3,32,32]
- Source layout: Not specified
- Target layout: Not specified
- Layout: Not specified
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP16
- Enable fusing: True
- User transformations: Not specified
- Reverse input channels: False
- Enable IR generation for fixed input shape: False
- Use the transformations config file: None
Advanced parameters:
- Force the usage of legacy Frontend of Model Optimizer for model conversion into IR: False
- Force the usage of new Frontend of Model Optimizer for model conversion into IR: False
OpenVINO runtime found in: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino
OpenVINO runtime version: 2022.2.0-7713-af16ea1d79a-releases/2022/2
Model Optimizer version: 2022.2.0-7713-af16ea1d79a-releases/2022/2
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/notebooks/113-image-classification-quantization/model/mobilenet_v2.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-275/.workspace/scm/ov-notebook/notebooks/113-image-classification-quantization/model/mobilenet_v2.bin
[ SUCCESS ] Total execution time: 0.48 seconds.
[ SUCCESS ] Memory consumed: 87 MB.
[ 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
Define Data Loader¶
In this step, the DataLoader interface from POT API is implemented.
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))])
dataset = CIFAR10(root=DATA_DIR, train=False, transform=transform, download=True)
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz
0%| | 0/170498071 [00:00<?, ?it/s]
Extracting data/cifar-10-python.tar.gz to data
# Create a DataLoader from a CIFAR10 dataset.
class CifarDataLoader(DataLoader):
def __init__(self, config):
"""
Initialize config and dataset.
:param config: created config with DATA_DIR path.
"""
if not isinstance(config, Dict):
config = Dict(config)
super().__init__(config)
self.indexes, self.pictures, self.labels = self.load_data(dataset)
def __len__(self):
return len(self.labels)
def __getitem__(self, index):
"""
Return one sample of index, label and picture.
:param index: index of the taken sample.
"""
if index >= len(self):
raise IndexError
return (self.indexes[index], self.labels[index]), self.pictures[index].numpy()
def load_data(self, dataset):
"""
Load dataset in needed format.
:param dataset: downloaded dataset.
"""
pictures, labels, indexes = [], [], []
for idx, sample in enumerate(dataset):
pictures.append(sample[0])
labels.append(sample[1])
indexes.append(idx)
return indexes, pictures, labels
Define Accuracy Metric Calculation¶
In this step, the Metric interface for accuracy Top-1 metric is
implemented. It is used for validating accuracy of quantized model.
# Custom implementation of classification accuracy metric.
class Accuracy(Metric):
# Required methods
def __init__(self, top_k=1):
super().__init__()
self._top_k = top_k
self._name = 'accuracy@top{}'.format(self._top_k)
self._matches = []
@property
def value(self):
""" Returns accuracy metric value for the last model output. """
return {self._name: self._matches[-1]}
@property
def avg_value(self):
""" Returns accuracy metric value for all model outputs. """
return {self._name: np.ravel(self._matches).mean()}
def update(self, output, target):
""" Updates prediction matches.
:param output: model output
:param target: annotations
"""
if len(output) > 1:
raise Exception('The accuracy metric cannot be calculated '
'for a model with multiple outputs')
if isinstance(target, dict):
target = list(target.values())
predictions = np.argsort(output[0], axis=1)[:, -self._top_k:]
match = [float(t in predictions[i]) for i, t in enumerate(target)]
self._matches.append(match)
def reset(self):
""" Resets collected matches """
self._matches = []
def get_attributes(self):
"""
Returns a dictionary of metric attributes {metric_name: {attribute_name: value}}.
Required attributes: 'direction': 'higher-better' or 'higher-worse'
'type': metric type
"""
return {self._name: {'direction': 'higher-better',
'type': 'accuracy'}}
Run Quantization Pipeline and compare the accuracy of the original and quantized models¶
In this step, define a configuration for the quantization pipeline and run it.
Note: Use built-in
IEEngineimplementation of theEngineinterface from the POT API for model inference.IEEngineis built on top of OpenVINO Python API for inference and provides basic functionality for inference of simple models. If you have a more complicated inference flow for your model/models, you should create your own implementation ofEngineinterface, for example, by inheriting fromIEEngineand extending it.
model_config = Dict({
'model_name': 'mobilenet_v2',
'model': ir_model_xml,
'weights': ir_model_bin
})
engine_config = Dict({
'device': 'CPU',
'stat_requests_number': 2,
'eval_requests_number': 2
})
dataset_config = {
'data_source': DATA_DIR
}
algorithms = [
{
'name': 'DefaultQuantization',
'params': {
'target_device': 'CPU',
'preset': 'performance',
'stat_subset_size': 300
}
}
]
# Steps 1-7: Model optimization
# Step 1: Load the model.
model = load_model(model_config)
# Step 2: Initialize the data loader.
data_loader = CifarDataLoader(dataset_config)
# Step 3 (Optional. Required for AccuracyAwareQuantization): Initialize the metric.
metric = Accuracy(top_k=1)
# Step 4: Initialize the engine for metric calculation and statistics collection.
engine = IEEngine(engine_config, data_loader, metric)
# Step 5: Create a pipeline of compression algorithms.
pipeline = create_pipeline(algorithms, engine)
# Step 6: Execute the pipeline.
compressed_model = pipeline.run(model)
# Step 7 (Optional): Compress model weights quantized precision
# in order to reduce the size of final .bin file.
compress_model_weights(compressed_model)
# Step 8: Save the compressed model to the desired path.
compressed_model_paths = save_model(model=compressed_model, save_path=MODEL_DIR, model_name="quantized_mobilenet_v2"
)
compressed_model_xml = compressed_model_paths[0]["model"]
compressed_model_bin = Path(compressed_model_paths[0]["model"]).with_suffix(".bin")
# Step 9: Compare accuracy of the original and quantized models.
metric_results = pipeline.evaluate(model)
if metric_results:
for name, value in metric_results.items():
print(f"Accuracy of the original model: {name}: {value}")
metric_results = pipeline.evaluate(compressed_model)
if metric_results:
for name, value in metric_results.items():
print(f"Accuracy of the optimized model: {name}: {value}")
Accuracy of the original model: accuracy@top1: 0.9348
Accuracy of the optimized model: accuracy@top1: 0.9348
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 CPUto benchmark async inference on CPU for one minute. Change CPU to GPU to benchmark on GPU. Runbenchmark_app --helpto see an overview of all command-line options.
# Inference FP16 model (OpenVINO IR)
!benchmark_app -m $ir_model_xml -d CPU -api async
[Step 1/11] Parsing and validating input arguments
[ WARNING ] -nstreams default value is determined automatically for a device. Although the automatic selection usually provides a reasonable performance, but it still may be non-optimal for some cases, for more information look at README.
[Step 2/11] Loading OpenVINO
[ WARNING ] PerformanceMode was not explicitly specified in command line. Device CPU performance hint will be set to THROUGHPUT.
[ INFO ] OpenVINO:
API version............. 2022.2.0-7713-af16ea1d79a-releases/2022/2
[ INFO ] Device info
CPU
openvino_intel_cpu_plugin version 2022.2
Build................... 2022.2.0-7713-af16ea1d79a-releases/2022/2
[Step 3/11] Setting device configuration
[ WARNING ] -nstreams default value is determined automatically for CPU device. Although the automatic selection usually provides a reasonable performance, but it still may be non-optimal for some cases, for more information look at README.
[Step 4/11] Reading network files
[ INFO ] Read model took 26.04 ms
[Step 5/11] Resizing network to match image sizes and given batch
[ INFO ] Network batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model input 'input.1' precision u8, dimensions ([N,C,H,W]): 1 3 32 32
[ INFO ] Model output '466' precision f32, dimensions ([...]): 1 10
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 188.28 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] DEVICE: CPU
[ INFO ] AVAILABLE_DEVICES , ['']
[ INFO ] RANGE_FOR_ASYNC_INFER_REQUESTS , (1, 1, 1)
[ INFO ] RANGE_FOR_STREAMS , (1, 24)
[ INFO ] FULL_DEVICE_NAME , Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
[ INFO ] OPTIMIZATION_CAPABILITIES , ['WINOGRAD', 'FP32', 'FP16', 'INT8', 'BIN', 'EXPORT_IMPORT']
[ INFO ] CACHE_DIR ,
[ INFO ] NUM_STREAMS , 6
[ INFO ] AFFINITY , Affinity.CORE
[ INFO ] INFERENCE_NUM_THREADS , 0
[ 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 data
[ INFO ] Create 6 infer requests took 1.78 ms
[ 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, 6 inference requests using 6 streams for CPU, inference only: True, limits: 60000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 2.23 ms
[Step 11/11] Dumping statistics report
Count: 287994 iterations
Duration: 60001.23 ms
Latency:
Median: 1.17 ms
AVG: 1.18 ms
MIN: 0.83 ms
MAX: 5.05 ms
Throughput: 4799.80 FPS
# Inference INT8 model (OpenVINO IR)
!benchmark_app -m $compressed_model_xml -d CPU -api async
[Step 1/11] Parsing and validating input arguments
[ WARNING ] -nstreams default value is determined automatically for a device. Although the automatic selection usually provides a reasonable performance, but it still may be non-optimal for some cases, for more information look at README.
[Step 2/11] Loading OpenVINO
[ WARNING ] PerformanceMode was not explicitly specified in command line. Device CPU performance hint will be set to THROUGHPUT.
[ INFO ] OpenVINO:
API version............. 2022.2.0-7713-af16ea1d79a-releases/2022/2
[ INFO ] Device info
CPU
openvino_intel_cpu_plugin version 2022.2
Build................... 2022.2.0-7713-af16ea1d79a-releases/2022/2
[Step 3/11] Setting device configuration
[ WARNING ] -nstreams default value is determined automatically for CPU device. Although the automatic selection usually provides a reasonable performance, but it still may be non-optimal for some cases, for more information look at README.
[Step 4/11] Reading network files
[ INFO ] Read model took 17.33 ms
[Step 5/11] Resizing network to match image sizes and given batch
[ INFO ] Network batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model input 'input.1' precision u8, dimensions ([N,C,H,W]): 1 3 32 32
[ INFO ] Model output '466' precision f32, dimensions ([...]): 1 10
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 165.33 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] DEVICE: CPU
[ INFO ] AVAILABLE_DEVICES , ['']
[ INFO ] RANGE_FOR_ASYNC_INFER_REQUESTS , (1, 1, 1)
[ INFO ] RANGE_FOR_STREAMS , (1, 24)
[ INFO ] FULL_DEVICE_NAME , Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
[ INFO ] OPTIMIZATION_CAPABILITIES , ['WINOGRAD', 'FP32', 'FP16', 'INT8', 'BIN', 'EXPORT_IMPORT']
[ INFO ] CACHE_DIR ,
[ INFO ] NUM_STREAMS , 6
[ INFO ] AFFINITY , Affinity.CORE
[ INFO ] INFERENCE_NUM_THREADS , 0
[ 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 data
[ INFO ] Create 6 infer requests took 1.96 ms
[ 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, 6 inference requests using 6 streams for CPU, inference only: True, limits: 60000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 1.31 ms
[Step 11/11] Dumping statistics report
Count: 506658 iterations
Duration: 60001.35 ms
Latency:
Median: 0.67 ms
AVG: 0.69 ms
MIN: 0.49 ms
MAX: 35.27 ms
Throughput: 8444.11 FPS
Compare results on four pictures.¶
ie = Core()
# Read and load a float model.
float_model = ie.read_model(
model=ir_model_xml, weights=ir_model_bin
)
float_compiled_model = ie.compile_model(model=float_model, device_name="CPU")
# Read and load a quantized model.
quantized_model = ie.read_model(
model=compressed_model_xml, weights=compressed_model_bin
)
quantized_compiled_model = ie.compile_model(model=quantized_model, device_name="CPU")
# Define all possible labels from the CIFAR10 dataset.
labels_names = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
all_pictures = []
all_labels = []
# Get all pictures and their labels.
for i, batch in enumerate(data_loader):
all_pictures.append(batch[1])
all_labels.append(batch[0][1])
def plot_pictures(indexes: list, all_pictures=all_pictures, all_labels=all_labels):
"""Plot 4 pictures.
:param indexes: a list of indexes of pictures to be displayed.
:param all_batches: batches with pictures.
"""
images, labels = [], []
num_pics = len(indexes)
assert num_pics == 4, f'No enough indexes for pictures to be displayed, got {num_pics}'
for idx in indexes:
assert idx < 10000, 'Cannot get such index, there are only 10000'
pic = np.rollaxis(all_pictures[idx].squeeze(), 0, 3)
images.append(pic)
labels.append(labels_names[all_labels[idx]])
f, axarr = plt.subplots(1, 4)
axarr[0].imshow(images[0])
axarr[0].set_title(labels[0])
axarr[1].imshow(images[1])
axarr[1].set_title(labels[1])
axarr[2].imshow(images[2])
axarr[2].set_title(labels[2])
axarr[3].imshow(images[3])
axarr[3].set_title(labels[3])
def infer_on_pictures(model, indexes: list, all_pictures=all_pictures):
""" Inference model on a few pictures.
:param net: model on which do inference
:param indexes: list of indexes
"""
output_key = model.output(0)
predicted_labels = []
for idx in indexes:
assert idx < 10000, 'Cannot get such index, there are only 10000'
result = model([all_pictures[idx][None,]])[output_key]
result = labels_names[np.argmax(result[0])]
predicted_labels.append(result)
return predicted_labels
indexes_to_infer = [7, 12, 15, 20] # To plot, specify 4 indexes.
plot_pictures(indexes_to_infer)
results_float = infer_on_pictures(float_compiled_model, indexes_to_infer)
results_quanized = infer_on_pictures(quantized_compiled_model, indexes_to_infer)
print(f"Labels for picture from float model : {results_float}.")
print(f"Labels for picture from quantized model : {results_quanized}.")
Labels for picture from float model : ['frog', 'dog', 'ship', 'horse'].
Labels for picture from quantized model : ['frog', 'dog', 'ship', 'horse'].
