Quantize Model Pytorch

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PyTorch (实验性）在 PyTorch 中使用 Eager 模式进行静态量化 PyTorch (实验性）计算机视觉教程的量化转移学习 PyTorch (实验）BERT 上的动态量化

The PyTorch → ONNX → Core ML conversion gave errors on the channel padding layer, so I wasn’t able to make a Core ML model to do speed measurements fuse_model() # Step 2 model_ft = create_combined_model(model) model_ft0 . Now SE-ResNet (18, 34, 50, 101, 152/20, 32) an,senet Quantized Neural Networks (QNNs) are often used to improve network efficiency during the inference phase, i .

I am using the pre-trained models from PyTorch's Torchvision library

PyTorch has the ability to quantize models on the CPU - Jermmy/pytorch-quantization-demo output = model . Francis Williams email protected +1-650-701-7891 Summary I am a PhD student in theMath and DataandGeometric Computing Labat New York University Specifically, pytorch caches chunks of memory spaces to speed up allocation used in every tensor creation .

0 and the graphics driver supports the desired version of cuda

quantize_per_channel (input, scales, zero_points, axis, dtype) → Tensor¶ Converts a float tensor to a per-channel quantized tensor with given scales and zero points It shows how you can take an existing model built with a deep learning framework and use that to build a TensorRT engine using the provided parsers . pytorch-quantization-demo / quantization_aware_training After all these, Quantized model output is what I expected, but inference time gets about 3~4 times longer .

Integer Quantization from saved_model (8-bit integer quantization) 4-2-9-5

One way of doing this is changing the bit-width of the floating-point number used for storing the weights In this section, we provide a segmentation training wrapper that extends the LightningModule . The script started downloading the file named by model_file of the model named by model rhs = attach_simulated_quantize(lhs, sign=False, rounding='stochastic_round') return expr .

Pytorch provides three approaches to quantize models

I use PyTorch as well (basically all NLP via “transformers” library) and used ONNX with great success The version of PyTorch used for ResNet-18 training was 1 . Because the TensorRT can run the quantized model (8bit 16bit) with cuda cudnn to speed up model, so i think pytorch should also be possible to do so Apache MXNet includes the Gluon API which gives you the simplicity and flexibility of PyTorch and allows you to hybridize your network to leverage performance optimizations of the symbolic graph .

Did you install using conda install pytorch torchvision cudatoolkit=10

1 release all the quantized models are represented using so-called FakeQuantize layer which is a very expressive primitive and is able to represent such operations as Quantize, Dequantize, Requantize, and even more All our experiments can be reproduced using the Permute, Quantize, and Fine-tune (PQF), an . Introduction to Quantization¶ Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision Prepare INT8 model, quantize parameters and collect calibration data .

All other functions should be moved outside or move to a derived class

Within each of these scripts we (1) build either the split or unsplit tensorflow model (2) import the trained weights from the TSM pytorch checkpoints (import_pytorch_weights), (3) export the split model for quantization, and finally (4) generate quantization inputs as described in “DPU Quantization Strategy” quantize_qat(model, run_fn, run_args, inplace=False)¶ 进行量化意识训练并输出量化模型 . Additionally, we will change the data format of our inputs and weights from expensive oating-point numbers to cheap xed-point numbers PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable Nvidia GPU .

The PyTorch frontend has support for converting a quantized PyTorch model to an equivalent Relay module enriched with quantization-aware operators

To follow along, feel free to download this sample cube The core of the pytorch lightning is the LightningModule that provides a warpper for the training framework . Below is the link to the tutorial on how to quantize a model However, we may only want to quantize some parts of a model, e .

Generate the quantized inference model by running the following command to quantize the model But we might be able to provide a minimal app with simple model soon to reproduce . pb file is theinput file for the XIR-based compiler We can quantize directly in the tensorflow js converter: .

PyTorch, the popular open-source ML framework, has continued to evolve rapidly since the introduction of PyTorch 1

For example, if the loaded model sees high accuracy loss when element-wise Add is quantized, it can be left in floating point Pytorch Post-training 量化的基本步骤【9】对于一个已经训练好的模型，post-training 量化的基本步骤如下： 1）准备模型：插桩：在需要 quantize 和 dequantize 操作的 module 中插入 QuantStub 和DeQuantStub。 . I will retrain my ResNet-18 model using PyTorch 1 最后是 quantize_inference 函数，这个函数在实际 inference 的时候会被调用，对应的就是上面的公式 (7)。注意，这个函数里面的卷积操作是在 int 上进行的，这是量化推理加速的关键「当然，由于 pytorch 的限制，我们仍然是在 float 上计算，只不过数值都是整数。 .

(Keep it for evaluation of quantized model) ResNet_int

To create INT8 model based on FP32 model, we will run QuantizeGraph pass inside subgraph to replace FP32 operators with INT8 operators if MKL-DNN supports and insert dequantize operator on proper position Tensorflow 官方已经集成了 Keras 作为自己推荐的 High-Level API，Keras 的确使用非常方便，而且代码美观简洁，不像 Tensorflow 那样有很多形式化的代码。对于我们进行快速原型和实验是非常有帮助的。然而在一些场合我们可能需要混合使用 Keras 和 Tensorflow 定义模型或者保存模型的操作，这时就需要一些 . Our theoretical analysis suggests that most of the training process is robust I was suggesting adding a larger model test in this file .

How do I build a quantized model like this ResNet tutorial from other deep learning frameworks (TensorFlow, PyTorch, etc

PyTorch Version (vai_q_pytorch) Added new topics: Module Partial Quantization, vai_q_pytorch Fast Finetuning, and vai_q_pytorch Quantize Finetuning Seq2Seq Model is a kind of model that use Encoder and a Decoder on top of the model . The first command will launch naive calibration to quantize your ssd_mobilenet1 In PyTorch we can register a hook on the gradient computation, so a callback is called when they are ready: for layer , ( name , module ) in enumerate ( self .

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scales - float 1D tensor of scales to use, size should match input Load PyTorch model in the form of a scripted PyTorch model and convert into relay . When the loaded model is the quantized model, do_quantization=False will use the quantization parameter of the model, for more details please refer to the answer of 1 from pytorch_jacinto_ai import xnn model = build_detection_model() # create a dummy input - this is required to analyze the model - fill in the input image size expected by your model .

If it fails to find the memory space, it splits an existing cached memory or allocate new space if there's no cached memory large enough for the requested size

Since the BlazeFace feature extractor only has 100k parameters and works on smallish 128×128 images, I’m betting it will be plenty fast… For background on Quantization - please read this link (INT8 quantization proposal) This thread only focuses on quantizing the models, i . We will also examine divergence from the accuracy of the full-precision model pytorch现在已经支持模型量化，主要支持两种量化模式(qnnpack和fbgemm)：qnnpack是基于tensor的模式，这种相当于一种全局量化模式；另外fbgemm是基于通道的模式，相比qnnpack，fbgemm是以及基于通道的局部量化模式；需要说明的是这两种模式主要是针对权重而言的，激活函数的量化都是一样的。 .

1 Getting Started This lab will require a beginner’s level of pro ciency with PyTorch

convert(quantized_model, inplace=True) quantized_model 4, as specified respectively by ONNX opsets 7 and 8, and 9 . quantize(model, run_fn, run_args, mapping=None, inplace=False)¶ 将浮点模型转换为量化模型。首先，它将准备进行校准或训练的模型，然后调用 run_fn ，它将运行校准步骤或训练步骤，之后我们将调用 convert ，它将模型转换为量化模型。 Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model .

PyTorch now offers three kinds of quantization methods for models: Post Training Dynamic Quantization — Quantize weights ahead of time but dynamically quantize the network activations at runtime

For x86, the best performance can be achieved on CPUs with AVX512 instructions set Students are expected to perform system simulations with python (or pytorch) for the bit quantization of neural network models . We tried to re-use some of the existing functionality of converting traced ops from pytorch to onnx for quantized models hence it is necessary to first trace it The cloud runtime introduces a new AI resource manager, to simplify scaling applications over multiple FPGA resources .

Note For the Release Notes for the 2020 version, refer to Release Notes for Intel® Distribution of OpenVINO™ toolkit 2020

Recently, my proposed solution is to reconstruct and quantize the 3D data as CAD models and textures on top of that qint8 model_inputs = tokenizer(大家好, 我是卖切糕的小男孩, 毕业于华中科技大学, return_tensors=pt) . We will rewrite Pytorch model code, perform ONNX graph surgery, optimize a TensorRT plugin and finally we’ll quantize the model to an 8-bit representation The quantized model learns latent knowledge from the generator and the full-precision model .

Hardware support for INT8 computations is typically 2 to 4 times faster compared to FP32 compute

nnieqat-pytorch Nnieqat is a quantize aware training package for Neural Network Inference Engine (NNIE) on pytorch, it uses hisilicon quantization library to quantize module's weight and activation as fake fp32 format How to use Tune with PyTorch Using PyTorch Lightning with Tune Model selection and serving with Ray Tune and Ray Serve Tune’s Scikit Learn Adapters Tuning XGBoost parameters Using Weights & Biases with Tune Examples Tune API Reference Execution (tune . This trained model is passed to AIMET using APIs for compression and quantization that we will look at in detail Repeat until convergence: Make the forward pass with the quantized model .

Variational Auto Encoders (VAEs) can be thought of as what all but the last layer of a neural network is doing, namely feature extraction or seperating out the data

A simple network quantization demo using pytorch from scratch dzhulgakov mentioned this issue Oct 14, 2019 Add note that cuda quantization is not supported #27829 . What: quantize weights once, activations at runtime At this point you need to modify the pretrained model .

A recorder records what operations have performed, and then it replays it backward to compute the gradients

NeurIPS 15146-15155 2019 Conference and Workshop Papers conf/nips/0001PSVW19 http://papers Saving the model can break the code in various ways, so the preferred method is to save and load only the model state . spikeLayer (neuronDesc, simulationDesc) source ¶ 서 론 tfmot 패키지는 TF Team에서 공식으로 배포하고 있는 AI 모델 최적화 패키지이다 .

linear layers and convolutions), but leaves some layers in FP32 (e

For the trained model we exemplary use the updated tf This event will always occur between model_download_begin and model_download_end events for the model, and will always be followed by a corresponding model_file_download_end event . 370 >> You're not going to want to miss this episode of the AI Show, 00:00:02 We cannot quantize the internal stages of the block at all .

The input and the network should always be on the same device

--- title: DefaultQuantization, AccuracyAwareQuantization by OpenVINO's post training optimization toolkit, INT8 (Integer Quantization) tags: OpenVINO Python DeepLearning PyTorch quantization author: PINTO slide: false --- # 1． It provides a framework for implementing state-of-the-art compression algorithms like pruning, quantization and knowledge distillation with a vast set of tools for evaluating . the redundant connections and quantize weights so that multiple connections share the same weight, and then they use Huffman coding to compress the weights Based on the Lesson 1 code, I want to use the pretrained resnet34 over the MNIST dataset to convert it into ONNX .

the precision of the weights and inputs that correspond to a model which has already been trained at a high precision

The only part of the model exposed at the Python level are the parameters of the fully-connected layers After calibration, quantized model and parameter will be saved on your disk . Pytorch模型转onnx模型 import io import torch import torch Glenn introduced the YOLOv5 Pytorch based approach, and Yes! YOLOv5 is written in the Pytorch framework .

PyTorch 中的动态量化支持将权重模型的浮点模型转换为具有静态 int8 或 float16 数据类型的量化模型，并为激活提供动态量化

The PyTorch or TensorFlow-Keras toolchain can be used to develop a model for the MAX78000 Extensive research in the field suggests many different quantization schemes . quantize (bool) – Whether to quantize the flow and save it to 2 jpeg images inplace – carry out model transformations in-place, the original module is mutated; 映射 –将子模块的类型映射到需要替换子模块的对应动态量化版本的类型; torch .

mobilenet from pathlib import Path #import a function from a different module that returns the model in eval

py --framework pt --model bert-base-uncased --quantize bert-base-uncased However, the MobileNetV3 architecture contains nn . This can be done by passing on the command line the node name via the option -keep-original-precision-for-nodes High-level overview A popular technique to quantize the models is to start from a pre-trained model .

resnet50() If a model is not entirely defined by module, than TensorQuantizer should be manually created and added to the right place in the model

I am guessing it is due to very low loss value but am not sure It will contain the quantized model parameters (the data type will still be FP32, but the values will be integers) . It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications attrs) # assuming we have an existed mxnet model, convert it to relay graph graph, params = relay .

03499v2 Github tensorflow-wavenet by Igor Babuschkin @google Generative Model-Based Text-to-Speech Synthesis by Heiga Zen @google In this post, Pytorch is used to implement Wavenet

pytorch An implementation of SENet, proposed in Squeeze-and-Excitation Networks by Jie Hu, Li Shen and Gang Sun, who are the winners of ILSVRC 2017 classification competition quantize(model, run_fn, run_args, mapping=None, inplace=False) source Quantize the input float model with post training static quantization . To quantize the model to 8 bit fixed point, see snpe-dlc-quantize The Python Imaging Library, or PIL for short, is one of the core libraries for image manipulation in Python .

A model can be post training quantized by simply by calling quant_modules

PyTorch现在为模型提供了三种量化方法：训练后的动态量化 -提前量化权重，但在运行时动态量化网络激活。可以这样完成：torch The major compression and acceleration technique provided by POT is uniform model quantization . import os import torch from craft import CRAFT trained_model='craft_mlt_25k spikeLayer with Loihi specific implementation for neuron model, weight quantization .

import tvm from tvm import te from tvm import relay import mxnet as mx from tvm

0, which brought an accelerated workflow from research to production default_qat_qconfig＃使用默认 QAT 配置＃步骤 3 model_ft = torch . By leveraging this quantization scheme, we can get reasonable quantized model accuracy across many models without resorting to retraining a model with quantization-aware This article is a deep dive into the techniques needed to get SSD300 object detection throughput to 2530 FPS .

Do you have any model that required this functionality? That may be a good candidate for using for the model-level test

state_dict () #将就解决了该错误，但是仍会报错，因为由于tensorflow版本更新，quantize_graph However, pytorch is implemented assuming that the number of point, or size of the activations do not change at every iteration . The masks can be adjusted at runtime by the algorithms Some operations like pooling or activations are fused to 1D or 2D convolution layers, and fully connected layers .

The quantized models generated in this mode are normally 10-20% slower than those with calibrations during inference

Quantize your continuous data in order to reduce the size of your model by up to 75% without sacrificing a significant amount of accuracy , from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed . Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive The inference ran just fine ( it was a bit slower with a 3% accuracy drop but .

Why Quantize? Neural network models can take up a lot of space on disk, with the original AlexNet being over 200 MB in float format for example

First it will prepare the model for calibration, then it calls run_fn which will run the calibration step, after that we will convert the model to a quantized model In contrast, quantization focuses on reducing the bits per weight . Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption This operation is inserted into the model during quantization procedure and is aimed to store quantization parameters for the layers .

To support the customized quantize layers, the quantized model should be loaded to quantize_scope, for example: from tensorflow_model_optimization

An example is available in Vitis AI Github The model to be quantized should include forward method only To automatically transform an existing model to a quantized model using this method, use the PostTrainLinearQuantizerclass . pth to ONNX with a single command without having to make any changes to the PyTorch program In this blog post, I would like to show how to use PyTorch to do dynamic quantizations .

Hi, I am trying to quantize a MobileNetV3 for use in a pytorch mobile/android application

, representing the weights/biases from their current FP32 format to INT8 format, while controlling the drop in the accuracy introduced by the quantization EfficientNet-Lite was only recently released and belongs to the family of image classification models capable of achieving state-of-the-art accuracy on . if accuracy loss is too high when all node kinds are quantized Thus, the GPU memory caching used by pytorch can result in unnecessarily large memory consumption .

To quantize a module, we need to quantize the input and weights if present

py running a model in # training mode could result in So, if you save the_model, it will save the entire model object, including its architecture definition and some other internal aspects . json file that there are many operations in the ResNet tutorial: The resultant TensorFlow model holds both the model architecture and its associated weights .

Linear W int8 bias float X float Y float # load or train your model model = WordLanguageModel() model

Quantization-aware training: quantize the weights during training Pytorch Lightning has over 3800 stars on Github and has recently hit 110k downloads . I am trying to quantize an ONNX model using the onnxruntime quantization tool In subscribing to our newsletter by entering your email address above you confirm you are over the age of 18 (or have obtained your parent’s/guardian’s permission to subscribe) and agree to .

To make a PyTorch model quantizable, it is necessary to modify the model definition to make sure the modified model meets the following conditions

Based on Torch, PyTorch has become a powerful machine learning framework favored by esteemed researchers around the world Train a model of your choice as usual (or take a trained model) . Returns a ParameterDict containing this Block and all of its children’s Parameters(default), also can returns the select ParameterDict which match some given regular expressions The model is created with a series of defined subclasses representing the hardware .

For example, I replaced ReLU with leakyReLU on ResNet50 then applied quantization

mnn list out mnn commands;mnnops get supported ops in mnn engine;mnnconvert convert other model to mnn model Under the hood, the Model Maker API uses transfer learning to re-train a model with a different dataset and categories . zero_points - integer 1D tensor of offset to use, size should Pre-trained transformer language models (GPT radford2018improving, XLNet yang2019xlnet, XLM lample2019cross, BERT Devlin2018BERTPO) have demonstrated State-of-the-Art (SOTA) results for a variety of NLP tasks such as sentence classification, sequence tagging and question answering, by extracting contextual word representations or by fine-tuning the whole model on a target task .

9% probability) that it saw the Inseego 5G MiFi mobile hotspot (Class 0) in the input image

unet = quantized_model Where did I make mistakes? Enderdead/Pytorch_Quantize_impls 61 Jopyth/BMXNet MODEL METRIC NAME METRIC VALUE . Run calibration algorithm → optimal scaling factors To train model which is more friendly to NNIE, just import nnieqat and replace torch .

Scalable: Pyro scales to large data sets with little overhead compared to hand-written code

PyTorch supports multiple approaches to quantizing a deep learning model The trained TensorFlow model has to be converted into a TFlite model and can be directly quantize as described in the following code block . 0 featuring Stable C++ frontend, distributed RPC framework, new experimental higher-level autograd API, Channels Last memory format, and more Following are 3 major use-cases: Create quantized wrapper for modules that have only inputs .

concat_axis ( int ) – The axis that dx and dy are concatenated, can be either 0 or 1

We have devices in our pockets that facilitate the creation of huge amounts of data, such as photos, gps coordinates, audio, and all kinds of personal information we consciously and unconsciously reveal A different batch dimension can be used during inference, by resizing the network during initialization . pytorch uses matplotlib and tensorboard to visualize the model and training In the process of pytorch building and training deep learning models, it is often necessary to be able to intuitively observe the visualization process, such as drawing a training curve org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month .

bit precision is reduced when supply voltage drops or battery life is ending

Scaled YOLOv4 consists of a series of object detection models of varying size that are the new state of the art in object detection NVIDIA Transfer Learning Toolkit Speedup AI training and create highly accurate and efficient domain-specific AI models . Copy all full precision weights that you want to quantize These weights can be quantized to 8bit to reduce the size to a quarter, whereas the accuracy usually decreases only by 1%-3% .

DeepLearningのモデル軽量化の気になっていたライブラリを使ってみました。今回はざっくりと導入の仕方と簡単な使い方、ライブラリの仕組みなどを調べた内容を書きたいと思います。はじめて使う人のガイドになればと思います。IntelのNeural Network Distiller。pruningや8-bit quantizationなど軽量化

from_tensorflow (graph, layout, shape, outputs) Load tensorflow graph which is a python tensorflow graph object into relay A checkpoint with the quantized model will be dumped in the run directory . 2020-12-10 12:41:59,162 INFO Thu Dec 10 12:41:59 2020 End to quantize the model resnet18(pretrained=True, progress=True, quantize=False) num_ftrs = model .

A Conversion tool to convert YOLO v3 Darknet weights to TF Lite model (YOLO v3 PyTorch > ONNX > TensorFlow > TF Lite), and to TensorRT (YOLO v3 Pytorch > ONNX > TensorRT)

The easiest way to do that is to wrap the model in the nn Unfortunately, its development has stagnated, with its last release in 2009 . download import download_testdata from mxnet import gluon import logging import os batch_size = 1 model_name However, often when quantizing a machine learning model (e .

Arithmetic in the quantized model is done using vectorized INT8 instructions

Chapter 5: Compiling the Model Added new section: Compiling with an XIR-based Toolchain I am trying to train an object detection model using Mask-RCNN with Resnet50 as backbone . The flow is slightly different for quantized ops (so the regular pytorch -> onnx conversion flow rule doesn't directly apply) References: WaveNet: A Generative Model for Raw Audio arXiv:1609 .

pytorch实现简单的straight-through estimator(STE) Quantization aware training 即量化感知训练，主要是使用tfmot This allows users to use lower precision backends such as FBGEMM and QNNPACK to accelerate performance on servers and mobile devices respectively while also lowering memory bandwidth footprint . Quantize Model Pytorch This article is a deep dive into the techniques needed to get SSD300 object detection throughput to 2530 FPS 3开始正式支持量化，在可量化的Tensor之外，PyTorch开始支持CNN中最常见的operator的量化操作，包括： .

For details on ways to invoke the quantizer see here

#Prepare the trained model for NNAPI on Android device import sys import os import torch import torch Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch . In terms of inference time, the winner is the Jetson Nano in combination with ResNet-50, TensorRT, and PyTorch quantized：提供了很多已经融合好的模块，如ConvBn2d，ConvBnReLU2d，直接对这些模型进行量化 .

Quoting the official PyTorch documentation: > PyTorch supports INT8 quantization compared to typical FP32 models allowing for a 4x reduction in the model size and a 4x reduction in memory bandwidth requirements

Do the Quantization - Here you instantiate a floating point model and then create quantized version of it Then, the quantize_params function can be called, which will iterate over all parameters and quantize them using params_quantization_fn . Good for: LSTMs/Transformers and MLPs with small batch size Next version of Transfer Learning Toolkit with support for conversational AI models will be available in early 2021 .

This can potentially lead to lower accuracy, but can reduce model size significantly (and has the side benefit of making the model more compressible over gzip)

PyTorch supports quantization in both eager and graph modes allowing for full freedom and control For the C++ API, it is the last release that supports C++11: you should start migrating to Python 3 and building with C++14 to . After a few days of the release of the YOLOv4 model on 27 May 2020, YOLOv5 got released by Glenn Jocher(Founder & CEO of Utralytics) 4: Distiller: 是 Intel 基于 Pytorch 开源的模型优化工具, 自然也支持 Pytorch 中的量化技术； distiller: NNI .

qint8) 训练后的静态量化 -提前量化权重，激活偏差和比例因子。这可以通过调用校准函数来完成。

pytorch-playground包含基础预训练模型和pytorch中的数据集（MNIST，SVHN，CIFAR10，CIFAR100，STL10，AlexNet，VGG16，VGG19，ResNet，Inception，SqueezeNet）这是 pytorch 初学者的游乐场(即资源列表，你可以随意使用如下模型)，其中包含流行数据集的预定义模型。 Users can tune the int8 accuracy by setting different calibration configurations . Step1: Freeze Keras model and convert to RKNN model quantize_dynamic函数，对模型中的某些层进行量化文章目录pytorch 提供torch .

py / Jump to Code definitions quantize_aware_training Function full_inference Function quantize_inference Function

One of the core component of the OpenVINO™ toolkit is the Model Optimizer a cross-platform command-line tool that converts a trained neural network from its source framework to an open-source, nGraph-compatible Intermediate Representation (IR) for use in inference In addition, PyTorch also supports quantization aware training, which models quantization errors in both the forward and backward passes using fake-quantization modules . You can print the output from the frontend to see how quantized models are represented quantize_per_channel() ：per-channel量化，除了需要指定上述三个参数之外，还需要额外指定执行per-channel量化的维度； torch .

A 3D model before and after applying the Decimate modifier in Blender on Planar mode with an angle limit of 1

Notice that there are two pb files generated by vai_q_tensorflow In most cases the model is trained in FP32 and then the model is converted to INT8 . This is a quantize aware training package for Neural Network Inference Engine(NNIE) on pytorch, it uses hisilicon quantization library to quantize module's weight and input data as fake fp32 format Look at Model Size - Here you show that the model size gets smaller .

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