Visualizer for neural network, deep learning, and machine learning models

Overview

Netron is a viewer for neural network, deep learning and machine learning models.

Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), TensorFlow Lite (.tflite), Caffe (.caffemodel, .prototxt), Darknet (.cfg), Core ML (.mlmodel), MNN (.mnn), MXNet (.model, -symbol.json), ncnn (.param), PaddlePaddle (.zip, __model__), Caffe2 (predict_net.pb), Barracuda (.nn), Tengine (.tmfile), TNN (.tnnproto), RKNN (.rknn), MindSpore Lite (.ms), UFF (.uff).

Netron has experimental support for TensorFlow (.pb, .meta, .pbtxt, .ckpt, .index), PyTorch (.pt, .pth), TorchScript (.pt, .pth), OpenVINO (.xml), Torch (.t7), Arm NN (.armnn), BigDL (.bigdl, .model), Chainer (.npz, .h5), CNTK (.model, .cntk), Deeplearning4j (.zip), MediaPipe (.pbtxt), ML.NET (.zip), scikit-learn (.pkl), TensorFlow.js (model.json, .pb).

Install

macOS: Download the .dmg file or run brew install netron

Linux: Download the .AppImage file or run snap install netron

Windows: Download the .exe installer or run winget install netron

Browser: Start the browser version.

Python Server: Run pip install netron and netron [FILE] or netron.start('[FILE]').

Models

Sample model files to download or open using the browser version:

Comments
  • Windows app not closing properly

    Windows app not closing properly

    After the latest update, Netron remains open taking up memory and CPU after closing the program. I must close it through task manager each time. I am on Windows 10

    no repro 
    opened by idenc 22
  • TorchScript: ValueError: not enough values to unpack

    TorchScript: ValueError: not enough values to unpack

    • Netron app and version: web app 5.5.9?
    • OS and browser version: Manjaro GNOME on firefox 97.0.1

    Steps to Reproduce:

    1. use torch.broadcast_tensors
    2. export with torch.trace(...).save()
    3. open in netron.app

    I have also gotten a Unsupported function 'torch.broadcast_tensors', but have been unable to reproduce it due to this current error. Most likely, the fix for the following repro will cover two bugs.

    Please attach or link model files to reproduce the issue if necessary.

    image

    Repro:

    import torch
    
    class Test(torch.nn.Module):
        def forward(self, a, b):
            a, b = torch.broadcast_tensors(a, b)
            assert a.shape == b.shape == (3, 5)
            return a + b
    
    torch.jit.trace(
        Test(),
        (torch.ones(3, 1), torch.ones(1, 5)),
    ).save("foobar.pt")
    

    Zipped foobar.pt: foobar.zip

    help wanted bug 
    opened by pbsds 15
  • OpenVINO support

    OpenVINO support

    • [x] 1. Opening rm_lstm4f.xml results in TypeError (#192)
    • [x] 2. dot files are not opened any more - need to fix it (#192)
    • [x] 3. add preflight check for invalid xml and dot content
    • [x] 6. Add test files to ./test/models.json (#195) (#211)
    • [x] 9. Add support for the version 3 of IR (#196)
    • [x] 10. Category color support (#203)
    • [x] 11. -metadata.json for coloring, documentation and attribute default filtering (#203).
    • [x] 5. Filter attribute defaults based on -metadata.json to show fewer attributes in the graph
    • [ ] 7. Show weight tensors
    • [x] 8. Graph inputs and outputs should be exposed as Graph.inputs and Graph.outputs
    • [x] 12. Move to DOMParser
    • [x] 13. Remove dot support
    feature 
    opened by lutzroeder 15
  • RangeError: Maximum call stack size exceeded

    RangeError: Maximum call stack size exceeded

    • Netron app and version: 4.4.8 App and Browser
    • OS and browser version: Windows 10 + Chrome Version 84.0.4147.135

    Steps to Reproduce:

    EfficientDet-d0.zip

    Please attach or link model files to reproduce the issue if necessary.

    help wanted no repro bug 
    opened by ryusaeba 14
  • Debugging Tensorflow Lite Model

    Debugging Tensorflow Lite Model

    Hi there,

    First off, just wanted to say thanks for creating such a great tool - Netron is very useful.

    I'm having an issue that likely stems from Tensorflow, rather than from Netron, but thought you might have some insights. In my flow, I use TF 1.15 to go from .ckpt --> frozen .pb --> .tflite. Normally it works reasonably smoothly, but a recent run shows an issue with the .tflite file: it is created without errors, it runs, but it performs poorly. Opening it with Netron shows that the activation functions (relu6 in this case) have been removed for every layer. Opening the equivalent .pb file in Netron shows the relu6 functions are present.

    Have you seen any cases in which Netron struggled with a TF Lite model (perhaps it can open, but isn't displaying correctly)? Also, how did you figure out the format for .tflite files (perhaps knowing this would allow me to debug it more deeply)?

    Thanks in advance.

    no repro 
    opened by mm7721 12
  • add armnn serialized format support

    add armnn serialized format support

    here's patch to support armnn format. (experimental)

    armnn-schema.js is compiled from ArmnnSchema.fbs included in armNN serailizer.

    see also:

    armnn: https://github.com/ARM-software/armnn

    As mensioned in #363, I will check items in below:

    • [x] Add sample files to test/models.json and run node test/test.js armnn
    • [x] Add tools/armnn script and sync, schema to automate regenerating armnn-schema.js
    • [x] Add tools/armnn script to run as part of ./Makefile
    • [x] Run make lint
    opened by Tee0125 12
  • TorchScript: Argument names to match runtime

    TorchScript: Argument names to match runtime

    Hi, there is some questions about node's name which in pt model saved by TorchScript. I use netron to view my pt model exported by torch.jit.save(),but the node's name doesn't match with it's real name resolved by TorchScript interface. It looks like the names in pt are arranged numerically from smallest to largest,but this is clearly not the case when they are parsed from TorchScript's interface. I wonder how this kind of situation can be solved, thanks a lot !! Looking forward to your reply.

    help wanted 
    opened by daodaoawaker 11
  • Support torch.fx IR visualization using netron

    Support torch.fx IR visualization using netron

    torch.fx is a library in PyTorch 1.8 that allows python-python model transformations. It works by symbolically tracing the PyTorch model into a graph (fx.GraphModule), which can be transformed and finally exported back to code, or used as a nn.Module directly. Currently there is no mechanism to import the graph IR into netron. An indirect path is to export to ONNX to visualize, which is not as useful if debugging transformations that potentially break ONNX exportability. It seems valuable to visualize the traced graph directly in netron.

    feature help wanted no repro 
    opened by sjain-stanford 11
  • TorchScript unsupported functions in after update

    TorchScript unsupported functions in after update

    I have a lot of basic model files saved in TorchScript and they were able to be opened weeks ago. However I cannot many of them after update Netron to v3.9.1. Many common functions are not supported not, e.g. torch.constant_pad_nd, torch.bmm, torch.avg_pool3d.

    opened by lujq96 11
  • OpenVINO IR v10 LSTM support

    OpenVINO IR v10 LSTM support

    • Netron app and version: 4.4.4
    • OS and browser version: Windows 10 64bit

    Steps to Reproduce:

    1. Open OpenVINO IR XML file in netron

    Please attach or link model files to reproduce the issue if necessary.

    I cannot share the proprietary model that shows dozens of disconnected nodes, but the one linked below does show disconnected subgraphs after conversion to OpenVINO IR. Note that the IR generated using the --generate_deprecated_IR_V7 option displays correctly.

    https://github.com/ARM-software/ML-KWS-for-MCU/blob/master/Pretrained_models/Basic_LSTM/Basic_LSTM_S.pb

    Convert using:

    python 'C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\mo.py' --input_model .\Basic_LSTM_S.pb --input=Reshape:0 --input_shape=[1,490] --output=Output-Layer/add

    This results in the following disconnected graph display:

    image

    no repro external bug 
    opened by mdeisher 10
  • Full support for scikit-learn (joblib)

    Full support for scikit-learn (joblib)

    For recoverable estimator persistence scikit-learn recommends to use joblib (instead of pickle). Sidenote: It is possible to export trained models into ONNX or PMML but the estimators are not recoverable. For more info refer to here.

    bug 
    opened by fkromer 9
  • Export full size image

    Export full size image

    I have onnx file successfully exported from mmsegmentation (swin-transformer), huge model (975.4) MB, I managed to open it in netron, however when I try to export it and preview in full size its blured.

    Any way I can fix it ? Thanks

    no repro bug 
    opened by adrianodac 0
  • TorchScript: torch.jit.mobile.serialization support

    TorchScript: torch.jit.mobile.serialization support

    Export PyTorch model to FlatBuffers file:

    import torch
    import torchvision
    model = torchvision.models.resnet34(weights=torchvision.models.ResNet34_Weights.DEFAULT)
    torch.jit.save_jit_module_to_flatbuffer(torch.jit.script(model), 'resnet34.ff')
    

    Sample files: scriptmodule.ff.zip squeezenet1_1_traced.ff.zip

    feature 
    opened by lutzroeder 0
  • MegEngine: fix some bugs

    MegEngine: fix some bugs

    fix some bugs of megengine C++ model (.mge) visualization:

    1. show the shape of the middle tensor;
    2. fix scope matching model identifier (mgv2) due to possible leading information;

    please help review, thanks~

    opened by Ysllllll 0
  • TorchScript server

    TorchScript server

    import torch
    import torchvision
    import torch.utils.tensorboard
    model = torchvision.models.detection.fasterrcnn_resnet50_fpn()
    script = torch.jit.script(model)
    script.save('fasterrcnn_resnet50_fpn.pt')
    with torch.utils.tensorboard.SummaryWriter('log') as writer:
        writer.add_graph(script, ())
    

    fasterrcnn_resnet50_fpn.pt.zip

    feature 
    opened by lutzroeder 0
MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice

Sampyl May 29, 2018: version 0.3 Sampyl is a package for sampling from probability distributions using MCMC methods. Similar to PyMC3 using theano to

Mat Leonard 304 Dec 25, 2022
Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction".

TGIN Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction". Files in the folder dataset/ electr

Alibaba 21 Dec 21, 2022
OpenMMLab Computer Vision Foundation

English | 简体中文 Introduction MMCV is a foundational library for computer vision research and supports many research projects as below: MMCV: OpenMMLab

OpenMMLab 4.6k Jan 09, 2023
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

SSTNet Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui J

83 Nov 29, 2022
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Learning to Reconstruct 3D Manhattan Wireframes From a Single Image This repository contains the PyTorch implementation of the paper: Yichao Zhou, Hao

Yichao Zhou 50 Dec 27, 2022
Deep Learning for Human Part Discovery in Images - Chainer implementation

Deep Learning for Human Part Discovery in Images - Chainer implementation NOTE: This is not official implementation. Original paper is Deep Learning f

Shintaro Shiba 63 Sep 25, 2022
3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos This repository contains the source code and dataset for the pa

54 Oct 09, 2022
Repo for parser tensorflow(.pb) and tflite(.tflite)

tfmodel_parser .pb file is the format of tensorflow model .tflite file is the format of tflite model, which usually used in mobile devices before star

1 Dec 23, 2021
Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.

VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks. VMAgent is constructed based on one month r

56 Dec 12, 2022
GAN-based 3D human pose estimation model for 3DV'17 paper

Tensorflow implementation for 3DV 2017 conference paper "Adversarially Parameterized Optimization for 3D Human Pose Estimation". @inproceedings{jack20

Dominic Jack 15 Feb 27, 2021
Pytorch implementation of Decoupled Spatial-Temporal Transformer for Video Inpainting

Decoupled Spatial-Temporal Transformer for Video Inpainting By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, J

51 Dec 13, 2022
A collection of differentiable SVD methods and also the official implementation of the ICCV21 paper "Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?"

Differentiable SVD Introduction This repository contains: The official Pytorch implementation of ICCV21 paper Why Approximate Matrix Square Root Outpe

YueSong 32 Dec 25, 2022
implementation for paper "ShelfNet for fast semantic segmentation"

ShelfNet-lightweight for paper (ShelfNet for fast semantic segmentation) This repo contains implementation of ShelfNet-lightweight models for real-tim

Juntang Zhuang 252 Sep 16, 2022
Cowsay - A rewrite of cowsay in python

Python Cowsay A rewrite of cowsay in python. Allows for parsing of existing .cow

James Ansley 3 Jun 27, 2022
Semantic segmentation models, datasets and losses implemented in PyTorch.

Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm

Yassine 1.3k Jan 07, 2023
Offline Reinforcement Learning with Implicit Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning This repository contains the official implementation of Offline Reinforcement Learning with Im

Ilya Kostrikov 126 Jan 06, 2023
Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

CDN Code for our NeurIPS 2021 paper "Mining the Benefits of Two-stage and One-stage HOI Detection". Contributed by Aixi Zhang*, Yue Liao*, Si Liu, Mia

71 Dec 14, 2022
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation [Paper] [PyTorch] [MXNet] [Video] This repository provides code for training

Visual Understanding Lab @ Samsung AI Center Moscow 516 Dec 21, 2022
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 917 Jan 03, 2023
Fashion Landmark Estimation with HRNet

HRNet for Fashion Landmark Estimation (Modified from deep-high-resolution-net.pytorch) Introduction This code applies the HRNet (Deep High-Resolution

SVIP Lab 91 Dec 26, 2022