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
Libtorch yolov3 deepsort

Overview It is for my undergrad thesis in Tsinghua University. There are four modules in the project: Detection: YOLOv3 Tracking: SORT and DeepSORT Pr

Xu Wei 226 Dec 13, 2022
Blender add-on: Add to Cameras menu: View → Camera, View → Add Camera, Camera → View, Previous Camera, Next Camera

Blender add-on: Camera additions In 3D view, it adds these actions to the View|Cameras menu: View → Camera : set the current camera to the 3D view Vie

German Bauer 11 Feb 08, 2022
Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works

GDAP Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works Environment Python (verified: v3.8) CUDA

45 Oct 29, 2022
An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

Jina AI 2 Mar 15, 2022
Explainability for Vision Transformers (in PyTorch)

Explainability for Vision Transformers (in PyTorch) This repository implements methods for explainability in Vision Transformers

Jacob Gildenblat 442 Jan 04, 2023
A PyTorch implementation of "From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network" (ICCV2021)

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network The official code of VisionLAN (ICCV2021). VisionLAN successfully a

81 Dec 12, 2022
JORLDY an open-source Reinforcement Learning (RL) framework provided by KakaoEnterprise

Repository for Open Source Reinforcement Learning Framework JORLDY

Kakao Enterprise Corp. 330 Dec 30, 2022
For visualizing the dair-v2x-i dataset

3D Detection & Tracking Viewer The project is based on hailanyi/3D-Detection-Tracking-Viewer and is modified, you can find the original version of the

34 Dec 29, 2022
Code for "Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans" CVPR 2021 best paper candidate

News 05/17/2021 To make the comparison on ZJU-MoCap easier, we save quantitative and qualitative results of other methods at here, including Neural Vo

ZJU3DV 748 Jan 07, 2023
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

EFENet EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation Code is a bit messy now. I woud clean up soon. For training the EF

Yaping Zhao 19 Nov 05, 2022
Omniverse sample scripts - A guide for developing with Python scripts on NVIDIA Ominverse

Omniverse sample scripts ここでは、NVIDIA Omniverse ( https://www.nvidia.com/ja-jp/om

ft-lab (Yutaka Yoshisaka) 37 Nov 17, 2022
Official implementation for paper Knowledge Bridging for Empathetic Dialogue Generation (AAAI 2021).

Knowledge Bridging for Empathetic Dialogue Generation This is the official implementation for paper Knowledge Bridging for Empathetic Dialogue Generat

Qintong Li 50 Dec 20, 2022
Simple implementation of Mobile-Former on Pytorch

Simple-implementation-of-Mobile-Former At present, only the model but no trained. There may be some bug in the code, and some details may be different

Acheung 103 Dec 31, 2022
Differentiable rasterization applied to 3D model simplification tasks

nvdiffmodeling Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Automatic 3D Model

NVIDIA Research Projects 336 Dec 30, 2022
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Fudan Zhang Vision Group 897 Jan 05, 2023
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
This is the second place solution for : UmojaHack Africa 2022: African Snake Antivenom Binding Challenge

UmojaHack-Africa-2022-African-Snake-Antivenom-Binding-Challenge This is the second place solution for : UmojaHack Africa 2022: African Snake Antivenom

Mami Mokhtar 10 Dec 03, 2022
Fang Zhonghao 13 Nov 19, 2022
The Power of Scale for Parameter-Efficient Prompt Tuning

The Power of Scale for Parameter-Efficient Prompt Tuning Implementation of soft embeddings from https://arxiv.org/abs/2104.08691v1 using Pytorch and H

Kip Parker 208 Dec 30, 2022
A crossplatform menu bar application using mpv as DLNA Media Renderer.

Macast Chinese README A menu bar application using mpv as DLNA Media Renderer. Install MacOS || Windows || Debian Download link: Macast release latest

4.4k Jan 01, 2023