code for Multi-scale Matching Networks for Semantic Correspondence, ICCV

Related tags

Deep LearningMMNet
Overview

MMNet

This repo is the official implementation of ICCV 2021 paper "Multi-scale Matching Networks for Semantic Correspondence.".

Pre-requisite

conda create -n mmnet python==3.8.0
conda activate mmnet
conda install torch==1.8.1 torchvision==0.9.1
pip install matplotlib scikit-image pandas

for installation of gluoncvth (fcn-resnet101):

git clone https://github.com/StacyYang/gluoncv-torch.git
cd gluoncv-torch
python setup.py install

Reproduction

for test

Trained models are available on [google drive].

pascal with fcn-resnet101 backbone([email protected]:81.6%):

python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name path\to\ckp_pascal_fcnres101.pth --resize 224,320

spair with fcn-resnet101 backbone([email protected]:46.6%):

python test.py --alpha 0.05 --benchmark spair --backbone fcn-resnet101 --ckp_name path\to\ckp_spair_fcnres101.pth --resize 224,320

Bibtex

If you use this code for your research, please consider citing:

@article{zhao2021multi,
  title={Multi-scale Matching Networks for Semantic Correspondence},
  author={Zhao, Dongyang and Song, Ziyang and Ji, Zhenghao and Zhao, Gangming and Ge, Weifeng and Yu, Yizhou},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}
You might also like...
A Pytorch implementation of
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

null 69 Dec 10, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 26 Sep 26, 2021
Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM [News] The code for ACMH is released!!! [News] The code for ACMP is released!!! About ACMM is a multi-scale geometric consistency guided multi-vi

Qingshan Xu 118 Jan 4, 2023
Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task
Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Siamese Deep Neural Networks for Semantic Text Similarity PyTorch A repository c

Shahrukh Khan 32 Dec 15, 2022
A PyTorch implementation of
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 2, 2023
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

Memory-Efficient Multi-Level In-Situ Generation (MLG) By Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen and David Z. Pan

Jiaqi Gu 2 Jan 4, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
《Dual-Resolution Correspondence Network》(NeurIPS 2020)
《Dual-Resolution Correspondence Network》(NeurIPS 2020)

Dual-Resolution Correspondence Network Dual-Resolution Correspondence Network, NeurIPS 2020 Dependency All dependencies are included in asset/dualrcne

Active Vision Laboratory 45 Nov 21, 2022
Comments
  • NaN during training

    NaN during training

    Hi, congrats on your paper! I was trying to run your training code (with resnet 101 on pf-pascal) but directly after a couple of iterations, nan appear in the input. Have you ever seen this issue? Thanks

    opened by PruneTruong 2
  • In def calLayer1,i do not know where are self.conv1_1_down,self.conv1_2_down,self.conv1_3_down,self.wa_1

    In def calLayer1,i do not know where are self.conv1_1_down,self.conv1_2_down,self.conv1_3_down,self.wa_1

    Hello,this paper is very nice,i am very love it. I read your code,in Model.py, def calLayer1(self, feats): sum1 = self.conv1_1_down(self.msblock1_1(feats[1])) +
    self.conv1_2_down(self.msblock1_2(feats[2])) +
    self.conv1_3_down(self.msblock1_3(feats[3])) sum1 = self.wa_1(sum1) return sum1 I do not find where are these operation,self.conv1_1_down,self.conv1_2_down,self.conv1_3_down,self.wa_1,so where are these ,in which document.Thank you,looking forward to your reply.

    opened by liang532 1
  • How to prepare the PF-Pascal dataset?

    How to prepare the PF-Pascal dataset?

    I downloaded the PF-dataset-Pascal.zip from the Proposal Flow paper's web page, extracted it, and run the next line of command, but get errors about missing data files.

    Input:

    python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name assets/model/mmnet_fcnresnet101_pascal.pth --resize 224,320

    Expected output: some results about the benchmark results.

    Actual output:

    currently executing test.py file.
2021-11-19 02:01:59,172 - INFO - Options listed below:----------------
2021-11-19 02:01:59,172 - INFO - name: framework_train
2021-11-19 02:01:59,172 - INFO - benchmark: pfpascal
2021-11-19 02:01:59,172 - INFO - thresh_type: auto
2021-11-19 02:01:59,172 - INFO - backbone_name: fcn-resnet101
2021-11-19 02:01:59,172 - INFO - ms_rate: 4
2021-11-19 02:01:59,173 - INFO - feature_channel: 21
2021-11-19 02:01:59,173 - INFO - batch: 5
2021-11-19 02:01:59,173 - INFO - gpu: 0
2021-11-19 02:01:59,173 - INFO - data_path: /data/SC_Dataset
2021-11-19 02:01:59,173 - INFO - ckp_path: ./checkpoints_debug
2021-11-19 02:01:59,173 - INFO - visualization_path: visualization
2021-11-19 02:01:59,173 - INFO - model_type: MMNet
2021-11-19 02:01:59,173 - INFO - ckp_name: assets/model/mmnet_fcnresnet101_pascal.pth
2021-11-19 02:01:59,173 - INFO - log_path: ./logs/
2021-11-19 02:01:59,173 - INFO - resize: 224,320
2021-11-19 02:01:59,173 - INFO - max_kps_num: 50
2021-11-19 02:01:59,173 - INFO - split_type: test
2021-11-19 02:01:59,173 - INFO - alpha: 0.05
2021-11-19 02:01:59,173 - INFO - resolution: 2
2021-11-19 02:01:59,173 - INFO - Options all listed.------------------
2021-11-19 02:01:59,173 - INFO - ckp file: assets/model/mmnet_fcnresnet101_pascal.pth
Traceback (most recent call last):
  File "/home/runner/MMNet/test.py", line 127, in <module>
    test(logger, options)
  File "/home/runner/MMNet/test.py", line 65, in test
    test_dataset = Dataset.CorrespondenceDataset(
  File "/home/runner/MMNet/data/PascalDataset.py", line 32, in __init__
    self.train_data = pd.read_csv(self.spt_path)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/util/_decorators.py", line 311, in wrapper
    return func(*args, **kwargs)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv
    return _read(filepath_or_buffer, kwds)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 482, in _read
    parser = TextFileReader(filepath_or_buffer, **kwds)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 811, in __init__
    self._engine = self._make_engine(self.engine)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1040, in _make_engine
    return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 51, in __init__
    self._open_handles(src, kwds)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py", line 222, in _open_handles
    self.handles = get_handle(
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/common.py", line 702, in get_handle
    handle = open(
FileNotFoundError: [Errno 2] No such file or directory: '/data/SC_Dataset/PF-PASCAL/test_pairs.csv'

    P.S. Output of executing ls /data/SC_Dataset/PF-PASCAL/:

    Annotations  html  index.html  JPEGImages  parsePascalVOC.mat  ShowMatchingPairs
    opened by tjyuyao 2
  • How to reproduce the reported test accuracy?

    How to reproduce the reported test accuracy?

    By running given following command with code on the main branch:

    python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name assets/model/mmnet_fcnresnet101_spair.pth --resize 224,320 --benchmark spair

    I expect to get the reported accuracy in the Table.2 of paper, i.e. 50.4 "all" accuracy, or spair with fcn-resnet101 backbone([email protected]:46.6%): as noted in the README.md file. However I get the following output, finding nowhere the related results. Can you point out the steps to reproduce the test accuracy?

    2021-11-19 00:49:54,452 - INFO - Options listed below:----------------
2021-11-19 00:49:54,452 - INFO - name: framework_train
2021-11-19 00:49:54,453 - INFO - benchmark: spair
2021-11-19 00:49:54,453 - INFO - thresh_type: auto
2021-11-19 00:49:54,454 - INFO - backbone_name: fcn-resnet101
2021-11-19 00:49:54,455 - INFO - ms_rate: 4
2021-11-19 00:49:54,455 - INFO - feature_channel: 21
2021-11-19 00:49:54,456 - INFO - batch: 5
2021-11-19 00:49:54,456 - INFO - gpu: 0
2021-11-19 00:49:54,457 - INFO - data_path: /data/SC_Dataset
2021-11-19 00:49:54,457 - INFO - ckp_path: ./checkpoints_debug
2021-11-19 00:49:54,458 - INFO - visualization_path: visualization
2021-11-19 00:49:54,458 - INFO - model_type: MMNet
2021-11-19 00:49:54,459 - INFO - ckp_name: assets/model/mmnet_fcnresnet101_spair.pth
2021-11-19 00:49:54,459 - INFO - log_path: ./logs/
2021-11-19 00:49:54,460 - INFO - resize: 224,320
2021-11-19 00:49:54,460 - INFO - max_kps_num: 50
2021-11-19 00:49:54,461 - INFO - split_type: test
2021-11-19 00:49:54,461 - INFO - alpha: 0.05
2021-11-19 00:49:54,462 - INFO - resolution: 2
2021-11-19 00:49:54,462 - INFO - Options all listed.------------------
2021-11-19 00:49:54,463 - INFO - ckp file: assets/model/mmnet_fcnresnet101_spair.pth
2021-11-19 00:50:04,950 - INFO - [    0/12234]: 	 [Pair PCK: 0.333]	[Average: 0.333] aeroplane
2021-11-19 00:50:04,953 - INFO - [    1/12234]: 	 [Pair PCK: 0.100]	[Average: 0.217] aeroplane
2021-11-19 00:50:04,956 - INFO - [    2/12234]: 	 [Pair PCK: 0.308]	[Average: 0.247] aeroplane
2021-11-19 00:50:04,958 - INFO - [    3/12234]: 	 [Pair PCK: 0.364]	[Average: 0.276] aeroplane
2021-11-19 00:50:04,960 - INFO - [    4/12234]: 	 [Pair PCK: 0.000]	[Average: 0.221] aeroplane
2021-11-19 00:50:05,575 - INFO - [    5/12234]: 	 [Pair PCK: 0.200]	[Average: 0.217] aeroplane
2021-11-19 00:50:05,577 - INFO - [    6/12234]: 	 [Pair PCK: 0.250]	[Average: 0.222] aeroplane
2021-11-19 00:50:05,580 - INFO - [    7/12234]: 	 [Pair PCK: 0.308]	[Average: 0.233] aeroplane
2021-11-19 00:50:05,583 - INFO - [    8/12234]: 	 [Pair PCK: 0.182]	[Average: 0.227] aeroplane
2021-11-19 00:50:05,585 - INFO - [    9/12234]: 	 [Pair PCK: 0.636]	[Average: 0.268] aeroplane
2021-11-19 00:50:06,153 - INFO - [   10/12234]: 	 [Pair PCK: 0.667]	[Average: 0.304] aeroplane
2021-11-19 00:50:06,156 - INFO - [   11/12234]: 	 [Pair PCK: 0.385]	[Average: 0.311] aeroplane
2021-11-19 00:50:06,158 - INFO - [   12/12234]: 	 [Pair PCK: 0.455]	[Average: 0.322] aeroplane
2021-11-19 00:50:06,160 - INFO - [   13/12234]: 	 [Pair PCK: 0.250]	[Average: 0.317] aeroplane
2021-11-19 00:50:06,163 - INFO - [   14/12234]: 	 [Pair PCK: 0.615]	[Average: 0.337] aeroplane
2021-11-19 00:50:06,731 - INFO - [   15/12234]: 	 [Pair PCK: 0.000]	[Average: 0.316] aeroplane
...
2021-11-19 01:13:47,264 - INFO - [12216/12234]: 	 [Pair PCK: 0.222]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,265 - INFO - [12217/12234]: 	 [Pair PCK: 0.200]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,266 - INFO - [12218/12234]: 	 [Pair PCK: 0.250]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,268 - INFO - [12219/12234]: 	 [Pair PCK: 0.222]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,837 - INFO - [12220/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,838 - INFO - [12221/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,848 - INFO - [12222/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,850 - INFO - [12223/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,853 - INFO - [12224/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,422 - INFO - [12225/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,424 - INFO - [12226/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,425 - INFO - [12227/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,427 - INFO - [12228/12234]: 	 [Pair PCK: 0.333]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,429 - INFO - [12229/12234]: 	 [Pair PCK: 0.222]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,896 - INFO - [12230/12234]: 	 [Pair PCK: 0.333]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,899 - INFO - [12231/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,899 - INFO - [12232/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,901 - INFO - [12233/12234]: 	 [Pair PCK: 0.111]	[Average: 0.333] tvmonitor
    opened by tjyuyao 1
Releases(v0.1.0)
Owner
joey zhao
Master in Computer Sciences and Technology at Fudan University
joey zhao
GitHub Repository
Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network This repository is the implementation of ACE-HGNN in PyTorch. Environment pyt

9 Nov 28, 2022
AOT (Associating Objects with Transformers) in PyTorch

An efficient modular implementation of Associating Objects with Transformers for Video Object Segmentation in PyTorch

162 Dec 14, 2022
A New Approach to Overgenerating and Scoring Abstractive Summaries

We provide the source code for the paper "A New Approach to Overgenerating and Scoring Abstractive Summaries" accepted at NAACL'21. If you find the code useful, please cite the following paper.

Kaiqiang Song 4 Apr 03, 2022
This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

PyTorch Infer Utils This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model infer

Alex Gorodnitskiy 11 Mar 20, 2022
[CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation

RCIL [CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation Chang-Bin Zhang1, Jia-Wen Xiao1, Xialei Liu1, Ying-Cong Chen2

Chang-Bin Zhang 71 Dec 28, 2022
Implementation of Heterogeneous Graph Attention Network

HetGAN Implementation of Heterogeneous Graph Attention Network This is the code repository of paper "Prediction of Metro Ridership During the COVID-19

5 Dec 28, 2021
Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Dominic Rampas 247 Dec 16, 2022
LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

Deep-Leafsnap Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhev

Sujith Vishwajith 48 Nov 27, 2022
MQBench Quantization Aware Training with PyTorch

MQBench Quantization Aware Training with PyTorch I am using MQBench(Model Quantization Benchmark)(http://mqbench.tech/) to quantize the model for depl

Ling Zhang 29 Nov 18, 2022
A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

PokeGAN A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon. Dataset The model has been trained on dataset that includes 8

19 Jul 26, 2022
The world's largest toxicity dataset.

The Toxicity Dataset by Surge AI Saving the internet is fun. Combing through thousands of online comments to build a toxicity dataset isn't. That's wh

Surge AI 134 Dec 19, 2022
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021 [WIP] The code for CVPR 2021 paper 'Disentangled Cycle Consistency for H

ChongjianGE 94 Dec 11, 2022
Constructing Neural Network-Based Models for Simulating Dynamical Systems

Constructing Neural Network-Based Models for Simulating Dynamical Systems Note this repo is work in progress prior to reviewing This is a companion re

Christian Møldrup Legaard 21 Nov 25, 2022
Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning

isvd Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning If you find this code useful, you may cite us as: @inprocee

Sami Abu-El-Haija 16 Jan 08, 2023
Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

Intelligent Systems Lab Org 416 Jan 03, 2023
EMNLP 2021 - Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling This is the official implementation for "Frustratingly Simple Pretraining Al

Atsuki Yamaguchi 31 Nov 18, 2022
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine

FlyEgle 214 Dec 29, 2022
Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19 (Oral).

Pose-Transfer Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19(Oral). The paper is available here. Video generation

Tengteng Huang 679 Jan 04, 2023
Utilities and information for the signals.numer.ai tournament

dsignals Utilities and information for the signals.numer.ai tournament using eodhistoricaldata.com eodhistoricaldata.com provides excellent historical

Degerhan Usluel 23 Dec 18, 2022
AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

Abel 2 Apr 12, 2022