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}
}
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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
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