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This is code for the paper Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification (ICLR 2022)

@inproceedings{tang2021omni,
  title={Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification},
  author={Tang, Wensi and Long, Guodong and Liu, Lu and Zhou, Tianyi and Blumenstein, Michael and Jiang, Jing},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

OS-CNN achieves SOTA on

UCR and UEA archives and some private datasets.

with just the default hyperpermeter setting! no need to search!

Just have a try!!!

Environment

python == 3.5
pytorch == 1.1.0
scikit-learn == 0.21.3

Easy use

Try Google Colab

Import this file OS_CNN_Colab_demo.ipynb

or

Run With Jupyter Notebook

1_1_OS-CNN_easy_use_Run_and_Save_Model.ipynb

This is an easy use of OS-CNN on univeriate dataset
Search X_train, y_train, X_test, y_test = TSC_data_loader(dataset_path, dataset_name)
you could replace the X_train, y_train, X_test, y_test as you want, or you could change dataset_name to determine which UCR dataset you want to run

2_1_1_OS-CNN_easy_use_Run_and_Save_Model_for_multivariate.ipynb

This is an easy use OS-CNN on multivariate dataset
search X_train, y_train, X_test, y_test = TSC_multivariate_data_loader(dataset_path, dataset_name)
you could replace the X_train, y_train, X_test, y_test as you want, or you could change dataset_name to determine which UEA dataset you want to run

Full Results

In ./Full_Results folder We have results of OS-CNN for UCR 85 datasets, UCR 128 datasets, and UEA 30 datasets.

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Detailed description of each file

1_1_OS-CNN_easy_use_Run_and_Save_Model.ipynb

This is an easy use OS-CNN
search X_train, y_train, X_test, y_test = TSC_data_loader(dataset_path, dataset_name)
you could replace the X_train, y_train, X_test, y_test as you like, or you could change dataset_name to determine which UCR dataset you want to run

1_2_OS-CNN_load_saved_model_for_prediction.ipynb

This code could help you to load morel and use the model for prediction (it needs model trained by 1_1_OS-CNN_easy_use_Run_and_Save_Model.ipynb)

2_1_1_OS-CNN_easy_use_Run_and_Save_Model_for_multivariate.ipynb

This is an easy use OS-CNN on multivariate dataset
search X_train, y_train, X_test, y_test = TSC_multivariate_data_loader(dataset_path, dataset_name)
you could replace the X_train, y_train, X_test, y_test as you like, or you could change dataset_name to determine which UEA dataset you want to run

2_2_1_OS_OS-CNN_easy_use_Run_and_Save_Model_for_multivariate.ipynb

This is an easy use OS_OS-CNN on multivariate dataset
the OS_OS-CNN is using OS layer on each variate of multivariate then put the feature map into an OS-CNN
search X_train, y_train, X_test, y_test = TSC_multivariate_data_loader(dataset_path, dataset_name)
you could replace the X_train, y_train, X_test, y_test as you like, or you could change dataset_name to determine which UEA dataset you want to run

3_1_compare_result.ipynb

In here, you could select different models to compare with os-cnn

Folder ./Code_example_of_theoretical_proof/ has the code verification of theoretical proof for our paper

1_1_Deep_Learning_Convolution_and_Convolution_theorem.ipynb
    Code verification of Section 3.2

2_1_Time_and_Space_Complexity_of_OS-CNN_Vs_FCN_ResNet.ipynb
    This code shows the model size of OS-CNN and Resnet and FCN. 
    It shows the OS-CNN is of better time and space complexity than SOTA

3_1_verification _of_Pytorch_FCN_&_ResNet_implementation.ipynb
    This code verifies the FCN and ResNet Pytorch implementation is correct
    
3_2_FCN_with_different_kernel_size.ipynb
    This code gets the classification result of FCN with different kernel sizes. Section 6.2 Table 3

3_3_Positional_information_loss_of_FCN_and_how_OS-CNN_overcome_this.ipynb
    This code shows the positional information loss of fixed kernel size design. Section 3.4

4_1_OS-CNN_load_saved_model_and_visualization_weight.ipynb 
    Check the initial noise and its influence on the feature extraction. Section 3.4
    
4_2_Frequency_Resolution.ipynb
    Check frequency resolution of small kernel size. Section 3.4
    
4_3_Check_Capability_Equivalent.ipynb
    This is code for Section 5: No representation ability lose 
    
4_4_calculate_prime_model_size.ipynb
    This is code for Section 5: Smaller model size
    
4_5_Enough_channel.ipynb
    This is code for Section 5.

Folder ./Appendix has some supplementary material:

1. Proof of No representation ability lose is a theoretical proof of no representation ability lose
2. The novelty of OS-CNN is a demonstration for why it can reduce model size
3. OS-CNN_network_structure.ipynb  It shows the network structure of OS-CNN

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Folder ./Texas_Sharpshooter_plot has materials for comparison between OS-CNN and cDTW by Texas Sharpshooter plot
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