Rank 3 : Source code for OPPO 6G Data Generation Challenge

Overview

OPPO 6G Data Generation with an E2E Framework

Homepage of OPPO 6G Data Generation Challenge

Datasets

  • H1_32T4R.mat
  • H2_32T4R.mat
  • Please put the original data in data folder.

Data Augmentation Scheme

  • Complex number is special : a+bj has a same square similarity with -a-bj, b-aj and -b+aj
  • With the strategy above, you can quadruple the amount of training data compared to the raw.
  • We randomly scale the data with a factor between 0.8~1.2, random gaussian noise with mean equals to 0 and std equals to 1e-4 are adopted.

Architectures

  • Auto encoder with reconstruction loss.
  • ResNet18 as an Encoder.
  • 3D Conv as a Decoder.
  • Position Attention Module and Channel Attention Module are important.
  • Normalization such as BatchNorm2d after Decoder is important.
  • Latent Quantization.

Pretrained Models

We provide several pretrained models in the folder of saved_models.

  • Sim : similarity score tested on the raw data.
  • Multi : multi score tested on the raw data.
  • Score : tested on the local raw data.
  • Feel free to use the pretrained weights or training from scratch.

Training

  • Modify the data_type in train.py, maybe you have to choose a suitable GPU id.
  • Online validation, only save the models with best scores so far.
  • Hints : smaller batch size may result in higher similarity score and higher multi score.
  • Epochs : we perform no ablation study on this parameter, you can just let it run.
  • Benchmark : data1: local score approx 0.82~0.83
  • Benchmark : data2: local score approx 0.76~0.77

Boost Scheme

  • We use adaboost weights to ensemble several models for acquiring performance gain.
  • Without model ensembles, you can still achieve an online score up to 0.72 easily.

Submit_pt

  • You can just use the single model without ensembles which is much easier.
  • Without deep ensembles, it is still trivial to achieve a score up to 0.72

Reference

Owner
Sen Pei
.........
Sen Pei
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