Allow training on customized dataset

README.md

@@ -1,48 +1,56 @@
-# MixMatch
-This is an unofficial PyTorch implementation of [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://arxiv.org/abs/1905.02249). 
-The official Tensorflow implementation is [here](https://github.com/google-research/mixmatch).
+MixMatch-pytorch-customized-dataset
 
-Now only experiments on CIFAR-10 are available.
+This is a PyTorch implementation of MixMatch, which allows training with customized dataset.
 
-This repository carefully implemented important details of the official implementation to reproduce the results.
+The official Tensorflow implementation is [here](https://github.com/google-research/mixmatch) and the forked Pytorch implementation is [here](https://github.com/YU1ut/MixMatch-pytorch).
 
+## Revision Note
 
-## Requirements
-- Python 3.6+
-- PyTorch 1.0
-- **torchvision 0.2.2 (older versions are not compatible with this code)** 
-- tensorboardX
-- progress
-- matplotlib
-- numpy
+Two revised training functions are updated compared to the original forked [repository](https://github.com/YU1ut/MixMatch-pytorch).
+
+
+In addition, I adjusted the code structure of the original Pytorch implementation and made necessary notes for better understanding.
+
+train.py is the original Pytorch Implementation of [that](https://github.com/YU1ut/MixMatch-pytorch), which is trained on CIFAR-10 only.
+
+1. train_SSL.py
+
+   Revised the dataset part to allow customized dataset for training.
+
+   Revised the original MixMatch loss function by considering the potential class imbalance issue in the training data.
+
+2. train_TL.py
+
+   This is a simple baseline training process by supervised learning only using labeled data with the same number as that of SSL training.
+
+   This allows performance evaluation with SSL training.
 
 ## Usage
 
+### Environment
+
+Check code environment "requirements.txt".
+
 ### Train
-Train the model by 250 labeled data of CIFAR-10 dataset:
 
-```
-python train.py --gpu <gpu_id> --n-labeled 250 --out cifar10@250
-```
+1. Customized dataset preparation.
 
-Train the model by 4000 labeled data of CIFAR-10 dataset:
+   Put the data under "dataset/".
 
-```
-python train.py --gpu <gpu_id> --n-labeled 4000 --out cifar10@4000
-```
+   Put the training/validatioin/test txt under the current location.
 
-### Monitoring training progress
-```
-tensorboard.sh --port 6006 --logdir cifar10@250
-```
+   Update the path information both in the train_SSL.py and train_TL.py.
+
+2. Parameter settting by users. For example, update the number of labeled data for training.
 
-## Results (Accuracy)
-| #Labels | 250 | 500 | 1000 | 2000| 4000 |
-|:---|:---:|:---:|:---:|:---:|:---:|
-|Paper | 88.92 ± 0.87 | 90.35 ± 0.94 | 92.25 ± 0.32| 92.97 ± 0.15 |93.76 ± 0.06|
-|This code | 88.71 | 88.96 | 90.52 | 92.23 | 93.52 |
+3. Train the model in SSL mode:
+
+   python train_SSL.py
+
+4. Train the model in TL mode:
+
+   python train_TL.py
 
-(Results of this code were evaluated on 1 run. Results of 5 runs with different seeds will be updated later. )
 
 ## References
 ```
@@ -52,4 +60,6 @@ tensorboard.sh --port 6006 --logdir cifar10@250
   journal={arXiv preprint arXiv:1905.02249},
   year={2019}
 }
-```
+```
+
+

requirements.txt

@@ -0,0 +1,38 @@
+brotlipy==0.7.0
+certifi==2021.10.8
+cffi @ file:///opt/conda/conda-bld/cffi_1642701102775/work
+charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
+cryptography @ file:///tmp/build/80754af9/cryptography_1639414570729/work
+cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
+fonttools==4.25.0
+idna @ file:///tmp/build/80754af9/idna_1637925883363/work
+joblib @ file:///tmp/build/80754af9/joblib_1635411271373/work
+kiwisolver @ file:///opt/conda/conda-bld/kiwisolver_1638569886207/work
+matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1647441664166/work
+mkl-fft==1.3.1
+mkl-random @ file:///tmp/build/80754af9/mkl_random_1626179032232/work
+mkl-service==2.4.0
+munkres==1.1.4
+numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1634106693478/work
+opencv-python==4.5.5.64
+packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work
+Pillow==9.0.1
+progress @ file:///tmp/build/80754af9/progress_1614270514501/work
+protobuf==3.19.1
+pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
+pyOpenSSL @ file:///opt/conda/conda-bld/pyopenssl_1643788558760/work
+pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
+PySocks @ file:///tmp/build/80754af9/pysocks_1594394576006/work
+python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
+requests @ file:///opt/conda/conda-bld/requests_1641824580448/work
+scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1642601761909/work
+scipy @ file:///tmp/build/80754af9/scipy_1641536880743/work
+six @ file:///tmp/build/80754af9/six_1644875935023/work
+tensorboardX @ file:///tmp/build/80754af9/tensorboardx_1621440489103/work
+threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
+torch==1.11.0
+torchaudio==0.11.0
+torchvision==0.12.0
+tornado @ file:///tmp/build/80754af9/tornado_1606942283357/work
+typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work
+urllib3 @ file:///opt/conda/conda-bld/urllib3_1643638302206/work