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Semi-Supervised Learning using USB built upon PyTorch

Author: Hao Chen

Unified Semi-supervised learning Benchmark (USB) is a semi-supervised learning framework built upon PyTorch. Based on Datasets and Modules provided by PyTorch, USB becomes a flexible, modular, and easy-to-use framework for semi-supervised learning. It supports a variety of semi-supervised learning algorithms, including FixMatch, FreeMatch, DeFixMatch, SoftMatch, and so on. It also supports a variety of imbalanced semi-supervised learning algorithms. The benchmark results across different datasets of computer vision, natural language processing, and speech processing are included in USB.

This tutorial will walk you through the basics of using the USB lighting package. Let’s get started by training a FreeMatch/SoftMatch model on CIFAR-10 using pretrained ViT! And we will show it is easy to change the semi-supervised algorithm and train on imbalanced datasets.

USB framework illustration

Introduction to FreeMatch and SoftMatch in Semi-Supervised Learning

Here we provide a brief introduction to FreeMatch and SoftMatch. First, we introduce a famous baseline for semi-supervised learning called FixMatch. FixMatch is a very simple framework for semi-supervised learning, where it utilizes a strong augmentation to generate pseudo labels for unlabeled data. It adopts a confidence thresholding strategy to filter out the low-confidence pseudo labels with a fixed threshold set. FreeMatch and SoftMatch are two algorithms that improve upon FixMatch. FreeMatch proposes adaptive thresholding strategy to replace the fixed thresholding strategy in FixMatch. The adaptive thresholding progressively increases the threshold according to the learning status of the model on each class. SoftMatch absorbs the idea of confidence thresholding as an weighting mechanism. It proposes a Gaussian weighting mechanism to overcome the quantity-quality trade-off in pseudo-labels. In this tutorial, we will use USB to train FreeMatch and SoftMatch.

Use USB to Train FreeMatch/SoftMatch on CIFAR-10 with only 40 labels

USB is easy to use and extend, affordable to small groups, and comprehensive for developing and evaluating SSL algorithms. USB provides the implementation of 14 SSL algorithms based on Consistency Regularization, and 15 tasks for evaluation from CV, NLP, and Audio domain. It has a modular design that allows users to easily extend the package by adding new algorithms and tasks. It also supports a Python API for easier adaptation to different SSL algorithms on new data.

Now, let’s use USB to train FreeMatch and SoftMatch on CIFAR-10. First, we need to install USB package semilearn and import necessary API functions from USB. Below is a list of functions we will use from semilearn:

  • get_dataset to load dataset, here we use CIFAR-10

  • get_data_loader to create train (labeled and unlabeled) and test data

loaders, the train unlabeled loaders will provide both strong and weak augmentation of unlabeled data - get_net_builder to create a model, here we use pretrained ViT - get_algorithm to create the semi-supervised learning algorithm, here we use FreeMatch and SoftMatch - get_config: to get default configuration of the algorithm - Trainer: a Trainer class for training and evaluating the algorithm on dataset

import semilearn
from semilearn import get_dataset, get_data_loader, get_net_builder, get_algorithm, get_config, Trainer

After importing necessary functions, we first set the hyper-parameters of the algorithm.

config = {
    'algorithm': 'freematch',
    'net': 'vit_tiny_patch2_32',
    'use_pretrain': True,
    'pretrain_path': 'https://github.com/microsoft/Semi-supervised-learning/releases/download/v.0.0.0/vit_tiny_patch2_32_mlp_im_1k_32.pth',

    # optimization configs
    'epoch': 1,
    'num_train_iter': 4000,
    'num_eval_iter': 500,
    'num_log_iter': 50,
    'optim': 'AdamW',
    'lr': 5e-4,
    'layer_decay': 0.5,
    'batch_size': 16,
    'eval_batch_size': 16,


    # dataset configs
    'dataset': 'cifar10',
    'num_labels': 40,
    'num_classes': 10,
    'img_size': 32,
    'crop_ratio': 0.875,
    'data_dir': './data',
    'ulb_samples_per_class': None,

    # algorithm specific configs
    'hard_label': True,
    'T': 0.5,
    'ema_p': 0.999,
    'ent_loss_ratio': 0.001,
    'uratio': 2,
    'ulb_loss_ratio': 1.0,

    # device configs
    'gpu': 0,
    'world_size': 1,
    'distributed': False,
    "num_workers": 4,
}
config = get_config(config)

Then, we load the dataset and create data loaders for training and testing. And we specify the model and algorithm to use.

dataset_dict = get_dataset(config, config.algorithm, config.dataset, config.num_labels, config.num_classes, data_dir=config.data_dir, include_lb_to_ulb=config.include_lb_to_ulb)
train_lb_loader = get_data_loader(config, dataset_dict['train_lb'], config.batch_size)
train_ulb_loader = get_data_loader(config, dataset_dict['train_ulb'], int(config.batch_size * config.uratio))
eval_loader = get_data_loader(config, dataset_dict['eval'], config.eval_batch_size)
algorithm = get_algorithm(config,  get_net_builder(config.net, from_name=False), tb_log=None, logger=None)

We can start training the algorithms on CIFAR-10 with 40 labels now. We train for 4000 iterations and evaluate every 500 iterations.

trainer = Trainer(config, algorithm)
trainer.fit(train_lb_loader, train_ulb_loader, eval_loader)

Finally, let’s evaluate the trained model on the validation set. After training 4000 iterations with FreeMatch on only 40 labels of CIFAR-10, we obtain a classifier that achieves above 93 accuracy on the validation set.

trainer.evaluate(eval_loader)

Use USB to Train SoftMatch with specific imbalanced algorithm on imbalanced CIFAR-10

Now let’s say we have imbalanced labeled set and unlabeled set of CIFAR-10, and we want to train a SoftMatch model on it. We create an imbalanced labeled set and imbalanced unlabeled set of CIFAR-10, by setting the lb_imb_ratio and ulb_imb_ratio to 10. Also, we replace the algorithm with softmatch and set the imbalanced to True.

config = {
    'algorithm': 'softmatch',
    'net': 'vit_tiny_patch2_32',
    'use_pretrain': True,
    'pretrain_path': 'https://github.com/microsoft/Semi-supervised-learning/releases/download/v.0.0.0/vit_tiny_patch2_32_mlp_im_1k_32.pth',

    # optimization configs
    'epoch': 1,
    'num_train_iter': 4000,
    'num_eval_iter': 500,
    'num_log_iter': 50,
    'optim': 'AdamW',
    'lr': 5e-4,
    'layer_decay': 0.5,
    'batch_size': 16,
    'eval_batch_size': 16,


    # dataset configs
    'dataset': 'cifar10',
    'num_labels': 1500,
    'num_classes': 10,
    'img_size': 32,
    'crop_ratio': 0.875,
    'data_dir': './data',
    'ulb_samples_per_class': None,
    'lb_imb_ratio': 10,
    'ulb_imb_ratio': 10,
    'ulb_num_labels': 3000,

    # algorithm specific configs
    'hard_label': True,
    'T': 0.5,
    'ema_p': 0.999,
    'ent_loss_ratio': 0.001,
    'uratio': 2,
    'ulb_loss_ratio': 1.0,

    # device configs
    'gpu': 0,
    'world_size': 1,
    'distributed': False,
    "num_workers": 4,
}
config = get_config(config)

Then, we re-load the dataset and create data loaders for training and testing. And we specify the model and algorithm to use.

dataset_dict = get_dataset(config, config.algorithm, config.dataset, config.num_labels, config.num_classes, data_dir=config.data_dir, include_lb_to_ulb=config.include_lb_to_ulb)
train_lb_loader = get_data_loader(config, dataset_dict['train_lb'], config.batch_size)
train_ulb_loader = get_data_loader(config, dataset_dict['train_ulb'], int(config.batch_size * config.uratio))
eval_loader = get_data_loader(config, dataset_dict['eval'], config.eval_batch_size)
algorithm = get_algorithm(config,  get_net_builder(config.net, from_name=False), tb_log=None, logger=None)

We can start Train the algorithms on CIFAR-10 with 40 labels now. We train for 4000 iterations and evaluate every 500 iterations.

trainer = Trainer(config, algorithm)
trainer.fit(train_lb_loader, train_ulb_loader, eval_loader)

Finally, let’s evaluate the trained model on the validation set.

trainer.evaluate(eval_loader)

References [1] USB: https://github.com/microsoft/Semi-supervised-learning [2] Kihyuk Sohn et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence [3] Yidong Wang et al. FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning [4] Hao Chen et al. SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning

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