AK-SSL 0.2.0

A Self-Supervised Learning Library

AK_SSL: A Self-Supervised Learning Library

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📒 Table of Contents

• 📒 Table of Contents
• 📍 Overview
• ✍️ Self Supervised Learning
• 🔎 Supported Methods
• 📦 Installation
• 💡 Tutorial
• 📊 Benchmarks
• 📜 References Used
• 💯 License
• 🤝 Collaborators

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📍 Overview

Welcome to the Self-Supervised Learning Library! This repository hosts a collection of tools and implementations for self-supervised learning. Self-supervised learning is a powerful paradigm that leverages unlabeled data to pre-trained models, which can then be fine-tuned on specific tasks with smaller labeled datasets. This library aims to provide researchers and practitioners with a comprehensive set of tools to experiment, learn, and apply self-supervised learning techniques effectively. This project was our assignment during the summer apprenticeship and final project in the newly established Intelligent and Learning System (ILS) laboratory at the University of Isfahan.

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✍️ Self Supervised Learning

Self-supervised learning is a subfield of machine learning where models are trained to predict certain aspects of the input data without relying on manual labeling. This approach has gained significant attention due to its ability to leverage large amounts of unlabeled data, which is often easier to obtain than fully annotated datasets. This library provides implementations of various self-supervised techniques, allowing you to experiment with and apply these methods in your own projects.

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🔎 Supported Methods

Vision Models

• BarlowTwins
• BYOL
• DINO
• MoCo v2
• MoCo v3
• SimCLR v1
• SimCLR v2
• SimSiam
• SwAV

Multimodal Models

• CLIP
• ALBEF
• SLIP
• VSE
• SimVLM
• UNITER

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📦 Installation

You can install AK_SSL and its dependencies from PyPI with:

pip install AK-SSL

We strongly recommend that you install AK_SSL in a dedicated virtualenv, to avoid conflicting with your system packages

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💡 Tutorial

Using AK_SSL, you have the flexibility to leverage the most recent self-supervised learning techniques seamlessly, harnessing the complete capabilities of PyTorch. You can explore diverse backbones, models, and optimizer while benefiting from a user-friendly framework that has been purposefully crafted for ease of use.

Initializing the Trainer for Vision Models

You can easily import Trainer module from AK_SSL library and start utilizing it right away.

from AK_SSL.vision import Trainer

Now, let's initialize the self-supervised trainer with our chosen method, backbone, dataset, and other configurations.

trainer = Trainer(
    method="barlowtwins",           # training method as string (BarlowTwins, BYOL, DINO, MoCov2, MoCov3, SimCLR, SimSiam, SwAV)
    backbone=backbone,              # backbone architecture as torch.Module
    feature_size=feature_size,      # size of the extracted features as integer
    image_size=32,                  # dataset image size as integer
    save_dir="./save_for_report/",  # directory to save training checkpoints and Tensorboard logs as string
    checkpoint_interval=50,         # interval (in epochs) for saving checkpoints as integer
    reload_checkpoint=False,        # reload a previously saved checkpoint as boolean
    verbose=True,                   # enable verbose output for training progress as a boolean
    **kwargs                        # other arguments 
)

Note: The use of **kwargs can differ between methods, depending on the specific method, loss function, transformation, and other factors. If you are utilizing any of the objectives listed below, you must provide their arguments during the initialization of the Trainer class.

• SimCLR Transformation

    color_jitter_strength     # a float to Set the strength of color
    use_blur                  # a boolean to specify whether to apply blur augmentation
    mean                      # a float to specify the mean values for each channel
    std                       # a float to specify the standard deviation values for each channel
  

• BarlowTwins

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
      hidden_dim              # an integer to specify dimensionality of the hidden layers in the neural network
      moving_average_decay    # a float to specify decay rate for moving averages during training
    

  • Loss

      lambda_param            # a float to controlling the balance between the main loss and the orthogonality loss
    

• DINO Method

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
      hidden_dim              # an integer to specify dimensionality of the hidden layers in the projection head neural network
      bottleneck_dim          # an integer to specify dimensionality of the bottleneck layer in the student network
      temp_student            # a float to specify temperature parameter for the student's logits
      temp_teacher            # a float to specify temperature parameter for the teacher's logits
      norm_last_layer         # a boolean to specify whether to normalize the last layer of the network
      momentum_teacher        # a float to control momentum coefficient for updating the teacher network
      num_crops               # an integer to determines the number of augmentations applied to each input image
      use_bn_in_head          # a boolean to spcecify whether to use batch normalization in the projection head
    

  • Loss

      center_momentum        # a float to control momentum coefficient for updating the center of cluster assignments
    

• MoCo v2

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
      K                       # an integer to specify number of negative samples per positive sample in the contrastive loss
      m                       # a float to control momentum coefficient for updating the moving-average encoder
    

  • Loss

      temperature             # a float to control the temperature for the contrastive loss function
    

• MoCo v3

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
      hidden_dim              # an integer to specify dimensionality of the hidden layers in the projection head neural network
      moving_average_decay    # a float to specify decay rate for moving averages during training
    

  • Loss

      temperature             # a float to control the temperature for the contrastive loss function
    

• SimCLR

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
      projection_num_layers   # an integer to specify the number of layers in the projection head (1: SimCLR v1, 2: SimCLR v2)
      projection_batch_norm   # a boolean to indicate whether to use batch normalization in the projection head
    

  • Loss

      temperature             # a float to control the temperature for the contrastive loss function
    

• SimSiam

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
    

  • Loss

      eps                     # a float to control the stability of the loss function
    

• SwAV

  • Method

      projection_dim          # an integer to specify dimensionality of the projection head
      hidden_dim              # an integer to specify dimensionality of the hidden layers in the projection head neural network
      epsilon                 # a float to control numerical stability in the algorithm
      sinkhorn_iterations     # an integer to specify the number of iterations in the Sinkhorn-Knopp algorithm
      num_prototypes          # an integer to specify the number of prototypes or clusters for contrastive learning
      queue_length            # an integer to specify rhe length of the queue for maintaining negative samples
      use_the_queue           # a boolean to indicate whether to use the queue for negative samples
      num_crops               # an integer to determines the number of augmentations applied to each input image
    

  • Loss

      temperature             # a float to control the temperature for the contrastive loss function
    

Initializing the Trainer for Multimodal Models

You can easily import Trainer module from AK_SSL library and start utilizing it right away.

from AK_SSL.multimodal import Trainer

Now, let's initialize the self-supervised trainer with our chosen method, backbone, dataset, and other configurations.

trainer = Trainer(
    method="clip",                  # training method as string (CLIP, ALBEF, SLIP, SimVLM, UNITER, VSE)
    image_encoder=img_encoder,      # vision model to extract image features as nn.Module
    text_encoder=txt_encoder,       # text model to extract text features as nn.Module
    mixed_precision_training=True,  # whether to use mixed precision training or not as boolean
    save_dir="./save_for_report/",  # directory to save training checkpoints and Tensorboard logs as string
    checkpoint_interval=50,         # interval (in epochs) for saving checkpoints as integer
    reload_checkpoint=False,        # reload a previously saved checkpoint as boolean
    verbose=True,                   # enable verbose output for training progress as a boolean
    **kwargs                        # other arguments 
)

Note: The use of **kwargs can differ between methods, depending on the specific method, loss function, transformation, and other factors. If you are utilizing any of the objectives listed below, you must provide their arguments during the initialization of the Trainer class.

• CLIP

    image_feature_dim         # Dimension of the image features as integer
    text_feature_dim          # Dimension of the text features as integer
    embed_dim                 # Dimension of the embeddings as integer
    init_tau                  # Initial value of tau as float
    init_b                    # Initial value of b as float
  

• ALBEF

    mlm_probability           # Masked language modeling probability as float
    embed_dim                 # Dimension of the embeddings as integer
    vision_width              # Vision encoder output width as integer
    temp                      # Temperature parameter as float
    queue_size                # Queue size as integer
    momentum                  # Momentum parameter as float
  

• SimVLM

    transformer_encoder       # Transformer encoder for vision and text embeddings as nn.Module
    transformer_decoder       # Transformer decoder for embeddings as nn.Module
    vocab_size                # Size of the vocabulary as integer
    feature_dim               # Dimension of the features as integer
    max_seq_len               # Maximum sequence length as integer
    max_trunc_txt_len         # Maximum truncated text length as integer
    prefix_txt_len            # Prefix text length as integer
    target_txt_len            # Target text length as integer
    pad_idx                   # Padding index as integer
    image_resolution          # Image resolution as integer
    patch_size                # Patch size as integer
    num_channels              # Number of channels as integer
  

• SLIP

    mlp_dim                   # Dimension of the MLP as integer
    vision_feature_dim        # Dimension of the vision features as integer
    transformer_feature_dim   # Dimension of the transformer features as integer
    embed_dim                 # Dimension of the embeddings as integer
  

• UNITER

    pooler                          # pooler as nn.Module
    encoder                         # transformer encoder as nn.Module
    num_answer                      # number of answer classes as integer
    hidden_size                     # hidden size as integer
    attention_probs_dropout_prob    # dropout rate as float
    initializer_range               # initializer range as float
  

• VSE

    margin                   # Margin for contrastive loss as float
  

Training the Self-Supervised Model for Vision Models

Then, we'll train the self-supervised model using the specified parameters.

  trainer.train(
      dataset=train_dataset,          # training dataset as torch.utils.data.Dataset               
      batch_size=256,          # the number of training examples used in each iteration as integer
      start_epoch=1,           # the starting epoch for training as integer (if 'reload_checkpoint' parameter was True, start epoch equals to the latest checkpoint epoch)
      epochs=100,              # the total number of training epochs as integer
      optimizer="Adam",        # the optimization algorithm used for training as string (Adam, SGD, or AdamW)
      weight_decay=1e-6,       # a regularization term to prevent overfitting by penalizing large weights as float
      learning_rate=1e-3,      # the learning rate for the optimizer as float
)

Training the Self-Supervised Model for Multimodal Models

Then, we'll train the self-supervised model using the specified parameters.

  trainer.train(
      dataset=train_dataset,           # the training data set as torch.utils.data.Dataset             
      batch_size=256,          # the number of training examples used in each iteration as integer
      start_epoch=1,           # the starting epoch for training as integer (if 'reload_checkpoint' parameter was True, start epoch equals to the latest checkpoint epoch)
      epochs=100,              # the total number of training epochs as integer
      optimizer="Adam",        # the optimization algorithm used for training as string (Adam, SGD, or AdamW)
      weight_decay=1e-6,       # a regularization term to prevent overfitting by penalizing large weights as float
      learning_rate=1e-3,      # the learning rate for the optimizer as float
)

Evaluating the Vision Self-Supervised Models

This evaluation assesses how well the pre-trained model performs on a dataset, specifically for tasks related to linear evaluation.

trainer.evaluate(
    train_dataset=train_dataset,      # to specify the training dataset as torch.utils.data.Dataset
    test_dataset=test_dataset,        # to specify the testing dataset as torch.utils.data.Dataset
    eval_method="linear",             # the evaluation method to use as string (linear or finetune)
    top_k=1,                          # the number of top-k predictions to consider during evaluation as integer
    epochs=100,                       # the number of evaluation epochs as integer
    optimizer='Adam',                 # the optimization algorithm used during evaluation as string (Adam, SGD, or AdamW)
    weight_decay=1e-6,                # a regularization term applied during evaluation to prevent overfitting as float
    learning_rate=1e-3,               # the learning rate for the optimizer during evaluation as float
    batch_size=256,                   # the batch size used for evaluation in integer
    fine_tuning_data_proportion=1,    # the proportion of training data to use during evaluation as float in range of (0.0, 1]
)

Get the Vision Self-Supervised Models backbone

In case you want to use the pre-trained network in your own downstream task, you need to define a downstream task model. This model should include the self-supervised model backbone as one of its components. Here's an example of how to define a simple downstream model class:

  class DownstreamNet(nn.Module):
      def __init__(self, backbone, **kwargs):
          super().__init__()
          self.backbone = backbone
  
          # You can define your downstream task model here
  
      def forward(self, x):
          x = self.backbone(x)
          # ...
  
  
  downstream_model = DownstreamNet(trainer.get_backbone())

Loading Self-Supervised Model Checkpoint

To load a previous checkpoint into the network, you can do as below.

path = 'YOUR CHECKPOINT PATH'
trainer.load_checkpoint(path)

Saving Self-Supervised Model backbone

To save model backbone, you can do as below.

trainer.save_backbone()

That's it! You've successfully trained and evaluate a self-supervised model using the AK_SSL Python library. You can further customize and experiment with different self-supervised methods, backbones, and hyperparameters to suit your specific tasks. You can find the description of Trainer class and its function using help built in fuction in python.

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📊 Benchmarks

We executed models and obtained results on the CIFAR10 dataset, with plans to expand our experimentation to other datasets. Please note that hyperparameters were not optimized for maximum accuracy.

Method	Backbone	Batch Size	Epoch	Optimizer	Learning Rate	Weight Decay	Linear Top1	Fine-tune Top1	Download Backbone	Download Full Checkpoint
BarlowTwins	Resnet18	256	800	Adam	1e-3	1e-6	70.92%	79.50%	Link	Link
BYOL	Resnet18	256	800	Adam	1e-3	1e-6	71.06%	71.04%
DINO	Resnet18	256	800	Adam	1e-3	1e-6	9.91%	9.76%
MoCo v2	Resnet18	256	800	Adam	1e-3	1e-6	70.08%	78.71%	Link	Link
MoCo v3	Resnet18	256	800	Adam	1e-3	1e-6	59.98%	74.20%	Link	Link
SimCLR v1	Resnet18	256	800	Adam	1e-3	1e-6	73.09%	72.75%	Link	Link
SimCLR v2	Resnet18	256	800	Adam	1e-3	1e-6	73.07%	81.52%
SimSiam	Resnet18	256	800	Adam	1e-3	1e-6	19.77%	70.77%	Link	Link
SwAv	Resnet18	256	800	Adam	1e-3	1e-6	33.36%	74.14%

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📜 References Used

In the development of this project, we have drawn inspiration and utilized code, libraries, and resources from various sources. We would like to acknowledge and express our gratitude to the following references and their respective authors:

• Lightly Library
• PYSSL Library
• SimCLR Implementation
• All original codes of supported methods

These references have played a crucial role in enhancing the functionality and quality of our project. We extend our thanks to the authors and contributors of these resources for their valuable work.

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💯 License

This project is licensed under the MIT License.

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🤝 Collaborators

By:

• Kian Majlessi
• Audrina Ebrahimi

Thanks to Dr. Peyman Adibi and Dr. Hossein Karshenas, for their invaluable guidance and support throughout this project.