An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches

Related tags

Deep Learning machine-learning deep-learning tensorflow artificial-intelligence transformer neural-networks image-classification attention-mechanism tnt sota
Overview

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An Implementation of the Transformer in Transformer paper by Han et al. for image classification, attention inside local patches. Transformer in Transformer uses pixel level attention paired with patch level attention for image classification, in TensorFlow.

PyTorch Implementation

Installation

Run the following to install:

pip install tnt-tensorflow

Developing tnt-tensorflow

To install tnt-tensorflow, along with tools you need to develop and test, run the following in your virtualenv:

git clone https://github.com/Rishit-dagli/Transformer-in-Transformer.git
# or clone your own fork

cd tnt
pip install -e .[dev]

Usage

import tensorflow as tf
from tnt import TNT

tnt = TNT(
    image_size=256,  # size of image
    patch_dim=512,  # dimension of patch token
    pixel_dim=24,  # dimension of pixel token
    patch_size=16,  # patch size
    pixel_size=4,  # pixel size
    depth=5,  # depth
    num_classes=1000,  # output number of classes
    attn_dropout=0.1,  # attention dropout
    ff_dropout=0.1,  # feedforward dropout
)

img = tf.random.uniform(shape=[5, 3, 256, 256])
logits = tnt(img) # (5, 1000)

Want to Contribute 🙋‍♂️ ?

Awesome! If you want to contribute to this project, you're always welcome! See Contributing Guidelines. You can also take a look at open issues for getting more information about current or upcoming tasks.

Want to discuss? 💬

Have any questions, doubts or want to present your opinions, views? You're always welcome. You can start discussions.

Citation

@misc{han2021transformer,
      title={Transformer in Transformer}, 
      author={Kai Han and An Xiao and Enhua Wu and Jianyuan Guo and Chunjing Xu and Yunhe Wang},
      year={2021},
      eprint={2103.00112},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

Copyright 2020 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Comments
  • Add Unit Tests

    Add Unit Tests

    The tests should check for the rank and shape of the output tensors, the test should override tf.test.TestCase base class.

    • [x] #15
    • [x] #16
    • [x] #18
    • [x] #17

    Feel free to take inspiration from:

    • https://github.com/Rishit-dagli/Fast-Transformer/blob/main/fast_transformer/test_fast_transformer.py
    • For parametrization feel free to follow https://stackoverflow.com/a/34094/11878567, can be used in the exact same way with subTest in TensorFlow
    enhancement good first issue 
    opened by Rishit-dagli 3
  • Update Workflows to run tests

    Update Workflows to run tests

    This issue follows #11

    Update GitHub Workflows to:

    • [ ] Run Tests before uploading to PyPI
    • [ ] Create a workflow to run tests on commits

    Feel free to take inspiration from https://github.com/Rishit-dagli/Fast-Transformer/tree/main/.github/workflows

    enhancement good first issue 
    opened by Rishit-dagli 0
  • Creates an Attention layer

    Creates an Attention layer

    Verify output shapes just from the attention layer:

    import tensorflow as tf
Attention(dim=256)(tf.random.normal([3,256,256]))

# <tf.Tensor: shape=(3, 256, 256), dtype=float32,

    Closes #3

    opened by Rishit-dagli 0
  • Put together a TNT class

    Put together a TNT class

    Verify shapes:

    tnt = TNT(
    image_size=256,  # size of image
    patch_dim=512,  # dimension of patch token
    pixel_dim=24,  # dimension of pixel token
    patch_size=16,  # patch size
    pixel_size=4,  # pixel size
    depth=5,  # depth
    num_classes=1000,  # output number of classes
    attn_dropout=0.1,  # attention dropout
    ff_dropout=0.1,  # feedforward dropout
)

img = tf.random.uniform(shape=[1, 3, 256, 256])
print(tnt(img).shape)

# (1, 1000)
```
    opened by Rishit-dagli 0
  • Create an Attention layerr

    Create an Attention layerr

    Verify output shapes just from the attention layer:

    import tensorflow as tf
Attention(dim=256)(tf.random.normal([3,256,256]))

# <tf.Tensor: shape=(3, 256, 256), dtype=float32,
    opened by Rishit-dagli 0
  • Create a PreNorm layer

    Create a PreNorm layer

    Verify output shapes from this layer:

    import tensorflow as tf
PreNorm(dim=1, fn=tf.keras.layers.Dense(5))(tf.random.normal([10, 1]))

# <tf.Tensor: shape=(10, 1), dtype=float32,
    opened by Rishit-dagli 0
Releases(v0.2.0)

  • v0.2.0(Feb 2, 2022)

    This is an interesting release for the project, including a pre-trained model on ImageNet, reproducibility of paper results, tests, and end-to-end training.

    ✅ Bug Fixes / Improvements

    • Create an end-to-end training example demonstrating how to train a TNT model for image classification through a custom training loop on the TF Flowers dataset (#14)
    • Pre-trained model to reproduce the paper results have been made available (in this release as well as on TensorFlow Hub)
    • Create an off-the-shelf inference example, that highlights how you can directly use the pre-trained model made available
    • Unit Tests for the Attention class (#19)
    • Unit Tests for the main TNT class (#20)

    Full Changelog: https://github.com/Rishit-dagli/Transformer-in-Transformer/compare/v0.1.0...v0.2.0

    Source code(tar.gz)
    Source code(zip)
    tnt_s_patch16_224.tar.gz(84.42 MB)

  • v0.1.0(Dec 3, 2021)

    This is the initial release of TNT TensorFlow and implements Transformers in Transformers as a subclassed TensorFlow model.

    Classes

    • Attention: Implements attention as a TensorFlow Keras Layer making some modifications.
    • PreNorm: Normalize the activations of the previous layer for each given example in a batch independently and apply some function to it, implemented as a TensorFlow Keras Layer.
    • FeedForward: Create a FeedForward neural net with two Dense layers and GELU activation, implemented as a TensorFlow Keras Layer.
    • TNT: Implements the Transformers in Transformers model using all the other classes, and converts to logits. Implemented as a TensorFlow Keras Model.
    Source code(tar.gz)
    Source code(zip)
    tnt_s_patch16_224.tar.gz(84.42 MB)
Owner
Rishit Dagli
High School,TEDx,2xTED-Ed speaker | International Speaker | Microsoft Student Ambassador | Mentor, @TFUGMumbai | Organize @KotlinMumbai
Rishit Dagli
GitHub https://pypi.org/project/tnt-tensorflow
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