A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners

Related tags

Deep Learningkerasautoencoderself-supervised-learningtensorflow2masked-image-modeling
Overview

Masked Autoencoders Are Scalable Vision Learners

Open In Colab

A TensorFlow implementation of Masked Autoencoders Are Scalable Vision Learners [1]. Our implementation of the proposed method is available in mae-pretraining.ipynb notebook. It includes evaluation with linear probing as well. Furthermore, the notebook can be fully executed on Google Colab. Our main objective is to present the core idea of the proposed method in a minimal and readable manner. We have also prepared a blog for getting started with Masked Autoencoder easily.


With just 100 epochs of pre-training and a fairly lightweight and asymmetric Autoencoder architecture we achieve 49.33%% accuracy with linear probing on the CIFAR-10 dataset. Our training logs and encoder weights are released in Weights and Logs. For comparison, we took the encoder architecture and trained it from scratch (refer to regular-classification.ipynb) in a fully supervised manner. This gave us ~76% test top-1 accuracy.

We note that with further hyperparameter tuning and more epochs of pre-training, we can achieve a better performance with linear-probing. Below we present some more results:

Config Masking
proportion		 LP
performance		 Encoder weights
& logs
Encoder & decoder layers: 3 & 1
Batch size: 256		 0.6 44.25% Link
Do 0.75 46.84% Link
Encoder & decoder layers: 6 & 2
Batch size: 256		 0.75 48.16% Link
Encoder & decoder layers: 9 & 3
Batch size: 256
Weight deacy: 1e-5		 0.75 49.33% Link

LP denotes linear-probing. Config is mostly based on what we define in the hyperparameters section of this notebook: mae-pretraining.ipynb.

Acknowledgements

References

[1] Masked Autoencoders Are Scalable Vision Learners; He et al.; arXiv 2021; https://arxiv.org/abs/2111.06377.

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Comments
  • Excellent work (`mae.ipynb`)!

    Excellent work (`mae.ipynb`)!

    @ariG23498 this is fantastic stuff. Super clean, readable, and coherent with the original implementation. A couple of suggestions that would likely make things even better:

    • Since you have already implemented masking visualization utilities how about making them part of the PatchEncoder itself? That way you could let it accept a test image, apply random masking, and plot it just like the way you are doing in the earlier cells. This way I believe the notebook will be cleaner.
    • AdamW (tfa.optimizers.adamw) is a better choice when it comes to training Transformer-based models.
    • Are we taking the loss on the correct component? I remember you mentioning it being dealt with differently.

    After these points are addressed I will take a crack at porting the training loop to TPUs along with other performance monitoring callbacks.

    opened by sayakpaul 7
  • Unshuffle the patches?

    Unshuffle the patches?

    Your code helps me a lot! However, I still have some questions. In the paper, the authors say they unshuffle the full list before applying the deocder. In the MaskedAutoencoder class of your implementation, decoder_inputs = tf.concat([encoder_outputs, masked_embeddings], axis=1)
    no unshuffling is used. I wonder if you can tell me the purpose of doing so? Thanks a lot!

    opened by changtaoli 2
  • Could you also share the weight of the pretrained decoder?

    Could you also share the weight of the pretrained decoder?

    Hi,

    Thanks for your excellent implementation! I found that you have shared the weights of the encoder, but if we want to replicate the reconstruction, the pretrained decoder is still needed. So, could you also share the weight of the pretrained decoder?

    Best Regards, Hongxin

    opened by hongxin001 1
  • Issue with the plotting utility `show_masked_image`

    Issue with the plotting utility `show_masked_image`

    Should be:

    def show_masked_image(self, patches):
        # Utility function that helps visualize maksed images.
        _, unmask_indices = self.get_random_indices()
        unmasked_patches = tf.gather(patches, unmask_indices, axis=1, batch_dims=1)

        # Necessary for plotting.
        ids = tf.argsort(unmask_indices)
        sorted_unmask_indices = tf.sort(unmask_indices)
        unmasked_patches = tf.gather(unmasked_patches, ids, batch_dims=1)

        # Select a random index for visualization.
        idx = np.random.choice(len(sorted_unmask_indices))
        print(f"Index selected: {idx}.")

        n = int(np.sqrt(NUM_PATCHES))
        unmask_index = sorted_unmask_indices[idx]
        unmasked_patch = unmasked_patches[idx]

        plt.figure(figsize=(4, 4))

        count = 0
        for i in range(NUM_PATCHES):
            ax = plt.subplot(n, n, i + 1)

            if count < unmask_index.shape[0] and unmask_index[count].numpy() == i:
                patch = unmasked_patch[count]
                patch_img = tf.reshape(patch, (PATCH_SIZE, PATCH_SIZE, 3))
                plt.imshow(patch_img)
                plt.axis("off")
                count = count + 1
            else:
                patch_img = tf.zeros((PATCH_SIZE, PATCH_SIZE, 3))
                plt.imshow(patch_img)
                plt.axis("off")
        plt.show()

        # Return the random index to validate the image outside the method.
        return idx
    opened by ariG23498 1
Releases(v1.0.0)
Owner
Aritra Roy Gosthipaty
Learning with a learning rate of 1e-10.
Aritra Roy Gosthipaty
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