Full Code of facebookresearch/mae for AI

Repository: facebookresearch/mae
Branch: main
Commit: efb2a8062c20
Files: 24
Total size: 1.2 MB

Directory structure:
gitextract_3w_d5c5n/

├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── FINETUNE.md
├── LICENSE
├── PRETRAIN.md
├── README.md
├── demo/
│   └── mae_visualize.ipynb
├── engine_finetune.py
├── engine_pretrain.py
├── main_finetune.py
├── main_linprobe.py
├── main_pretrain.py
├── models_mae.py
├── models_vit.py
├── submitit_finetune.py
├── submitit_linprobe.py
├── submitit_pretrain.py
└── util/
    ├── crop.py
    ├── datasets.py
    ├── lars.py
    ├── lr_decay.py
    ├── lr_sched.py
    ├── misc.py
    └── pos_embed.py

================================================
FILE CONTENTS
================================================

================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Code of Conduct

## Our Pledge

In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to make participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, sex characteristics, gender identity and expression,
level of experience, education, socio-economic status, nationality, personal
appearance, race, religion, or sexual identity and orientation.

## Our Standards

Examples of behavior that contributes to creating a positive environment
include:

* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members

Examples of unacceptable behavior by participants include:

* The use of sexualized language or imagery and unwelcome sexual attention or
  advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
  address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
  professional setting

## Our Responsibilities

Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.

Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.

## Scope

This Code of Conduct applies within all project spaces, and it also applies when
an individual is representing the project or its community in public spaces.
Examples of representing a project or community include using an official
project e-mail address, posting via an official social media account, or acting
as an appointed representative at an online or offline event. Representation of
a project may be further defined and clarified by project maintainers.

This Code of Conduct also applies outside the project spaces when there is a
reasonable belief that an individual's behavior may have a negative impact on
the project or its community.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at <opensource-conduct@fb.com>. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.

Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.

## Attribution

This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html

[homepage]: https://www.contributor-covenant.org

For answers to common questions about this code of conduct, see
https://www.contributor-covenant.org/faq


================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to mae
We want to make contributing to this project as easy and transparent as
possible.

## Pull Requests
We actively welcome your pull requests.

1. Fork the repo and create your branch from `main`.
2. If you've added code that should be tested, add tests.
3. If you've changed APIs, update the documentation.
4. Ensure the test suite passes.
5. Make sure your code lints.
6. If you haven't already, complete the Contributor License Agreement ("CLA").

## Contributor License Agreement ("CLA")
In order to accept your pull request, we need you to submit a CLA. You only need
to do this once to work on any of Facebook's open source projects.

Complete your CLA here: <https://code.facebook.com/cla>

## Issues
We use GitHub issues to track public bugs. Please ensure your description is
clear and has sufficient instructions to be able to reproduce the issue.

Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
disclosure of security bugs. In those cases, please go through the process
outlined on that page and do not file a public issue.

## License
By contributing to mae, you agree that your contributions will be licensed
under the LICENSE file in the root directory of this source tree.

================================================
FILE: FINETUNE.md
================================================
## Fine-tuning Pre-trained MAE for Classification

### Evaluation

As a sanity check, run evaluation using our ImageNet **fine-tuned** models:

<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom"></th>
<th valign="bottom">ViT-Base</th>
<th valign="bottom">ViT-Large</th>
<th valign="bottom">ViT-Huge</th>
<!-- TABLE BODY -->
<tr><td align="left">fine-tuned checkpoint</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/finetune/mae_finetuned_vit_base.pth">download</a></td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/finetune/mae_finetuned_vit_large.pth">download</a></td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/finetune/mae_finetuned_vit_huge.pth">download</a></td>
</tr>
<tr><td align="left">md5</td>
<td align="center"><tt>1b25e9</tt></td>
<td align="center"><tt>51f550</tt></td>
<td align="center"><tt>2541f2</tt></td>
</tr>
<tr><td align="left">reference ImageNet accuracy</td>
<td align="center">83.664</td>
<td align="center">85.952</td>
<td align="center">86.928</td>
</tr>
</tbody></table>

Evaluate ViT-Base in a single GPU (`${IMAGENET_DIR}` is a directory containing `{train, val}` sets of ImageNet):
```
python main_finetune.py --eval --resume mae_finetuned_vit_base.pth --model vit_base_patch16 --batch_size 16 --data_path ${IMAGENET_DIR}
```
This should give:
```
* Acc@1 83.664 Acc@5 96.530 loss 0.731
```

Evaluate ViT-Large:
```
python main_finetune.py --eval --resume mae_finetuned_vit_large.pth --model vit_large_patch16 --batch_size 16 --data_path ${IMAGENET_DIR}
```
This should give:
```
* Acc@1 85.952 Acc@5 97.570 loss 0.646
```

Evaluate ViT-Huge:
```
python main_finetune.py --eval --resume mae_finetuned_vit_huge.pth --model vit_huge_patch14 --batch_size 16 --data_path ${IMAGENET_DIR}
```
This should give:
```
* Acc@1 86.928 Acc@5 98.088 loss 0.584
```

### Fine-tuning

Get our pre-trained checkpoints from [here](https://github.com/fairinternal/mae/#pre-trained-checkpoints).

To fine-tune with **multi-node distributed training**, run the following on 4 nodes with 8 GPUs each:
```
python submitit_finetune.py \
    --job_dir ${JOB_DIR} \
    --nodes 4 \
    --batch_size 32 \
    --model vit_base_patch16 \
    --finetune ${PRETRAIN_CHKPT} \
    --epochs 100 \
    --blr 5e-4 --layer_decay 0.65 \
    --weight_decay 0.05 --drop_path 0.1 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
    --dist_eval --data_path ${IMAGENET_DIR}
```
- Install submitit (`pip install submitit`) first.
- Here the effective batch size is 32 (`batch_size` per gpu) * 4 (`nodes`) * 8 (gpus per node) = 1024.
- `blr` is the base learning rate. The actual `lr` is computed by the [linear scaling rule](https://arxiv.org/abs/1706.02677): `lr` = `blr` * effective batch size / 256.
- We have run 4 trials with different random seeds. The resutls are 83.63, 83.66, 83.52, 83.46 (mean 83.57 and std 0.08).
- Training time is ~7h11m in 32 V100 GPUs.

Script for ViT-Large:
```
python submitit_finetune.py \
    --job_dir ${JOB_DIR} \
    --nodes 4 --use_volta32 \
    --batch_size 32 \
    --model vit_large_patch16 \
    --finetune ${PRETRAIN_CHKPT} \
    --epochs 50 \
    --blr 1e-3 --layer_decay 0.75 \
    --weight_decay 0.05 --drop_path 0.2 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
    --dist_eval --data_path ${IMAGENET_DIR}
```
- We have run 4 trials with different random seeds. The resutls are 85.95, 85.87, 85.76, 85.88 (mean 85.87 and std 0.07).
- Training time is ~8h52m in 32 V100 GPUs.

Script for ViT-Huge:
```
python submitit_finetune.py \
    --job_dir ${JOB_DIR} \
    --nodes 8 --use_volta32 \
    --batch_size 16 \
    --model vit_huge_patch14 \
    --finetune ${PRETRAIN_CHKPT} \
    --epochs 50 \
    --blr 1e-3 --layer_decay 0.75 \
    --weight_decay 0.05 --drop_path 0.3 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
    --dist_eval --data_path ${IMAGENET_DIR}
```
- Training time is ~13h9m in 64 V100 GPUs.

To fine-tune our pre-trained ViT-Base with **single-node training**, run the following on 1 node with 8 GPUs:
```
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8 main_finetune.py \
    --accum_iter 4 \
    --batch_size 32 \
    --model vit_base_patch16 \
    --finetune ${PRETRAIN_CHKPT} \
    --epochs 100 \
    --blr 5e-4 --layer_decay 0.65 \
    --weight_decay 0.05 --drop_path 0.1 --mixup 0.8 --cutmix 1.0 --reprob 0.25 \
    --dist_eval --data_path ${IMAGENET_DIR}
```
- Here the effective batch size is 32 (`batch_size` per gpu) * 4 (`accum_iter`) * 8 (gpus) = 1024. `--accum_iter 4` simulates 4 nodes.

#### Notes

- The [pre-trained models we provide](https://github.com/fairinternal/mae/#pre-trained-checkpoints) are trained with *normalized* pixels `--norm_pix_loss` (1600 epochs, Table 3 in paper). The fine-tuning hyper-parameters are slightly different from the default baseline using *unnormalized* pixels.

- The original MAE implementation was in TensorFlow+TPU with no explicit mixed precision. This re-implementation is in PyTorch+GPU with automatic mixed precision (`torch.cuda.amp`). We have observed different numerical behavior between the two platforms. In this repo, we use `--global_pool` for fine-tuning; using `--cls_token` performs similarly, but there is a chance of producing NaN when fine-tuning ViT-Huge in GPUs. We did not observe this issue in TPUs. Turning off amp could solve this issue, but is slower.

- Here we use RandErase following DeiT: `--reprob 0.25`. Its effect is smaller than random variance.

### Linear Probing

Run the following on 4 nodes with 8 GPUs each:
```
python submitit_linprobe.py \
    --job_dir ${JOB_DIR} \
    --nodes 4 \
    --batch_size 512 \
    --model vit_base_patch16 --cls_token \
    --finetune ${PRETRAIN_CHKPT} \
    --epochs 90 \
    --blr 0.1 \
    --weight_decay 0.0 \
    --dist_eval --data_path ${IMAGENET_DIR}
```
- Here the effective batch size is 512 (`batch_size` per gpu) * 4 (`nodes`) * 8 (gpus per node) = 16384.
- `blr` is the base learning rate. The actual `lr` is computed by the [linear scaling rule](https://arxiv.org/abs/1706.02677): `lr` = `blr` * effective batch size / 256.
- Training time is ~2h20m for 90 epochs in 32 V100 GPUs.
- To run single-node training, follow the instruction in fine-tuning.

To train ViT-Large or ViT-Huge, set `--model vit_large_patch16` or `--model vit_huge_patch14`. It is sufficient to train 50 epochs `--epochs 50`.

This PT/GPU code produces *better* results for ViT-L/H (see the table below). This is likely caused by the system difference between TF and PT.

<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom"></th>
<th valign="bottom">ViT-Base</th>
<th valign="bottom">ViT-Large</th>
<th valign="bottom">ViT-Huge</th>
<!-- TABLE BODY -->
<tr><td align="left">paper (TF/TPU)</td>
<td align="center">68.0</td>
<td align="center">75.8</td>
<td align="center">76.6</td>
</tr>
<tr><td align="left">this repo (PT/GPU)</td>
<td align="center">67.8</td>
<td align="center">76.0</td>
<td align="center">77.2</td>
</tr>
</tbody></table>


================================================
FILE: LICENSE
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================================================
FILE: PRETRAIN.md
================================================
## Pre-training MAE

To pre-train ViT-Large (recommended default) with **multi-node distributed training**, run the following on 8 nodes with 8 GPUs each:
```
python submitit_pretrain.py \
    --job_dir ${JOB_DIR} \
    --nodes 8 \
    --use_volta32 \
    --batch_size 64 \
    --model mae_vit_large_patch16 \
    --norm_pix_loss \
    --mask_ratio 0.75 \
    --epochs 800 \
    --warmup_epochs 40 \
    --blr 1.5e-4 --weight_decay 0.05 \
    --data_path ${IMAGENET_DIR}
```
- Here the effective batch size is 64 (`batch_size` per gpu) * 8 (`nodes`) * 8 (gpus per node) = 4096. If memory or # gpus is limited, use `--accum_iter` to maintain the effective batch size, which is `batch_size` (per gpu) * `nodes` * 8 (gpus per node) * `accum_iter`.
- `blr` is the base learning rate. The actual `lr` is computed by the [linear scaling rule](https://arxiv.org/abs/1706.02677): `lr` = `blr` * effective batch size / 256.
- Here we use `--norm_pix_loss` as the target for better representation learning. To train a baseline model (e.g., for visualization), use pixel-based construction and turn off `--norm_pix_loss`.
- The exact same hyper-parameters and configs (initialization, augmentation, etc.) are used as our TF/TPU implementation. In our sanity checks, this PT/GPU re-implementation can reproduce the TF/TPU results within reasonable random variation. We get 85.5% [fine-tuning](FINETUNE.md) accuracy by pre-training ViT-Large for 800 epochs (85.4% in paper Table 1d with TF/TPU).
- Training time is ~42h in 64 V100 GPUs (800 epochs).

To train ViT-Base or ViT-Huge, set `--model mae_vit_base_patch16` or `--model mae_vit_huge_patch14`.


================================================
FILE: README.md
================================================
## Masked Autoencoders: A PyTorch Implementation

<p align="center">
  <img src="https://user-images.githubusercontent.com/11435359/146857310-f258c86c-fde6-48e8-9cee-badd2b21bd2c.png" width="480">
</p>


This is a PyTorch/GPU re-implementation of the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377):
```
@Article{MaskedAutoencoders2021,
  author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{\'a}r and Ross Girshick},
  journal = {arXiv:2111.06377},
  title   = {Masked Autoencoders Are Scalable Vision Learners},
  year    = {2021},
}
```

* The original implementation was in TensorFlow+TPU. This re-implementation is in PyTorch+GPU.

* This repo is a modification on the [DeiT repo](https://github.com/facebookresearch/deit). Installation and preparation follow that repo.

* This repo is based on [`timm==0.3.2`](https://github.com/rwightman/pytorch-image-models), for which a [fix](https://github.com/rwightman/pytorch-image-models/issues/420#issuecomment-776459842) is needed to work with PyTorch 1.8.1+.

### Catalog

- [x] Visualization demo
- [x] Pre-trained checkpoints + fine-tuning code
- [x] Pre-training code

### Visualization demo

Run our interactive visualization demo using [Colab notebook](https://colab.research.google.com/github/facebookresearch/mae/blob/main/demo/mae_visualize.ipynb) (no GPU needed):
<p align="center">
  <img src="https://user-images.githubusercontent.com/11435359/147859292-77341c70-2ed8-4703-b153-f505dcb6f2f8.png" width="600">
</p>

### Fine-tuning with pre-trained checkpoints

The following table provides the pre-trained checkpoints used in the paper, converted from TF/TPU to PT/GPU:
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom"></th>
<th valign="bottom">ViT-Base</th>
<th valign="bottom">ViT-Large</th>
<th valign="bottom">ViT-Huge</th>
<!-- TABLE BODY -->
<tr><td align="left">pre-trained checkpoint</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth">download</a></td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth">download</a></td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_huge.pth">download</a></td>
</tr>
<tr><td align="left">md5</td>
<td align="center"><tt>8cad7c</tt></td>
<td align="center"><tt>b8b06e</tt></td>
<td align="center"><tt>9bdbb0</tt></td>
</tr>
</tbody></table>

The fine-tuning instruction is in [FINETUNE.md](FINETUNE.md).

By fine-tuning these pre-trained models, we rank #1 in these classification tasks (detailed in the paper):
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom"></th>
<th valign="bottom">ViT-B</th>
<th valign="bottom">ViT-L</th>
<th valign="bottom">ViT-H</th>
<th valign="bottom">ViT-H<sub>448</sub></th>
<td valign="bottom" style="color:#C0C0C0">prev best</td>
<!-- TABLE BODY -->
<tr><td align="left">ImageNet-1K (no external data)</td>
<td align="center">83.6</td>
<td align="center">85.9</td>
<td align="center">86.9</td>
<td align="center"><b>87.8</b></td>
<td align="center" style="color:#C0C0C0">87.1</td>
</tr>
<td colspan="5"><font size="1"><em>following are evaluation of the same model weights (fine-tuned in original ImageNet-1K):</em></font></td>
<tr>
</tr>
<tr><td align="left">ImageNet-Corruption (error rate) </td>
<td align="center">51.7</td>
<td align="center">41.8</td>
<td align="center"><b>33.8</b></td>
<td align="center">36.8</td>
<td align="center" style="color:#C0C0C0">42.5</td>
</tr>
<tr><td align="left">ImageNet-Adversarial</td>
<td align="center">35.9</td>
<td align="center">57.1</td>
<td align="center">68.2</td>
<td align="center"><b>76.7</b></td>
<td align="center" style="color:#C0C0C0">35.8</td>
</tr>
<tr><td align="left">ImageNet-Rendition</td>
<td align="center">48.3</td>
<td align="center">59.9</td>
<td align="center">64.4</td>
<td align="center"><b>66.5</b></td>
<td align="center" style="color:#C0C0C0">48.7</td>
</tr>
<tr><td align="left">ImageNet-Sketch</td>
<td align="center">34.5</td>
<td align="center">45.3</td>
<td align="center">49.6</td>
<td align="center"><b>50.9</b></td>
<td align="center" style="color:#C0C0C0">36.0</td>
</tr>
<td colspan="5"><font size="1"><em>following are transfer learning by fine-tuning the pre-trained MAE on the target dataset:</em></font></td>
</tr>
<tr><td align="left">iNaturalists 2017</td>
<td align="center">70.5</td>
<td align="center">75.7</td>
<td align="center">79.3</td>
<td align="center"><b>83.4</b></td>
<td align="center" style="color:#C0C0C0">75.4</td>
</tr>
<tr><td align="left">iNaturalists 2018</td>
<td align="center">75.4</td>
<td align="center">80.1</td>
<td align="center">83.0</td>
<td align="center"><b>86.8</b></td>
<td align="center" style="color:#C0C0C0">81.2</td>
</tr>
<tr><td align="left">iNaturalists 2019</td>
<td align="center">80.5</td>
<td align="center">83.4</td>
<td align="center">85.7</td>
<td align="center"><b>88.3</b></td>
<td align="center" style="color:#C0C0C0">84.1</td>
</tr>
<tr><td align="left">Places205</td>
<td align="center">63.9</td>
<td align="center">65.8</td>
<td align="center">65.9</td>
<td align="center"><b>66.8</b></td>
<td align="center" style="color:#C0C0C0">66.0</td>
</tr>
<tr><td align="left">Places365</td>
<td align="center">57.9</td>
<td align="center">59.4</td>
<td align="center">59.8</td>
<td align="center"><b>60.3</b></td>
<td align="center" style="color:#C0C0C0">58.0</td>
</tr>
</tbody></table>

### Pre-training

The pre-training instruction is in [PRETRAIN.md](PRETRAIN.md).

### License

This project is under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for details.


================================================
FILE: demo/mae_visualize.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "15c2148f-c1b0-46e0-87f6-2db29e13d5b8",
   "metadata": {},
   "source": [
    "## Masked Autoencoders: Visualization Demo\n",
    "\n",
    "This is a visualization demo using our pre-trained MAE models. No GPU is needed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffa39c9-ca9b-4da0-90a4-de96bebbf755",
   "metadata": {},
   "source": [
    "### Prepare\n",
    "Check environment. Install packages if in Colab.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1eae7403-f458-4f55-a557-4e045bd6f679",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import requests\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "# check whether run in Colab\n",
    "if 'google.colab' in sys.modules:\n",
    "    print('Running in Colab.')\n",
    "    !pip3 install timm==0.4.5  # 0.3.2 does not work in Colab\n",
    "    !git clone https://github.com/facebookresearch/mae.git\n",
    "    sys.path.append('./mae')\n",
    "else:\n",
    "    sys.path.append('..')\n",
    "import models_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7797ef-412a-439f-911e-3be294047629",
   "metadata": {},
   "source": [
    "### Define utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4573e6be-935a-4106-8c06-e467552b0e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the utils\n",
    "\n",
    "imagenet_mean = np.array([0.485, 0.456, 0.406])\n",
    "imagenet_std = np.array([0.229, 0.224, 0.225])\n",
    "\n",
    "def show_image(image, title=''):\n",
    "    # image is [H, W, 3]\n",
    "    assert image.shape[2] == 3\n",
    "    plt.imshow(torch.clip((image * imagenet_std + imagenet_mean) * 255, 0, 255).int())\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.axis('off')\n",
    "    return\n",
    "\n",
    "def prepare_model(chkpt_dir, arch='mae_vit_large_patch16'):\n",
    "    # build model\n",
    "    model = getattr(models_mae, arch)()\n",
    "    # load model\n",
    "    checkpoint = torch.load(chkpt_dir, map_location='cpu')\n",
    "    msg = model.load_state_dict(checkpoint['model'], strict=False)\n",
    "    print(msg)\n",
    "    return model\n",
    "\n",
    "def run_one_image(img, model):\n",
    "    x = torch.tensor(img)\n",
    "\n",
    "    # make it a batch-like\n",
    "    x = x.unsqueeze(dim=0)\n",
    "    x = torch.einsum('nhwc->nchw', x)\n",
    "\n",
    "    # run MAE\n",
    "    loss, y, mask = model(x.float(), mask_ratio=0.75)\n",
    "    y = model.unpatchify(y)\n",
    "    y = torch.einsum('nchw->nhwc', y).detach().cpu()\n",
    "\n",
    "    # visualize the mask\n",
    "    mask = mask.detach()\n",
    "    mask = mask.unsqueeze(-1).repeat(1, 1, model.patch_embed.patch_size[0]**2 *3)  # (N, H*W, p*p*3)\n",
    "    mask = model.unpatchify(mask)  # 1 is removing, 0 is keeping\n",
    "    mask = torch.einsum('nchw->nhwc', mask).detach().cpu()\n",
    "    \n",
    "    x = torch.einsum('nchw->nhwc', x)\n",
    "\n",
    "    # masked image\n",
    "    im_masked = x * (1 - mask)\n",
    "\n",
    "    # MAE reconstruction pasted with visible patches\n",
    "    im_paste = x * (1 - mask) + y * mask\n",
    "\n",
    "    # make the plt figure larger\n",
    "    plt.rcParams['figure.figsize'] = [24, 24]\n",
    "\n",
    "    plt.subplot(1, 4, 1)\n",
    "    show_image(x[0], \"original\")\n",
    "\n",
    "    plt.subplot(1, 4, 2)\n",
    "    show_image(im_masked[0], \"masked\")\n",
    "\n",
    "    plt.subplot(1, 4, 3)\n",
    "    show_image(y[0], \"reconstruction\")\n",
    "\n",
    "    plt.subplot(1, 4, 4)\n",
    "    show_image(im_paste[0], \"reconstruction + visible\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92a06e7-3b6d-4c33-9eb2-15e560a4ce42",
   "metadata": {},
   "source": [
    "### Load an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27755296-05cc-4344-90de-a8ab3878f485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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