[
  {
    "path": ".flake8",
    "content": "[flake8]\nmax-line-length = 88\nextend-ignore =\n    # See https://github.com/PyCQA/pycodestyle/issues/373\n    E203,\n"
  },
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# downstream tasks\ndownstream_tasks/segmentation/dataset/*\ndownstream_tasks/classification/dataset/*\n\n# C extensions\n*.so\n*.pth\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n#   For a library or package, you might want to ignore these files since the code is\n#   intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n#   This is especially recommended for binary packages to ensure reproducibility, and is more\n#   commonly ignored for libraries.\n#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n#   in version control.\n#   https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n#  and can be added to the global gitignore or merged into this file.  For a more nuclear\n#  option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2024 Zhitong Xiong\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "\n# [DOFA](https://github.com/ShadowXZT/DOFA-pytorch)\n\n>\n> 🚨 Examples for using DOFA and DOFAv2 for object detection and instance segmentation with [terratorch](https://github.com/xiong-zhitong/terratorch/tree/main/examples/confs/od_dofav1).\n>\n> 🚨 Please use the new version of codes and [weights](https://huggingface.co/XShadow/DOFA). The performance is much better!\n> \n> 🚨 If you are using the new version of weights, please make sure that the new version of [wave_dynamic_layer.py](https://github.com/zhu-xlab/DOFA/blob/master/wave_dynamic_layer.py#L209) is used.\n> \n\n--- \n\n## Dynamic One-For-All foundation model for Remote sensing and Earth observation\n\n**Object Detection**: DOFA and DOFA v2 can be used for object detection tasks, which are fundamental remote sensing applications.\n<p align=\"center\">\n<img src=\"assets/dofa_od.png\" width=\"1200\">\n</p>\n\n**What is DOFA**: DOFA is a unified multimodal foundation model for different data modalities in remote sensing and Earth observation.\n<p align=\"center\">\n<img src=\"assets/DOFA-main.png\" width=\"500\">\n</p>\n\n**Differences with existing foundation models**: DOFA is pre-trained using five different data modalities in remote sensing and Earth observation. It can handle images with any number of input channels.\n\n**DOFA is inspired by neuroplasticity** Neuroplasticity is an\nimportant brain mechanism for adjusting to new experiences or environmental shifts. Inspired by this concept, we design DOFA to emulate this mechanism for processing multimodal EO data.\n\n<p align=\"center\">\n<img src=\"assets/DOFA-model.png\" width=\"500\">\n</p>\n\n\nFor more details, please take a look at the paper [Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities](https://arxiv.org/abs/2403.15356).\n\n## Why develop DOFA\n- The learned multimodal representation may not effectively capture such an intersensor relationship.\n\n- The performance of foundation models will degrade when downstream tasks require the utilization of data from unseen sensors with varying numbers of spectral bands and spatial resolutions or different wavelength regimes.\n\n- The development of individual, customized foundation models requires considerably more computing resources and human efforts.\n\n- The increasing number of specialized foundation models makes it difficult to select the most appropriate one for a specific downstream task.\n\n## Installation\n\n### Requirements\n\nThe requirements of DOFA can be installed as follows:\n\n```console\n> pip install -r requirements.txt\n```\n\n### Weights\n\nPre-trained model weights can be downloaded from [HuggingFace](https://huggingface.co/XShadow/DOFA).\n\n## Usage\n\nPlease refer to [demo.ipynb](https://github.com/zhu-xlab/DOFA/blob/master/demo.ipynb) for more details.\n\nDOFA supports input images with any number of channels using our pre-trained foundation models. The following examples show how to use DOFA for **Sentinel-1 (SAR)**, **Sentinel-2**, **NAIP RGB**. We will add example usage for Gaofen Multispectral, and Hyperspectral data soon.\n\n---\n\n### Using `torch.hub` to Load the DOFA ViT Base Model\n\nThis snippet demonstrates how to load a ViT model—specifically, **DOFA ViT Base**—from a GitHub repository that includes a `hubconf.py` entrypoint. The model weights are hosted on Hugging Face via a direct download URL, so **no additional dependencies** beyond PyTorch are required.\n\n```python\nimport torch\n\nmodel = torch.hub.load(\n    'zhu-xlab/DOFA',\n    'vit_base_dofa',  # The entry point defined in hubconf.py\n    pretrained=True,\n)\n\nmodel = model.cuda()\nmodel.eval()\n```\n\nNow the model is ready for inference or further fine-tuning.\nIf you would like to fine-tune DOFA on different downstream tasks, please refer to [DOFA-pytorch](https://github.com/xiong-zhitong/DOFA-pytorch).\n\n\n### TorchGeo\n\nAlternatively, DOFA can be used via the [TorchGeo](https://github.com/microsoft/torchgeo) library:\n\n```python\nimport torch\nfrom torchgeo.models import DOFABase16_Weights, dofa_base_patch16_224\n\n# Example NAIP image (wavelengths in $\\mu$m)\nx = torch.rand(2, 4, 224, 224)\nwavelengths = [0.48, 0.56, 0.64, 0.81]\n\n# Use pre-trained model weights\nmodel = dofa_base_patch16_224(weights=DOFABase16_Weights.DOFA_MAE)\n\n# Make a prediction (model may need to be fine-tuned first)\ny = model(x, wavelengths)\n```\n\n---\n\n\n### Demo usage of DOFA on different data modalities\n\n---\n\n### Download the pre-trained weights for DOFA from huggingface\nPlease move to the `checkpoints` directory and run\n```python\npython download_weights.py\n```\n\nPlease download the `data.zip` for the demo usage from [HF](https://huggingface.co/earthflow/DOFA/resolve/main/data.zip). Then unzip it to the `data` directory.\n\n\n```python\nfrom models_dwv import vit_base_patch16\n\ncheck_point = torch.load('./checkpoints/DOFA_ViT_base_e100.pth')\nvit_model = vit_base_patch16()\nmsg = vit_model.load_state_dict(check_point, strict=False)\n# missing_keys=['fc_norm.weight', 'fc_norm.bias', 'head.weight', 'head.bias'], unexpected_keys=['mask_token', 'norm.weight', 'norm.bias', 'projector.weight', 'projector.bias']\nvit_model = vit_model.cuda()\n```\n\nNow you can use **the loaded single DOFA model** to process image data from **different modalities** with **any number of channels**!\n\n\n### Preprare for the data loading and preprocessing\n\n```python\n# Step 1: Data preprocessing (normalization and resize)\n\nimport torch\nimport rasterio\nimport kornia as K\nimport numpy as np\n# vh,vv\nS1_MEAN = [166.36275909, 88.45542715]# / 255.0\nS1_STD = [64.83126309, 43.07350145]# /255.0\n\nS2_MEAN = [114.1099739 , 114.81779093, 126.63977424,  84.33539309,\n        97.84789168, 103.94461911, 101.435633  ,  72.32804172,\n        56.66528851]\nS2_STD = [77.84352553, 69.96844919, 67.42465279, 64.57022983, 61.72545487,\n       61.34187099, 60.29744676, 47.88519516, 42.55886798]\n\nNAIP_MEAN = [123.675, 116.28, 103.53] # ImageNet stats for now\nNAIP_STD = [58.395, 57.12, 57.375] # ImageNet stats for now\n\nGaufen_MEAN = [123.94924583,  92.58088583,  97.28130189,  90.31526596]\nGaufen_STD = [67.34487297, 62.8271046 , 60.5856767 , 60.3946299]\n```\n\n\n```python\nclass DataAugmentation(torch.nn.Module):\n    def __init__(self, mean, std):\n        super().__init__()\n        self.transform = torch.nn.Sequential(\n            K.augmentation.RandomResizedCrop(size=(224,224), scale=(0.2,1.0)),\n            K.augmentation.Normalize(mean=mean,std=std)\n        )\n    @torch.no_grad()\n    def forward(self,x):\n        x_out = self.transform(x)\n        return x_out\n```\n\n### Load Sentinel-1 data with 2 channels\n\n```python\ntransform = DataAugmentation(mean=S1_MEAN,std=S1_STD)\n\ndef preprocess_s1(vh_path, vv_path):\n    with rasterio.open(vh_path) as f1:\n        vh = f1.read()\n    with rasterio.open(vv_path) as f2:\n        vv = f2.read()\n    s1_img = np.concatenate((vh,vv),0).astype('float32')\n    s1_img = torch.from_numpy(s1_img)\n    s1_img = transform(s1_img).squeeze(0)\n    return s1_img\n```\n\n\n```python\nimport matplotlib.pyplot as plt\n# Load Sentinel-1 images from the given example data\nC = 2  # can be 2,3,4,6,9,12,13,202 or any number if you can provide the wavelengths of them\n\nimage1 = './data/s1/vv/1869_3575.png'\nimage2 = './data/s1/vh/1869_3575.png'\ns1_img = preprocess_s1(image1,image2)\n\nfig, ax = plt.subplots(nrows=1, ncols=C, figsize=(10, 10))\n\nfor i,row in enumerate(ax):\n    row.imshow(s1_img[i])\n\ns1_img = s1_img.view([1,2,224,224]).cuda()\n```\n\n\n    \n![png](assets/demo_4_0.png)\n\n\n```python\nwavelengths = [5.405, 5.405]\nout_feat = vit_model.forward_features(s1_img, wave_list=wavelengths)\nout_logits = vit_model.forward(s1_img, wave_list=wavelengths)\nprint(out_feat.shape)\nprint(out_logits.shape)\n```\n\n\n### Load Sentinel-2 data with 9 channels\n\n\n\n```python\nimport glob\n\nC = 9\n\ntransform = DataAugmentation(mean=S2_MEAN,std=S2_STD)\n\ndef preprocess_s2(img_path):\n    chs = []\n    s2_files = glob.glob(f\"{img_path}/*/*.png\")\n    for path in s2_files:\n        with rasterio.open(path) as f:\n            ch = f.read()\n        chs.append(ch)\n    s2_img = np.concatenate(chs, 0).astype(\"float32\")\n    s2_img = torch.from_numpy(s2_img)\n    s2_img = transform(s2_img).squeeze(0)\n    return s2_img\n```\n\nVisualize the Sentinel-2 imagery\n\n\n```python\ns2_img = preprocess_s2(\"data/s2/S2A_MSIL1C_20170528T050611_N0205_R076_T44NMM_20170528T050606\")\nfig, ax = plt.subplots(nrows=1, ncols=C, figsize=(10, 10))\n\nfor i,row in enumerate(ax):\n    row.imshow(s2_img[i])\n    row.axis(\"off\")\ns2_img = s2_img.view([1,C,224,224]).cuda()\n```\n\n    /opt/miniconda3/lib/python3.11/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n      dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n\n\n\n    \n![png](assets/demo_11_1.png)\n    \n\n\n### We use the same DOFA model to inference the Sentinel-2 image\n\n\n\n```python\n\nwavelengths = [0.665, 0.56, 0.49, 0.705, 0.74, 0.783, 0.842, 1.61, 2.19]\n\nout_feat = vit_model.forward_features(s2_img, wave_list=wavelengths)\nout_logits = vit_model.forward(s2_img, wave_list=wavelengths)\nprint(out_feat.shape)\nprint(out_logits.shape)\n```\n\n### What if I only want to use a subset of Sentinel-2 data?\n\n\n```python\n# Let's only keep the first 5 channels\nwavelengths = [0.665, 0.56, 0.49, 0.705, 0.74]\n\nout_feat = vit_model.forward_features(s2_img[:,:5,...], wave_list=wavelengths)\nout_logits = vit_model.forward(s2_img[:,:5,...], wave_list=wavelengths)\nprint(out_feat.shape)\nprint(out_logits.shape)\n```\n\n### Usage for RGB optical data\n\n\n```python\nC = 3\n\ntransform = DataAugmentation(mean=NAIP_MEAN, std=NAIP_STD)\n\n\ndef preprocess_rgb(img_path):\n    with rasterio.open(img_path) as f:\n        rgb_img = f.read().astype(\"float32\")\n    rgb_img = torch.from_numpy(rgb_img)\n    rgb_img = transform(rgb_img).squeeze(0)\n    return rgb_img\n```\n\n\n```python\nimport cv2\n\nrgb_path = 'data/naip/36861_49963.png'\nnaip_img = preprocess_rgb(rgb_path)\n\nplt.imshow(cv2.cvtColor(cv2.imread(rgb_path), cv2.COLOR_BGR2RGB))\nplt.axis(\"off\")\n\nnaip_img = naip_img.view([1,C,224,224]).cuda()\n```\n\n\n    \n![png](assets/demo_18_0.png)\n    \n\n\n\n```python\nwavelengths = [0.665, 0.56, 0.49]\n\nout_feat = vit_model.forward_features(naip_img, wave_list=wavelengths)\nout_logits = vit_model.forward(naip_img, wave_list=wavelengths)\nprint(out_feat.shape)\nprint(out_logits.shape)\n```\n\nUsage for Hyperspectral images is similar to other images.\n\n\n\n---\n\nIf you find the DOFA useful in your research, please kindly cite our paper:\n```\n@article{xiong2024neural,\n  title={Neural Plasticity-Inspired Foundation Model for Observing the {Earth} Crossing Modalities},\n  author={Xiong, Zhitong and Wang, Yi and Zhang, Fahong and Stewart, Adam J and Hanna, Jo{\\\"e}lle and Borth, Damian and Papoutsis, Ioannis and Saux, Bertrand Le and Camps-Valls, Gustau and Zhu, Xiao Xiang},\n  journal={arXiv preprint arXiv:2403.15356},\n  year={2024}\n}\n```\n"
  },
  {
    "path": "checkpoints/download_weights.py",
    "content": "# Download the pre-trained weights of DOFA\n\nfrom huggingface_hub import hf_hub_download\nimport os\nfile_path = os.path.dirname(os.path.abspath(__file__))\n\nhf_hub_download(repo_id=\"XShadow/DOFA\", filename=\"DOFA_ViT_base_e100.pth\", local_dir=file_path)\n"
  },
  {
    "path": "demo.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Demo for DOFA\\n\",\n    \"### DOFA supports input images with any number of channels using our pre-trained foundation models\\n\",\n    \"### The following examples show how to use DOFA for Sentinel-1 (SAR), Sentinel-2, NAIP RGB, Gaofen Multispectral, and Hyperspectral data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Download the pre-trained weights for DOFA from huggingface\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%run checkpoints/download_weights.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Step 1: Data preprocessing (normalization and resize)\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import rasterio\\n\",\n    \"import kornia as K\\n\",\n    \"import numpy as np\\n\",\n    \"# vh,vv\\n\",\n    \"S1_MEAN = [166.36275909, 88.45542715]# / 255.0\\n\",\n    \"S1_STD = [64.83126309, 43.07350145]# /255.0\\n\",\n    \"\\n\",\n    \"S2_MEAN = [114.1099739 , 114.81779093, 126.63977424,  84.33539309,\\n\",\n    \"        97.84789168, 103.94461911, 101.435633  ,  72.32804172,\\n\",\n    \"        56.66528851]\\n\",\n    \"S2_STD = [77.84352553, 69.96844919, 67.42465279, 64.57022983, 61.72545487,\\n\",\n    \"       61.34187099, 60.29744676, 47.88519516, 42.55886798]\\n\",\n    \"\\n\",\n    \"NAIP_MEAN = [123.675, 116.28, 103.53] # ImageNet stats for now\\n\",\n    \"NAIP_STD = [58.395, 57.12, 57.375] # ImageNet stats for now\\n\",\n    \"\\n\",\n    \"Hyper_MEAN = [0.04904891, 0.04734517, 0.04881499, 0.0521312 , 0.05371449,\\n\",\n    \"       0.05431649, 0.05600387, 0.05753566, 0.05837488, 0.06014395,\\n\",\n    \"       0.06120129, 0.06187369, 0.06262351, 0.0640324 , 0.06544493,\\n\",\n    \"       0.06583978, 0.06657578, 0.06818208, 0.06887893, 0.07050433,\\n\",\n    \"       0.0730939 , 0.07500546, 0.07658557, 0.07914317, 0.08149088,\\n\",\n    \"       0.08413605, 0.08584346, 0.0875968 , 0.08949799, 0.09126373,\\n\",\n    \"       0.09284472, 0.09385966, 0.09429929, 0.09644857, 0.09758445,\\n\",\n    \"       0.09888336, 0.1000851 , 0.1012402 , 0.10217949, 0.10292227,\\n\",\n    \"       0.10441044, 0.10482953, 0.10586129, 0.10771345, 0.10875386,\\n\",\n    \"       0.10872938, 0.10955398, 0.11022133, 0.11095442, 0.11202578,\\n\",\n    \"       0.1144143 , 0.11816488, 0.12615164, 0.13405322, 0.14239053,\\n\",\n    \"       0.14940845, 0.15952006, 0.16786492, 0.17470501, 0.17793106,\\n\",\n    \"       0.18344983, 0.1717895 , 0.18624028, 0.18920699, 0.19094643,\\n\",\n    \"       0.19191591, 0.19321348, 0.19543689, 0.19453696, 0.197083  ,\\n\",\n    \"       0.19759373, 0.20008718, 0.20109805, 0.20273463, 0.20447857,\\n\",\n    \"       0.20549563, 0.20768884, 0.20616769, 0.20696713, 0.21603036,\\n\",\n    \"       0.20431315, 0.20028888, 0.21381801, 0.19553433, 0.22010142,\\n\",\n    \"       0.20745818, 0.20469215, 0.20106881, 0.21624712, 0.20217295,\\n\",\n    \"       0.21258003, 0.19276747, 0.19084313, 0.21547508, 0.1990552 ,\\n\",\n    \"       0.2220764 , 0.20010307, 0.22556668, 0.20294108, 0.20432738,\\n\",\n    \"       0.22884114, 0.23084754, 0.23361147, 0.23613915, 0.23835524,\\n\",\n    \"       0.24002708, 0.24240751, 0.24512835, 0.24625339, 0.24729841,\\n\",\n    \"       0.24621868, 0.2335448 , 0.23611045, 0.23486711, 0.22491438,\\n\",\n    \"       0.23376624, 0.23624881, 0.23806269, 0.23892529, 0.24048481,\\n\",\n    \"       0.24448228, 0.24877857, 0.25137802, 0.25356494, 0.25809049,\\n\",\n    \"       0.25776253, 0.19769041, 0.20087005, 0.20429956, 0.20702436,\\n\",\n    \"       0.21026749, 0.21287441, 0.21557267, 0.21920979, 0.22113606,\\n\",\n    \"       0.2227512 , 0.22380759, 0.22556949, 0.22486467, 0.22534442,\\n\",\n    \"       0.22393468, 0.22259283, 0.22103291, 0.21938812, 0.21726496,\\n\",\n    \"       0.15029096, 0.15920778, 0.14992988, 0.1402144 , 0.14215149,\\n\",\n    \"       0.16192129, 0.16663248, 0.16954731, 0.16092901, 0.16268809,\\n\",\n    \"       0.16303404, 0.16922207, 0.16708257, 0.16741623, 0.16829468,\\n\",\n    \"       0.16974312, 0.17043488, 0.17164688, 0.17061502, 0.17135934,\\n\",\n    \"       0.17061249, 0.17054758, 0.17006451, 0.17125258, 0.16988001,\\n\",\n    \"       0.16870924, 0.16756754, 0.1703907 , 0.17044655, 0.16995895,\\n\",\n    \"       0.16583233, 0.16387971, 0.15977418, 0.15813546, 0.15500555,\\n\",\n    \"       0.15475487, 0.1500904 , 0.14842632, 0.14555257, 0.1444372 ,\\n\",\n    \"       0.14345487, 0.1439716 , 0.13948618, 0.13974816, 0.1388893 ,\\n\",\n    \"       0.1425306 , 0.1399015 , 0.14124387, 0.13716652, 0.13908459,\\n\",\n    \"       0.13517979, 0.13579579, 0.12699047, 0.13110322, 0.12600956,\\n\",\n    \"       0.12683088, 0.11266357]\\n\",\n    \"\\n\",\n    \"Hyper_STD = [0.05438845, 0.05425398, 0.05519192, 0.05621342, 0.05694766,\\n\",\n    \"       0.05704381, 0.05763639, 0.05804253, 0.05834612, 0.05888857,\\n\",\n    \"       0.05920108, 0.05948167, 0.06002252, 0.06070791, 0.06148699,\\n\",\n    \"       0.06196402, 0.06251209, 0.063477  , 0.06412124, 0.06480832,\\n\",\n    \"       0.06584631, 0.06655134, 0.06710579, 0.06817766, 0.06930595,\\n\",\n    \"       0.07081702, 0.07204193, 0.07329206, 0.07491294, 0.07681551,\\n\",\n    \"       0.07890642, 0.08096196, 0.08243044, 0.08428552, 0.08603505,\\n\",\n    \"       0.08763417, 0.08869326, 0.08985463, 0.09081571, 0.09164355,\\n\",\n    \"       0.09288558, 0.09362094, 0.09377934, 0.09490612, 0.09628371,\\n\",\n    \"       0.09688408, 0.09770997, 0.09843717, 0.099185  , 0.09957288,\\n\",\n    \"       0.10124085, 0.10085674, 0.0999966 , 0.09884673, 0.09924151,\\n\",\n    \"       0.10034673, 0.10382162, 0.10637773, 0.10945914, 0.11091637,\\n\",\n    \"       0.1143651 , 0.11309448, 0.11714786, 0.11771383, 0.11835992,\\n\",\n    \"       0.11911578, 0.11971312, 0.12100819, 0.12134505, 0.12288926,\\n\",\n    \"       0.1223688 , 0.12337272, 0.12378503, 0.1247803 , 0.12574014,\\n\",\n    \"       0.12591301, 0.12706246, 0.12643132, 0.1274808 , 0.12616546,\\n\",\n    \"       0.12669385, 0.12547143, 0.12466624, 0.12086743, 0.1281701 ,\\n\",\n    \"       0.12939227, 0.11918587, 0.1359367 , 0.12719588, 0.13615098,\\n\",\n    \"       0.12415102, 0.12939233, 0.12601028, 0.12531339, 0.12763924,\\n\",\n    \"       0.12921317, 0.12880291, 0.13102435, 0.13017717, 0.13047819,\\n\",\n    \"       0.13304893, 0.13441405, 0.13619871, 0.13785246, 0.13942554,\\n\",\n    \"       0.14062946, 0.14236353, 0.14349961, 0.14351399, 0.14394955,\\n\",\n    \"       0.14339238, 0.13597313, 0.13902099, 0.13897383, 0.13261457,\\n\",\n    \"       0.1377756 , 0.13982825, 0.14176949, 0.14285451, 0.14382317,\\n\",\n    \"       0.14602028, 0.1482343 , 0.15007727, 0.15194561, 0.153546  ,\\n\",\n    \"       0.15312391, 0.14626474, 0.1466966 , 0.14744146, 0.14778844,\\n\",\n    \"       0.14876473, 0.14891526, 0.14976731, 0.15107008, 0.15135105,\\n\",\n    \"       0.15174565, 0.15199847, 0.15274257, 0.15231952, 0.15232084,\\n\",\n    \"       0.1517344 , 0.15127988, 0.15108911, 0.15042051, 0.1496356 ,\\n\",\n    \"       0.13592469, 0.14091663, 0.13328618, 0.11833   , 0.12461805,\\n\",\n    \"       0.13769715, 0.1433956 , 0.14432674, 0.13841473, 0.13815018,\\n\",\n    \"       0.13944786, 0.14269211, 0.14142959, 0.14021556, 0.14112666,\\n\",\n    \"       0.14058631, 0.14122906, 0.14030282, 0.1395203 , 0.13781546,\\n\",\n    \"       0.13659598, 0.13416635, 0.13343848, 0.13207203, 0.13057547,\\n\",\n    \"       0.12746956, 0.12577166, 0.12779064, 0.12974892, 0.12913598,\\n\",\n    \"       0.12803291, 0.12678831, 0.12626308, 0.12507224, 0.1248102 ,\\n\",\n    \"       0.12383852, 0.121826  , 0.11985217, 0.11986669, 0.11762512,\\n\",\n    \"       0.1181166 , 0.11759082, 0.11575512, 0.11410792, 0.11531784,\\n\",\n    \"       0.1169539 , 0.11614721, 0.11519321, 0.11456495, 0.11471919,\\n\",\n    \"       0.11496413, 0.11304017, 0.10994165, 0.11212726, 0.11287158,\\n\",\n    \"       0.11276065, 0.10822126]\\n\",\n    \"\\n\",\n    \"Gaufen_MEAN = [123.94924583,  92.58088583,  97.28130189,  90.31526596]\\n\",\n    \"Gaufen_STD = [67.34487297, 62.8271046 , 60.5856767 , 60.3946299]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class DataAugmentation(torch.nn.Module):\\n\",\n    \"    def __init__(self, mean, std):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.transform = torch.nn.Sequential(\\n\",\n    \"            K.augmentation.RandomResizedCrop(size=(224,224), scale=(0.2,1.0)),\\n\",\n    \"            K.augmentation.Normalize(mean=mean,std=std)\\n\",\n    \"        )\\n\",\n    \"    @torch.no_grad()\\n\",\n    \"    def forward(self,x):\\n\",\n    \"        x_out = self.transform(x)\\n\",\n    \"        return x_out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = DataAugmentation(mean=S1_MEAN,std=S1_STD)\\n\",\n    \"\\n\",\n    \"def preprocess_s1(vh_path, vv_path):\\n\",\n    \"    with rasterio.open(vh_path) as f1:\\n\",\n    \"        vh = f1.read()\\n\",\n    \"    with rasterio.open(vv_path) as f2:\\n\",\n    \"        vv = f2.read()\\n\",\n    \"    s1_img = np.concatenate((vh,vv),0).astype('float32')\\n\",\n    \"    s1_img = torch.from_numpy(s1_img)\\n\",\n    \"    s1_img = transform(s1_img).squeeze(0)\\n\",\n    \"    return s1_img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x1000 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"# Load Sentinel-1 images from the given example data\\n\",\n    \"C = 2  # can be 2,3,4,6,9,12,13,202 or any number if you can provide the wavelengths of them\\n\",\n    \"\\n\",\n    \"image1 = './data/s1/vv/1869_3575.png'\\n\",\n    \"image2 = './data/s1/vh/1869_3575.png'\\n\",\n    \"s1_img = preprocess_s1(image1,image2)\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(nrows=1, ncols=C, figsize=(10, 10))\\n\",\n    \"\\n\",\n    \"for i,row in enumerate(ax):\\n\",\n    \"    row.imshow(s1_img[i])\\n\",\n    \"\\n\",\n    \"s1_img = s1_img.view([1,2,224,224]).cuda()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load the pre-trained weights of DOFA base model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from dofa_v1 import vit_base_patch16\\n\",\n    \"\\n\",\n    \"check_point = torch.load('./checkpoints/DOFA_ViT_base_e100.pth')\\n\",\n    \"vit_model = vit_base_patch16()\\n\",\n    \"msg = vit_model.load_state_dict(check_point, strict=False)\\n\",\n    \"# missing_keys=['fc_norm.weight', 'fc_norm.bias', 'head.weight', 'head.bias'], unexpected_keys=['mask_token', 'norm.weight', 'norm.bias', 'projector.weight', 'projector.bias']\\n\",\n    \"vit_model = vit_model.cuda()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 768])\\n\",\n      \"torch.Size([1, 45])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"wavelengths = [3.75, 3.75]\\n\",\n    \"out_feat = vit_model.forward_features(s1_img, wave_list=wavelengths)\\n\",\n    \"out_logits = vit_model.forward(s1_img, wave_list=wavelengths)\\n\",\n    \"print(out_feat.shape)\\n\",\n    \"print(out_logits.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load Sentinel-2 data with 9 channels\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"\\n\",\n    \"C = 9\\n\",\n    \"\\n\",\n    \"transform = DataAugmentation(mean=S2_MEAN,std=S2_STD)\\n\",\n    \"\\n\",\n    \"def preprocess_s2(img_path):\\n\",\n    \"    chs = []\\n\",\n    \"    s2_files = glob.glob(f\\\"{img_path}/*/*.png\\\")\\n\",\n    \"    for path in s2_files:\\n\",\n    \"        with rasterio.open(path) as f:\\n\",\n    \"            ch = f.read()\\n\",\n    \"        chs.append(ch)\\n\",\n    \"    s2_img = np.concatenate(chs, 0).astype(\\\"float32\\\")\\n\",\n    \"    s2_img = torch.from_numpy(s2_img)\\n\",\n    \"    s2_img = transform(s2_img).squeeze(0)\\n\",\n    \"    return s2_img\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Visualize the Sentinel-2 imagery\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/miniconda3/lib/python3.11/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\\n\",\n      \"  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x1000 with 9 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"s2_img = preprocess_s2(\\\"data/s2/S2A_MSIL1C_20170528T050611_N0205_R076_T44NMM_20170528T050606\\\")\\n\",\n    \"fig, ax = plt.subplots(nrows=1, ncols=C, figsize=(10, 10))\\n\",\n    \"\\n\",\n    \"for i,row in enumerate(ax):\\n\",\n    \"    row.imshow(s2_img[i])\\n\",\n    \"    row.axis(\\\"off\\\")\\n\",\n    \"s2_img = s2_img.view([1,C,224,224]).cuda()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### We use the same DOFA model to inference the Sentinel-2 image\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 768])\\n\",\n      \"torch.Size([1, 45])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"wavelengths = [0.665, 0.56, 0.49, 0.705, 0.74, 0.783, 0.842, 1.61, 2.19]\\n\",\n    \"\\n\",\n    \"out_feat = vit_model.forward_features(s2_img, wave_list=wavelengths)\\n\",\n    \"out_logits = vit_model.forward(s2_img, wave_list=wavelengths)\\n\",\n    \"print(out_feat.shape)\\n\",\n    \"print(out_logits.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### What if I only want to use a subset of Sentinel-2 data?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 768])\\n\",\n      \"torch.Size([1, 45])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Let's only keep the first 5 channels\\n\",\n    \"wavelengths = [0.665, 0.56, 0.49, 0.705, 0.74]\\n\",\n    \"\\n\",\n    \"out_feat = vit_model.forward_features(s2_img[:,:5,...], wave_list=wavelengths)\\n\",\n    \"out_logits = vit_model.forward(s2_img[:,:5,...], wave_list=wavelengths)\\n\",\n    \"print(out_feat.shape)\\n\",\n    \"print(out_logits.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Usage for RGB optical data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"C = 3\\n\",\n    \"\\n\",\n    \"transform = DataAugmentation(mean=NAIP_MEAN, std=NAIP_STD)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def preprocess_rgb(img_path):\\n\",\n    \"    with rasterio.open(img_path) as f:\\n\",\n    \"        rgb_img = f.read().astype(\\\"float32\\\")\\n\",\n    \"    rgb_img = torch.from_numpy(rgb_img)\\n\",\n    \"    rgb_img = transform(rgb_img).squeeze(0)\\n\",\n    \"    return rgb_img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Please install cv2 by \\\"pip install opencv-python\\\"\\n\",\n    \"# or other lib for image reading\\n\",\n    \"\\n\",\n    \"import cv2\\n\",\n    \"\\n\",\n    \"rgb_path = 'data/naip/36861_49963.png'\\n\",\n    \"naip_img = preprocess_rgb(rgb_path)\\n\",\n    \"\\n\",\n    \"plt.imshow(cv2.cvtColor(cv2.imread(rgb_path), cv2.COLOR_BGR2RGB))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"\\n\",\n    \"naip_img = naip_img.view([1,C,224,224]).cuda()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 768])\\n\",\n      \"torch.Size([1, 45])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"wavelengths = [0.665, 0.56, 0.49]\\n\",\n    \"\\n\",\n    \"out_feat = vit_model.forward_features(naip_img, wave_list=wavelengths)\\n\",\n    \"out_logits = vit_model.forward(naip_img, wave_list=wavelengths)\\n\",\n    \"print(out_feat.shape)\\n\",\n    \"print(out_logits.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Coming soon: Usage for Hyperspectral data\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "dofa_v1.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\n\nfrom functools import partial\nfrom wave_dynamic_layer import Dynamic_MLP_OFA\nfrom operator import mul\nfrom torch.nn.modules.utils import _pair\nfrom torch.nn import Conv2d, Dropout\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport pdb\nimport math\nfrom functools import reduce\nimport json\n\nfrom timm.models.vision_transformer import PatchEmbed, Block\n\nclass OFAViT(nn.Module):\n    \"\"\" Masked Autoencoder with VisionTransformer backbone\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, drop_rate=0.,\n                 embed_dim=1024, depth=24, num_heads=16, wv_planes=128, num_classes=45,\n                 global_pool=True, mlp_ratio=4., norm_layer=nn.LayerNorm):\n        super().__init__()\n\n        self.wv_planes = wv_planes\n        self.global_pool = global_pool\n        if self.global_pool:\n            norm_layer = norm_layer\n            embed_dim = embed_dim\n            self.fc_norm = norm_layer(embed_dim)\n        else:\n            self.norm = norm_layer(embed_dim)\n\n        self.patch_embed = Dynamic_MLP_OFA(wv_planes=128, inter_dim=128, kernel_size=patch_size, embed_dim=embed_dim)\n        self.num_patches = (img_size // patch_size) ** 2\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding\n\n        self.blocks = nn.ModuleList([\n            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)\n            for i in range(depth)])\n\n        self.head_drop = nn.Dropout(drop_rate)\n        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n\n\n    def forward_features(self, x, wave_list):\n        # embed patches\n        wavelist = torch.tensor(wave_list, device=x.device).float()\n        self.waves = wavelist\n\n        x, _ = self.patch_embed(x, self.waves)\n\n        x = x + self.pos_embed[:, 1:, :]\n        # append cls token\n        cls_token = self.cls_token + self.pos_embed[:, :1, :]\n        cls_tokens = cls_token.expand(x.shape[0], -1, -1)\n        x = torch.cat((cls_tokens, x), dim=1)\n\n        # apply Transformer blocks\n        for block in self.blocks:\n            x = block(x)\n\n        if self.global_pool:\n            x = x[:, 1:, :].mean(dim=1)  # global pool without cls token\n            outcome = self.fc_norm(x)\n        else:\n            x = self.norm(x)\n            outcome = x[:, 0]\n        return outcome\n\n    def forward_head(self, x, pre_logits=False):\n        x = self.head_drop(x)\n        return x if pre_logits else self.head(x)\n\n    def forward(self, x, wave_list):\n        x = self.forward_features(x, wave_list)\n        x = self.forward_head(x)\n        return x\n\n\ndef vit_small_patch16(**kwargs):\n    model = OFAViT(\n        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\ndef vit_base_patch16(**kwargs):\n    model = OFAViT(\n        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\n\ndef vit_large_patch16(**kwargs):\n    model = OFAViT(\n        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\n\ndef vit_huge_patch14(**kwargs):\n    model = OFAViT(\n        patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n"
  },
  {
    "path": "downstream_tasks/README.md",
    "content": "TODO:\n\nOngoing: add more examples of using DOFAv1 and DOFAv2 based on [Terratorch](https://github.com/IBM/terratorch/tree/main/examples/confs).\n\n- classification\n- segmentation \n- object detection\n- instance segmentation\n- regression examples\n\n"
  },
  {
    "path": "hubconf.py",
    "content": "MODEL = \"https://huggingface.co/earthflow/DOFA/resolve/main/DOFA_ViT_base_e100.pth\"\n\n# hubconf.py\n\ndependencies = [\"torch\"]\nimport torch\nfrom dofa_v1 import vit_base_patch16\n\ndef vit_base_dofa(pretrained=True, strict=False, **kwargs):\n    model = vit_base_patch16(**kwargs)\n\n    if pretrained:\n        # Direct URL to your checkpoint on Hugging Face\n        url = MODEL\n\n        # Download & load\n        state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu')\n        model.load_state_dict(state_dict, strict=strict)\n\n    return model\n\n\n#TODO: add support to more model types\n"
  },
  {
    "path": "pretraining/datasets/__init__.py",
    "content": "#\n"
  },
  {
    "path": "pretraining/datasets/enmap_waves.txt",
    "content": " 1: {'wavelengthCenterOfBand': 418.416, 'FWHMOfBand': 6.99561},\n 2: {'wavelengthCenterOfBand': 424.043, 'FWHMOfBand': 6.6675},\n 3: {'wavelengthCenterOfBand': 429.457, 'FWHMOfBand': 6.42408},\n 4: {'wavelengthCenterOfBand': 434.686, 'FWHMOfBand': 6.25124},\n 5: {'wavelengthCenterOfBand': 439.758, 'FWHMOfBand': 6.13485},\n 6: {'wavelengthCenterOfBand': 444.699, 'FWHMOfBand': 6.06076},\n 7: {'wavelengthCenterOfBand': 449.539, 'FWHMOfBand': 6.01486},\n 8: {'wavelengthCenterOfBand': 454.306, 'FWHMOfBand': 5.98302},\n 9: {'wavelengthCenterOfBand': 459.031, 'FWHMOfBand': 5.95232},\n 10: {'wavelengthCenterOfBand': 463.73, 'FWHMOfBand': 5.9199},\n 11: {'wavelengthCenterOfBand': 468.411, 'FWHMOfBand': 5.88775},\n 12: {'wavelengthCenterOfBand': 473.08, 'FWHMOfBand': 5.8579},\n 13: {'wavelengthCenterOfBand': 477.744, 'FWHMOfBand': 5.83239},\n 14: {'wavelengthCenterOfBand': 482.411, 'FWHMOfBand': 5.81325},\n 15: {'wavelengthCenterOfBand': 487.087, 'FWHMOfBand': 5.8025},\n 16: {'wavelengthCenterOfBand': 491.78, 'FWHMOfBand': 5.8022},\n 17: {'wavelengthCenterOfBand': 496.497, 'FWHMOfBand': 5.81407},\n 18: {'wavelengthCenterOfBand': 501.243, 'FWHMOfBand': 5.83751},\n 19: {'wavelengthCenterOfBand': 506.02, 'FWHMOfBand': 5.87076},\n 20: {'wavelengthCenterOfBand': 510.829, 'FWHMOfBand': 5.91208},\n 21: {'wavelengthCenterOfBand': 515.672, 'FWHMOfBand': 5.95969},\n 22: {'wavelengthCenterOfBand': 520.551, 'FWHMOfBand': 6.01183},\n 23: {'wavelengthCenterOfBand': 525.467, 'FWHMOfBand': 6.06674},\n 24: {'wavelengthCenterOfBand': 530.424, 'FWHMOfBand': 6.12279},\n 25: {'wavelengthCenterOfBand': 535.422, 'FWHMOfBand': 6.1794},\n 26: {'wavelengthCenterOfBand': 540.463, 'FWHMOfBand': 6.23648},\n 27: {'wavelengthCenterOfBand': 545.551, 'FWHMOfBand': 6.29397},\n 28: {'wavelengthCenterOfBand': 550.687, 'FWHMOfBand': 6.35179},\n 29: {'wavelengthCenterOfBand': 555.873, 'FWHMOfBand': 6.40987},\n 30: {'wavelengthCenterOfBand': 561.112, 'FWHMOfBand': 6.46815},\n 31: {'wavelengthCenterOfBand': 566.405, 'FWHMOfBand': 6.52654},\n 32: {'wavelengthCenterOfBand': 571.756, 'FWHMOfBand': 6.58497},\n 33: {'wavelengthCenterOfBand': 577.166, 'FWHMOfBand': 6.64338},\n 34: {'wavelengthCenterOfBand': 582.636, 'FWHMOfBand': 6.70177},\n 35: {'wavelengthCenterOfBand': 588.171, 'FWHMOfBand': 6.76072},\n 36: {'wavelengthCenterOfBand': 593.773, 'FWHMOfBand': 6.82113},\n 37: {'wavelengthCenterOfBand': 599.446, 'FWHMOfBand': 6.88388},\n 38: {'wavelengthCenterOfBand': 605.193, 'FWHMOfBand': 6.94987},\n 39: {'wavelengthCenterOfBand': 611.017, 'FWHMOfBand': 7.01999},\n 40: {'wavelengthCenterOfBand': 616.923, 'FWHMOfBand': 7.09511},\n 41: {'wavelengthCenterOfBand': 622.921, 'FWHMOfBand': 7.17533},\n 42: {'wavelengthCenterOfBand': 628.987, 'FWHMOfBand': 7.25495},\n 43: {'wavelengthCenterOfBand': 635.112, 'FWHMOfBand': 7.33145},\n 44: {'wavelengthCenterOfBand': 641.294, 'FWHMOfBand': 7.40537},\n 45: {'wavelengthCenterOfBand': 647.537, 'FWHMOfBand': 7.47729},\n 46: {'wavelengthCenterOfBand': 653.841, 'FWHMOfBand': 7.54777},\n 47: {'wavelengthCenterOfBand': 660.207, 'FWHMOfBand': 7.61738},\n 48: {'wavelengthCenterOfBand': 666.637, 'FWHMOfBand': 7.68669},\n 49: {'wavelengthCenterOfBand': 673.131, 'FWHMOfBand': 7.75628},\n 50: {'wavelengthCenterOfBand': 679.691, 'FWHMOfBand': 7.8267},\n 51: {'wavelengthCenterOfBand': 686.319, 'FWHMOfBand': 7.89853},\n 52: {'wavelengthCenterOfBand': 693.014, 'FWHMOfBand': 7.97235},\n 53: {'wavelengthCenterOfBand': 699.78, 'FWHMOfBand': 8.04871},\n 54: {'wavelengthCenterOfBand': 706.617, 'FWHMOfBand': 8.1281},\n 55: {'wavelengthCenterOfBand': 713.524, 'FWHMOfBand': 8.2102},\n 56: {'wavelengthCenterOfBand': 720.501, 'FWHMOfBand': 8.29433},\n 57: {'wavelengthCenterOfBand': 727.545, 'FWHMOfBand': 8.3798},\n 58: {'wavelengthCenterOfBand': 734.654, 'FWHMOfBand': 8.46592},\n 59: {'wavelengthCenterOfBand': 741.826, 'FWHMOfBand': 8.552},\n 60: {'wavelengthCenterOfBand': 749.06, 'FWHMOfBand': 8.63735},\n 61: {'wavelengthCenterOfBand': 756.353, 'FWHMOfBand': 8.72128},\n 62: {'wavelengthCenterOfBand': 763.703, 'FWHMOfBand': 8.80311},\n 63: {'wavelengthCenterOfBand': 771.108, 'FWHMOfBand': 8.88214},\n 64: {'wavelengthCenterOfBand': 778.567, 'FWHMOfBand': 8.95779},\n 65: {'wavelengthCenterOfBand': 786.078, 'FWHMOfBand': 9.03034},\n 66: {'wavelengthCenterOfBand': 793.639, 'FWHMOfBand': 9.1005},\n 67: {'wavelengthCenterOfBand': 801.249, 'FWHMOfBand': 9.16897},\n 68: {'wavelengthCenterOfBand': 808.905, 'FWHMOfBand': 9.23646},\n 69: {'wavelengthCenterOfBand': 816.608, 'FWHMOfBand': 9.30368},\n 70: {'wavelengthCenterOfBand': 824.355, 'FWHMOfBand': 9.37133},\n 71: {'wavelengthCenterOfBand': 832.145, 'FWHMOfBand': 9.44012},\n 72: {'wavelengthCenterOfBand': 839.976, 'FWHMOfBand': 9.51076},\n 73: {'wavelengthCenterOfBand': 847.847, 'FWHMOfBand': 9.58396},\n 74: {'wavelengthCenterOfBand': 855.757, 'FWHMOfBand': 9.66032},\n 75: {'wavelengthCenterOfBand': 863.703, 'FWHMOfBand': 9.73971},\n 76: {'wavelengthCenterOfBand': 871.683, 'FWHMOfBand': 9.82161},\n 77: {'wavelengthCenterOfBand': 879.693, 'FWHMOfBand': 9.90551},\n 78: {'wavelengthCenterOfBand': 887.729, 'FWHMOfBand': 9.99089},\n 79: {'wavelengthCenterOfBand': 895.789, 'FWHMOfBand': 10.0772},\n 80: {'wavelengthCenterOfBand': 901.961, 'FWHMOfBand': 9.17725},\n 81: {'wavelengthCenterOfBand': 903.87, 'FWHMOfBand': 10.164},\n 82: {'wavelengthCenterOfBand': 911.571, 'FWHMOfBand': 9.31901},\n 83: {'wavelengthCenterOfBand': 911.968, 'FWHMOfBand': 10.2508},\n 84: {'wavelengthCenterOfBand': 920.081, 'FWHMOfBand': 10.3369},\n 85: {'wavelengthCenterOfBand': 921.32, 'FWHMOfBand': 9.45441},\n 86: {'wavelengthCenterOfBand': 928.204, 'FWHMOfBand': 10.422},\n 87: {'wavelengthCenterOfBand': 931.203, 'FWHMOfBand': 9.58399},\n 88: {'wavelengthCenterOfBand': 936.335, 'FWHMOfBand': 10.5054},\n 89: {'wavelengthCenterOfBand': 941.218, 'FWHMOfBand': 9.7083},\n 90: {'wavelengthCenterOfBand': 944.47, 'FWHMOfBand': 10.5862},\n 91: {'wavelengthCenterOfBand': 951.36, 'FWHMOfBand': 9.82789},\n 92: {'wavelengthCenterOfBand': 952.608, 'FWHMOfBand': 10.6589},\n 93: {'wavelengthCenterOfBand': 960.748, 'FWHMOfBand': 10.716},\n 94: {'wavelengthCenterOfBand': 961.628, 'FWHMOfBand': 9.94331},\n 95: {'wavelengthCenterOfBand': 968.892, 'FWHMOfBand': 10.75},\n 96: {'wavelengthCenterOfBand': 972.016, 'FWHMOfBand': 10.0551},\n 97: {'wavelengthCenterOfBand': 977.037, 'FWHMOfBand': 10.7533},\n 98: {'wavelengthCenterOfBand': 982.523, 'FWHMOfBand': 10.1638},\n 99: {'wavelengthCenterOfBand': 985.186, 'FWHMOfBand': 10.7183},\n 100: {'wavelengthCenterOfBand': 993.144, 'FWHMOfBand': 10.27},\n 101: {'wavelengthCenterOfBand': 993.338, 'FWHMOfBand': 10.6375},\n 102: {'wavelengthCenterOfBand': 1003.88, 'FWHMOfBand': 10.3742},\n 103: {'wavelengthCenterOfBand': 1014.72, 'FWHMOfBand': 10.4768},\n 104: {'wavelengthCenterOfBand': 1025.66, 'FWHMOfBand': 10.5775},\n 105: {'wavelengthCenterOfBand': 1036.7, 'FWHMOfBand': 10.6753},\n 106: {'wavelengthCenterOfBand': 1047.84, 'FWHMOfBand': 10.769},\n 107: {'wavelengthCenterOfBand': 1059.07, 'FWHMOfBand': 10.8578},\n 108: {'wavelengthCenterOfBand': 1070.39, 'FWHMOfBand': 10.9405},\n 109: {'wavelengthCenterOfBand': 1081.78, 'FWHMOfBand': 11.0162},\n 110: {'wavelengthCenterOfBand': 1093.26, 'FWHMOfBand': 11.0839},\n 111: {'wavelengthCenterOfBand': 1104.81, 'FWHMOfBand': 11.1437},\n 112: {'wavelengthCenterOfBand': 1116.43, 'FWHMOfBand': 11.196},\n 113: {'wavelengthCenterOfBand': 1128.1, 'FWHMOfBand': 11.2414},\n 114: {'wavelengthCenterOfBand': 1139.84, 'FWHMOfBand': 11.2806},\n 115: {'wavelengthCenterOfBand': 1151.62, 'FWHMOfBand': 11.3139},\n 116: {'wavelengthCenterOfBand': 1163.44, 'FWHMOfBand': 11.3421},\n 117: {'wavelengthCenterOfBand': 1175.3, 'FWHMOfBand': 11.3656},\n 118: {'wavelengthCenterOfBand': 1187.2, 'FWHMOfBand': 11.385},\n 119: {'wavelengthCenterOfBand': 1199.11, 'FWHMOfBand': 11.4009},\n 120: {'wavelengthCenterOfBand': 1211.05, 'FWHMOfBand': 11.4135},\n 121: {'wavelengthCenterOfBand': 1223.0, 'FWHMOfBand': 11.4229},\n 122: {'wavelengthCenterOfBand': 1234.97, 'FWHMOfBand': 11.4292},\n 123: {'wavelengthCenterOfBand': 1246.94, 'FWHMOfBand': 11.4325},\n 124: {'wavelengthCenterOfBand': 1258.93, 'FWHMOfBand': 11.4329},\n 125: {'wavelengthCenterOfBand': 1270.92, 'FWHMOfBand': 11.4305},\n 126: {'wavelengthCenterOfBand': 1282.92, 'FWHMOfBand': 11.4253},\n 127: {'wavelengthCenterOfBand': 1294.91, 'FWHMOfBand': 11.4176},\n 128: {'wavelengthCenterOfBand': 1306.9, 'FWHMOfBand': 11.4073},\n 129: {'wavelengthCenterOfBand': 1318.88, 'FWHMOfBand': 11.3946},\n 130: {'wavelengthCenterOfBand': 1330.85, 'FWHMOfBand': 11.3796},\n 131: {'wavelengthCenterOfBand': 1342.82, 'FWHMOfBand': 11.3624},\n 132: {'wavelengthCenterOfBand': 1354.76, 'FWHMOfBand': 11.343},\n 133: {'wavelengthCenterOfBand': 1366.69, 'FWHMOfBand': 11.3215},\n 134: {'wavelengthCenterOfBand': 1378.6, 'FWHMOfBand': 11.2981},\n 135: {'wavelengthCenterOfBand': 1390.48, 'FWHMOfBand': 11.2728},\n 136: {'wavelengthCenterOfBand': 1461.1, 'FWHMOfBand': 11.0876},\n 137: {'wavelengthCenterOfBand': 1472.74, 'FWHMOfBand': 11.052},\n 138: {'wavelengthCenterOfBand': 1484.34, 'FWHMOfBand': 11.0151},\n 139: {'wavelengthCenterOfBand': 1495.89, 'FWHMOfBand': 10.9772},\n 140: {'wavelengthCenterOfBand': 1507.4, 'FWHMOfBand': 10.9383},\n 141: {'wavelengthCenterOfBand': 1518.87, 'FWHMOfBand': 10.8984},\n 142: {'wavelengthCenterOfBand': 1530.29, 'FWHMOfBand': 10.8577},\n 143: {'wavelengthCenterOfBand': 1541.67, 'FWHMOfBand': 10.8161},\n 144: {'wavelengthCenterOfBand': 1553.01, 'FWHMOfBand': 10.7738},\n 145: {'wavelengthCenterOfBand': 1564.3, 'FWHMOfBand': 10.7308},\n 146: {'wavelengthCenterOfBand': 1575.55, 'FWHMOfBand': 10.6872},\n 147: {'wavelengthCenterOfBand': 1586.76, 'FWHMOfBand': 10.643},\n 148: {'wavelengthCenterOfBand': 1597.91, 'FWHMOfBand': 10.5985},\n 149: {'wavelengthCenterOfBand': 1609.02, 'FWHMOfBand': 10.5535},\n 150: {'wavelengthCenterOfBand': 1620.09, 'FWHMOfBand': 10.5082},\n 151: {'wavelengthCenterOfBand': 1631.11, 'FWHMOfBand': 10.4626},\n 152: {'wavelengthCenterOfBand': 1642.07, 'FWHMOfBand': 10.4169},\n 153: {'wavelengthCenterOfBand': 1653.0, 'FWHMOfBand': 10.371},\n 154: {'wavelengthCenterOfBand': 1663.87, 'FWHMOfBand': 10.3251},\n 155: {'wavelengthCenterOfBand': 1674.7, 'FWHMOfBand': 10.2793},\n 156: {'wavelengthCenterOfBand': 1685.47, 'FWHMOfBand': 10.2335},\n 157: {'wavelengthCenterOfBand': 1696.2, 'FWHMOfBand': 10.1879},\n 158: {'wavelengthCenterOfBand': 1706.87, 'FWHMOfBand': 10.1425},\n 159: {'wavelengthCenterOfBand': 1717.5, 'FWHMOfBand': 10.0974},\n 160: {'wavelengthCenterOfBand': 1728.08, 'FWHMOfBand': 10.0525},\n 161: {'wavelengthCenterOfBand': 1738.6, 'FWHMOfBand': 10.0079},\n 162: {'wavelengthCenterOfBand': 1749.08, 'FWHMOfBand': 9.96359},\n 163: {'wavelengthCenterOfBand': 1759.51, 'FWHMOfBand': 9.91949},\n 164: {'wavelengthCenterOfBand': 1939.14, 'FWHMOfBand': 9.16572},\n 165: {'wavelengthCenterOfBand': 1948.69, 'FWHMOfBand': 9.12592},\n 166: {'wavelengthCenterOfBand': 1958.2, 'FWHMOfBand': 9.08633},\n 167: {'wavelengthCenterOfBand': 1967.66, 'FWHMOfBand': 9.04694},\n 168: {'wavelengthCenterOfBand': 1977.08, 'FWHMOfBand': 9.00775},\n 169: {'wavelengthCenterOfBand': 1986.45, 'FWHMOfBand': 8.96875},\n 170: {'wavelengthCenterOfBand': 1995.79, 'FWHMOfBand': 8.92995},\n 171: {'wavelengthCenterOfBand': 2005.08, 'FWHMOfBand': 8.89134},\n 172: {'wavelengthCenterOfBand': 2014.33, 'FWHMOfBand': 8.85292},\n 173: {'wavelengthCenterOfBand': 2023.54, 'FWHMOfBand': 8.81469},\n 174: {'wavelengthCenterOfBand': 2032.7, 'FWHMOfBand': 8.77665},\n 175: {'wavelengthCenterOfBand': 2041.83, 'FWHMOfBand': 8.73878},\n 176: {'wavelengthCenterOfBand': 2050.92, 'FWHMOfBand': 8.70111},\n 177: {'wavelengthCenterOfBand': 2059.96, 'FWHMOfBand': 8.66361},\n 178: {'wavelengthCenterOfBand': 2068.97, 'FWHMOfBand': 8.62632},\n 179: {'wavelengthCenterOfBand': 2077.93, 'FWHMOfBand': 8.58923},\n 180: {'wavelengthCenterOfBand': 2086.86, 'FWHMOfBand': 8.55236},\n 181: {'wavelengthCenterOfBand': 2095.74, 'FWHMOfBand': 8.51572},\n 182: {'wavelengthCenterOfBand': 2104.59, 'FWHMOfBand': 8.47932},\n 183: {'wavelengthCenterOfBand': 2113.4, 'FWHMOfBand': 8.44317},\n 184: {'wavelengthCenterOfBand': 2122.17, 'FWHMOfBand': 8.40728},\n 185: {'wavelengthCenterOfBand': 2130.9, 'FWHMOfBand': 8.37166},\n 186: {'wavelengthCenterOfBand': 2139.6, 'FWHMOfBand': 8.33632},\n 187: {'wavelengthCenterOfBand': 2148.26, 'FWHMOfBand': 8.30128},\n 188: {'wavelengthCenterOfBand': 2156.88, 'FWHMOfBand': 8.26653},\n 189: {'wavelengthCenterOfBand': 2165.47, 'FWHMOfBand': 8.2321},\n 190: {'wavelengthCenterOfBand': 2174.02, 'FWHMOfBand': 8.198},\n 191: {'wavelengthCenterOfBand': 2182.53, 'FWHMOfBand': 8.16422},\n 192: {'wavelengthCenterOfBand': 2191.01, 'FWHMOfBand': 8.13079},\n 193: {'wavelengthCenterOfBand': 2199.45, 'FWHMOfBand': 8.09771},\n 194: {'wavelengthCenterOfBand': 2207.86, 'FWHMOfBand': 8.065},\n 195: {'wavelengthCenterOfBand': 2216.24, 'FWHMOfBand': 8.03267},\n 196: {'wavelengthCenterOfBand': 2224.58, 'FWHMOfBand': 8.00072},\n 197: {'wavelengthCenterOfBand': 2232.89, 'FWHMOfBand': 7.96916},\n 198: {'wavelengthCenterOfBand': 2241.16, 'FWHMOfBand': 7.93802},\n 199: {'wavelengthCenterOfBand': 2249.4, 'FWHMOfBand': 7.90728},\n 200: {'wavelengthCenterOfBand': 2257.61, 'FWHMOfBand': 7.87694},\n 201: {'wavelengthCenterOfBand': 2265.79, 'FWHMOfBand': 7.84695},\n 202: {'wavelengthCenterOfBand': 2273.93, 'FWHMOfBand': 7.81727},\n 203: {'wavelengthCenterOfBand': 2282.04, 'FWHMOfBand': 7.78785},\n 204: {'wavelengthCenterOfBand': 2290.12, 'FWHMOfBand': 7.75866},\n 205: {'wavelengthCenterOfBand': 2298.17, 'FWHMOfBand': 7.72966},\n 206: {'wavelengthCenterOfBand': 2306.19, 'FWHMOfBand': 7.70079},\n 207: {'wavelengthCenterOfBand': 2314.17, 'FWHMOfBand': 7.67203},\n 208: {'wavelengthCenterOfBand': 2322.13, 'FWHMOfBand': 7.64333},\n 209: {'wavelengthCenterOfBand': 2330.05, 'FWHMOfBand': 7.61464},\n 210: {'wavelengthCenterOfBand': 2337.94, 'FWHMOfBand': 7.58593},\n 211: {'wavelengthCenterOfBand': 2345.81, 'FWHMOfBand': 7.55715},\n 212: {'wavelengthCenterOfBand': 2353.64, 'FWHMOfBand': 7.52827},\n 213: {'wavelengthCenterOfBand': 2361.44, 'FWHMOfBand': 7.49924},\n 214: {'wavelengthCenterOfBand': 2369.21, 'FWHMOfBand': 7.47001},\n 215: {'wavelengthCenterOfBand': 2376.95, 'FWHMOfBand': 7.44055},\n 216: {'wavelengthCenterOfBand': 2384.66, 'FWHMOfBand': 7.41082},\n 217: {'wavelengthCenterOfBand': 2392.34, 'FWHMOfBand': 7.38077},\n 218: {'wavelengthCenterOfBand': 2400.0, 'FWHMOfBand': 7.35037},\n 219: {'wavelengthCenterOfBand': 2407.62, 'FWHMOfBand': 7.31957},\n 220: {'wavelengthCenterOfBand': 2415.21, 'FWHMOfBand': 7.28832},\n 221: {'wavelengthCenterOfBand': 2422.78, 'FWHMOfBand': 7.25659},\n 222: {'wavelengthCenterOfBand': 2430.32, 'FWHMOfBand': 7.22434},\n 223: {'wavelengthCenterOfBand': 2437.82, 'FWHMOfBand': 7.19152},\n 224: {'wavelengthCenterOfBand': 2445.3, 'FWHMOfBand': 7.1581}\n"
  },
  {
    "path": "pretraining/datasets/ofall_dataset.py",
    "content": "import sys\nimport os\nimport pdb\nfrom Dataset4EO.datasets import SatlasS1,SatlasS2,SatlasNAIP,Hyper11k,five_billion\nimport time\nfrom tqdm import tqdm\nimport numpy as np\nimport cv2\nimport torch\nfrom torchdata.dataloader2 import MultiProcessingReadingService\nfrom torch.utils.data import Dataset, DataLoader, Sampler, DistributedSampler\nimport rasterio\nimport kornia as K\nimport random\nimport pdb\nimport math\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=rasterio.errors.NotGeoreferencedWarning)\n\n# vh,vv\nS1_MEAN = [166.36275909, 88.45542715]# / 255.0\nS1_STD = [64.83126309, 43.07350145]# /255.0\n\nS2_MEAN = [114.1099739 , 114.81779093, 126.63977424,  84.33539309,\n        97.84789168, 103.94461911, 101.435633  ,  72.32804172,\n        56.66528851]\nS2_STD = [77.84352553, 69.96844919, 67.42465279, 64.57022983, 61.72545487,\n       61.34187099, 60.29744676, 47.88519516, 42.55886798]\n\nNAIP_MEAN = [123.675, 116.28, 103.53] # ImageNet stats for now\nNAIP_STD = [58.395, 57.12, 57.375] # ImageNet stats for now\n\nHyper_MEAN = [0.04904891, 0.04734517, 0.04881499, 0.0521312 , 0.05371449,\n       0.05431649, 0.05600387, 0.05753566, 0.05837488, 0.06014395,\n       0.06120129, 0.06187369, 0.06262351, 0.0640324 , 0.06544493,\n       0.06583978, 0.06657578, 0.06818208, 0.06887893, 0.07050433,\n       0.0730939 , 0.07500546, 0.07658557, 0.07914317, 0.08149088,\n       0.08413605, 0.08584346, 0.0875968 , 0.08949799, 0.09126373,\n       0.09284472, 0.09385966, 0.09429929, 0.09644857, 0.09758445,\n       0.09888336, 0.1000851 , 0.1012402 , 0.10217949, 0.10292227,\n       0.10441044, 0.10482953, 0.10586129, 0.10771345, 0.10875386,\n       0.10872938, 0.10955398, 0.11022133, 0.11095442, 0.11202578,\n       0.1144143 , 0.11816488, 0.12615164, 0.13405322, 0.14239053,\n       0.14940845, 0.15952006, 0.16786492, 0.17470501, 0.17793106,\n       0.18344983, 0.1717895 , 0.18624028, 0.18920699, 0.19094643,\n       0.19191591, 0.19321348, 0.19543689, 0.19453696, 0.197083  ,\n       0.19759373, 0.20008718, 0.20109805, 0.20273463, 0.20447857,\n       0.20549563, 0.20768884, 0.20616769, 0.20696713, 0.21603036,\n       0.20431315, 0.20028888, 0.21381801, 0.19553433, 0.22010142,\n       0.20745818, 0.20469215, 0.20106881, 0.21624712, 0.20217295,\n       0.21258003, 0.19276747, 0.19084313, 0.21547508, 0.1990552 ,\n       0.2220764 , 0.20010307, 0.22556668, 0.20294108, 0.20432738,\n       0.22884114, 0.23084754, 0.23361147, 0.23613915, 0.23835524,\n       0.24002708, 0.24240751, 0.24512835, 0.24625339, 0.24729841,\n       0.24621868, 0.2335448 , 0.23611045, 0.23486711, 0.22491438,\n       0.23376624, 0.23624881, 0.23806269, 0.23892529, 0.24048481,\n       0.24448228, 0.24877857, 0.25137802, 0.25356494, 0.25809049,\n       0.25776253, 0.19769041, 0.20087005, 0.20429956, 0.20702436,\n       0.21026749, 0.21287441, 0.21557267, 0.21920979, 0.22113606,\n       0.2227512 , 0.22380759, 0.22556949, 0.22486467, 0.22534442,\n       0.22393468, 0.22259283, 0.22103291, 0.21938812, 0.21726496,\n       0.15029096, 0.15920778, 0.14992988, 0.1402144 , 0.14215149,\n       0.16192129, 0.16663248, 0.16954731, 0.16092901, 0.16268809,\n       0.16303404, 0.16922207, 0.16708257, 0.16741623, 0.16829468,\n       0.16974312, 0.17043488, 0.17164688, 0.17061502, 0.17135934,\n       0.17061249, 0.17054758, 0.17006451, 0.17125258, 0.16988001,\n       0.16870924, 0.16756754, 0.1703907 , 0.17044655, 0.16995895,\n       0.16583233, 0.16387971, 0.15977418, 0.15813546, 0.15500555,\n       0.15475487, 0.1500904 , 0.14842632, 0.14555257, 0.1444372 ,\n       0.14345487, 0.1439716 , 0.13948618, 0.13974816, 0.1388893 ,\n       0.1425306 , 0.1399015 , 0.14124387, 0.13716652, 0.13908459,\n       0.13517979, 0.13579579, 0.12699047, 0.13110322, 0.12600956,\n       0.12683088, 0.11266357]\n\nHyper_MEAN = Hyper_MEAN[7:77]\n\nHyper_STD = [0.05438845, 0.05425398, 0.05519192, 0.05621342, 0.05694766,\n       0.05704381, 0.05763639, 0.05804253, 0.05834612, 0.05888857,\n       0.05920108, 0.05948167, 0.06002252, 0.06070791, 0.06148699,\n       0.06196402, 0.06251209, 0.063477  , 0.06412124, 0.06480832,\n       0.06584631, 0.06655134, 0.06710579, 0.06817766, 0.06930595,\n       0.07081702, 0.07204193, 0.07329206, 0.07491294, 0.07681551,\n       0.07890642, 0.08096196, 0.08243044, 0.08428552, 0.08603505,\n       0.08763417, 0.08869326, 0.08985463, 0.09081571, 0.09164355,\n       0.09288558, 0.09362094, 0.09377934, 0.09490612, 0.09628371,\n       0.09688408, 0.09770997, 0.09843717, 0.099185  , 0.09957288,\n       0.10124085, 0.10085674, 0.0999966 , 0.09884673, 0.09924151,\n       0.10034673, 0.10382162, 0.10637773, 0.10945914, 0.11091637,\n       0.1143651 , 0.11309448, 0.11714786, 0.11771383, 0.11835992,\n       0.11911578, 0.11971312, 0.12100819, 0.12134505, 0.12288926,\n       0.1223688 , 0.12337272, 0.12378503, 0.1247803 , 0.12574014,\n       0.12591301, 0.12706246, 0.12643132, 0.1274808 , 0.12616546,\n       0.12669385, 0.12547143, 0.12466624, 0.12086743, 0.1281701 ,\n       0.12939227, 0.11918587, 0.1359367 , 0.12719588, 0.13615098,\n       0.12415102, 0.12939233, 0.12601028, 0.12531339, 0.12763924,\n       0.12921317, 0.12880291, 0.13102435, 0.13017717, 0.13047819,\n       0.13304893, 0.13441405, 0.13619871, 0.13785246, 0.13942554,\n       0.14062946, 0.14236353, 0.14349961, 0.14351399, 0.14394955,\n       0.14339238, 0.13597313, 0.13902099, 0.13897383, 0.13261457,\n       0.1377756 , 0.13982825, 0.14176949, 0.14285451, 0.14382317,\n       0.14602028, 0.1482343 , 0.15007727, 0.15194561, 0.153546  ,\n       0.15312391, 0.14626474, 0.1466966 , 0.14744146, 0.14778844,\n       0.14876473, 0.14891526, 0.14976731, 0.15107008, 0.15135105,\n       0.15174565, 0.15199847, 0.15274257, 0.15231952, 0.15232084,\n       0.1517344 , 0.15127988, 0.15108911, 0.15042051, 0.1496356 ,\n       0.13592469, 0.14091663, 0.13328618, 0.11833   , 0.12461805,\n       0.13769715, 0.1433956 , 0.14432674, 0.13841473, 0.13815018,\n       0.13944786, 0.14269211, 0.14142959, 0.14021556, 0.14112666,\n       0.14058631, 0.14122906, 0.14030282, 0.1395203 , 0.13781546,\n       0.13659598, 0.13416635, 0.13343848, 0.13207203, 0.13057547,\n       0.12746956, 0.12577166, 0.12779064, 0.12974892, 0.12913598,\n       0.12803291, 0.12678831, 0.12626308, 0.12507224, 0.1248102 ,\n       0.12383852, 0.121826  , 0.11985217, 0.11986669, 0.11762512,\n       0.1181166 , 0.11759082, 0.11575512, 0.11410792, 0.11531784,\n       0.1169539 , 0.11614721, 0.11519321, 0.11456495, 0.11471919,\n       0.11496413, 0.11304017, 0.10994165, 0.11212726, 0.11287158,\n       0.11276065, 0.10822126]\n\nHyper_STD = Hyper_STD[7:77]\nGaufen_MEAN = [123.94924583,  92.58088583,  97.28130189,  90.31526596]\nGaufen_STD = [67.34487297, 62.8271046 , 60.5856767 , 60.3946299]\n\nclass DataAugmentation(torch.nn.Module):\n    def __init__(self, mean, std):\n        super().__init__()\n        self.transform = torch.nn.Sequential(\n            K.augmentation.RandomResizedCrop(size=(224,224), scale=(0.2,1.0)),\n            K.augmentation.RandomHorizontalFlip(p=0.5),\n            K.augmentation.Normalize(mean=mean,std=std)\n        )\n\n    @torch.no_grad()\n    def forward(self,x):\n        #x = kornia.image_to_tensor(x_np, keepdim=True)  # CxHxW\n        x_out = self.transform(x)\n        return x_out\n\n\nclass Sentinel1Dataset(Dataset):\n    def __init__(self, root_dir, split='train', transform=True):\n        #self.root_dir = root_dir\n        #self.split = split\n        if transform:\n            self.transform = DataAugmentation(mean=S1_MEAN,std=S1_STD)\n        else:\n            self.transform = None\n\n        dp = SatlasS1(root_dir, split=split)\n        self.meta_list = list(dp)\n\n    def __getitem__(self, index):\n        meta_idx = self.meta_list[index]\n        vh_path, vv_path = meta_idx['filename']\n        #vh = cv2.imread(vh_path) * 1.0\n        #vv = cv2.imread(vv_path) * 1.0\n        with rasterio.open(vh_path) as f1:\n            vh = f1.read()\n            #vh = (vh - S1_MEAN[0])/S1_STD[0]\n        with rasterio.open(vv_path) as f2:\n            vv = f2.read()\n            #vv = (vv - S1_MEAN[1])/S1_STD[1]\n        s1_img = np.concatenate((vh,vv),0).astype('float32')\n        #pdb.set_trace()\n        if self.transform:\n            s1_img = torch.from_numpy(s1_img)\n            s1_img = self.transform(s1_img).squeeze(0)\n        else:\n            s1_img = torch.from_numpy(s1_img)\n\n        return s1_img\n\n    def __len__(self):\n        return len(self.meta_list)\n\n\nclass Sentinel2Dataset(Dataset):\n    def __init__(self, root_dir, split='train', transform=True):\n        #self.root_dir = root_dir\n        #self.split = split\n        if transform:\n            self.transform = DataAugmentation(mean=S2_MEAN,std=S2_STD)\n        else:\n            self.transform = None\n\n        dp = SatlasS2(root_dir, split=split)\n        self.meta_list = list(dp)\n\n\n    def __getitem__(self, index):\n        meta_idx = self.meta_list[index]\n\n        chs = []\n        for i, path in enumerate(meta_idx['filename']):\n            with rasterio.open(path) as f:\n                ch = f.read()\n            chs.append(ch)\n        s2_img = np.concatenate(chs,0).astype('float32')\n        #pdb.set_trace()\n        if self.transform:\n            s2_img = torch.from_numpy(s2_img)\n            s2_img = self.transform(s2_img).squeeze(0)\n        else:\n            s2_img = torch.from_numpy(s2_img)\n\n        return s2_img\n\n    def __len__(self):\n        return len(self.meta_list)\n\nclass NAIPDataset(Dataset):\n    def __init__(self, root_dir, split='train', transform=True):\n        #self.root_dir = root_dir\n        #self.split = split\n        if transform:\n            self.transform = DataAugmentation(mean=NAIP_MEAN,std=NAIP_STD)\n        else:\n            self.transform = None\n        dp = SatlasNAIP(root_dir, split=split)\n        self.meta_list = list(dp)\n\n    def __getitem__(self, index):\n        meta_idx = self.meta_list[index]\n        im_path = meta_idx['filename']\n        with rasterio.open(im_path) as f:\n            try:\n                naip_img = f.read().astype('float32')\n            except rasterio.errors.RasterioIOError:\n                naip_img = np.zeros([3,512,512],dtype=np.float32)\n        if self.transform:\n            naip_img = torch.from_numpy(naip_img)\n            naip_img = self.transform(naip_img).squeeze(0)\n        else:\n            naip_img = torch.from_numpy(naip_img)\n\n        return naip_img\n\n    def __len__(self):\n        return len(self.meta_list)\n\nclass HyperDataset(Dataset):\n    def __init__(self, root_dir, split='train', transform=True):\n        #self.root_dir = root_dir\n        #self.split = split\n        if transform:\n            self.transform = DataAugmentation(mean=Hyper_MEAN,std=Hyper_STD)\n        else:\n            self.transform = None\n\n        dp = Hyper11k(root_dir, split=split)\n        self.meta_list = list(dp)\n\n        invalid_channels = [126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 160,\n                    161, 162, 163, 164, 165, 166]\n        #self.valid_channels_ids = [c for c in range(224) if c not in invalid_channels]\n        self.valid_channels_ids = [c for c in range(7,77)]\n        self.num_valid_channels = len(self.valid_channels_ids)\n\n\n    def __getitem__(self, index):\n        meta_idx = self.meta_list[index]\n        im_path = meta_idx['filename']\n        with rasterio.open(im_path) as f:\n            hyper_img = f.read()#.astype('float32')\n            hyper_img = hyper_img[self.valid_channels_ids]\n            hyper_img = np.clip(hyper_img, a_min=0, a_max=10000)\n            hyper_img = (hyper_img - 0) / (10000 - 0)\n            hyper_img = hyper_img.astype(np.float32)\n            #print(hyper_img.shape)  \n\n        if self.transform:\n            hyper_img = torch.from_numpy(hyper_img)\n            hyper_img = self.transform(hyper_img).squeeze(0)\n        else:\n            hyper_img = torch.from_numpy(hyper_img)\n\n        return hyper_img\n\n    def __len__(self):\n        return len(self.meta_list)\n\nclass GaufenDataset(Dataset):\n    def __init__(self, root_dir, split='train', transform=True):\n        #self.root_dir = root_dir\n        #self.split = split\n        if transform:\n            self.transform = DataAugmentation(mean=Gaufen_MEAN,std=Gaufen_STD)\n        else:\n            self.transform = None\n\n        dp = five_billion.FiveBillion(root_dir, split=split)\n        self.meta_list = list(dp)\n\n    def __getitem__(self, index):\n        meta_idx = self.meta_list[index]\n        im_path = meta_idx['filename']\n        with rasterio.open(im_path) as f:\n            gaufen_img = f.read().astype('float32')\n        if self.transform:\n            gaufen_img = torch.from_numpy(gaufen_img)\n            gaufen_img = self.transform(gaufen_img).squeeze(0)\n        else:\n            gaufen_img = torch.from_numpy(gaufen_img)\n\n        return gaufen_img\n\n    def __len__(self):\n        return len(self.meta_list)\n\n\n\n\ndef chunk(indices, size):\n    return torch.split(torch.tensor(indices), size)\n\n# Define a custom batch sampler\nclass MyBatchSampler(Sampler):\n    def __init__(self, dataset, dataset1, dataset2, dataset3, dataset4, dataset5, batch_size, shuffle=True, drop_last=True):\n        self.dataset = dataset\n        self.batch_size = batch_size\n        self.drop_last = drop_last\n        self.shuffle = shuffle\n\n        # Split the dataset into two subsets based on their lengths\n        self.indices = list(range(len(dataset)))\n        self.indices1 = self.indices[:len(dataset1)]\n        self.indices2 = self.indices[len(dataset1):len(dataset1)+len(dataset2)]\n        self.indices3 = self.indices[len(dataset1)+len(dataset2):len(dataset1)+len(dataset2)+len(dataset3)]\n        self.indices4 = self.indices[len(dataset1)+len(dataset2)+len(dataset3):len(dataset1)+len(dataset2)+len(dataset3)+len(dataset4)]\n        self.indices5 = self.indices[len(dataset1)+len(dataset2)+len(dataset3)+len(dataset4):]\n\n\n    def __iter__(self):\n\n        # Randomly shuffle the indices within each subset\n        if self.shuffle:\n            random.shuffle(self.indices1)\n            random.shuffle(self.indices2)\n            random.shuffle(self.indices3)\n            random.shuffle(self.indices4)\n            random.shuffle(self.indices5)\n\n        a_batches  = chunk(self.indices1, self.batch_size)\n        b_batches = chunk(self.indices2, self.batch_size)\n        c_batches  = chunk(self.indices3, self.batch_size)\n        d_batches = chunk(self.indices4, self.batch_size)\n        e_batches = chunk(self.indices5, self.batch_size)\n\n        if self.drop_last:\n            a_batches = a_batches[:-1]\n            b_batches = b_batches[:-1]\n            c_batches = c_batches[:-1]\n            d_batches = d_batches[:-1]\n            e_batches = e_batches[:-1]\n\n        all_batches = list(a_batches + b_batches + c_batches + d_batches + e_batches)\n        all_batches = [batch.tolist() for batch in all_batches]\n\n\n        if self.shuffle:\n            random.shuffle(all_batches)\n\n        for batch in all_batches:\n            yield batch\n\n    def __len__(self):\n        total_samples = len(self.indices)\n        if self.drop_last:\n            return total_samples // self.batch_size\n        else:\n            return (total_samples + self.batch_size - 1) // self.batch_size\n\n\n\n\nclass MyDistributedBatchSampler(DistributedSampler):\n    def __init__(self, dataset, dataset1, dataset2, dataset3, dataset4, dataset5,\n                 num_replicas=None,\n                 rank=None, shuffle=True,\n                 seed = 0, drop_last = False, batch_size = 10):\n        super().__init__(\n            dataset=dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle, seed=seed, drop_last=drop_last)\n        self.batch_size = batch_size\n        self.dataset1 = dataset1\n        self.dataset2 = dataset2\n        self.dataset3 = dataset3\n        self.dataset4 = dataset4\n        self.dataset5 = dataset5\n\n        self.indices = list(range(len(dataset)))\n        self.indices1 = self.indices[:len(dataset1)]\n        self.indices2 = self.indices[len(dataset1):len(dataset1)+len(dataset2)]\n        self.indices3 = self.indices[len(dataset1)+len(dataset2):len(dataset1)+len(dataset2)+len(dataset3)]\n        self.indices4 = self.indices[len(dataset1)+len(dataset2)+len(dataset3):len(dataset1)+len(dataset2)+len(dataset3)+len(dataset4)]\n        self.indices5 = self.indices[len(dataset1)+len(dataset2)+len(dataset3)+len(dataset4):]\n\n        if self.drop_last:\n            num_batches1 = len(self.indices1) // self.batch_size\n            num_batches2 = len(self.indices2) // self.batch_size\n            num_batches3 = len(self.indices3) // self.batch_size\n            num_batches4 = len(self.indices4) // self.batch_size\n            num_batches5 = len(self.indices5) // self.batch_size\n        else:\n            num_batches1 = len(self.indices1) // self.batch_size + 1\n            num_batches2 = len(self.indices2) // self.batch_size + 1\n            num_batches3 = len(self.indices3) // self.batch_size + 1\n            num_batches4 = len(self.indices4) // self.batch_size + 1\n            num_batches5 = len(self.indices5) // self.batch_size + 1\n\n        num_batches = num_batches1 + num_batches2 + num_batches3 + num_batches4 + num_batches5\n\n        self.num_batches = math.ceil(num_batches / self.num_replicas)\n        self.total_size = self.num_batches * self.num_replicas\n\n\n    def __iter__(self):\n        if self.shuffle:\n            random.shuffle(self.indices1)\n            random.shuffle(self.indices2)\n            random.shuffle(self.indices3)\n            random.shuffle(self.indices4)\n            random.shuffle(self.indices5)\n\n        a_batches  = chunk(self.indices1, self.batch_size)\n        b_batches = chunk(self.indices2, self.batch_size)\n        c_batches  = chunk(self.indices3, self.batch_size)\n        d_batches = chunk(self.indices4, self.batch_size)\n        e_batches = chunk(self.indices5, self.batch_size)\n\n        if self.drop_last:\n            if len(self.indices1) % self.batch_size != 0:\n                a_batches = a_batches[:-1]\n            if len(self.indices2) % self.batch_size != 0:\n                b_batches = b_batches[:-1]\n            if len(self.indices3) % self.batch_size != 0:\n                c_batches = c_batches[:-1]\n            if len(self.indices4) % self.batch_size != 0:\n                d_batches = d_batches[:-1]\n            if len(self.indices5) % self.batch_size != 0:\n                e_batches = e_batches[:-1]\n\n        all_batches = list(a_batches + b_batches + c_batches + d_batches + e_batches)\n        all_batches = [batch.tolist() for batch in all_batches]\n\n        if len(all_batches) % self.num_replicas != 0:\n            padding_size = self.total_size - len(all_batches)\n            all_batches += all_batches[:padding_size]\n        assert len(all_batches) == self.total_size\n\n        rank_batches = all_batches[self.rank:len(all_batches):self.num_replicas]\n        return iter(rank_batches)\n\n    def __len__(self) -> int:\n        return self.num_batches\n\n"
  },
  {
    "path": "pretraining/datasets/waves.json",
    "content": "{\"2\": [3.75, 3.75], \"3\": [0.665, 0.56, 0.49], \"4\": [0.49, 0.56, 0.665, 0.83], \"9\": [0.665, 0.56, 0.49, 0.705, 0.74, 0.783, 0.842, 1.61, 2.19], \"202\": [0.418, 0.424, 0.429, 0.435, 0.44, 0.445, 0.45, 0.454, 0.459, 0.464, 0.468, 0.473, 0.478, 0.482, 0.487, 0.492, 0.496, 0.501, 0.506, 0.511, 0.516, 0.521, 0.525, 0.53, 0.535, 0.54, 0.546, 0.551, 0.556, 0.561, 0.566, 0.572, 0.577, 0.583, 0.588, 0.594, 0.599, 0.605, 0.611, 0.617, 0.623, 0.629, 0.635, 0.641, 0.648, 0.654, 0.66, 0.667, 0.673, 0.68, 0.686, 0.693, 0.7, 0.707, 0.714, 0.721, 0.728, 0.735, 0.742, 0.749, 0.756, 0.764, 0.771, 0.779, 0.786, 0.794, 0.801, 0.809, 0.817, 0.824, 0.832, 0.84, 0.848, 0.856, 0.864, 0.872, 0.88, 0.888, 0.896, 0.902, 0.904, 0.912, 0.912, 0.92, 0.921, 0.928, 0.931, 0.936, 0.941, 0.944, 0.951, 0.953, 0.961, 0.962, 0.969, 0.972, 0.977, 0.983, 0.985, 0.993, 0.993, 1.004, 1.015, 1.026, 1.037, 1.048, 1.059, 1.07, 1.082, 1.093, 1.105, 1.116, 1.128, 1.14, 1.152, 1.163, 1.175, 1.187, 1.199, 1.211, 1.223, 1.235, 1.247, 1.259, 1.271, 1.283, 1.53, 1.542, 1.553, 1.564, 1.576, 1.587, 1.598, 1.609, 1.62, 1.631, 1.642, 1.653, 1.664, 1.675, 1.685, 1.696, 1.707, 1.718, 1.728, 1.977, 1.986, 1.996, 2.005, 2.014, 2.024, 2.033, 2.042, 2.051, 2.06, 2.069, 2.078, 2.087, 2.096, 2.105, 2.113, 2.122, 2.131, 2.14, 2.148, 2.157, 2.165, 2.174, 2.183, 2.191, 2.199, 2.208, 2.216, 2.225, 2.233, 2.241, 2.249, 2.258, 2.266, 2.274, 2.282, 2.29, 2.298, 2.306, 2.314, 2.322, 2.33, 2.338, 2.346, 2.354, 2.361, 2.369, 2.377, 2.385, 2.392, 2.4, 2.408, 2.415, 2.423, 2.43, 2.438, 2.445]}"
  },
  {
    "path": "pretraining/engine_pretrain.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# BEiT: https://github.com/microsoft/unilm/tree/master/beit\n# --------------------------------------------------------\nimport math\nimport sys\nfrom typing import Iterable\n\nimport torch\n\nimport util.misc as misc\nimport util.lr_sched as lr_sched\nimport pdb\nimport json\n\nwith open('datasets/waves.json','r') as read_jsonf:\n    wave_lists = json.load(read_jsonf)\n\ndef train_one_epoch(model: torch.nn.Module,\n                    data_loader: Iterable, optimizer: torch.optim.Optimizer,\n                    device: torch.device, epoch: int, loss_scaler,\n                    log_writer=None,\n                    args=None):\n    model.train(True)\n    metric_logger = misc.MetricLogger(delimiter=\"  \")\n    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))\n    header = 'Epoch: [{}]'.format(epoch)\n    print_freq = 20\n\n    accum_iter = args.accum_iter\n\n    optimizer.zero_grad()\n\n    if log_writer is not None:\n        print('log_dir: {}'.format(log_writer.log_dir))\n\n    for data_iter_step, samples in enumerate(metric_logger.log_every(data_loader, print_freq, header)):\n        #print(data_iter_step,'start')\n        # we use a per iteration (instead of per epoch) lr scheduler\n        wave_list = wave_lists[str(samples.size(1))]\n        if data_iter_step % accum_iter == 0:\n            lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)\n\n        samples = samples.to(device, non_blocking=True)\n\n        with torch.cuda.amp.autocast():\n            loss, dloss, _, _ = model(samples, mask_ratio=args.mask_ratio, in_chans=len(wave_list), wave_list=wave_list)\n\n        loss_value = loss.item()\n        dloss_value = dloss.item()\n\n        if not math.isfinite(loss_value):\n            print(\"Loss is {}, stopping training\".format(loss_value))\n            sys.exit(1)\n\n        loss /= accum_iter\n        dloss /= accum_iter\n        if data_iter_step % 100 ==0:\n            print('\\n')\n            print('===============================')\n            print(f'Loss: {loss}, distill loss: {dloss}, MIM loss: {loss-dloss}')\n            print('===============================')\n        loss_scaler(loss, optimizer, parameters=model.parameters(),\n                    update_grad=(data_iter_step + 1) % accum_iter == 0)\n        if (data_iter_step + 1) % accum_iter == 0:\n            optimizer.zero_grad()\n\n        torch.cuda.synchronize()\n\n        metric_logger.update(loss=loss_value)\n\n        lr = optimizer.param_groups[0][\"lr\"]\n        metric_logger.update(lr=lr)\n\n        loss_value_reduce = misc.all_reduce_mean(loss_value)\n        if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:\n            \"\"\" We use epoch_1000x as the x-axis in tensorboard.\n            This calibrates different curves when batch size changes.\n            \"\"\"\n            epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)\n            log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)\n            log_writer.add_scalar('lr', lr, epoch_1000x)\n        #print(data_iter_step,'end')\n\n    # gather the stats from all processes\n    metric_logger.synchronize_between_processes()\n    print(\"Averaged stats:\", metric_logger)\n    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}\n"
  },
  {
    "path": "pretraining/main_pretrain_ofa.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# BEiT: https://github.com/microsoft/unilm/tree/master/beit\n# --------------------------------------------------------\nimport argparse\nimport datetime\nimport json\nimport numpy as np\nimport os\nimport time\nfrom pathlib import Path\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.utils.tensorboard import SummaryWriter\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\n\nimport resource\n\nimport timm\n\nimport timm.optim.optim_factory as optim_factory\n\nimport util.misc as misc\nfrom util.misc import NativeScalerWithGradNormCount as NativeScaler\n\nimport models_base_ofa_mae as models_mae\n#import models_large_ofa_mae as models_mae\n\nfrom engine_pretrain import train_one_epoch\nfrom datasets.ofall_dataset import Sentinel1Dataset, Sentinel2Dataset, NAIPDataset, HyperDataset, GaufenDataset, MyBatchSampler, MyDistributedBatchSampler\n\ndef get_args_parser():\n    parser = argparse.ArgumentParser('MAE pre-training', add_help=False)\n    parser.add_argument('--batch_size', default=64, type=int,\n                        help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')\n    parser.add_argument('--epochs', default=400, type=int)\n    parser.add_argument('--accum_iter', default=1, type=int,\n                        help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')\n\n    # Model parameters\n    parser.add_argument('--model', default='mae_vit_large_patch16', type=str, metavar='MODEL',\n                        help='Name of model to train')\n\n    parser.add_argument('--input_size', default=224, type=int,\n                        help='images input size')\n\n    parser.add_argument('--mask_ratio', default=0.75, type=float,\n                        help='Masking ratio (percentage of removed patches).')\n\n    parser.add_argument('--norm_pix_loss', action='store_true',\n                        help='Use (per-patch) normalized pixels as targets for computing loss')\n    parser.set_defaults(norm_pix_loss=False)\n\n    # Optimizer parameters\n    parser.add_argument('--weight_decay', type=float, default=0.05,\n                        help='weight decay (default: 0.05)')\n\n    parser.add_argument('--lr', type=float, default=None, metavar='LR',\n                        help='learning rate (absolute lr)')\n    parser.add_argument('--blr', type=float, default=1e-3, metavar='LR',\n                        help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')\n    parser.add_argument('--min_lr', type=float, default=0., metavar='LR',\n                        help='lower lr bound for cyclic schedulers that hit 0')\n\n    parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N',\n                        help='epochs to warmup LR')\n\n    # Dataset parameters\n    parser.add_argument('--data_path_s1', type=str, help='dataset path s1')\n    parser.add_argument('--data_path_s2', type=str, help='dataset path s2')\n    parser.add_argument('--data_path_naip', type=str, help='dataset path naip')\n    parser.add_argument('--data_path_hyper', type=str, help='dataset path hyper')\n    parser.add_argument('--data_path_gaufen', type=str, help='dataset path gaufen')\n\n    parser.add_argument('--output_dir', default='./output_dir',\n                        help='path where to save, empty for no saving')\n    parser.add_argument('--log_dir', default='./output_dir',\n                        help='path where to tensorboard log')\n    parser.add_argument('--device', default='cuda',\n                        help='device to use for training / testing')\n    parser.add_argument('--seed', default=0, type=int)\n    parser.add_argument('--resume', default='',\n                        help='resume from checkpoint')\n\n    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',\n                        help='start epoch')\n    parser.add_argument('--num_workers', default=10, type=int)\n    parser.add_argument('--pin_mem', action='store_true',\n                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')\n    parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')\n    parser.set_defaults(pin_mem=True)\n\n    # distributed training parameters\n    parser.add_argument('--world_size', default=1, type=int,\n                        help='number of distributed processes')\n    parser.add_argument('--local_rank', default=-1, type=int)\n    parser.add_argument('--dist_on_itp', action='store_true')\n    parser.add_argument('--dist_url', default='env://',\n                        help='url used to set up distributed training')\n\n    parser.add_argument('--in_chans', nargs='*', default=[2,9,3,70,4], type=int)\n    parser.add_argument(\"--local-rank\", type=int, default=0)\n    parser.add_argument(\"--split\", type=str, default='train_10k')\n    parser.add_argument(\"--wave_list\", type=list, default=None)\n\n    return parser\n\n\ndef main(args):\n    misc.init_distributed_mode(args)\n\n    print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))\n    print(\"{}\".format(args).replace(', ', ',\\n'))\n\n    device = torch.device(args.device)\n\n    # fix the seed for reproducibility\n    seed = args.seed + misc.get_rank()\n    torch.manual_seed(seed)\n    np.random.seed(seed)\n\n    cudnn.benchmark = True\n\n    if True:\n        print(f'Loading data with split: {args.split}')\n        dataset_s1 = Sentinel1Dataset(root_dir=args.data_path_s1, split=args.split)\n        dataset_s2 = Sentinel2Dataset(root_dir=args.data_path_s2, split=args.split)\n        dataset_naip = NAIPDataset(root_dir=args.data_path_naip, split=args.split)\n        dataset_hyper = HyperDataset(root_dir=args.data_path_hyper, split='train_10k')\n        dataset_gaufen = GaufenDataset(root_dir=args.data_path_gaufen, split=args.split)\n\n        dataset_train = torch.utils.data.ConcatDataset([dataset_s1,dataset_s2,dataset_naip,dataset_hyper,dataset_gaufen])\n\n    else:\n        raise NotImplementedError\n\n\n    #pdb.set_trace()\n\n    if True:  # args.distributed:\n        num_tasks = misc.get_world_size()\n        global_rank = misc.get_rank()\n        batch_sampler_train = MyDistributedBatchSampler(\n            dataset_train, dataset_s1, dataset_s2, dataset_naip, dataset_hyper, dataset_gaufen,\n            num_replicas=num_tasks, rank=global_rank, shuffle=True, batch_size = args.batch_size, drop_last=True\n            )\n\n        print(\"Sampler_train = %s\" % str(batch_sampler_train))\n    else:\n        batch_sampler_train = MyBatchSampler(\n            dataset_train, dataset_s1, dataset_s2, dataset_naip, dataset_hyper, dataset_gaufen,\n            batch_size=args.batch_size, shuffle=True, drop_last=True\n        )\n\n    if global_rank == 0 and args.log_dir is not None:\n        os.makedirs(args.log_dir, exist_ok=True)\n        log_writer = SummaryWriter(log_dir=args.log_dir)\n    else:\n        log_writer = None\n\n    data_loader_train = torch.utils.data.DataLoader(\n        dataset_train,\n        batch_sampler=batch_sampler_train,\n        num_workers=args.num_workers,\n        pin_memory=args.pin_mem,\n    )\n    print(len(dataset_train),len(data_loader_train))\n    #pdb.set_trace()\n    # define the model\n    model = models_mae.__dict__[args.model](norm_pix_loss=args.norm_pix_loss, in_chans=args.in_chans)\n\n    model.to(device)\n\n    model_without_ddp = model\n    print(\"Model = %s\" % str(model_without_ddp))\n\n    eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()\n    if args.lr is None:  # only base_lr is specified\n        args.lr = args.blr * eff_batch_size / 256\n\n    print(\"base lr: %.2e\" % (args.lr * 256 / eff_batch_size))\n    print(\"actual lr: %.2e\" % args.lr)\n\n    print(\"accumulate grad iterations: %d\" % args.accum_iter)\n    print(\"effective batch size: %d\" % eff_batch_size)\n\n    if args.distributed:\n        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)\n        model_without_ddp = model.module\n    # following timm: set wd as 0 for bias and norm layers\n    param_groups = optim_factory.param_groups_weight_decay(model_without_ddp, args.weight_decay) # timm 1.x\n\n    optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))\n    loss_scaler = NativeScaler()\n\n    misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)\n\n    print(f\"Start training for {args.epochs} epochs\")\n    start_time = time.time()\n    for epoch in range(args.start_epoch, args.epochs):\n        if args.distributed:\n            data_loader_train.batch_sampler.set_epoch(epoch)\n        train_stats = train_one_epoch(\n            model, data_loader_train,\n            optimizer, device, epoch, loss_scaler,\n            log_writer=log_writer,\n            args=args\n        )\n        if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs):\n            misc.save_model(\n                args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,\n                loss_scaler=loss_scaler, epoch=epoch)\n\n        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},\n                        'epoch': epoch,}\n\n        if args.output_dir and misc.is_main_process():\n            if log_writer is not None:\n                log_writer.flush()\n            with open(os.path.join(args.output_dir, \"log.txt\"), mode=\"a\", encoding=\"utf-8\") as f:\n                f.write(json.dumps(log_stats) + \"\\n\")\n\n    total_time = time.time() - start_time\n    total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n    print('Training time {}'.format(total_time_str))\n\n\nif __name__ == '__main__':\n    args = get_args_parser()\n    args = args.parse_args()\n    if args.output_dir:\n        Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n    main(args)\n"
  },
  {
    "path": "pretraining/models_base_ofa_mae.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\n\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\n\nfrom timm.models.vision_transformer import PatchEmbed, Block\nfrom wave_dynamic_layer import Dynamic_MLP_OFA, Dynamic_MLP_Decoder, GaussianFourierFeatureTransform\n\nfrom util.pos_embed import get_2d_sincos_pos_embed, get_1d_sincos_pos_embed_from_grid_torch\nfrom util.misc import CLIPLoss\nimport timm\nimport random\nimport pdb\n\n\nclass MaskedAutoencoderViT(nn.Module):\n    \"\"\" Masked Autoencoder with VisionTransformer backbone\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=[2,9,3,202,4],\n                 embed_dim=1024, depth=24, num_heads=16,\n                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,\n                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):\n        super().__init__()\n        self.in_chans = in_chans\n        self.wv_planes = 128\n        self.patch_size = (patch_size,patch_size)\n\n        self.patch_embed = Dynamic_MLP_OFA(wv_planes=128, inter_dim=128, kernel_size=16, embed_dim=embed_dim)\n\n        #num_patches = self.patch_embed_s1.num_patches\n        self.num_patches = (img_size // patch_size) ** 2\n        self.waves = None\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding\n\n        self.blocks = nn.ModuleList([\n            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)\n            for i in range(depth)])\n        self.norm = norm_layer(embed_dim)\n        # --------------------------------------------------------------------------\n\n        # MAE decoder specifics\n        # --------------------------------------------------------------------------\n        self.teacher_alpha = 1.0\n        self.cos = nn.CosineSimilarity(dim=1)\n        self.teacher_avgpool = torch.nn.AdaptiveAvgPool1d(1)\n        self.projector = torch.nn.Linear(embed_dim, embed_dim)\n        # --------------------------------------------------------------------------\n\n        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)\n\n        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))\n\n        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, decoder_embed_dim), requires_grad=False)  # fixed sin-cos embedding\n\n        self.decoder_blocks = nn.ModuleList([\n            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)\n            for i in range(decoder_depth)])\n\n        self.decoder_norm = norm_layer(decoder_embed_dim)\n        self.decoder_pred = Dynamic_MLP_Decoder(wv_planes=128, inter_dim=128, kernel_size=16, decoder_embed=decoder_embed_dim)\n\n        # --------------------------------------------------------------------------\n\n        self.norm_pix_loss = norm_pix_loss\n\n        self.initialize_weights()\n\n    def initialize_weights(self):\n        # initialization\n        # initialize (and freeze) pos_embed by sin-cos embedding\n        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.num_patches**.5), cls_token=True)\n        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))\n\n        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.num_patches**.5), cls_token=True)\n        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))\n\n        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)\n        torch.nn.init.normal_(self.cls_token, std=.02)\n        torch.nn.init.normal_(self.mask_token, std=.02)\n\n        # initialize nn.Linear and nn.LayerNorm\n        self.apply(self._init_weights)\n\n        # load per-trained weights for the continoul pretraining\n        self.teacher = timm.create_model('vit_base_patch16_224.augreg2_in21k_ft_in1k', pretrained=True)\n        pre_state_dict = self.teacher.state_dict()\n        del pre_state_dict['patch_embed.proj.weight']\n        del pre_state_dict['patch_embed.proj.bias']\n        wg_weights = torch.load('weight_generator_1000_0.01_er50k.pt')\n        msg = self.load_state_dict(pre_state_dict, strict=False)\n        self.patch_embed.weight_generator.load_state_dict(wg_weights['weight_generator'])\n        self.patch_embed.fclayer.load_state_dict(wg_weights['fclayer'])\n        print('**************************************************')\n        n_parameters = sum(p.numel() for p in self.parameters() if p.requires_grad)\n        print(n_parameters)\n        print('**************************************************')\n\n\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            # we use xavier_uniform following official JAX ViT:\n            torch.nn.init.xavier_uniform_(m.weight)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    def patchify(self, imgs):\n        \"\"\"\n        imgs: (N, 3, H, W)\n        x: (N, L, patch_size**2 *3)\n        \"\"\"\n        p = self.patch_size[0]\n        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0\n\n        h = w = imgs.shape[2] // p\n        x = imgs.reshape(shape=(imgs.shape[0], imgs.shape[1], h, p, w, p))\n        x = torch.einsum('nchpwq->nhwpqc', x)\n        x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * imgs.shape[1]))\n        return x\n\n    def unpatchify(self, x):\n        \"\"\"\n        x: (N, L, patch_size**2 *3)\n        imgs: (N, 3, H, W)\n        \"\"\"\n        p = self.patch_size[0]\n        h = w = int(x.shape[1]**.5)\n        assert h * w == x.shape[1]\n        in_chan = x.shape[2] / (p**2)\n        x = x.reshape(shape=(x.shape[0], h, w, p, p, in_chan))\n        x = torch.einsum('nhwpqc->nchpwq', x)\n        imgs = x.reshape(shape=(x.shape[0], in_chan, h * p, h * p))\n        return imgs\n\n    def random_select_channels(self, imgs, wave_list):\n        \"\"\"\n        imgs: input origginal\n        return: new_imgs with randomly selected channels, wave_list\n        \"\"\"\n        batch_size, num_channels, height, width = imgs.shape\n        num_selected_channels = random.randint(int(num_channels*0.75)+1,num_channels)\n        selected_indices = torch.randperm(num_channels)[:num_selected_channels]\n        selected_channels = imgs[:, selected_indices, :, :]\n        nwave_list = [wave_list[int(it)] for it in selected_indices]\n        return selected_channels, nwave_list\n\n\n    def random_masking(self, x, mask_ratio):\n        \"\"\"\n        Perform per-sample random masking by per-sample shuffling.\n        Per-sample shuffling is done by argsort random noise.\n        x: [N, L, D], sequence\n        \"\"\"\n        N, L, D = x.shape  # batch, length, dim\n        len_keep = int(L * (1 - mask_ratio))\n        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]\n        # sort noise for each sample\n        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove\n        ids_restore = torch.argsort(ids_shuffle, dim=1)\n\n        # keep the first subset\n        ids_keep = ids_shuffle[:, :len_keep]\n        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))\n\n        # generate the binary mask: 0 is keep, 1 is remove\n        mask = torch.ones([N, L], device=x.device)\n        mask[:, :len_keep] = 0\n        # unshuffle to get the binary mask\n        mask = torch.gather(mask, dim=1, index=ids_restore)\n\n        return x_masked, mask, ids_restore\n\n    def forward_zs(self, x, wave_list):\n        waves = torch.tensor(wave_list, device=x.device).float()\n        x,_ = self.patch_embed(x, waves)\n        x = x + self.pos_embed[:, 1:, :]\n        cls_token = self.cls_token + self.pos_embed[:, :1, :]\n        cls_tokens = cls_token.expand(x.shape[0], -1, -1)\n        x = torch.cat((cls_tokens, x), dim=1)\n        for blk in self.blocks[:-1]:\n            x = blk(x)\n        #mx = x\n        x = self.teacher_avgpool(x.transpose(1, 2))\n        x = torch.flatten(x, 1)\n        x = self.projector(x)\n\n        return x\n\n    def forward_encoder(self, x, mask_ratio, wave_list):\n        # embed patches\n        waves = torch.tensor(wave_list, device=x.device).float()\n\n        x,waves = self.patch_embed(x, waves)\n\n        self.waves = waves\n        # add pos embed w/o cls token\n        x = x + self.pos_embed[:, 1:, :]\n        # masking: length -> length * mask_ratio\n        x, mask, ids_restore = self.random_masking(x, mask_ratio)\n\n        # append cls token\n        cls_token = self.cls_token + self.pos_embed[:, :1, :]\n        cls_tokens = cls_token.expand(x.shape[0], -1, -1)\n        x = torch.cat((cls_tokens, x), dim=1)\n\n        # apply Transformer blocks\n        for blk in self.blocks:\n            x = blk(x)\n        x = self.norm(x)\n\n        return x, mask, ids_restore\n\n    def forward_decoder(self, x, ids_restore, in_chan):\n        # embed tokens\n        x = self.decoder_embed(x)\n\n        # append mask tokens to sequence\n        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)\n        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token\n        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle\n        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token\n\n        # add pos embed\n        x = x + self.decoder_pos_embed\n\n        # apply Transformer blocks\n        for blk in self.decoder_blocks:\n            x = blk(x)\n        x = self.decoder_norm(x)\n\n        # predictor projection\n        x = self.decoder_pred(x, self.waves)\n\n        # remove cls token\n        x = x[:, 1:, :]\n\n        return x\n\n    def forward_loss(self, imgs, pred, mask):\n        \"\"\"\n        imgs: [N, 3, H, W]\n        pred: [N, L, p*p*3]\n        mask: [N, L], 0 is keep, 1 is remove,\n        \"\"\"\n        target = self.patchify(imgs)\n        if self.norm_pix_loss:\n            mean = target.mean(dim=-1, keepdim=True)\n            var = target.var(dim=-1, keepdim=True)\n            target = (target - mean) / (var + 1.e-6)**.5\n\n        loss = (pred - target) ** 2\n        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch\n\n        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches\n        return loss\n\n    def forward_zt(self, imgs, wave_list):\n        with torch.no_grad():\n            if imgs.size(1)==2:\n                timgs = torch.cat([imgs, torch.zeros([imgs.shape[0],1,imgs.shape[2],imgs.shape[3]], device=imgs.device)],dim=1)\n                twave_list = [wave_list[0]] * 3\n            elif imgs.size(1)==4:\n                timgs = imgs[:,:3,...]\n                twave_list = wave_list[:3]\n            elif imgs.size(1)==9:\n                timgs = imgs[:,:3,...]\n                twave_list = wave_list[:3]\n            elif imgs.size(1)==202:\n                timgs = torch.cat([imgs[:,47:48,...],imgs[:,29:30,...],imgs[:,15:16,...]],dim=1)\n                twave_list = [wave_list[47], wave_list[29], wave_list[15]]\n            else:\n                timgs = imgs\n                twave_list = wave_list\n            assert timgs.size(1) == 3 and len(twave_list) == 3\n            z_t = self.teacher._intermediate_layers(timgs,2)[0]\n            z_t = self.teacher_avgpool(z_t.transpose(1, 2))\n            z_t = torch.flatten(z_t, 1)\n            return z_t, timgs, twave_list\n\n    def forward(self, imgs, in_chans, wave_list, mask_ratio=0.75):\n        z_t, timgs, twave_list = self.forward_zt(imgs, wave_list)\n        latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio, wave_list)\n        z_s = self.forward_zs(timgs, twave_list)\n        distill_loss = -(self.cos(z_s, z_t.detach()).mean()) * self.teacher_alpha\n        pred = self.forward_decoder(latent, ids_restore, imgs.shape[1])  # [N, L, p*p*3]\n        loss = self.forward_loss(imgs, pred, mask) + distill_loss\n        torch.cuda.empty_cache()\n        return loss, distill_loss, pred, mask\n\ndef mae_vit_small_patch16_dec512d8b(**kwargs):\n    model = MaskedAutoencoderViT(\n        patch_size=16, embed_dim=384, depth=12, num_heads=6,\n        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,\n        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\ndef mae_vit_base_patch16_dec512d8b(**kwargs):\n    model = MaskedAutoencoderViT(\n        patch_size=16, embed_dim=768, depth=12, num_heads=12,\n        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,\n        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\n\ndef mae_vit_large_patch16_dec512d8b(**kwargs):\n    model = MaskedAutoencoderViT(\n        patch_size=16, embed_dim=1024, depth=24, num_heads=16,\n        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,\n        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\n\ndef mae_vit_huge_patch14_dec512d8b(**kwargs):\n    model = MaskedAutoencoderViT(\n        patch_size=14, embed_dim=1280, depth=32, num_heads=16,\n        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,\n        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)\n    return model\n\n\n# set recommended archs\nmae_vit_small_patch16 = mae_vit_small_patch16_dec512d8b  # decoder: 512 dim, 8 blocks\nmae_vit_base_patch16 = mae_vit_base_patch16_dec512d8b  # decoder: 512 dim, 8 blocks\nmae_vit_large_patch16 = mae_vit_large_patch16_dec512d8b  # decoder: 512 dim, 8 blocks\nmae_vit_huge_patch14 = mae_vit_huge_patch14_dec512d8b  # decoder: 512 dim, 8 blocks\n"
  },
  {
    "path": "pretraining/samplers/distributed.py",
    "content": "import math\nfrom typing import Iterator, Optional, Tuple, Union\n\nimport torch\nimport torch.distributed as dist\nfrom torchgeo.datasets import BoundingBox, GeoDataset\nfrom torchgeo.samplers import RandomGeoSampler, Units\nfrom torchgeo.samplers.utils import get_random_bounding_box\n\n\nclass DistributedRandomGeoSampler(RandomGeoSampler):\n    \"\"\"Samples elements from a region of interest randomly.\n\n    This is particularly useful during training when you want to maximize the size of\n    the dataset and return as many random :term:`chips <chip>` as possible.\n\n    This sampler is not recommended for use with tile-based datasets. Use\n    :class:`RandomBatchGeoSampler` instead.\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: GeoDataset,\n        size: Union[Tuple[float, float], float],\n        length: int,\n        roi: Optional[BoundingBox] = None,\n        units: Units = Units.PIXELS,\n        num_replicas: Optional[int] = None,\n        rank: Optional[int] = None,\n        seed: int = 0,\n    ) -> None:\n\n        if num_replicas is None:\n            if not dist.is_available():\n                raise RuntimeError(\"Requires distributed package to be available\")\n            num_replicas = dist.get_world_size()\n        if rank is None:\n            if not dist.is_available():\n                raise RuntimeError(\"Requires distributed package to be available\")\n            rank = dist.get_rank()\n        if rank >= num_replicas or rank < 0:\n            raise ValueError(\n                \"Invalid rank {}, rank should be in the interval\"\n                \" [0, {}]\".format(rank, num_replicas - 1)\n            )\n        self.dataset = dataset\n        self.num_replicas = num_replicas\n        self.rank = rank\n        self.epoch = 0\n        # Pad the last batch\n        self.num_samples = math.ceil(length / self.num_replicas)\n        self.total_size = self.num_samples * self.num_replicas\n        self.seed = seed\n\n        #####  TODO: Check if this is actually working####\n        super().__init__(dataset, size, length, roi, units)\n\n    def __iter__(self) -> Iterator[BoundingBox]:\n        \"\"\"Return the index of a dataset.\n\n        Returns:\n            (minx, maxx, miny, maxy, mint, maxt) coordinates to index a dataset\n        \"\"\"\n        g = torch.Generator()\n        g.manual_seed(self.seed + self.epoch)\n        indices = torch.multinomial(\n            self.areas, self.total_size, generator=g, replacement=True\n        ).tolist()\n        assert len(indices) == self.total_size\n        indices = indices[self.rank : self.total_size : self.num_replicas]\n        assert len(indices) == self.num_samples\n\n        for idx in indices:\n            # Choose a random tile, weighted by area\n            hit = self.hits[idx]\n            bounds = BoundingBox(*hit.bounds)\n\n            # Choose a random index within that tile\n            bounding_box = get_random_bounding_box(bounds, self.size, self.res)\n\n            yield bounding_box\n\n    def __len__(self) -> int:\n        \"\"\"Return the number of samples in a single epoch.\n\n        Returns:\n            length of the epoch\n        \"\"\"\n        return self.num_samples\n\n    def set_epoch(self, epoch: int) -> None:\n        r\"\"\"\n        Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas\n        use a different random ordering for each epoch. Otherwise, the next iteration of this\n        sampler will yield the same ordering.\n\n        Args:\n            epoch (int): Epoch number.\n        \"\"\"\n        self.epoch = epoch\n"
  },
  {
    "path": "pretraining/train_mae_all.sh",
    "content": "export CUDA_VISIBLE_DEVICES=6,7\npython -m torch.distributed.launch --nproc_per_node=2 --master_port=25678 main_pretrain_ofa.py \\\n--data_path_s1 /home/zhitong/Datasets/Sentinel/ \\\n--data_path_s2 /home/zhitong/Datasets/Sentinel/ \\\n--data_path_naip /home/zhitong/Datasets/NAIP/ \\\n--data_path_hyper /home/zhitong/Datasets/Hyperspectral/ \\\n--data_path_gaufen /home/zhitong/Datasets/Gaofen/FiveBillion/ \\\n--output_dir checkpoints/mae_base_ofa_all_vit16_224_Ball \\\n--log_dir checkpoints/mae_base_ofa_all_vit16_224_Ball/log \\\n--model mae_vit_base_patch16 \\\n--norm_pix_loss \\\n--mask_ratio 0.75 \\\n--num_workers 4 \\\n--batch_size 96 \\\n--epochs 120 \\\n--warmup_epochs 20 \\\n--blr 1.0e-4 \\\n--weight_decay 0.05 \\\n--seed 42 \\\n--input_size 224 \\\n--in_chans 2 9 3 202 4 \\\n--split train_100k \\\n#--dist_url $dist_url \\\n#--dist_backend 'nccl' \\\n"
  },
  {
    "path": "pretraining/util/crop.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport torch\n\nfrom torchvision import transforms\nfrom torchvision.transforms import functional as F\n\n\nclass RandomResizedCrop(transforms.RandomResizedCrop):\n    \"\"\"\n    RandomResizedCrop for matching TF/TPU implementation: no for-loop is used.\n    This may lead to results different with torchvision's version.\n    Following BYOL's TF code:\n    https://github.com/deepmind/deepmind-research/blob/master/byol/utils/dataset.py#L206\n    \"\"\"\n    @staticmethod\n    def get_params(img, scale, ratio):\n        width, height = F.get_image_size(img)\n        area = height * width\n\n        target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()\n        log_ratio = torch.log(torch.tensor(ratio))\n        aspect_ratio = torch.exp(\n            torch.empty(1).uniform_(log_ratio[0], log_ratio[1])\n        ).item()\n\n        w = int(round(math.sqrt(target_area * aspect_ratio)))\n        h = int(round(math.sqrt(target_area / aspect_ratio)))\n\n        w = min(w, width)\n        h = min(h, height)\n\n        i = torch.randint(0, height - h + 1, size=(1,)).item()\n        j = torch.randint(0, width - w + 1, size=(1,)).item()\n\n        return i, j, h, w\n"
  },
  {
    "path": "pretraining/util/datasets.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\n\nimport os\nimport PIL\n\nfrom torchvision import datasets, transforms\n\nfrom timm.data import create_transform\nfrom timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD\n\n\ndef build_dataset(is_train, args):\n    transform = build_transform(is_train, args)\n\n    root = os.path.join(args.data_path, 'train' if is_train else 'val')\n    dataset = datasets.ImageFolder(root, transform=transform)\n\n    print(dataset)\n\n    return dataset\n\n\ndef build_transform(is_train, args):\n    mean = IMAGENET_DEFAULT_MEAN\n    std = IMAGENET_DEFAULT_STD\n    # train transform\n    if is_train:\n        # this should always dispatch to transforms_imagenet_train\n        transform = create_transform(\n            input_size=args.input_size,\n            is_training=True,\n            color_jitter=args.color_jitter,\n            auto_augment=args.aa,\n            interpolation='bicubic',\n            re_prob=args.reprob,\n            re_mode=args.remode,\n            re_count=args.recount,\n            mean=mean,\n            std=std,\n        )\n        return transform\n\n    # eval transform\n    t = []\n    if args.input_size <= 224:\n        crop_pct = 224 / 256\n    else:\n        crop_pct = 1.0\n    size = int(args.input_size / crop_pct)\n    t.append(\n        transforms.Resize(size, interpolation=PIL.Image.BICUBIC),  # to maintain same ratio w.r.t. 224 images\n    )\n    t.append(transforms.CenterCrop(args.input_size))\n\n    t.append(transforms.ToTensor())\n    t.append(transforms.Normalize(mean, std))\n    return transforms.Compose(t)\n"
  },
  {
    "path": "pretraining/util/lars.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# LARS optimizer, implementation from MoCo v3:\n# https://github.com/facebookresearch/moco-v3\n# --------------------------------------------------------\n\nimport torch\n\n\nclass LARS(torch.optim.Optimizer):\n    \"\"\"\n    LARS optimizer, no rate scaling or weight decay for parameters <= 1D.\n    \"\"\"\n    def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_coefficient=0.001):\n        defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, trust_coefficient=trust_coefficient)\n        super().__init__(params, defaults)\n\n    @torch.no_grad()\n    def step(self):\n        for g in self.param_groups:\n            for p in g['params']:\n                dp = p.grad\n\n                if dp is None:\n                    continue\n\n                if p.ndim > 1: # if not normalization gamma/beta or bias\n                    dp = dp.add(p, alpha=g['weight_decay'])\n                    param_norm = torch.norm(p)\n                    update_norm = torch.norm(dp)\n                    one = torch.ones_like(param_norm)\n                    q = torch.where(param_norm > 0.,\n                                    torch.where(update_norm > 0,\n                                    (g['trust_coefficient'] * param_norm / update_norm), one),\n                                    one)\n                    dp = dp.mul(q)\n\n                param_state = self.state[p]\n                if 'mu' not in param_state:\n                    param_state['mu'] = torch.zeros_like(p)\n                mu = param_state['mu']\n                mu.mul_(g['momentum']).add_(dp)\n                p.add_(mu, alpha=-g['lr'])"
  },
  {
    "path": "pretraining/util/lr_decay.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# ELECTRA https://github.com/google-research/electra\n# BEiT: https://github.com/microsoft/unilm/tree/master/beit\n# --------------------------------------------------------\n\nimport json\n\n\ndef param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75):\n    \"\"\"\n    Parameter groups for layer-wise lr decay\n    Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58\n    \"\"\"\n    param_group_names = {}\n    param_groups = {}\n\n    num_layers = len(model.blocks) + 1\n\n    layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1))\n\n    for n, p in model.named_parameters():\n        if not p.requires_grad:\n            continue\n\n        # no decay: all 1D parameters and model specific ones\n        if p.ndim == 1 or n in no_weight_decay_list:\n            g_decay = \"no_decay\"\n            this_decay = 0.\n        else:\n            g_decay = \"decay\"\n            this_decay = weight_decay\n            \n        layer_id = get_layer_id_for_vit(n, num_layers)\n        group_name = \"layer_%d_%s\" % (layer_id, g_decay)\n\n        if group_name not in param_group_names:\n            this_scale = layer_scales[layer_id]\n\n            param_group_names[group_name] = {\n                \"lr_scale\": this_scale,\n                \"weight_decay\": this_decay,\n                \"params\": [],\n            }\n            param_groups[group_name] = {\n                \"lr_scale\": this_scale,\n                \"weight_decay\": this_decay,\n                \"params\": [],\n            }\n\n        param_group_names[group_name][\"params\"].append(n)\n        param_groups[group_name][\"params\"].append(p)\n\n    # print(\"parameter groups: \\n%s\" % json.dumps(param_group_names, indent=2))\n\n    return list(param_groups.values())\n\n\ndef get_layer_id_for_vit(name, num_layers):\n    \"\"\"\n    Assign a parameter with its layer id\n    Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33\n    \"\"\"\n    if name in ['cls_token', 'pos_embed']:\n        return 0\n    elif name.startswith('patch_embed'):\n        return 0\n    elif name.startswith('blocks'):\n        return int(name.split('.')[1]) + 1\n    else:\n        return num_layers"
  },
  {
    "path": "pretraining/util/lr_sched.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\ndef adjust_learning_rate(optimizer, epoch, args):\n    \"\"\"Decay the learning rate with half-cycle cosine after warmup\"\"\"\n    if epoch < args.warmup_epochs:\n        lr = args.lr * epoch / args.warmup_epochs \n    else:\n        lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \\\n            (1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))\n    for param_group in optimizer.param_groups:\n        if \"lr_scale\" in param_group:\n            param_group[\"lr\"] = lr * param_group[\"lr_scale\"]\n        else:\n            param_group[\"lr\"] = lr\n    return lr\n"
  },
  {
    "path": "pretraining/util/misc.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# BEiT: https://github.com/microsoft/unilm/tree/master/beit\n# --------------------------------------------------------\n\nimport builtins\nimport datetime\nimport os\nimport time\nfrom collections import defaultdict, deque\nfrom pathlib import Path\nimport torch.nn.functional as F\nimport torch.nn as nn\n\nimport torch\nimport torch.distributed as dist\n#from torch._six import inf\nfrom torch import inf # pytorch 2.1\n\n\nclass SmoothedValue(object):\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=20, fmt=None):\n        if fmt is None:\n            fmt = \"{median:.4f} ({global_avg:.4f})\"\n        self.deque = deque(maxlen=window_size)\n        self.total = 0.0\n        self.count = 0\n        self.fmt = fmt\n\n    def update(self, value, n=1):\n        self.deque.append(value)\n        self.count += n\n        self.total += value * n\n\n    def synchronize_between_processes(self):\n        \"\"\"\n        Warning: does not synchronize the deque!\n        \"\"\"\n        if not is_dist_avail_and_initialized():\n            return\n        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')\n        dist.barrier()\n        dist.all_reduce(t)\n        t = t.tolist()\n        self.count = int(t[0])\n        self.total = t[1]\n\n    @property\n    def median(self):\n        d = torch.tensor(list(self.deque))\n        return d.median().item()\n\n    @property\n    def avg(self):\n        d = torch.tensor(list(self.deque), dtype=torch.float32)\n        return d.mean().item()\n\n    @property\n    def global_avg(self):\n        return self.total / self.count\n\n    @property\n    def max(self):\n        return max(self.deque)\n\n    @property\n    def value(self):\n        return self.deque[-1]\n\n    def __str__(self):\n        return self.fmt.format(\n            median=self.median,\n            avg=self.avg,\n            global_avg=self.global_avg,\n            max=self.max,\n            value=self.value)\n\n\nclass MetricLogger(object):\n    def __init__(self, delimiter=\"\\t\"):\n        self.meters = defaultdict(SmoothedValue)\n        self.delimiter = delimiter\n\n    def update(self, **kwargs):\n        for k, v in kwargs.items():\n            if v is None:\n                continue\n            if isinstance(v, torch.Tensor):\n                v = v.item()\n            assert isinstance(v, (float, int))\n            self.meters[k].update(v)\n\n    def __getattr__(self, attr):\n        if attr in self.meters:\n            return self.meters[attr]\n        if attr in self.__dict__:\n            return self.__dict__[attr]\n        raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n            type(self).__name__, attr))\n\n    def __str__(self):\n        loss_str = []\n        for name, meter in self.meters.items():\n            loss_str.append(\n                \"{}: {}\".format(name, str(meter))\n            )\n        return self.delimiter.join(loss_str)\n\n    def synchronize_between_processes(self):\n        for meter in self.meters.values():\n            meter.synchronize_between_processes()\n\n    def add_meter(self, name, meter):\n        self.meters[name] = meter\n\n    def log_every(self, iterable, print_freq, header=None):\n        i = 0\n        if not header:\n            header = ''\n        start_time = time.time()\n        end = time.time()\n        iter_time = SmoothedValue(fmt='{avg:.4f}')\n        data_time = SmoothedValue(fmt='{avg:.4f}')\n        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'\n        log_msg = [\n            header,\n            '[{0' + space_fmt + '}/{1}]',\n            'eta: {eta}',\n            '{meters}',\n            'time: {time}',\n            'data: {data}'\n        ]\n        if torch.cuda.is_available():\n            log_msg.append('max mem: {memory:.0f}')\n        log_msg = self.delimiter.join(log_msg)\n        MB = 1024.0 * 1024.0\n        for obj in iterable:\n            data_time.update(time.time() - end)\n            yield obj\n            iter_time.update(time.time() - end)\n            if i % print_freq == 0 or i == len(iterable) - 1:\n                eta_seconds = iter_time.global_avg * (len(iterable) - i)\n                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n                if torch.cuda.is_available():\n                    print(log_msg.format(\n                        i, len(iterable), eta=eta_string,\n                        meters=str(self),\n                        time=str(iter_time), data=str(data_time),\n                        memory=torch.cuda.max_memory_allocated() / MB))\n                else:\n                    print(log_msg.format(\n                        i, len(iterable), eta=eta_string,\n                        meters=str(self),\n                        time=str(iter_time), data=str(data_time)))\n            i += 1\n            end = time.time()\n        total_time = time.time() - start_time\n        total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n        print('{} Total time: {} ({:.4f} s / it)'.format(\n            header, total_time_str, total_time / len(iterable)))\n\n\ndef setup_for_distributed(is_master):\n    \"\"\"\n    This function disables printing when not in master process\n    \"\"\"\n    builtin_print = builtins.print\n\n    def print(*args, **kwargs):\n        force = kwargs.pop('force', False)\n        force = force or (get_world_size() > 8)\n        if is_master or force:\n            now = datetime.datetime.now().time()\n            builtin_print('[{}] '.format(now), end='')  # print with time stamp\n            builtin_print(*args, **kwargs)\n\n    builtins.print = print\n\n\ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\n\ndef get_world_size():\n    if not is_dist_avail_and_initialized():\n        return 1\n    return dist.get_world_size()\n\n\ndef get_rank():\n    if not is_dist_avail_and_initialized():\n        return 0\n    return dist.get_rank()\n\n\ndef is_main_process():\n    return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n    if is_main_process():\n        torch.save(*args, **kwargs)\n\n\ndef init_distributed_mode(args):\n    if args.dist_on_itp:\n        args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])\n        args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])\n        args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])\n        args.dist_url = \"tcp://%s:%s\" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])\n        os.environ['LOCAL_RANK'] = str(args.gpu)\n        os.environ['RANK'] = str(args.rank)\n        os.environ['WORLD_SIZE'] = str(args.world_size)\n        # [\"RANK\", \"WORLD_SIZE\", \"MASTER_ADDR\", \"MASTER_PORT\", \"LOCAL_RANK\"]\n    elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:\n        args.rank = int(os.environ[\"RANK\"])\n        args.world_size = int(os.environ['WORLD_SIZE'])\n        args.gpu = int(os.environ['LOCAL_RANK'])\n    elif 'SLURM_PROCID' in os.environ:\n        args.rank = int(os.environ['SLURM_PROCID'])\n        args.gpu = args.rank % torch.cuda.device_count()\n    else:\n        print('Not using distributed mode')\n        setup_for_distributed(is_master=True)  # hack\n        args.distributed = False\n        return\n\n    args.distributed = True\n\n    torch.cuda.set_device(args.gpu)\n    args.dist_backend = 'nccl'\n    print('| distributed init (rank {}): {}, gpu {}'.format(\n        args.rank, args.dist_url, args.gpu), flush=True)\n    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,\n                                         world_size=args.world_size, rank=args.rank)\n    torch.distributed.barrier()\n    setup_for_distributed(args.rank == 0)\n\n\nclass NativeScalerWithGradNormCount:\n    state_dict_key = \"amp_scaler\"\n\n    def __init__(self):\n        self._scaler = torch.cuda.amp.GradScaler()\n\n    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):\n        self._scaler.scale(loss).backward(create_graph=create_graph)\n        if update_grad:\n            if clip_grad is not None:\n                assert parameters is not None\n                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place\n                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)\n            else:\n                self._scaler.unscale_(optimizer)\n                norm = get_grad_norm_(parameters)\n            self._scaler.step(optimizer)\n            self._scaler.update()\n        else:\n            norm = None\n        return norm\n\n    def state_dict(self):\n        return self._scaler.state_dict()\n\n    def load_state_dict(self, state_dict):\n        self._scaler.load_state_dict(state_dict)\n\n\ndef get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:\n    if isinstance(parameters, torch.Tensor):\n        parameters = [parameters]\n    parameters = [p for p in parameters if p.grad is not None]\n    norm_type = float(norm_type)\n    if len(parameters) == 0:\n        return torch.tensor(0.)\n    device = parameters[0].grad.device\n    if norm_type == inf:\n        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)\n    else:\n        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)\n    return total_norm\n\n\ndef save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):\n    output_dir = Path(args.output_dir)\n    epoch_name = str(epoch)\n    if loss_scaler is not None:\n        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]\n        for checkpoint_path in checkpoint_paths:\n            to_save = {\n                'model': model_without_ddp.state_dict(),\n                'optimizer': optimizer.state_dict(),\n                'epoch': epoch,\n                'scaler': loss_scaler.state_dict(),\n                'args': args,\n            }\n\n            save_on_master(to_save, checkpoint_path)\n    else:\n        client_state = {'epoch': epoch}\n        model.save_checkpoint(save_dir=args.output_dir, tag=\"checkpoint-%s\" % epoch_name, client_state=client_state)\n\n\ndef load_model(args, model_without_ddp, optimizer, loss_scaler):\n    if args.resume:\n        if args.resume.startswith('https'):\n            checkpoint = torch.hub.load_state_dict_from_url(\n                args.resume, map_location='cpu', check_hash=True)\n        else:\n            checkpoint = torch.load(args.resume, map_location='cpu')\n        model_without_ddp.load_state_dict(checkpoint['model'])\n        print(\"Resume checkpoint %s\" % args.resume)\n        if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval):\n            optimizer.load_state_dict(checkpoint['optimizer'])\n            args.start_epoch = checkpoint['epoch'] + 1\n            if 'scaler' in checkpoint:\n                loss_scaler.load_state_dict(checkpoint['scaler'])\n            print(\"With optim & sched!\")\n\n\ndef all_reduce_mean(x):\n    world_size = get_world_size()\n    if world_size > 1:\n        x_reduce = torch.tensor(x).cuda()\n        dist.all_reduce(x_reduce)\n        x_reduce /= world_size\n        return x_reduce.item()\n    else:\n        return x\n\n\n\nclass CLIPLoss(nn.Module):\n    def __init__(self, temperature=1.0):\n        super(CLIPLoss, self).__init__()\n        self.temperature = temperature\n\n    def forward(self, image_features, text_features):\n        # Normalize features\n        image_features = F.normalize(image_features, p=2, dim=-1)\n        text_features = F.normalize(text_features, p=2, dim=-1)\n\n        # Cosine similarity as logits\n        logits = torch.matmul(image_features, text_features.t()) / self.temperature\n\n        # Shape of logits: [batch_size, batch_size]\n        labels = torch.arange(logits.shape[0], device=logits.device)\n\n        # Calculate loss\n        loss_i = F.cross_entropy(logits, labels)\n        loss_t = F.cross_entropy(logits.t(), labels)\n\n        # Symmetric loss\n        loss = (loss_i + loss_t) / 2\n        return loss\n\n\n\nif __name__=='__main__':\n    clip_loss = CLIPLoss()\n    embeddings_1 = torch.randn([128, 768])\n    embeddings_2 = torch.randn([128, 768])\n    clip_loss(embeddings_1, embeddings_2)\n"
  },
  {
    "path": "pretraining/util/pos_embed.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# --------------------------------------------------------\n# Position embedding utils\n# --------------------------------------------------------\n\nimport numpy as np\nimport torch\n\n\n# --------------------------------------------------------\n# 2D sine-cosine position embedding\n# References:\n# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py\n# MoCo v3: https://github.com/facebookresearch/moco-v3\n# --------------------------------------------------------\ndef get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, prompt=False):\n    \"\"\"\n    grid_size: int of the grid height and width\n    return:\n    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n    \"\"\"\n    grid_h = np.arange(grid_size, dtype=np.float32)\n    grid_w = np.arange(grid_size, dtype=np.float32)\n    grid = np.meshgrid(grid_w, grid_h)  # here w goes first\n    grid = np.stack(grid, axis=0)\n\n    grid = grid.reshape([2, 1, grid_size, grid_size])\n    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n    if cls_token:\n        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)\n    if prompt:\n        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)\n    return pos_embed\n\n\ndef get_2d_sincos_pos_embed_with_resolution(\n    embed_dim, grid_size, res, cls_token=False, device=\"cpu\"\n):\n    \"\"\"\n    grid_size: int of the grid height and width\n    res: array of size n, representing the resolution of a pixel (say, in meters),\n    return:\n    pos_embed: [n,grid_size*grid_size, embed_dim] or [n,1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n    \"\"\"\n    # res = torch.FloatTensor(res).to(device)\n    res = res.to(device)\n    grid_h = torch.arange(grid_size, dtype=torch.float32, device=device)\n    grid_w = torch.arange(grid_size, dtype=torch.float32, device=device)\n    grid = torch.meshgrid(\n        grid_w, grid_h, indexing=\"xy\"\n    )  # here h goes first,direction reversed for numpy\n    grid = torch.stack(grid, dim=0)  # 2 x h x w\n\n    # grid = grid.reshape([2, 1, grid_size, grid_size])\n    grid = torch.einsum(\"chw,n->cnhw\", grid, res)  # 2 x n x h x w\n    _, n, h, w = grid.shape\n    pos_embed = get_2d_sincos_pos_embed_from_grid_torch(\n        embed_dim, grid\n    )  #  # (nxH*W, D/2)\n    pos_embed = pos_embed.reshape(n, h * w, embed_dim)\n    if cls_token:\n        pos_embed = torch.cat(\n            [\n                torch.zeros(\n                    [n, 1, embed_dim], dtype=torch.float32, device=pos_embed.device\n                ),\n                pos_embed,\n            ],\n            dim=1,\n        )\n    return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n    assert embed_dim % 2 == 0\n\n    # use half of dimensions to encode grid_h\n    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)\n    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)\n\n    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)\n    return emb\n\n\ndef get_2d_sincos_pos_embed_from_grid_torch(embed_dim, grid):\n    assert embed_dim % 2 == 0\n\n    # use half of dimensions to encode grid_h\n    emb_h = get_1d_sincos_pos_embed_from_grid_torch(\n        embed_dim // 2, grid[0]\n    )  # (H*W, D/2)\n    emb_w = get_1d_sincos_pos_embed_from_grid_torch(\n        embed_dim // 2, grid[1]\n    )  # (H*W, D/2)\n\n    emb = torch.cat([emb_h, emb_w], dim=1)  # (H*W, D)\n    return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos):\n    \"\"\"\n    embed_dim: output dimension for each position\n    pos: a list of positions to be encoded: size (M,)\n    out: (M, D)\n    \"\"\"\n    assert embed_dim % 2 == 0\n    old_shape = pos\n    omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device)\n    omega /= embed_dim / 2.0\n    omega = 1.0 / 10000**omega  # (D/2,)\n\n    pos = pos.reshape(-1)  # (M,)\n    out = torch.einsum(\"m,d->md\", pos, omega)  # (M, D/2), outer product\n\n    emb_sin = torch.sin(out)  # (M, D/2)\n    emb_cos = torch.cos(out)  # (M, D/2)\n\n    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)\n    return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n    \"\"\"\n    embed_dim: output dimension for each position\n    pos: a list of positions to be encoded: size (M,)\n    out: (M, D)\n    \"\"\"\n    assert embed_dim % 2 == 0\n    omega = np.arange(embed_dim // 2, dtype=np.float32)\n    omega /= embed_dim / 2.0\n    omega = 1.0 / 10000**omega  # (D/2,)\n\n    pos = pos.reshape(-1)  # (M,)\n    out = np.einsum(\"m,d->md\", pos, omega)  # (M, D/2), outer product\n\n    emb_sin = np.sin(out)  # (M, D/2)\n    emb_cos = np.cos(out)  # (M, D/2)\n\n    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)\n    return emb\n\n\n# --------------------------------------------------------\n# Interpolate position embeddings for high-resolution\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\ndef interpolate_pos_embed(model, checkpoint_model, num_patches=None):\n    if \"pos_embed\" in checkpoint_model:\n        pos_embed_checkpoint = checkpoint_model[\"pos_embed\"]\n        embedding_size = pos_embed_checkpoint.shape[-1]\n        if num_patches is not None:\n            num_patches = num_patches\n        else:\n            num_patches = model.patch_embed.num_patches\n        num_extra_tokens = model.pos_embed.shape[-2] - num_patches\n        # height (== width) for the checkpoint position embedding\n        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n        # height (== width) for the new position embedding\n        new_size = int(num_patches**0.5)\n        # class_token and dist_token are kept unchanged\n        if orig_size != new_size:\n            print(\n                \"Position interpolate from %dx%d to %dx%d\"\n                % (orig_size, orig_size, new_size, new_size)\n            )\n            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n            # only the position tokens are interpolated\n            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n            pos_tokens = pos_tokens.reshape(\n                -1, orig_size, orig_size, embedding_size\n            ).permute(0, 3, 1, 2)\n            pos_tokens = torch.nn.functional.interpolate(\n                pos_tokens,\n                size=(new_size, new_size),\n                mode=\"bicubic\",\n                align_corners=False,\n            )\n            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n            checkpoint_model[\"pos_embed\"] = new_pos_embed\n"
  },
  {
    "path": "pretraining/util/vision_transformer.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom functools import partial\n\nfrom timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD\nfrom timm.models.helpers import load_pretrained\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\nfrom timm.models.resnet import resnet26d, resnet50d, resnet26, resnet50\nfrom timm.models.registry import register_model\n\nimport logging\n_logger = logging.getLogger(__name__)\n\nfrom torchvision.ops import roi_align\nimport math\n_DEFAULT_SCALE_CLAMP = math.log(100000.0 / 16)\n\nimport pdb\n\ndef _cfg(url='', **kwargs):\n    return {\n        'url': url,\n        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,\n        'crop_pct': .9, 'interpolation': 'bicubic',\n        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,\n        'first_conv': 'patch_embed.proj', 'classifier': 'head',\n        **kwargs\n    }\n\n\ndefault_cfgs = {\n    # patch models\n    'vit_small_patch16_224': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',\n    ),\n    'vit_base_patch16_224': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',\n        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),\n    ),\n    'vit_base_patch16_384': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',\n        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),\n    'vit_base_patch32_384': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',\n        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),\n    'vit_large_patch16_224': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',\n        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n    'vit_large_patch16_384': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',\n        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),\n    'vit_large_patch32_384': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',\n        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),\n\n    # patch models, imagenet21k (weights ported from official Google JAX impl)\n    'vit_base_patch16_224_in21k': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth',\n        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n    'vit_base_patch32_224_in21k': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth',\n        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n    'vit_large_patch16_224_in21k': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth',\n        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n    'vit_large_patch32_224_in21k': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',\n        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n    'vit_huge_patch14_224_in21k': _cfg(\n        url='',  # FIXME I have weights for this but > 2GB limit for github release binaries\n        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n\n    # hybrid models (weights ported from official Google JAX impl)\n    'vit_base_resnet50_224_in21k': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',\n        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),\n    'vit_base_resnet50_384': _cfg(\n        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',\n        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),\n\n    # hybrid models (my experiments)\n    'vit_small_resnet26d_224': _cfg(),\n    'vit_small_resnet50d_s3_224': _cfg(),\n    'vit_base_resnet26d_224': _cfg(),\n    'vit_base_resnet50d_224': _cfg(),\n}\n\n\nclass Mlp(nn.Module):\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\nclass Attention(nn.Module):\n    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim ** -0.5\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n    def forward(self, x):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass Block(nn.Module):\n\n    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=partial(nn.LayerNorm, eps=1e-6), vis=False):\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n    def forward(self, x):\n        x = x + self.drop_path(self.attn(self.norm1(x)))\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n        return x\n\n\nclass PatchEmbed(nn.Module):\n    \"\"\" Image to Patch Embedding\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.num_patches = num_patches\n\n        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n\n    def forward(self, x):\n        B, C, H, W = x.shape\n        # FIXME look at relaxing size constraints\n        # assert H == self.img_size[0] and W == self.img_size[1], \\\n        #     f\"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).\"\n        x = self.proj(x).flatten(2).transpose(1, 2)\n        return x\n\n\nclass HybridEmbed(nn.Module):\n    \"\"\" CNN Feature Map Embedding\n    Extract feature map from CNN, flatten, project to embedding dim.\n    \"\"\"\n    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):\n        super().__init__()\n        assert isinstance(backbone, nn.Module)\n        img_size = to_2tuple(img_size)\n        self.img_size = img_size\n        self.backbone = backbone\n        if feature_size is None:\n            with torch.no_grad():\n                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature\n                # map for all networks, the feature metadata has reliable channel and stride info, but using\n                # stride to calc feature dim requires info about padding of each stage that isn't captured.\n                training = backbone.training\n                if training:\n                    backbone.eval()\n                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))\n                if isinstance(o, (list, tuple)):\n                    o = o[-1]  # last feature if backbone outputs list/tuple of features\n                feature_size = o.shape[-2:]\n                feature_dim = o.shape[1]\n                backbone.train(training)\n        else:\n            feature_size = to_2tuple(feature_size)\n            if hasattr(self.backbone, 'feature_info'):\n                feature_dim = self.backbone.feature_info.channels()[-1]\n            else:\n                feature_dim = self.backbone.num_features\n        self.num_patches = feature_size[0] * feature_size[1]\n        self.proj = nn.Conv2d(feature_dim, embed_dim, 1)\n\n    def forward(self, x):\n        x = self.backbone(x)\n        if isinstance(x, (list, tuple)):\n            x = x[-1]  # last feature if backbone outputs list/tuple of features\n        x = self.proj(x).flatten(2).transpose(1, 2)\n        return x\n\n\nclass VisionTransformer(nn.Module):\n    \"\"\" Vision Transformer with support for patch or hybrid CNN input stage\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,\n                 num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,\n                 drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.num_classes = num_classes\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)\n\n        if hybrid_backbone is not None:\n            self.patch_embed = HybridEmbed(\n                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)\n        else:\n            self.patch_embed = PatchEmbed(\n                img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        self.dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n        self.blocks = nn.ModuleList([\n            Block(\n                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=self.dpr[i], norm_layer=norm_layer,\n            )\n            for i in range(depth)])\n        self.norm = norm_layer(embed_dim)\n\n        # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here\n        #self.repr = nn.Linear(embed_dim, representation_size)\n        #self.repr_act = nn.Tanh()\n\n        # Classifier head\n        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n\n        trunc_normal_(self.pos_embed, std=.02)\n        trunc_normal_(self.cls_token, std=.02)\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'pos_embed', 'cls_token'}\n\n    def get_classifier(self):\n        return self.head\n\n    def reset_classifier(self, num_classes, global_pool=''):\n        self.num_classes = num_classes\n        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n\n    def forward_features(self, x):\n        B = x.shape[0]\n        x = self.patch_embed(x)\n\n        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks\n        x = torch.cat((cls_tokens, x), dim=1)\n        x = x + self.pos_embed\n        x = self.pos_drop(x)\n\n        for blk in self.blocks:\n            x = blk(x)\n\n        x = self.norm(x)\n        return x[:, 0]\n\n    def forward(self, x):\n        x = self.forward_features(x)\n        x = self.head(x)\n        return x\n\n\ndef resize_pos_embed(posemb, posemb_new):\n    # Rescale the grid of position embeddings when loading from state_dict. Adapted from\n    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224\n    _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)\n    ntok_new = posemb_new.shape[1]\n    if True:\n        posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]\n        ntok_new -= 1\n    else:\n        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]\n    gs_old = int(math.sqrt(len(posemb_grid)))\n    gs_new = int(math.sqrt(ntok_new))\n    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)\n    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)\n    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear')\n    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)\n    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)\n    return posemb\n\n\ndef checkpoint_filter_fn(state_dict, model):\n    \"\"\" convert patch embedding weight from manual patchify + linear proj to conv\"\"\"\n    out_dict = {}\n    if 'model' in state_dict:\n        # For deit models\n        state_dict = state_dict['model']\n    for k, v in state_dict.items():\n        if 'patch_embed.proj.weight' in k and len(v.shape) < 4:\n            # For old models that I trained prior to conv based patchification\n            O, I, H, W = model.patch_embed.proj.weight.shape\n            v = v.reshape(O, -1, H, W)\n        elif k == 'pos_embed' and v.shape != model.pos_embed.shape:\n            # To resize pos embedding when using model at different size from pretrained weights\n            v = resize_pos_embed(v, model.pos_embed)\n        out_dict[k] = v\n    return out_dict\n\n\ndef _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs):\n    default_cfg = default_cfgs[variant]\n    default_num_classes = default_cfg['num_classes']\n    default_img_size = default_cfg['input_size'][-1]\n\n    num_classes = kwargs.pop('num_classes', default_num_classes)\n    img_size = kwargs.pop('img_size', default_img_size)\n    repr_size = kwargs.pop('representation_size', None)\n    if repr_size is not None and num_classes != default_num_classes:\n        # Remove representation layer if fine-tuning. This may not always be the desired action,\n        # but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?\n        _logger.warning(\"Removing representation layer for fine-tuning.\")\n        repr_size = None\n\n    # model_cls = DistilledVisionTransformer if distilled else VisionTransformer\n    model_cls =  VisionTransformer\n    # model = model_cls(img_size=img_size, num_classes=num_classes, representation_size=repr_size, **kwargs)\n    model = model_cls(img_size=img_size, num_classes=num_classes, **kwargs)\n    model.default_cfg = default_cfg\n\n    if pretrained:\n        load_pretrained(\n            model, num_classes=num_classes, in_chans=kwargs.get('in_chans', 3),\n            filter_fn=partial(checkpoint_filter_fn, model=model))\n    return model\n\n\n@register_model\ndef vit_small_patch16_224(pretrained=False, **kwargs):\n    \"\"\" My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3.\"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,\n        qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)\n    if pretrained:\n        # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model\n        model_kwargs.setdefault('qk_scale', 768 ** -0.5)\n    model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_patch16_224(pretrained=False, **kwargs):\n    \"\"\" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)\n    model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_patch32_224(pretrained=False, **kwargs):\n    \"\"\" ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.\n    \"\"\"\n    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)\n    model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_patch16_384(pretrained=False, **kwargs):\n    \"\"\" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)\n    model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_patch32_384(pretrained=False, **kwargs):\n    \"\"\" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)\n    model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_large_patch16_224(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n    model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_large_patch32_224(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.\n    \"\"\"\n    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n    model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_large_patch16_384(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n    model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_large_patch32_384(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n    model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_patch16_224_in21k(pretrained=False, **kwargs):\n    \"\"\" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)\n    model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_patch32_224_in21k(pretrained=False, **kwargs):\n    \"\"\" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)\n    model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_large_patch16_224_in21k(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=16, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)\n    model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_large_patch32_224_in21k(pretrained=False, **kwargs):\n    \"\"\" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)\n    model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_huge_patch14_224_in21k(pretrained=False, **kwargs):\n    \"\"\" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.\n    NOTE: converted weights not currently available, too large for github release hosting.\n    \"\"\"\n    model_kwargs = dict(\n        patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)\n    model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_resnet50_224_in21k(pretrained=False, **kwargs):\n    \"\"\" R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head\n    backbone = ResNetV2(\n        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),\n        preact=False, stem_type='same', conv_layer=StdConv2dSame)\n    model_kwargs = dict(\n        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone,\n        representation_size=768, **kwargs)\n    model = _create_vision_transformer('vit_base_resnet50_224_in21k', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_resnet50_384(pretrained=False, **kwargs):\n    \"\"\" R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).\n    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.\n    \"\"\"\n    # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head\n    backbone = ResNetV2(\n        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),\n        preact=False, stem_type='same', conv_layer=StdConv2dSame)\n    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)\n    model = _create_vision_transformer('vit_base_resnet50_384', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_small_resnet26d_224(pretrained=False, **kwargs):\n    \"\"\" Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.\n    \"\"\"\n    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])\n    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)\n    model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_small_resnet50d_s3_224(pretrained=False, **kwargs):\n    \"\"\" Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.\n    \"\"\"\n    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])\n    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)\n    model = _create_vision_transformer('vit_small_resnet50d_s3_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_resnet26d_224(pretrained=False, **kwargs):\n    \"\"\" Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.\n    \"\"\"\n    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])\n    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)\n    model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_base_resnet50d_224(pretrained=False, **kwargs):\n    \"\"\" Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.\n    \"\"\"\n    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])\n    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)\n    model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@register_model\ndef vit_deit_tiny_patch16_224(pretrained=False, **kwargs):\n    \"\"\" DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).\n    ImageNet-1k weights from https://github.com/facebookresearch/deit.\n    \"\"\"\n    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)\n    model = _create_vision_transformer('vit_deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)\n    return model\n\n@register_model\ndef deit_small_resnet50_224(pretrained=False, **kwargs):\n    pretrained_backbone = kwargs.get('pretrained_backbone', False)  # default to True for now, for testing\n    backbone = resnet50(pretrained=pretrained_backbone, features_only=True, out_indices=[4])\n    model = VisionTransformer(patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6), hybrid_backbone=backbone, **kwargs)\n    model.default_cfg = default_cfgs['vit_small_resnet50_224']\n    return model\n"
  },
  {
    "path": "pretraining/util/wandb_log.py",
    "content": "import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport util.misc as misc\nimport wandb\n\n# def equalize(x):\n#     x = (x - x.min()) / (x.max()-x.min()+1e-6) * 255\n#     x = x.astype(np.uint8)\n#     r_image, g_image, b_image = cv2.split(x)\n#     r_image_eq = cv2.equalizeHist(r_image)\n#     g_image_eq = cv2.equalizeHist(g_image)\n#     b_image_eq = cv2.equalizeHist(b_image)\n\n#     image_eq = cv2.merge((r_image_eq, g_image_eq, b_image_eq))\n#     return image_eq\n\n\nclass WANDB_LOG_IMG_CONFIG:\n    mean = np.zeros(3)\n    std = np.ones(3)\n    factor = 1.0\n\n\ndef equalize(x):\n    x = x * WANDB_LOG_IMG_CONFIG.std.reshape(\n        1, 1, 3\n    ) + WANDB_LOG_IMG_CONFIG.mean.reshape(1, 1, 3)\n    if x.max() > 2.0:\n        x /= WANDB_LOG_IMG_CONFIG.factor\n    return x\n\n\ndef wandb_dump_input_output(x, ys, epoch=0, texts=\"\"):\n    \"\"\"\n    x: H X W X C\n    y: H X W X C\n    \"\"\"\n    if misc.is_main_process():\n        n_imgs = 1 + len(ys)\n        x = x.numpy()\n        ys = [y.numpy().astype(float) for y in ys]\n        ys = [equalize(y) for y in ys]\n        x = equalize(x)\n        fig, axes = plt.subplots(1, n_imgs, figsize=(5 * n_imgs, 5))\n        if texts:\n            fig.suptitle(texts)\n        axes[0].imshow(x)\n        axes[0].title.set_text(f\"({x.shape[0]}, {x.shape[1]})\")\n        for idx, y in enumerate(ys):\n            axes[1 + idx].imshow(y)\n            axes[1 + idx].title.set_text(f\"({y.shape[0]}, {y.shape[1]})\")\n        wandb.log({\"vis\": wandb.Image(fig), \"epoch\": epoch})\n        plt.close(fig)\n\n\ndef wandb_dump_images(imgs, name=\"vis\", epoch=0):\n    \"\"\"\n    x: H X W X C\n    y: H X W X C\n    \"\"\"\n    if misc.is_main_process():\n        n_imgs = len(imgs)\n        fig, axes = plt.subplots(1, n_imgs, figsize=(5 * n_imgs, 5))\n        for idx, img in enumerate(imgs):\n            axes[idx].imshow(img)\n        wandb.log({name: wandb.Image(fig), \"epoch\": epoch})\n        plt.close(fig)\n\n\ndef compare_pos_embedding(posa, posb, ns=[0]):\n    \"\"\"\n    posa,posa: N X (L+1) X d_emb\n    \"\"\"\n    n, l1, d = posa.shape\n    _, l2, _ = posb.shape\n    dim1 = int((l1 - 1) ** 0.5)\n    dim2 = int((l2 - 1) ** 0.5)\n    idx = [0, d // 4, d // 2, d // 4 * 3, d - 1]\n    for j in ns:\n        imgs = []\n        for i in idx:\n            a = posa[j, 1:, i].reshape(dim1, dim1).cpu().numpy()\n            b = posb[j, 1:, i].reshape(dim2, dim2).cpu().numpy()\n            imgs.append(a)\n            imgs.append(b)\n        wandb_dump_images(imgs)\n\n\ndef wandb_log_metadata(metadata):\n    if misc.is_main_process():\n        payload = {}\n        if \"zero_ratio\" in metadata:\n            zero_ratio = metadata.get(\"zero_ratio\")\n            payload.update(\n                dict(\n                    zero_ratio_mean=zero_ratio.mean(),\n                    zero_ratio_max=zero_ratio.max(),\n                    zero_ratio_min=zero_ratio.min(),\n                    zero_ratio_std=zero_ratio.std(),\n                )\n            )\n\n        wandb.log(payload)\n"
  },
  {
    "path": "pretraining/wave_dynamic_layer.py",
    "content": "import torch\nimport torch.nn as nn\nfrom enum import Enum\nfrom typing import Callable, List, Optional, Tuple, Union\nimport math\nimport torch.nn.functional as F\nfrom util.pos_embed import get_1d_sincos_pos_embed_from_grid_torch\nimport numpy as np\nimport pdb\n\nfrom itertools import repeat as repeat_iter\nimport collections.abc\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom functools import reduce\nfrom operator import mul\nfrom torch import _assert\nimport torch.nn.init as init\nimport timm\n\nrandom_seed = 1234\ntorch.manual_seed(random_seed)\n\n\nclass TransformerWeightGenerator(nn.Module):\n    def __init__(self, input_dim, output_dim, embed_dim, num_heads=4, num_layers=1):\n        super(TransformerWeightGenerator, self).__init__()\n        encoder_layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=num_heads, activation='gelu', norm_first=False, batch_first=False, dropout=False)\n        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers,enable_nested_tensor=False)\n\n        # Linear layer to map transformer output to desired weight shape\n        self.fc_weight = nn.Linear(input_dim, output_dim)\n        self.fc_bias = nn.Linear(input_dim, embed_dim)\n        self.wt_num = 128\n        self.weight_tokens = nn.Parameter(torch.empty([self.wt_num,input_dim]))\n        self.bias_token = nn.Parameter(torch.empty([1,input_dim]))\n\n        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)\n        torch.nn.init.normal_(self.weight_tokens, std=.02)\n        torch.nn.init.normal_(self.bias_token, std=.02)\n\n    def forward(self, x):\n        # x should have shape [seq_len, batch, input_dim]\n        pos_wave = x\n        x = torch.cat([self.weight_tokens, pos_wave],dim=0)\n        x = torch.cat([x,self.bias_token], dim=0)\n        transformer_output = self.transformer_encoder(x)\n        weights = self.fc_weight(transformer_output[self.wt_num:-1]+pos_wave)\n        bias = self.fc_bias(transformer_output[-1])  # Using the last output to generate bias\n        return weights, bias\n\n\n\nclass GaussianFourierFeatureTransform(torch.nn.Module):\n    \"\"\"\n    An implementation of Gaussian Fourier feature mapping.\n\n    \"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains\":\n       https://arxiv.org/abs/2006.10739\n       https://people.eecs.berkeley.edu/~bmild/fourfeat/index.html\n\n    Given an input of size [batches, num_input_channels, width, height],\n     returns a tensor of size [batches, mapping_size*2, width, height].\n    \"\"\"\n\n    def __init__(self, num_input_channels, mapping_size=256, scale=10):\n        super().__init__()\n\n        self._num_input_channels = num_input_channels\n        self._mapping_size = mapping_size\n        torch.manual_seed(42)\n        self._B = torch.randn((num_input_channels, mapping_size)) * scale\n\n    def forward(self, x):\n        assert x.dim() == 4, 'Expected 4D input (got {}D input)'.format(x.dim())\n\n        batches, channels, width, height = x.shape\n\n        assert channels == self._num_input_channels,\\\n            \"Expected input to have {} channels (got {} channels)\".format(self._num_input_channels, channels)\n\n        # Make shape compatible for matmul with _B.\n        # From [B, C, W, H] to [(B*W*H), C].\n        x = x.permute(0, 2, 3, 1).reshape(batches * width * height, channels)\n\n        x = x @ self._B.to(x.device)\n\n        # From [(B*W*H), C] to [B, W, H, C]\n        x = x.view(batches, width, height, self._mapping_size)\n        # From [B, W, H, C] to [B, C, W, H]\n        x = x.permute(0, 3, 1, 2)\n\n        x = 2 * np.pi * x\n        return torch.cat([torch.sin(x), torch.cos(x)], dim=1)\n\n\nclass Basic1d(nn.Module):\n    def __init__(self, in_channels, out_channels, bias=True):\n        super().__init__()\n        conv = nn.Linear(in_channels, out_channels, bias)\n        self.conv = nn.Sequential(conv, )\n        if not bias:\n            self.conv.add_module('ln', nn.LayerNorm(out_channels))\n        self.conv.add_module('relu', nn.ReLU(inplace=True))\n\n    def forward(self, x):\n        out = self.conv(x)\n        return out\n\nclass FCResLayer(nn.Module):\n    def __init__(self, linear_size=128):\n        super(FCResLayer, self).__init__()\n        self.l_size = linear_size\n        self.nonlin1 = nn.ReLU(inplace=True)\n        self.nonlin2 = nn.ReLU(inplace=True)\n        #self.dropout1 = nn.Dropout()\n        self.w1 = nn.Linear(self.l_size, self.l_size)\n        self.w2 = nn.Linear(self.l_size, self.l_size)\n\n    def forward(self, x):\n        y = self.w1(x)\n        y = self.nonlin1(y)\n        #y = self.dropout1(y)\n        y = self.w2(y)\n        y = self.nonlin2(y)\n        out = x + y\n        return out\n\n\nclass Dynamic_MLP_Decoder(nn.Module):\n    def __init__(self, wv_planes, inter_dim=128, kernel_size=16, decoder_embed=512):\n        super().__init__()\n        self.kernel_size = kernel_size\n        self.wv_planes = wv_planes\n        self.inter_dim = inter_dim\n        self.decoder_embed = decoder_embed\n        self._num_kernel = self.kernel_size * self.kernel_size * self.decoder_embed\n\n        #self.weight_generator = nn.Sequential(Basic1d(wv_planes, self.inter_dim, bias=True),\n        #                                      nn.Linear(self.inter_dim, self._num_kernel))\n        self.weight_generator = TransformerWeightGenerator(wv_planes, self._num_kernel, decoder_embed)\n        self.scaler = 0.01\n\n        self._init_weights()\n\n    def _get_weights(self, waves, batch=True):\n        dweights = []\n        dynamic_weights = None\n        if batch:\n            dynamic_weights = self.weight_generator(waves)\n        else:\n            for i in range(waves.size(0)):\n                dweights.append(self.weight_generator(waves[i]))\n            dynamic_weights = torch.stack(dweights, dim=0)\n\n        return dynamic_weights\n\n    def weight_init(self, m):\n        if type(m) == nn.Linear:\n            init.xavier_uniform_(m.weight)\n            m.bias.data.fill_(0.01)\n\n    def _init_weights(self):\n        \"\"\"\n        initialize the base weights and dynamic mlp weights\n        \"\"\"\n        self.weight_generator.apply(self.weight_init)\n\n    def forward(self, img_feat, waves):\n        inplanes = waves.size(0)\n        #wv_feats: 9,128 -> 9*16*16,512\n        weight,bias = self._get_weights(waves) #9,16*16*512\n        dynamic_weight = weight.view(inplanes * self.kernel_size * self.kernel_size, self.decoder_embed) #9*16*16,512\n        weights = dynamic_weight * self.scaler\n\n        dynamic_out = F.linear(img_feat, weights, bias=None)\n        x = dynamic_out\n        return x\n\nclass Dynamic_MLP_OFA(nn.Module):\n    \"\"\"\n    Input: channels of wavelength (normalized): List -> List\n           kernel size of the depth-wise convolution: kernel_size, default 3x3\n           wv_planes\n           inplanes\n    \"\"\"\n\n    def __init__(self, wv_planes, inter_dim = 128, kernel_size=3, embed_dim=1024):\n        super().__init__()\n        self.kernel_size = kernel_size\n        self.wv_planes = wv_planes\n        self.embed_dim = embed_dim\n        self.kernel_size = kernel_size\n        self._num_kernel = self.kernel_size * self.kernel_size * self.embed_dim\n        self.inter_dim = inter_dim\n        self.patch_size = (kernel_size, kernel_size)\n\n        self.weight_generator = TransformerWeightGenerator(wv_planes, self._num_kernel, embed_dim)\n        self.scaler = 0.01\n\n        self.fclayer = FCResLayer(wv_planes)\n\n        self._init_weights()\n\n    def _get_weights(self, waves):\n        dweights = []\n        dynamic_weights = self.weight_generator(waves)\n\n        return dynamic_weights\n\n    def weight_init(self, m):\n        if type(m) == nn.Linear:\n            init.xavier_uniform_(m.weight)\n            m.bias.data.fill_(0.01)\n\n    def _init_weights(self):\n        \"\"\"\n        initialize the base weights and dynamic mlp weights\n        \"\"\"\n        self.weight_generator.apply(self.weight_init)\n        self.fclayer.apply(self.weight_init)\n\n\n    def forward(self, img_feat, wvs):\n        inplanes = wvs.size(0)\n        #wv_feats: 9,128 -> 9, 3x3x3\n        waves = get_1d_sincos_pos_embed_from_grid_torch(self.wv_planes, wvs*1000)\n        waves = self.fclayer(waves)\n        weight,bias = self._get_weights(waves) #3x3x3\n        #bias = None\n\n        dynamic_weight = weight.view(inplanes, self.kernel_size, self.kernel_size, self.embed_dim)\n        dynamic_weight = dynamic_weight.permute([3,0,1,2])\n        if bias is not None:\n            bias = bias.view([self.embed_dim]) * self.scaler\n\n        weights = dynamic_weight * self.scaler\n\n        dynamic_out = F.conv2d(img_feat, weights, bias=bias, stride=self.kernel_size, padding=1, dilation=1)\n\n        x = dynamic_out\n        x = x.flatten(2).transpose(1, 2)\n\n        return x, waves\n\n\n"
  },
  {
    "path": "pyproject.toml",
    "content": "[tool.isort]\nprofile = \"black\"\nknown_first_party = [\"util\"]\nskip_gitignore = true\ncolor_output = true\n"
  },
  {
    "path": "requirements.txt",
    "content": "kornia==0.7.0\nrasterio==1.3.9\ntimm==0.9.2\ntorch\n"
  },
  {
    "path": "wave_dynamic_layer.py",
    "content": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.init as init\n\nrandom_seed = 1234\ntorch.manual_seed(random_seed)\n\n\ndef get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos):\n    \"\"\"\n    embed_dim: output dimension for each position\n    pos: a list of positions to be encoded: size (M,)\n    out: (M, D)\n    \"\"\"\n    assert embed_dim % 2 == 0\n    omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device)\n    omega /= embed_dim / 2.0\n    omega = 1.0 / 10000**omega  # (D/2,)\n\n    pos = pos.reshape(-1)  # (M,)\n    out = torch.einsum(\"m,d->md\", pos, omega)  # (M, D/2), outer product\n\n    emb_sin = torch.sin(out)  # (M, D/2)\n    emb_cos = torch.cos(out)  # (M, D/2)\n\n    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)\n    return emb\n\nclass TransformerWeightGenerator(nn.Module):\n    def __init__(self, input_dim, output_dim, embed_dim, num_heads=4, num_layers=1):\n        super(TransformerWeightGenerator, self).__init__()\n        encoder_layer = nn.TransformerEncoderLayer(\n            d_model=input_dim,\n            nhead=num_heads,\n            activation=\"gelu\",\n            norm_first=False,\n            batch_first=False,\n            dropout=False,\n        )\n        self.transformer_encoder = nn.TransformerEncoder(\n            encoder_layer, num_layers=num_layers, enable_nested_tensor=False\n        )\n\n        # Linear layer to map transformer output to desired weight shape\n        self.fc_weight = nn.Linear(input_dim, output_dim)\n        self.fc_bias = nn.Linear(input_dim, embed_dim)\n        self.wt_num = 128\n        self.weight_tokens = nn.Parameter(torch.empty([self.wt_num, input_dim]))\n        self.bias_token = nn.Parameter(torch.empty([1, input_dim]))\n\n        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is\n        # too big (2.)\n        torch.nn.init.normal_(self.weight_tokens, std=0.02)\n        torch.nn.init.normal_(self.bias_token, std=0.02)\n\n    def forward(self, x):\n        # x should have shape [seq_len, batch, input_dim]\n        pos_wave = x\n        x = torch.cat([self.weight_tokens, pos_wave], dim=0)\n        x = torch.cat([x, self.bias_token], dim=0)\n        transformer_output = self.transformer_encoder(x)\n        weights = self.fc_weight(transformer_output[self.wt_num : -1] + pos_wave)\n        bias = self.fc_bias(\n            transformer_output[-1]\n        )  # Using the last output to generate bias\n        return weights, bias\n\n\nclass Basic1d(nn.Module):\n    def __init__(self, in_channels, out_channels, bias=True):\n        super().__init__()\n        conv = nn.Linear(in_channels, out_channels, bias)\n        self.conv = nn.Sequential(\n            conv,\n        )\n        if not bias:\n            self.conv.add_module(\"ln\", nn.LayerNorm(out_channels))\n        self.conv.add_module(\"relu\", nn.ReLU(inplace=True))\n\n    def forward(self, x):\n        out = self.conv(x)\n        return out\n\n\nclass FCResLayer(nn.Module):\n    def __init__(self, linear_size=128):\n        super(FCResLayer, self).__init__()\n        self.l_size = linear_size\n        self.nonlin1 = nn.ReLU(inplace=True)\n        self.nonlin2 = nn.ReLU(inplace=True)\n        self.w1 = nn.Linear(self.l_size, self.l_size)\n        self.w2 = nn.Linear(self.l_size, self.l_size)\n\n    def forward(self, x):\n        y = self.w1(x)\n        y = self.nonlin1(y)\n        y = self.w2(y)\n        y = self.nonlin2(y)\n        out = x + y\n        return out\n\n\nclass Dynamic_MLP_Decoder(nn.Module):\n    def __init__(self, wv_planes, inter_dim=128, kernel_size=16, decoder_embed=512):\n        super().__init__()\n        self.kernel_size = kernel_size\n        self.wv_planes = wv_planes\n        self.inter_dim = inter_dim\n        self.decoder_embed = decoder_embed\n        self._num_kernel = self.kernel_size * self.kernel_size * self.decoder_embed\n\n        self.weight_generator = TransformerWeightGenerator(\n            wv_planes, self._num_kernel, decoder_embed\n        )\n        self.scaler = 0.01\n\n        self._init_weights()\n\n    def _get_weights(self, waves, batch=True):\n        dweights = []\n        dynamic_weights = None\n        if batch:\n            dynamic_weights = self.weight_generator(waves)\n        else:\n            for i in range(waves.size(0)):\n                dweights.append(self.weight_generator(waves[i]))\n            dynamic_weights = torch.stack(dweights, dim=0)\n\n        return dynamic_weights\n\n    def weight_init(self, m):\n        if isinstance(m, nn.Linear):\n            init.xavier_uniform_(m.weight)\n            m.bias.data.fill_(0.01)\n\n    def _init_weights(self):\n        \"\"\"\n        initialize the base weights and dynamic mlp weights\n        \"\"\"\n        self.weight_generator.apply(self.weight_init)\n\n    def forward(self, img_feat, waves):\n        inplanes = waves.size(0)\n        # wv_feats: 9,128 -> 9*16*16,512\n        weight, bias = self._get_weights(waves)  # 9,16*16*512\n        dynamic_weight = weight.view(\n            inplanes * self.kernel_size * self.kernel_size, self.decoder_embed\n        )  # 9*16*16,512\n        weights = dynamic_weight * self.scaler\n\n        dynamic_out = F.linear(img_feat, weights, bias=None)\n        x = dynamic_out\n        return x\n\nclass Dynamic_MLP_OFA(nn.Module):\n    \"\"\"\n    Input: channels of wavelength (normalized): List -> List\n           kernel size of the depth-wise convolution: kernel_size, default 3x3\n           wv_planes\n           inplanes\n    \"\"\"\n\n    def __init__(self, wv_planes, inter_dim=128, kernel_size=3, embed_dim=1024):\n        super().__init__()\n        self.kernel_size = kernel_size\n        self.wv_planes = wv_planes\n        self.embed_dim = embed_dim\n        self.kernel_size = kernel_size\n        self._num_kernel = self.kernel_size * self.kernel_size * self.embed_dim\n        self.inter_dim = inter_dim\n        self.patch_size = (kernel_size, kernel_size)\n        self.num_patches = -1\n\n        self.weight_generator = TransformerWeightGenerator(\n            wv_planes, self._num_kernel, embed_dim\n        )\n        self.scaler = 0.01\n\n        self.fclayer = FCResLayer(wv_planes)\n\n        self._init_weights()\n\n    def _get_weights(self, waves):\n        dynamic_weights = self.weight_generator(waves)\n\n        return dynamic_weights\n\n    def weight_init(self, m):\n        if isinstance(m, nn.Linear):\n            init.xavier_uniform_(m.weight)\n            m.bias.data.fill_(0.01)\n\n    def _init_weights(self):\n        \"\"\"\n        initialize the base weights and dynamic mlp weights\n        \"\"\"\n        self.weight_generator.apply(self.weight_init)\n        self.fclayer.apply(self.weight_init)\n\n    def forward(self, img_feat, wvs):\n        inplanes = wvs.size(0)\n        # wv_feats: 9,128 -> 9, 3x3x3\n        waves = get_1d_sincos_pos_embed_from_grid_torch(self.wv_planes, wvs * 1000)\n        waves = self.fclayer(waves)\n        weight, bias = self._get_weights(waves)  # 3x3x3\n\n        # Fix bug        \n        dynamic_weight = weight.view(inplanes, self.kernel_size, self.kernel_size, self.embed_dim)\n        dynamic_weight = dynamic_weight.permute([3,0,1,2])\n        \n        if bias is not None:\n            bias = bias.view([self.embed_dim]) * self.scaler\n\n        weights = dynamic_weight * self.scaler\n\n        dynamic_out = F.conv2d(\n            img_feat, weights, bias=bias, stride=self.kernel_size, padding=1, dilation=1\n        )\n\n        x = dynamic_out\n        x = x.flatten(2).transpose(1, 2)\n\n        return x, waves\n\n"
  }
]