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Repository: zhu-xlab/DOFA
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Directory structure:
gitextract_hlyyc9jp/
├── .flake8
├── .gitignore
├── LICENSE
├── README.md
├── checkpoints/
│ └── download_weights.py
├── demo.ipynb
├── dofa_v1.py
├── downstream_tasks/
│ └── README.md
├── hubconf.py
├── pretraining/
│ ├── datasets/
│ │ ├── __init__.py
│ │ ├── enmap_waves.txt
│ │ ├── ofall_dataset.py
│ │ └── waves.json
│ ├── engine_pretrain.py
│ ├── main_pretrain_ofa.py
│ ├── models_base_ofa_mae.py
│ ├── samplers/
│ │ └── distributed.py
│ ├── train_mae_all.sh
│ ├── util/
│ │ ├── crop.py
│ │ ├── datasets.py
│ │ ├── lars.py
│ │ ├── lr_decay.py
│ │ ├── lr_sched.py
│ │ ├── misc.py
│ │ ├── pos_embed.py
│ │ ├── vision_transformer.py
│ │ └── wandb_log.py
│ └── wave_dynamic_layer.py
├── pyproject.toml
├── requirements.txt
└── wave_dynamic_layer.py
================================================
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================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2024 Zhitong Xiong
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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SOFTWARE.
================================================
FILE: README.md
================================================
# [DOFA](https://github.com/ShadowXZT/DOFA-pytorch)
>
> 🚨 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).
>
> 🚨 Please use the new version of codes and [weights](https://huggingface.co/XShadow/DOFA). The performance is much better!
>
> 🚨 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.
>
---
## Dynamic One-For-All foundation model for Remote sensing and Earth observation
**Object Detection**: DOFA and DOFA v2 can be used for object detection tasks, which are fundamental remote sensing applications.
<p align="center">
<img src="assets/dofa_od.png" width="1200">
</p>
**What is DOFA**: DOFA is a unified multimodal foundation model for different data modalities in remote sensing and Earth observation.
<p align="center">
<img src="assets/DOFA-main.png" width="500">
</p>
**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.
**DOFA is inspired by neuroplasticity** Neuroplasticity is an
important 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.
<p align="center">
<img src="assets/DOFA-model.png" width="500">
</p>
For 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).
## Why develop DOFA
- The learned multimodal representation may not effectively capture such an intersensor relationship.
- 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.
- The development of individual, customized foundation models requires considerably more computing resources and human efforts.
- The increasing number of specialized foundation models makes it difficult to select the most appropriate one for a specific downstream task.
## Installation
### Requirements
The requirements of DOFA can be installed as follows:
```console
> pip install -r requirements.txt
```
### Weights
Pre-trained model weights can be downloaded from [HuggingFace](https://huggingface.co/XShadow/DOFA).
## Usage
Please refer to [demo.ipynb](https://github.com/zhu-xlab/DOFA/blob/master/demo.ipynb) for more details.
DOFA 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.
---
### Using `torch.hub` to Load the DOFA ViT Base Model
This 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.
```python
import torch
model = torch.hub.load(
'zhu-xlab/DOFA',
'vit_base_dofa', # The entry point defined in hubconf.py
pretrained=True,
)
model = model.cuda()
model.eval()
```
Now the model is ready for inference or further fine-tuning.
If you would like to fine-tune DOFA on different downstream tasks, please refer to [DOFA-pytorch](https://github.com/xiong-zhitong/DOFA-pytorch).
### TorchGeo
Alternatively, DOFA can be used via the [TorchGeo](https://github.com/microsoft/torchgeo) library:
```python
import torch
from torchgeo.models import DOFABase16_Weights, dofa_base_patch16_224
# Example NAIP image (wavelengths in $\mu$m)
x = torch.rand(2, 4, 224, 224)
wavelengths = [0.48, 0.56, 0.64, 0.81]
# Use pre-trained model weights
model = dofa_base_patch16_224(weights=DOFABase16_Weights.DOFA_MAE)
# Make a prediction (model may need to be fine-tuned first)
y = model(x, wavelengths)
```
---
### Demo usage of DOFA on different data modalities
---
### Download the pre-trained weights for DOFA from huggingface
Please move to the `checkpoints` directory and run
```python
python download_weights.py
```
Please 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.
```python
from models_dwv import vit_base_patch16
check_point = torch.load('./checkpoints/DOFA_ViT_base_e100.pth')
vit_model = vit_base_patch16()
msg = vit_model.load_state_dict(check_point, strict=False)
# missing_keys=['fc_norm.weight', 'fc_norm.bias', 'head.weight', 'head.bias'], unexpected_keys=['mask_token', 'norm.weight', 'norm.bias', 'projector.weight', 'projector.bias']
vit_model = vit_model.cuda()
```
Now you can use **the loaded single DOFA model** to process image data from **different modalities** with **any number of channels**!
### Preprare for the data loading and preprocessing
```python
# Step 1: Data preprocessing (normalization and resize)
import torch
import rasterio
import kornia as K
import numpy as np
# vh,vv
S1_MEAN = [166.36275909, 88.45542715]# / 255.0
S1_STD = [64.83126309, 43.07350145]# /255.0
S2_MEAN = [114.1099739 , 114.81779093, 126.63977424, 84.33539309,
97.84789168, 103.94461911, 101.435633 , 72.32804172,
56.66528851]
S2_STD = [77.84352553, 69.96844919, 67.42465279, 64.57022983, 61.72545487,
61.34187099, 60.29744676, 47.88519516, 42.55886798]
NAIP_MEAN = [123.675, 116.28, 103.53] # ImageNet stats for now
NAIP_STD = [58.395, 57.12, 57.375] # ImageNet stats for now
Gaufen_MEAN = [123.94924583, 92.58088583, 97.28130189, 90.31526596]
Gaufen_STD = [67.34487297, 62.8271046 , 60.5856767 , 60.3946299]
```
```python
class DataAugmentation(torch.nn.Module):
def __init__(self, mean, std):
super().__init__()
self.transform = torch.nn.Sequential(
K.augmentation.RandomResizedCrop(size=(224,224), scale=(0.2,1.0)),
K.augmentation.Normalize(mean=mean,std=std)
)
@torch.no_grad()
def forward(self,x):
x_out = self.transform(x)
return x_out
```
### Load Sentinel-1 data with 2 channels
```python
transform = DataAugmentation(mean=S1_MEAN,std=S1_STD)
def preprocess_s1(vh_path, vv_path):
with rasterio.open(vh_path) as f1:
vh = f1.read()
with rasterio.open(vv_path) as f2:
vv = f2.read()
s1_img = np.concatenate((vh,vv),0).astype('float32')
s1_img = torch.from_numpy(s1_img)
s1_img = transform(s1_img).squeeze(0)
return s1_img
```
```python
import matplotlib.pyplot as plt
# Load Sentinel-1 images from the given example data
C = 2 # can be 2,3,4,6,9,12,13,202 or any number if you can provide the wavelengths of them
image1 = './data/s1/vv/1869_3575.png'
image2 = './data/s1/vh/1869_3575.png'
s1_img = preprocess_s1(image1,image2)
fig, ax = plt.subplots(nrows=1, ncols=C, figsize=(10, 10))
for i,row in enumerate(ax):
row.imshow(s1_img[i])
s1_img = s1_img.view([1,2,224,224]).cuda()
```
```python
wavelengths = [5.405, 5.405]
out_feat = vit_model.forward_features(s1_img, wave_list=wavelengths)
out_logits = vit_model.forward(s1_img, wave_list=wavelengths)
print(out_feat.shape)
print(out_logits.shape)
```
### Load Sentinel-2 data with 9 channels
```python
import glob
C = 9
transform = DataAugmentation(mean=S2_MEAN,std=S2_STD)
def preprocess_s2(img_path):
chs = []
s2_files = glob.glob(f"{img_path}/*/*.png")
for path in s2_files:
with rasterio.open(path) as f:
ch = f.read()
chs.append(ch)
s2_img = np.concatenate(chs, 0).astype("float32")
s2_img = torch.from_numpy(s2_img)
s2_img = transform(s2_img).squeeze(0)
return s2_img
```
Visualize the Sentinel-2 imagery
```python
s2_img = preprocess_s2("data/s2/S2A_MSIL1C_20170528T050611_N0205_R076_T44NMM_20170528T050606")
fig, ax = plt.subplots(nrows=1, ncols=C, figsize=(10, 10))
for i,row in enumerate(ax):
row.imshow(s2_img[i])
row.axis("off")
s2_img = s2_img.view([1,C,224,224]).cuda()
```
/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.
dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)
### We use the same DOFA model to inference the Sentinel-2 image
```python
wavelengths = [0.665, 0.56, 0.49, 0.705, 0.74, 0.783, 0.842, 1.61, 2.19]
out_feat = vit_model.forward_features(s2_img, wave_list=wavelengths)
out_logits = vit_model.forward(s2_img, wave_list=wavelengths)
print(out_feat.shape)
print(out_logits.shape)
```
### What if I only want to use a subset of Sentinel-2 data?
```python
# Let's only keep the first 5 channels
wavelengths = [0.665, 0.56, 0.49, 0.705, 0.74]
out_feat = vit_model.forward_features(s2_img[:,:5,...], wave_list=wavelengths)
out_logits = vit_model.forward(s2_img[:,:5,...], wave_list=wavelengths)
print(out_feat.shape)
print(out_logits.shape)
```
### Usage for RGB optical data
```python
C = 3
transform = DataAugmentation(mean=NAIP_MEAN, std=NAIP_STD)
def preprocess_rgb(img_path):
with rasterio.open(img_path) as f:
rgb_img = f.read().astype("float32")
rgb_img = torch.from_numpy(rgb_img)
rgb_img = transform(rgb_img).squeeze(0)
return rgb_img
```
```python
import cv2
rgb_path = 'data/naip/36861_49963.png'
naip_img = preprocess_rgb(rgb_path)
plt.imshow(cv2.cvtColor(cv2.imread(rgb_path), cv2.COLOR_BGR2RGB))
plt.axis("off")
naip_img = naip_img.view([1,C,224,224]).cuda()
```
```python
wavelengths = [0.665, 0.56, 0.49]
out_feat = vit_model.forward_features(naip_img, wave_list=wavelengths)
out_logits = vit_model.forward(naip_img, wave_list=wavelengths)
print(out_feat.shape)
print(out_logits.shape)
```
Usage for Hyperspectral images is similar to other images.
---
If you find the DOFA useful in your research, please kindly cite our paper:
```
@article{xiong2024neural,
title={Neural Plasticity-Inspired Foundation Model for Observing the {Earth} Crossing Modalities},
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},
journal={arXiv preprint arXiv:2403.15356},
year={2024}
}
```
================================================
FILE: checkpoints/download_weights.py
================================================
# Download the pre-trained weights of DOFA
from huggingface_hub import hf_hub_download
import os
file_path = os.path.dirname(os.path.abspath(__file__))
hf_hub_download(repo_id="XShadow/DOFA", filename="DOFA_ViT_base_e100.pth", local_dir=file_path)
================================================
FILE: demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Demo for DOFA\n",
"### DOFA supports input images with any number of channels using our pre-trained foundation models\n",
"### The following examples show how to use DOFA for Sentinel-1 (SAR), Sentinel-2, NAIP RGB, Gaofen Multispectral, and Hyperspectral data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download the pre-trained weights for DOFA from huggingface"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%run checkpoints/download_weights.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Step 1: Data preprocessing (normalization and resize)\n",
"\n",
"import torch\n",
"import rasterio\n",
"import kornia as K\n",
"import numpy as np\n",
"# vh,vv\n",
"S1_MEAN = [166.36275909, 88.45542715]# / 255.0\n",
"S1_STD = [64.83126309, 43.07350145]# /255.0\n",
"\n",
"S2_MEAN = [114.1099739 , 114.81779093, 126.63977424, 84.33539309,\n",
" 97.84789168, 103.94461911, 101.435633 , 72.32804172,\n",
" 56.66528851]\n",
"S2_STD = [77.84352553, 69.96844919, 67.42465279, 64.57022983, 61.72545487,\n",
" 61.34187099, 60.29744676, 47.88519516, 42.55886798]\n",
"\n",
"NAIP_MEAN = [123.675, 116.28, 103.53] # ImageNet stats for now\n",
"NAIP_STD = [58.395, 57.12, 57.375] # ImageNet stats for now\n",
"\n",
"Hyper_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",
"\n",
"Hyper_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",
"\n",
"Gaufen_MEAN = [123.94924583, 92.58088583, 97.28130189, 90.31526596]\n",
"Gaufen_STD = [67.34487297, 62.8271046 , 60.5856767 , 60.3946299]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class 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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"transform = DataAugmentation(mean=S1_MEAN,std=S1_STD)\n",
"\n",
"def 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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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
gitextract_hlyyc9jp/ ├── .flake8 ├── .gitignore ├── LICENSE ├── README.md ├── checkpoints/ │ └── download_weights.py ├── demo.ipynb ├── dofa_v1.py ├── downstream_tasks/ │ └── README.md ├── hubconf.py ├── pretraining/ │ ├── datasets/ │ │ ├── __init__.py │ │ ├── enmap_waves.txt │ │ ├── ofall_dataset.py │ │ └── waves.json │ ├── engine_pretrain.py │ ├── main_pretrain_ofa.py │ ├── models_base_ofa_mae.py │ ├── samplers/ │ │ └── distributed.py │ ├── train_mae_all.sh │ ├── util/ │ │ ├── crop.py │ │ ├── datasets.py │ │ ├── lars.py │ │ ├── lr_decay.py │ │ ├── lr_sched.py │ │ ├── misc.py │ │ ├── pos_embed.py │ │ ├── vision_transformer.py │ │ └── wandb_log.py │ └── wave_dynamic_layer.py ├── pyproject.toml ├── requirements.txt └── wave_dynamic_layer.py
SYMBOL INDEX (223 symbols across 18 files)
FILE: dofa_v1.py
class OFAViT (line 28) | class OFAViT(nn.Module):
method __init__ (line 31) | def __init__(self, img_size=224, patch_size=16, drop_rate=0.,
method forward_features (line 58) | def forward_features(self, x, wave_list):
method forward_head (line 83) | def forward_head(self, x, pre_logits=False):
method forward (line 87) | def forward(self, x, wave_list):
function vit_small_patch16 (line 93) | def vit_small_patch16(**kwargs):
function vit_base_patch16 (line 99) | def vit_base_patch16(**kwargs):
function vit_large_patch16 (line 106) | def vit_large_patch16(**kwargs):
function vit_huge_patch14 (line 113) | def vit_huge_patch14(**kwargs):
FILE: hubconf.py
function vit_base_dofa (line 9) | def vit_base_dofa(pretrained=True, strict=False, **kwargs):
FILE: pretraining/datasets/ofall_dataset.py
class DataAugmentation (line 123) | class DataAugmentation(torch.nn.Module):
method __init__ (line 124) | def __init__(self, mean, std):
method forward (line 133) | def forward(self,x):
class Sentinel1Dataset (line 139) | class Sentinel1Dataset(Dataset):
method __init__ (line 140) | def __init__(self, root_dir, split='train', transform=True):
method __getitem__ (line 151) | def __getitem__(self, index):
method __len__ (line 172) | def __len__(self):
class Sentinel2Dataset (line 176) | class Sentinel2Dataset(Dataset):
method __init__ (line 177) | def __init__(self, root_dir, split='train', transform=True):
method __getitem__ (line 189) | def __getitem__(self, index):
method __len__ (line 207) | def __len__(self):
class NAIPDataset (line 210) | class NAIPDataset(Dataset):
method __init__ (line 211) | def __init__(self, root_dir, split='train', transform=True):
method __getitem__ (line 221) | def __getitem__(self, index):
method __len__ (line 237) | def __len__(self):
class HyperDataset (line 240) | class HyperDataset(Dataset):
method __init__ (line 241) | def __init__(self, root_dir, split='train', transform=True):
method __getitem__ (line 259) | def __getitem__(self, index):
method __len__ (line 278) | def __len__(self):
class GaufenDataset (line 281) | class GaufenDataset(Dataset):
method __init__ (line 282) | def __init__(self, root_dir, split='train', transform=True):
method __getitem__ (line 293) | def __getitem__(self, index):
method __len__ (line 306) | def __len__(self):
function chunk (line 312) | def chunk(indices, size):
class MyBatchSampler (line 316) | class MyBatchSampler(Sampler):
method __init__ (line 317) | def __init__(self, dataset, dataset1, dataset2, dataset3, dataset4, da...
method __iter__ (line 332) | def __iter__(self):
method __len__ (line 365) | def __len__(self):
class MyDistributedBatchSampler (line 375) | class MyDistributedBatchSampler(DistributedSampler):
method __init__ (line 376) | def __init__(self, dataset, dataset1, dataset2, dataset3, dataset4, da...
method __iter__ (line 415) | def __iter__(self):
method __len__ (line 452) | def __len__(self) -> int:
FILE: pretraining/engine_pretrain.py
function train_one_epoch (line 25) | def train_one_epoch(model: torch.nn.Module,
FILE: pretraining/main_pretrain_ofa.py
function get_args_parser (line 40) | def get_args_parser():
function main (line 117) | def main(args):
FILE: pretraining/models_base_ofa_mae.py
class MaskedAutoencoderViT (line 27) | class MaskedAutoencoderViT(nn.Module):
method __init__ (line 30) | def __init__(self, img_size=224, patch_size=16, in_chans=[2,9,3,202,4],
method initialize_weights (line 81) | def initialize_weights(self):
method _init_weights (line 113) | def _init_weights(self, m):
method patchify (line 123) | def patchify(self, imgs):
method unpatchify (line 137) | def unpatchify(self, x):
method random_select_channels (line 151) | def random_select_channels(self, imgs, wave_list):
method random_masking (line 164) | def random_masking(self, x, mask_ratio):
method forward_zs (line 189) | def forward_zs(self, x, wave_list):
method forward_encoder (line 205) | def forward_encoder(self, x, mask_ratio, wave_list):
method forward_decoder (line 229) | def forward_decoder(self, x, ids_restore, in_chan):
method forward_loss (line 255) | def forward_loss(self, imgs, pred, mask):
method forward_zt (line 273) | def forward_zt(self, imgs, wave_list):
method forward (line 296) | def forward(self, imgs, in_chans, wave_list, mask_ratio=0.75):
function mae_vit_small_patch16_dec512d8b (line 306) | def mae_vit_small_patch16_dec512d8b(**kwargs):
function mae_vit_base_patch16_dec512d8b (line 313) | def mae_vit_base_patch16_dec512d8b(**kwargs):
function mae_vit_large_patch16_dec512d8b (line 321) | def mae_vit_large_patch16_dec512d8b(**kwargs):
function mae_vit_huge_patch14_dec512d8b (line 329) | def mae_vit_huge_patch14_dec512d8b(**kwargs):
FILE: pretraining/samplers/distributed.py
class DistributedRandomGeoSampler (line 11) | class DistributedRandomGeoSampler(RandomGeoSampler):
method __init__ (line 21) | def __init__(
method __iter__ (line 58) | def __iter__(self) -> Iterator[BoundingBox]:
method __len__ (line 83) | def __len__(self) -> int:
method set_epoch (line 91) | def set_epoch(self, epoch: int) -> None:
FILE: pretraining/util/crop.py
class RandomResizedCrop (line 15) | class RandomResizedCrop(transforms.RandomResizedCrop):
method get_params (line 23) | def get_params(img, scale, ratio):
FILE: pretraining/util/datasets.py
function build_dataset (line 20) | def build_dataset(is_train, args):
function build_transform (line 31) | def build_transform(is_train, args):
FILE: pretraining/util/lars.py
class LARS (line 14) | class LARS(torch.optim.Optimizer):
method __init__ (line 18) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
method step (line 23) | def step(self):
FILE: pretraining/util/lr_decay.py
function param_groups_lrd (line 15) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ...
function get_layer_id_for_vit (line 64) | def get_layer_id_for_vit(name, num_layers):
FILE: pretraining/util/lr_sched.py
function adjust_learning_rate (line 9) | def adjust_learning_rate(optimizer, epoch, args):
FILE: pretraining/util/misc.py
class SmoothedValue (line 27) | class SmoothedValue(object):
method __init__ (line 32) | def __init__(self, window_size=20, fmt=None):
method update (line 40) | def update(self, value, n=1):
method synchronize_between_processes (line 45) | def synchronize_between_processes(self):
method median (line 59) | def median(self):
method avg (line 64) | def avg(self):
method global_avg (line 69) | def global_avg(self):
method max (line 73) | def max(self):
method value (line 77) | def value(self):
method __str__ (line 80) | def __str__(self):
class MetricLogger (line 89) | class MetricLogger(object):
method __init__ (line 90) | def __init__(self, delimiter="\t"):
method update (line 94) | def update(self, **kwargs):
method __getattr__ (line 103) | def __getattr__(self, attr):
method __str__ (line 111) | def __str__(self):
method synchronize_between_processes (line 119) | def synchronize_between_processes(self):
method add_meter (line 123) | def add_meter(self, name, meter):
method log_every (line 126) | def log_every(self, iterable, print_freq, header=None):
function setup_for_distributed (line 173) | def setup_for_distributed(is_master):
function is_dist_avail_and_initialized (line 190) | def is_dist_avail_and_initialized():
function get_world_size (line 198) | def get_world_size():
function get_rank (line 204) | def get_rank():
function is_main_process (line 210) | def is_main_process():
function save_on_master (line 214) | def save_on_master(*args, **kwargs):
function init_distributed_mode (line 219) | def init_distributed_mode(args):
class NativeScalerWithGradNormCount (line 254) | class NativeScalerWithGradNormCount:
method __init__ (line 257) | def __init__(self):
method __call__ (line 260) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
method state_dict (line 276) | def state_dict(self):
method load_state_dict (line 279) | def load_state_dict(self, state_dict):
function get_grad_norm_ (line 283) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
function save_model (line 298) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
function load_model (line 318) | def load_model(args, model_without_ddp, optimizer, loss_scaler):
function all_reduce_mean (line 335) | def all_reduce_mean(x):
class CLIPLoss (line 347) | class CLIPLoss(nn.Module):
method __init__ (line 348) | def __init__(self, temperature=1.0):
method forward (line 352) | def forward(self, image_features, text_features):
FILE: pretraining/util/pos_embed.py
function get_2d_sincos_pos_embed (line 20) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, promp...
function get_2d_sincos_pos_embed_with_resolution (line 40) | def get_2d_sincos_pos_embed_with_resolution(
function get_2d_sincos_pos_embed_from_grid (line 78) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
function get_2d_sincos_pos_embed_from_grid_torch (line 89) | def get_2d_sincos_pos_embed_from_grid_torch(embed_dim, grid):
function get_1d_sincos_pos_embed_from_grid_torch (line 104) | def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos):
function get_1d_sincos_pos_embed_from_grid (line 126) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
function interpolate_pos_embed (line 152) | def interpolate_pos_embed(model, checkpoint_model, num_patches=None):
FILE: pretraining/util/vision_transformer.py
function _cfg (line 21) | def _cfg(url='', **kwargs):
class Mlp (line 90) | class Mlp(nn.Module):
method __init__ (line 91) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 100) | def forward(self, x):
class Attention (line 109) | class Attention(nn.Module):
method __init__ (line 110) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
method forward (line 122) | def forward(self, x):
class Block (line 137) | class Block(nn.Module):
method __init__ (line 139) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 151) | def forward(self, x):
class PatchEmbed (line 157) | class PatchEmbed(nn.Module):
method __init__ (line 160) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 171) | def forward(self, x):
class HybridEmbed (line 180) | class HybridEmbed(nn.Module):
method __init__ (line 184) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
method forward (line 213) | def forward(self, x):
class VisionTransformer (line 221) | class VisionTransformer(nn.Module):
method __init__ (line 224) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method _init_weights (line 264) | def _init_weights(self, m):
method no_weight_decay (line 274) | def no_weight_decay(self):
method get_classifier (line 277) | def get_classifier(self):
method reset_classifier (line 280) | def reset_classifier(self, num_classes, global_pool=''):
method forward_features (line 284) | def forward_features(self, x):
method forward (line 299) | def forward(self, x):
function resize_pos_embed (line 305) | def resize_pos_embed(posemb, posemb_new):
function checkpoint_filter_fn (line 325) | def checkpoint_filter_fn(state_dict, model):
function _create_vision_transformer (line 343) | def _create_vision_transformer(variant, pretrained=False, distilled=Fals...
function vit_small_patch16_224 (line 371) | def vit_small_patch16_224(pretrained=False, **kwargs):
function vit_base_patch16_224 (line 384) | def vit_base_patch16_224(pretrained=False, **kwargs):
function vit_base_patch32_224 (line 394) | def vit_base_patch32_224(pretrained=False, **kwargs):
function vit_base_patch16_384 (line 403) | def vit_base_patch16_384(pretrained=False, **kwargs):
function vit_base_patch32_384 (line 413) | def vit_base_patch32_384(pretrained=False, **kwargs):
function vit_large_patch16_224 (line 423) | def vit_large_patch16_224(pretrained=False, **kwargs):
function vit_large_patch32_224 (line 433) | def vit_large_patch32_224(pretrained=False, **kwargs):
function vit_large_patch16_384 (line 442) | def vit_large_patch16_384(pretrained=False, **kwargs):
function vit_large_patch32_384 (line 452) | def vit_large_patch32_384(pretrained=False, **kwargs):
function vit_base_patch16_224_in21k (line 462) | def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
function vit_base_patch32_224_in21k (line 473) | def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
function vit_large_patch16_224_in21k (line 484) | def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
function vit_large_patch32_224_in21k (line 495) | def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
function vit_huge_patch14_224_in21k (line 506) | def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
function vit_base_resnet50_224_in21k (line 518) | def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
function vit_base_resnet50_384 (line 534) | def vit_base_resnet50_384(pretrained=False, **kwargs):
function vit_small_resnet26d_224 (line 548) | def vit_small_resnet26d_224(pretrained=False, **kwargs):
function vit_small_resnet50d_s3_224 (line 558) | def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
function vit_base_resnet26d_224 (line 568) | def vit_base_resnet26d_224(pretrained=False, **kwargs):
function vit_base_resnet50d_224 (line 578) | def vit_base_resnet50d_224(pretrained=False, **kwargs):
function vit_deit_tiny_patch16_224 (line 588) | def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
function deit_small_resnet50_224 (line 597) | def deit_small_resnet50_224(pretrained=False, **kwargs):
FILE: pretraining/util/wandb_log.py
class WANDB_LOG_IMG_CONFIG (line 19) | class WANDB_LOG_IMG_CONFIG:
function equalize (line 25) | def equalize(x):
function wandb_dump_input_output (line 34) | def wandb_dump_input_output(x, ys, epoch=0, texts=""):
function wandb_dump_images (line 57) | def wandb_dump_images(imgs, name="vis", epoch=0):
function compare_pos_embedding (line 71) | def compare_pos_embedding(posa, posb, ns=[0]):
function wandb_log_metadata (line 90) | def wandb_log_metadata(metadata):
FILE: pretraining/wave_dynamic_layer.py
class TransformerWeightGenerator (line 26) | class TransformerWeightGenerator(nn.Module):
method __init__ (line 27) | def __init__(self, input_dim, output_dim, embed_dim, num_heads=4, num_...
method forward (line 43) | def forward(self, x):
class GaussianFourierFeatureTransform (line 55) | class GaussianFourierFeatureTransform(torch.nn.Module):
method __init__ (line 67) | def __init__(self, num_input_channels, mapping_size=256, scale=10):
method forward (line 75) | def forward(self, x):
class Basic1d (line 98) | class Basic1d(nn.Module):
method __init__ (line 99) | def __init__(self, in_channels, out_channels, bias=True):
method forward (line 107) | def forward(self, x):
class FCResLayer (line 111) | class FCResLayer(nn.Module):
method __init__ (line 112) | def __init__(self, linear_size=128):
method forward (line 121) | def forward(self, x):
class Dynamic_MLP_Decoder (line 131) | class Dynamic_MLP_Decoder(nn.Module):
method __init__ (line 132) | def __init__(self, wv_planes, inter_dim=128, kernel_size=16, decoder_e...
method _get_weights (line 147) | def _get_weights(self, waves, batch=True):
method weight_init (line 159) | def weight_init(self, m):
method _init_weights (line 164) | def _init_weights(self):
method forward (line 170) | def forward(self, img_feat, waves):
class Dynamic_MLP_OFA (line 181) | class Dynamic_MLP_OFA(nn.Module):
method __init__ (line 189) | def __init__(self, wv_planes, inter_dim = 128, kernel_size=3, embed_di...
method _get_weights (line 206) | def _get_weights(self, waves):
method weight_init (line 212) | def weight_init(self, m):
method _init_weights (line 217) | def _init_weights(self):
method forward (line 225) | def forward(self, img_feat, wvs):
FILE: wave_dynamic_layer.py
function get_1d_sincos_pos_embed_from_grid_torch (line 11) | def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos):
class TransformerWeightGenerator (line 31) | class TransformerWeightGenerator(nn.Module):
method __init__ (line 32) | def __init__(self, input_dim, output_dim, embed_dim, num_heads=4, num_...
method forward (line 58) | def forward(self, x):
class Basic1d (line 71) | class Basic1d(nn.Module):
method __init__ (line 72) | def __init__(self, in_channels, out_channels, bias=True):
method forward (line 82) | def forward(self, x):
class FCResLayer (line 87) | class FCResLayer(nn.Module):
method __init__ (line 88) | def __init__(self, linear_size=128):
method forward (line 96) | def forward(self, x):
class Dynamic_MLP_Decoder (line 105) | class Dynamic_MLP_Decoder(nn.Module):
method __init__ (line 106) | def __init__(self, wv_planes, inter_dim=128, kernel_size=16, decoder_e...
method _get_weights (line 121) | def _get_weights(self, waves, batch=True):
method weight_init (line 133) | def weight_init(self, m):
method _init_weights (line 138) | def _init_weights(self):
method forward (line 144) | def forward(self, img_feat, waves):
class Dynamic_MLP_OFA (line 157) | class Dynamic_MLP_OFA(nn.Module):
method __init__ (line 165) | def __init__(self, wv_planes, inter_dim=128, kernel_size=3, embed_dim=...
method _get_weights (line 185) | def _get_weights(self, waves):
method weight_init (line 190) | def weight_init(self, m):
method _init_weights (line 195) | def _init_weights(self):
method forward (line 202) | def forward(self, img_feat, wvs):
Condensed preview — 31 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,279K chars).
[
{
"path": ".flake8",
"chars": 114,
"preview": "[flake8]\nmax-line-length = 88\nextend-ignore =\n # See https://github.com/PyCQA/pycodestyle/issues/373\n E203,\n"
},
{
"path": ".gitignore",
"chars": 3185,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# downstream tasks\ndownstream_tasks/segmentat"
},
{
"path": "LICENSE",
"chars": 1070,
"preview": "MIT License\n\nCopyright (c) 2024 Zhitong Xiong\n\nPermission is hereby granted, free of charge, to any person obtaining a c"
},
{
"path": "README.md",
"chars": 10829,
"preview": "\n# [DOFA](https://github.com/ShadowXZT/DOFA-pytorch)\n\n>\n> 🚨 Examples for using DOFA and DOFAv2 for object detection and "
},
{
"path": "checkpoints/download_weights.py",
"chars": 250,
"preview": "# Download the pre-trained weights of DOFA\n\nfrom huggingface_hub import hf_hub_download\nimport os\nfile_path = os.path.di"
},
{
"path": "demo.ipynb",
"chars": 1107590,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Demo for DOFA\\n\",\n \"### DOFA s"
},
{
"path": "dofa_v1.py",
"chars": 4014,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "downstream_tasks/README.md",
"chars": 244,
"preview": "TODO:\n\nOngoing: add more examples of using DOFAv1 and DOFAv2 based on [Terratorch](https://github.com/IBM/terratorch/tre"
},
{
"path": "hubconf.py",
"chars": 594,
"preview": "MODEL = \"https://huggingface.co/earthflow/DOFA/resolve/main/DOFA_ViT_base_e100.pth\"\n\n# hubconf.py\n\ndependencies = [\"torc"
},
{
"path": "pretraining/datasets/__init__.py",
"chars": 2,
"preview": "#\n"
},
{
"path": "pretraining/datasets/enmap_waves.txt",
"chars": 14608,
"preview": " 1: {'wavelengthCenterOfBand': 418.416, 'FWHMOfBand': 6.99561},\n 2: {'wavelengthCenterOfBand': 424.043, 'FWHMOfBand': 6."
},
{
"path": "pretraining/datasets/ofall_dataset.py",
"chars": 18846,
"preview": "import sys\nimport os\nimport pdb\nfrom Dataset4EO.datasets import SatlasS1,SatlasS2,SatlasNAIP,Hyper11k,five_billion\nimpor"
},
{
"path": "pretraining/datasets/waves.json",
"chars": 1543,
"preview": "{\"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, 0.78"
},
{
"path": "pretraining/engine_pretrain.py",
"chars": 3602,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/main_pretrain_ofa.py",
"chars": 10110,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/models_base_ofa_mae.py",
"chars": 14103,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/samplers/distributed.py",
"chars": 3510,
"preview": "import math\nfrom typing import Iterator, Optional, Tuple, Union\n\nimport torch\nimport torch.distributed as dist\nfrom torc"
},
{
"path": "pretraining/train_mae_all.sh",
"chars": 822,
"preview": "export CUDA_VISIBLE_DEVICES=6,7\npython -m torch.distributed.launch --nproc_per_node=2 --master_port=25678 main_pretrain_"
},
{
"path": "pretraining/util/crop.py",
"chars": 1361,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/datasets.py",
"chars": 1902,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/lars.py",
"chars": 1851,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/lr_decay.py",
"chars": 2461,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/lr_sched.py",
"chars": 802,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/misc.py",
"chars": 12536,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/pos_embed.py",
"chars": 6866,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "pretraining/util/vision_transformer.py",
"chars": 28513,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom functools import partial\n\nfrom timm.data import "
},
{
"path": "pretraining/util/wandb_log.py",
"chars": 2940,
"preview": "import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport util.misc as misc\nimport wandb\n\n# def equalize(x):\n"
},
{
"path": "pretraining/wave_dynamic_layer.py",
"chars": 8757,
"preview": "import torch\nimport torch.nn as nn\nfrom enum import Enum\nfrom typing import Callable, List, Optional, Tuple, Union\nimpor"
},
{
"path": "pyproject.toml",
"chars": 102,
"preview": "[tool.isort]\nprofile = \"black\"\nknown_first_party = [\"util\"]\nskip_gitignore = true\ncolor_output = true\n"
},
{
"path": "requirements.txt",
"chars": 48,
"preview": "kornia==0.7.0\nrasterio==1.3.9\ntimm==0.9.2\ntorch\n"
},
{
"path": "wave_dynamic_layer.py",
"chars": 7434,
"preview": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.init as init\n\nrand"
}
]
About this extraction
This page contains the full source code of the zhu-xlab/DOFA GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 31 files (1.2 MB), approximately 797.3k tokens, and a symbol index with 223 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.