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Repository: NVlabs/GaussianSTORM
Branch: main
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Directory structure:
gitextract_we0tkx0h/
├── .gitignore
├── .vscode/
│ └── settings.json
├── LICENSE
├── README.md
├── assets/
│ └── Inter-Regular.otf
├── docs/
│ ├── WAYMO.md
│ └── example_data/
│ ├── segment-990914685337955114_980_000_1000_000_with_camera_labels.json
│ └── waymo_train.txt
├── engine_storm.py
├── extract_sky.py
├── inference.py
├── main_storm.py
├── preproc/
│ ├── utils.py
│ ├── waymo_download.py
│ └── waymo_preprocess.py
├── preprocess.py
├── requirements.txt
├── requirements_data_preprocess.txt
├── storm/
│ ├── dataset/
│ │ ├── constants.py
│ │ ├── data_utils.py
│ │ ├── samplers.py
│ │ └── storm_dataset.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── decoder.py
│ │ ├── embedders.py
│ │ ├── layers.py
│ │ ├── storm.py
│ │ └── vit.py
│ ├── utils/
│ │ ├── __init__.py
│ │ ├── distributed.py
│ │ ├── logging.py
│ │ ├── losses.py
│ │ ├── lpips.py
│ │ ├── lpips_loss.py
│ │ └── misc.py
│ └── visualization/
│ ├── annotation.py
│ ├── layout.py
│ ├── video_maker.py
│ └── visualization_tools.py
└── third_party/
└── depth_anything_v2/
├── dinov2.py
├── dinov2_layers/
│ ├── __init__.py
│ ├── attention.py
│ ├── block.py
│ ├── drop_path.py
│ ├── layer_scale.py
│ ├── mlp.py
│ ├── patch_embed.py
│ └── swiglu_ffn.py
├── dpt.py
└── util/
├── blocks.py
└── transform.py
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FILE CONTENTS
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FILE: .gitignore
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data/
data
work_dirs/
local_scripts/
sync_files.sh
*.mp4
*draft*
*.zip
*.tar
*.gz
# media
*.mp4
*.png
*.jpg
*.html
ckpts/
# wandb
wandb/
# work in progress
*wip*
*results*
*debug*
tmp*
# caches
*.pyc
*.swp
__pycache__
# comming soon
*viewer*
third_party/flow3d_viewer/
third_party/viser_extras/
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FILE: .vscode/settings.json
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FILE: LICENSE
================================================
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FILE: README.md
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<div align="center">
# **STORM**
### Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
[Project Page](https://jiawei-yang.github.io/STORM/) • [arXiv Paper](https://arxiv.org/abs/2501.00602)
</div>
---
## Highlights
* **Fast, feed-forward, and self-supervised** dynamic scene reconstruction from sparse multi-view sequences
* Learns **3D Gaussian** *and* **scene flow** jointly; supports real-time rendering (once Gaussians are generated) and motion segmentation
* Outperforms per‑scene optimization and other generalizable models by **+4 dB PSNR** on dynamic regions while being significantly faster
---
## Installation
> Tested with **CUDA 12.1**, **PyTorch 2.3** and an NVIDIA **A100**
> Replace the CUDA/PyTorch versions as needed for your environment
```bash
# clone project
git clone https://github.com/NVlabs/GaussianSTORM.git
cd GaussianSTORM
# create conda environment
conda create -n storm python=3.10 -y
conda activate storm
# install python dependencies
pip install -r requirements.txt
# install gsplat (for batch-wise rendering support)
pip install git+https://github.com/nerfstudio-project/gsplat.git@2b0de894232d21e8963179a7bbbd315f27c52c9c
# └─ if the above fails, drop the commit hash:
# pip install git+https://github.com/nerfstudio-project/gsplat.git
```
> Note: installing `gsplat` can be machine-dependent and sometimes be tricky. if you encounter issues, please refer to the original `gsplat` repository for troubleshooting.
## Quick Start (Playground)
We provide a tiny subset of Waymo Open Dataset (3 sequences) for quick experimentation:
```bash
# download dataset subset (≈ 600 MB)
gdown 14fapsAGoMCQ5Ky82cg2X6bk-mLQ7fdCF
tar -xf STORM_subset.tar.gz
```
```bash
# run single-GPU inference demo
python inference.py \
--project storm_playground --exp_name visualization \
--data_root data/STORM_subset \
--model STORM-B/8 --num_motion_tokens 16 \
--use_sky_token --use_affine_token \
--load_depth --load_flow --load_ground \
--load_from $CKPT_PTH
```
> `CKPT_PTH` refers to the checkpoint. We cannot share an official checkpoint at this moment. Please refer to the issue page for an unofficial checkpoint.
## Dataset Preparation
### Waymo Dataset
- To prepare the Waymo Open Dataset, please refer to [Waymo Data](docs/WAYMO.md)
### Other datasets
We haven't included instructions for preparing NuScenes and Argoverse2 datasets. We might include these based on the capacity.
## Training
Multi-GPU example that reproduces the paper's STORM-B/8 model:
```bash
# with a global batch size= num_gpus * batch_size = 8 * 4 = 32 (We used 64 global batch size for main experiments)
torchrun --nproc_per_node=8 main_storm.py \
--project 0504_storm \
--exp_name 0504_pixel_storm \
--data_root ../storm2.3/data/STORM2 \ # replace this with your data root.
--batch_size 4 --num_iterations 100000 --lr_sched constant \
--model STORM-B/8 --num_motion_tokens 16 \
--use_sky_token --use_affine_token \
--load_depth --load_flow --load_ground \
--enable_depth_loss --enable_flow_reg_loss --flow_reg_coeff 0.005 --enable_sky_opacity_loss \
--enable_perceptual_loss --perceptual_loss_start_iter 5000 \
--enable_wandb \
--auto_resume
```
> **Tips:**
> - Checkpoints and logs are saved at `work_dirs/<project>/<exp_name>`
> - `batch_size` is per-GPU; global batch = batch_size × #GPUs × #nodes
> - For additional arguments, see `main_storm.py`
## Evaluation
```bash
torchrun --nproc_per_node=8 main_storm.py \
--project 0504_storm \
--exp_name 0504_pixel_storm \
--data_root ../storm2.3/data/STORM2 \ # replace this with your data root.
--batch_size 4 --num_iterations 100000 --lr_sched constant \
--model STORM-B/8 --num_motion_tokens 16 \
--use_sky_token --use_affine_token \
--load_depth --load_flow --load_ground \
--enable_depth_loss --enable_flow_reg_loss --flow_reg_coeff 0.005 --enable_sky_opacity_loss \
--enable_perceptual_loss --perceptual_loss_start_iter 5000 \
--auto_resume \
--evaluate # this parameter specifies the evaluation mode
```
## TODO
[ ] Viewers.
## Citation
```bibtex
@inproceedings{yang2025storm,
title = {STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes},
author = {Jiawei Yang and Jiahui Huang and Yuxiao Chen and Yan Wang and Boyi Li and Yurong You and Maximilian Igl and Apoorva Sharma and Peter Karkus and Danfei Xu and Boris Ivanovic and Yue Wang and Marco Pavone},
booktitle = {ICLR},
year = {2025}
}
```
## License
This project is licensed under the **NVIDIA License**. See the [LICENSE](LICENSE) file for details.
## Acknowledgements
Our implementation builds upon **gsplat**.
We thank the respective authors for open‑sourcing their excellent work.
================================================
FILE: docs/WAYMO.md
================================================
# Preparing Waymo Dataset
> Note: This document is modified from [OmniRe](https://github.com/ziyc/drivestudio/blob/main/docs/Waymo.md) and [EmerNeRF](https://github.com/NVlabs/EmerNeRF/blob/main/docs/NOTR.md)
## 1. Register on Waymo Open Dataset
#### Sign Up for a Waymo Open Dataset Account and Install gcloud SDK
To download the Waymo dataset, you need to register an account at [Waymo Open Dataset](https://waymo.com/open/). You also need to install gcloud SDK and authenticate your account. Please refer to [this page](https://cloud.google.com/sdk/docs/install) for more details.
#### Set Up the Data Directory
Once you've registered and installed the gcloud SDK, create a directory to house the raw data:
```bash
# create the data directory or create a symbolic link to the data directory
mkdir -p ./data/waymo/raw
```
## 2. Environment
We highly recommend setting up another environment for data processing as the TensorFlow dependencies often conflict with our main environment.
```bash
conda create -n storm_data python=3.10
conda activate storm_data
pip install -r requirements_data_preprocess.txt
```
## 3. Download the Raw Data
For the Waymo Open Dataset, we first organize the scene names alphabetically and store them in `data/dataset_scene_list/waymo_train_list.txt` and `data/dataset_scene_list/waymo_val_list.txt`. The scene index is then determined by the line number minus one.
For example, you can download 3 sequences from the dataset by:
```bash
python preproc/waymo_download.py \
--target_dir ./data/waymo/raw/training \
--split_file data/dataset_scene_list/waymo_train_list.txt \
--scene_ids 700 754 23
```
If you wish to run experiments on different scenes, please specify your own list of scenes.
You can also omit the `scene_ids` to download all scenes specified in the `split_file`:
```bash
# training set
python preproc/waymo_download.py \
--target_dir ./data/waymo/raw/training \
--split_file data/dataset_scene_list/waymo_train_list.txt
# validation set
python preproc/waymo_download.py \
--target_dir ./data/waymo/raw/validation \
--split_file data/dataset_scene_list/waymo_val_list.txt
```
<details>
<summary>If this script doesn't work due to network issues, consider manual download:</summary>
Download the [scene flow version](https://console.cloud.google.com/storage/browser/waymo_open_dataset_scene_flow;tab=objects?prefix=&forceOnObjectsSortingFiltering=false) of Waymo.
> **Note**: Ensure you're downloading the scene flow version to avoid errors.
</details>
## 4. Preprocess the Data
After downloading the raw dataset, you'll need to preprocess this compressed data to extract and organize various components.
#### Run the Preprocessing Script
To preprocess specific scenes of the dataset, use the following command:
```bash
python preprocess.py \
--data_root data/waymo/raw/ \
--target_dir data/waymo/processed \
--dataset waymo \
--split training \
--scene_list_file data/dataset_scene_list/waymo_train_list.txt \
--scene_ids 700 754 23 \
--num_workers 8 \
--process_keys images lidar calib pose dynamic_masks ground \
--json_folder_to_save data/STORM_data/annotations/waymo
```
Alternatively, preprocess a batch of scenes by providing the split file:
```bash
# training set
python preprocess.py \
--data_root data/waymo/raw/ \
--target_dir data/waymo/processed \
--dataset waymo \
--split training \
--scene_list_file data/dataset_scene_list/waymo_train_list.txt \
--num_workers 8 \
--process_keys images lidar calib pose dynamic_masks ground \
--json_folder_to_save data/STORM_data/annotations/waymo
# validation set
python preprocess.py \
--data_root data/waymo/raw/ \
--target_dir data/waymo/processed \
--dataset waymo \
--split validation \
--scene_list_file data/dataset_scene_list/waymo_val_list.txt \
--num_workers 8 \
--process_keys images lidar calib pose dynamic_masks ground \
--json_folder_to_save data/STORM_data/annotations/waymo
```
The extracted data will be stored in the `data/waymo/processed` directory.
## 5. Extract Sky Masks
- We originally used `ViT-adapater`, following [EmerNeRF](https://github.com/NVlabs/EmerNeRF/blob/main/docs/NOTR.md#sky-mask), to extract sky masks. We later realized the non-trivial dependency of installing `mmcv` properly.
- Therefore, we recommend using `DepthAnything-v2` to extract the sky masks. Specifically, the predicted depth map from DepthAnything-v2 is a relative depth map, where lower values indicate farther regions, and zero values indicate infinitely far regions, which represent the sky.
- To do this:
1. Use whatever command you prefer to generate the file list to all image files, e.g., `find data/waymo/processed/training/*/images -name "*.jpg" > file_list.txt`
2. Download DpethAnything-v2 checkpoint, e.g., `mkdir ckpts && wget https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth -O ckpts/depth_anything_v2_vitl.pth`
2. Use `extract_sky_mask.py` to extract sky masks for these images by `python extract_sky.py --file_list ./file_list.txt`
## 6. Data Structure
After completing all preprocessing steps, the project files should be organized according to the following structure:
```bash
ProjectPath/data/
└── waymo/
├── raw/
│ ├── segment-454855130179746819_4580_000_4600_000_with_camera_labels.tfrecord
│ └── ...
└── processed/
└──training/
├── 000/
│ ├──cam_to_ego/ # camera to ego-vehicle transformations: {cam_id}.txt
│ ├──cam_to_world/ # camera to world transformations: {timestep:03d}_{cam_id}.txt
│ ├──depth_flows_4/ # downsampled (1/4) depth flow maps: {timestep:03d}_{cam_id}.npy
│ ├──dynamic_masks/ # bounding-box-generated dynamic masks: {timestep:03d}_{cam_id}.png
│ ├──ego_to_world/ # ego-vehicle to world transformations: {timestep:03d}.txt
│ ├──ground_label_4/ # downsampled (1/4) ground labels extracted from point cloud, used for flow evaluation only: {timestep:03d}.txt
│ ├──images/ # original camera images: {timestep:03d}_{cam_id}.jpg
│ ├──images_4/ # downsampled (1/4) camera images: {timestep:03d}_{cam_id}.jpg
│ ├──intrinsics/ # camera intrinsics: {cam_id}.txt
│ ├──lidar/ # lidar data: {timestep:03d}.bin
│ ├──sky_masks/ # sky masks: {timestep:03d}_{cam_id}.png
├── 001/
├── ...
```
================================================
FILE: docs/example_data/segment-990914685337955114_980_000_1000_000_with_camera_labels.json
================================================
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14.9, 15.0, 15.1, 15.2, 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16.0, 16.1, 16.2, 16.3, 16.4, 16.5, 16.6, 16.7, 16.8, 16.9, 17.0, 17.1, 17.2, 17.3, 17.4, 17.5, 17.6, 17.7, 17.8, 17.9, 18.0, 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7, 18.8, 18.9, 19.0, 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7], "normalized_intrinsics": {"0": [1.0760852812653638, 1.6141279218980458, 0.4971965468813711, 0.5072499859713261], "1": [1.0714336048044208, 1.6071504072066312, 0.5112488553103808, 0.5005822834492333], "2": [1.067119656762737, 1.6006794851441053, 0.49226577388679255, 0.4929978469775671], "3": [1.0787773443175446, 2.337756773238923, 0.502880797268437, 0.2763063252339018], "4": [1.0748925209023337, 2.329338194280452, 0.5118216810459222, 0.25624083627281696]}, "camera_to_ego": {"0": [[0.9998842873191989, -0.0024402666225806988, 0.015015227969911943, 1.5389990840669499], [0.002560104988162815, 0.9999649851086498, -0.007967083475600458, -0.023573164304480632], [-0.014995260405451157, 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gitextract_we0tkx0h/
├── .gitignore
├── .vscode/
│ └── settings.json
├── LICENSE
├── README.md
├── assets/
│ └── Inter-Regular.otf
├── docs/
│ ├── WAYMO.md
│ └── example_data/
│ ├── segment-990914685337955114_980_000_1000_000_with_camera_labels.json
│ └── waymo_train.txt
├── engine_storm.py
├── extract_sky.py
├── inference.py
├── main_storm.py
├── preproc/
│ ├── utils.py
│ ├── waymo_download.py
│ └── waymo_preprocess.py
├── preprocess.py
├── requirements.txt
├── requirements_data_preprocess.txt
├── storm/
│ ├── dataset/
│ │ ├── constants.py
│ │ ├── data_utils.py
│ │ ├── samplers.py
│ │ └── storm_dataset.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── decoder.py
│ │ ├── embedders.py
│ │ ├── layers.py
│ │ ├── storm.py
│ │ └── vit.py
│ ├── utils/
│ │ ├── __init__.py
│ │ ├── distributed.py
│ │ ├── logging.py
│ │ ├── losses.py
│ │ ├── lpips.py
│ │ ├── lpips_loss.py
│ │ └── misc.py
│ └── visualization/
│ ├── annotation.py
│ ├── layout.py
│ ├── video_maker.py
│ └── visualization_tools.py
└── third_party/
└── depth_anything_v2/
├── dinov2.py
├── dinov2_layers/
│ ├── __init__.py
│ ├── attention.py
│ ├── block.py
│ ├── drop_path.py
│ ├── layer_scale.py
│ ├── mlp.py
│ ├── patch_embed.py
│ └── swiglu_ffn.py
├── dpt.py
└── util/
├── blocks.py
└── transform.py
SYMBOL INDEX (374 symbols across 36 files)
FILE: engine_storm.py
function visualize (line 21) | def visualize(args, model, dset_train, step, train_vis_id, device, dset_...
function evaluate (line 50) | def evaluate(dataloader, model, args, name_str=None):
function evaluate_flow (line 307) | def evaluate_flow(dataloader, model, args, name_str=None):
FILE: extract_sky.py
class ListDataset (line 15) | class ListDataset:
method __init__ (line 18) | def __init__(
method _load_samples (line 37) | def _load_samples(self, data_list: str) -> list:
method __getitem__ (line 46) | def __getitem__(self, index: int) -> Tuple[torch.Tensor, str]:
method __len__ (line 63) | def __len__(self) -> int:
function get_args_parser (line 66) | def get_args_parser() -> argparse.ArgumentParser:
function setup_model (line 77) | def setup_model(device: torch.device, depth_ckpt: str) -> DepthAnythingV2:
function get_sky_mask (line 87) | def get_sky_mask(dataloader: torch.utils.data.DataLoader, depthv2: Dept...
function main (line 121) | def main(args: argparse.Namespace) -> None:
FILE: inference.py
function get_args_parser (line 27) | def get_args_parser():
function main (line 80) | def main(args):
FILE: main_storm.py
function get_args_parser (line 37) | def get_args_parser():
function main (line 132) | def main(args):
FILE: preproc/utils.py
class ProgressBar (line 12) | class ProgressBar:
method __init__ (line 15) | def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdo...
method terminal_width (line 24) | def terminal_width(self):
method start (line 28) | def start(self):
method update (line 36) | def update(self, num_tasks=1):
function init_pool (line 70) | def init_pool(process_num, initializer=None, initargs=None):
function track_parallel_progress (line 81) | def track_parallel_progress(
FILE: preproc/waymo_download.py
function download_file (line 8) | def download_file(filename, target_dir, source):
function download_files (line 25) | def download_files(
FILE: preproc/waymo_preprocess.py
function project_vehicle_to_image (line 49) | def project_vehicle_to_image(vehicle_pose, calibration, points):
function get_ground_np (line 91) | def get_ground_np(pts):
function compute_range_image_cartesian (line 132) | def compute_range_image_cartesian(
function extract_point_cloud_from_range_image (line 234) | def extract_point_cloud_from_range_image(
function parse_range_image_flow_and_camera_projection (line 283) | def parse_range_image_flow_and_camera_projection(frame):
function convert_range_image_to_point_cloud_flow (line 332) | def convert_range_image_to_point_cloud_flow(
class WaymoProcessor (line 467) | class WaymoProcessor:
method __init__ (line 468) | def __init__(
method convert (line 495) | def convert(self):
method convert_one (line 501) | def convert_one(self, file_id):
method make_json (line 601) | def make_json(self, file_id):
method __len__ (line 677) | def __len__(self):
method save_image (line 681) | def save_image(self, frame, file_id, frame_id):
method save_calib (line 713) | def save_calib(self, frame, file_id, frame_id):
method save_lidar (line 745) | def save_lidar(self, frame, file_id, frame_id):
method save_ground (line 884) | def save_ground(self, frame, file_id, frame_id):
method save_pose (line 958) | def save_pose(self, frame, file_id, frame_id):
method save_dynamic_mask (line 991) | def save_dynamic_mask(self, frame, file_id, frame_id):
method create_folder (line 1084) | def create_folder(self):
FILE: storm/dataset/data_utils.py
function forward_pose (line 11) | def forward_pose(pose: torch.tensor, transform: torch.tensor, inv=False)...
function get_path_front_left_lift_then_spiral_forward (line 20) | def get_path_front_left_lift_then_spiral_forward(
function to_tensor (line 142) | def to_tensor(x: Union[np.ndarray, List, Tuple]) -> torch.Tensor:
function to_float_tensor (line 150) | def to_float_tensor(d):
function to_batch_tensor (line 163) | def to_batch_tensor(d):
function resize_depth (line 174) | def resize_depth(depth, target_size):
function resize_flow (line 195) | def resize_flow(flow, target_size):
function prepare_inputs_and_targets (line 222) | def prepare_inputs_and_targets(data_dict, device=torch.device("cuda"), v...
function prepare_inputs_and_targets_novel_view (line 300) | def prepare_inputs_and_targets_novel_view(data_dict, device=torch.device...
FILE: storm/dataset/samplers.py
function _get_torch_dtype (line 10) | def _get_torch_dtype(size: int) -> Any:
function _generate_randperm_indices (line 15) | def _generate_randperm_indices(*, size: int, generator: torch.Generator):
class InfiniteSampler (line 33) | class InfiniteSampler(Sampler):
method __init__ (line 34) | def __init__(
method __iter__ (line 61) | def __iter__(self):
method _iterator (line 66) | def _iterator(self):
method _shuffled_iterator (line 73) | def _shuffled_iterator(self):
class NoPaddingDistributedSampler (line 82) | class NoPaddingDistributedSampler(Sampler):
method __init__ (line 83) | def __init__(self, dataset, num_replicas=None, rank=None, shuffle=False):
method __iter__ (line 108) | def __iter__(self):
method __len__ (line 118) | def __len__(self):
FILE: storm/dataset/storm_dataset.py
class STORMDataset (line 20) | class STORMDataset(Dataset):
method __init__ (line 21) | def __init__(
method __len__ (line 79) | def __len__(self) -> int:
method get_frame (line 82) | def get_frame(
method __getitem__ (line 249) | def __getitem__(
class STORMDatasetEval (line 364) | class STORMDatasetEval(STORMDataset):
method __init__ (line 365) | def __init__(
method __len__ (line 413) | def __len__(self) -> int:
method __getitem__ (line 416) | def __getitem__(self, index: int):
class SingleSequenceDataset (line 424) | class SingleSequenceDataset(Dataset):
method __init__ (line 425) | def __init__(
method __len__ (line 481) | def __len__(self) -> int:
method get_frame (line 484) | def get_frame(
method get_segment (line 647) | def get_segment(self, index: int, context_frame_idx: int = -1, return_...
method __getitem__ (line 746) | def __getitem__(self, index: int, start_index: int = 0, end_index: int...
FILE: storm/models/decoder.py
function check_results (line 15) | def check_results(result_dict) -> bool:
class Decoder (line 26) | class Decoder(nn.Module):
method __init__ (line 27) | def __init__(
method forward (line 57) | def forward(self, render_results):
class ConvDecoder (line 74) | class ConvDecoder(nn.Module):
method __init__ (line 75) | def __init__(
method forward (line 110) | def forward(self, render_results) -> Dict[str, Tensor]:
class ModulatedLinearLayer (line 149) | class ModulatedLinearLayer(nn.Module):
method __init__ (line 150) | def __init__(self, in_channels, hidden_channels=64, condition_channels...
method forward (line 160) | def forward(self, x, c):
class ModulatedMLP (line 171) | class ModulatedMLP(nn.Module):
method __init__ (line 172) | def __init__(
method forward (line 196) | def forward(self, x, c):
class DummyDecoder (line 207) | class DummyDecoder(nn.Module):
method __init__ (line 208) | def __init__(self, **kwargs):
method forward (line 211) | def forward(self, render_results):
FILE: storm/models/embedders.py
class PatchEmbed (line 18) | class PatchEmbed(nn.Module):
method __init__ (line 23) | def __init__(
method feat_ratio (line 51) | def feat_ratio(self, as_scalar=True) -> Union[Tuple[int, int], int]:
method dynamic_feat_size (line 57) | def dynamic_feat_size(self, img_size: Tuple[int, int]) -> Tuple[int, i...
method forward (line 68) | def forward(self, x: Tensor) -> Tensor:
class PluckerEmbedder (line 87) | class PluckerEmbedder(nn.Module):
method __init__ (line 92) | def __init__(
method forward (line 112) | def forward(
class TimestepEmbedder (line 186) | class TimestepEmbedder(nn.Module):
method __init__ (line 192) | def __init__(self, hidden_size, frequency_embedding_size=256):
method timestep_embedding (line 202) | def timestep_embedding(t, dim, max_period=10000):
method forward (line 222) | def forward(self, t):
class NeRFPosEmbedder (line 228) | class NeRFPosEmbedder(nn.Module):
method __init__ (line 229) | def __init__(
method get_out_dim (line 246) | def get_out_dim(self) -> int:
method forward (line 252) | def forward(self, in_tensor: Tensor) -> Tensor:
FILE: storm/models/layers.py
function modulate (line 17) | def modulate(x, shift=None, scale=None):
class Mlp (line 23) | class Mlp(nn.Module):
method __init__ (line 24) | def __init__(self, in_dim: int, hidden_dim=None, out_dim=None, act_lay...
method forward (line 32) | def forward(self, x):
class Attention (line 36) | class Attention(nn.Module):
method __init__ (line 39) | def __init__(
method forward (line 68) | def forward(self, x: Tensor, data: Tensor = None) -> Tensor:
class Block (line 100) | class Block(nn.Module):
method __init__ (line 103) | def __init__(
method forward (line 127) | def forward(self, x: Tensor, data: Tensor = None) -> Tensor:
class Transformer (line 135) | class Transformer(nn.Module):
method __init__ (line 136) | def __init__(
method forward (line 164) | def forward(self, x: torch.Tensor) -> torch.Tensor:
class GroupNorm (line 174) | class GroupNorm(nn.Module):
method __init__ (line 175) | def __init__(self, channels):
method forward (line 179) | def forward(self, x):
class Swish (line 183) | class Swish(nn.Module):
method forward (line 184) | def forward(self, x):
class ResidualBlock (line 188) | class ResidualBlock(nn.Module):
method __init__ (line 189) | def __init__(self, in_channels, out_channels):
method forward (line 205) | def forward(self, x):
class UpSampleBlock (line 212) | class UpSampleBlock(nn.Module):
method __init__ (line 213) | def __init__(self, channels):
method forward (line 217) | def forward(self, x):
class NonLocalBlock (line 222) | class NonLocalBlock(nn.Module):
method __init__ (line 223) | def __init__(self, channels):
method forward (line 230) | def forward(self, x):
class LayerNorm2d (line 238) | class LayerNorm2d(nn.Module):
method __init__ (line 239) | def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
method forward (line 245) | def forward(self, x: torch.Tensor) -> torch.Tensor:
function pos_enc (line 253) | def pos_enc(x, min_deg=0, max_deg=10, append_identity=True):
function get_2d_sincos_pos_embed (line 267) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, num_e...
function get_2d_sincos_pos_embed_from_grid (line 287) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
function get_1d_sincos_pos_embed_from_grid (line 298) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
function resample_abs_pos_embed (line 319) | def resample_abs_pos_embed(
FILE: storm/models/storm.py
class STORM (line 14) | class STORM(ViT):
method __init__ (line 15) | def __init__(
method _pos_embed (line 181) | def _pos_embed(self, x: Tensor) -> Tensor:
method _time_embed (line 186) | def _time_embed(self, x: Tensor, time: Tensor, num_views=1) -> Tensor:
method forward_decoder (line 205) | def forward_decoder(self, render_results):
method forward_features (line 220) | def forward_features(self, x, plucker_embeds, time):
method forward_motion_predictor (line 242) | def forward_motion_predictor(self, x, motion_tokens=None, gs_params=No...
method forward_gs_predictor (line 281) | def forward_gs_predictor(self, x, origins, directions):
method forward_renderer (line 302) | def forward_renderer(self, gs_params, data_dict, render_motion_seg=Tru...
method get_ray_dict (line 508) | def get_ray_dict(self, data_dict):
method forward (line 534) | def forward(self, data_dict):
method from_gs_params_to_output (line 608) | def from_gs_params_to_output(self, gs_params, target_dict, num_cams=1):
method get_gs_params (line 639) | def get_gs_params(self, data_dict):
function STORM_B_8 (line 669) | def STORM_B_8(**kwargs):
function STORM_L_8 (line 673) | def STORM_L_8(**kwargs):
function STORM_B_16 (line 677) | def STORM_B_16(**kwargs):
function STORM_L_16 (line 681) | def STORM_L_16(**kwargs):
function STORM_XL_8 (line 685) | def STORM_XL_8(**kwargs):
function STORM_H_8 (line 689) | def STORM_H_8(**kwargs):
function STORM_H_16 (line 693) | def STORM_H_16(**kwargs):
FILE: storm/models/vit.py
class VisionTransformer (line 17) | class VisionTransformer(nn.Module):
method __init__ (line 20) | def __init__(
method init_weights (line 64) | def init_weights(self):
method _pos_embed (line 78) | def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
method unpatchify (line 92) | def unpatchify(self, x, hw=None, channel_first=True, patch_size=None) ...
method forward (line 106) | def forward(self, x: torch.Tensor) -> torch.Tensor:
function ViT_S_16 (line 114) | def ViT_S_16(**kwargs):
function ViT_B_16 (line 118) | def ViT_B_16(**kwargs):
function ViT_L_16 (line 122) | def ViT_L_16(**kwargs):
function ViT_H_14 (line 126) | def ViT_H_14(**kwargs):
FILE: storm/utils/distributed.py
function is_enabled (line 28) | def is_enabled() -> bool:
function get_global_rank (line 33) | def get_global_rank() -> int:
function get_world_size (line 38) | def get_world_size() -> int:
function is_main_process (line 43) | def is_main_process() -> bool:
function _collect_env_vars (line 48) | def _collect_env_vars() -> Dict[str, str]:
function _is_slurm_job_process (line 60) | def _is_slurm_job_process() -> bool:
function _parse_slurm_node_list (line 65) | def _parse_slurm_node_list(s: str) -> List[str]:
function _check_env_variable (line 83) | def _check_env_variable(key: str, new_value: str):
function _restrict_print_to_main_process (line 89) | def _restrict_print_to_main_process() -> None:
function _get_master_port (line 105) | def _get_master_port(seed: int = 0) -> int:
function _get_available_port (line 117) | def _get_available_port() -> int:
class _TorchDistributedEnvironment (line 126) | class _TorchDistributedEnvironment:
method __init__ (line 129) | def __init__(self):
method _set_from_slurm_env (line 160) | def _set_from_slurm_env(self):
method _set_from_preset_env (line 180) | def _set_from_preset_env(self):
method _set_from_local (line 195) | def _set_from_local(self):
method export (line 205) | def export(self, *, overwrite: bool) -> "_TorchDistributedEnvironment":
function enable (line 223) | def enable(
FILE: storm/utils/logging.py
class SmoothedValue (line 20) | class SmoothedValue:
method __init__ (line 23) | def __init__(self, window_size=20, fmt=None):
method update (line 29) | def update(self, value, num=1):
method synchronize_between_processes (line 35) | def synchronize_between_processes(self):
method median (line 45) | def median(self):
method avg (line 50) | def avg(self):
method global_avg (line 55) | def global_avg(self):
method max (line 60) | def max(self):
method value (line 65) | def value(self):
method __str__ (line 69) | def __str__(self):
class MetricLogger (line 80) | class MetricLogger:
method __init__ (line 81) | def __init__(self, delimiter="\t", output_file=None):
method update (line 86) | def update(self, **kwargs):
method __getattr__ (line 95) | def __getattr__(self, attr):
method __str__ (line 102) | def __str__(self):
method synchronize_between_processes (line 108) | def synchronize_between_processes(self):
method add_meter (line 112) | def add_meter(self, name, meter):
method dump_in_output_file (line 115) | def dump_in_output_file(self, iteration, iter_time, data_time):
method log_every (line 127) | def log_every(self, iterable, print_freq, header=None, n_iterations=No...
class WandbLogger (line 203) | class WandbLogger:
method __init__ (line 204) | def __init__(self, args, resume="must", id=None):
method wandb_safe_log (line 221) | def wandb_safe_log(*args, **kwargs):
method set_step (line 228) | def set_step(self, step=None):
method update (line 232) | def update(self, metrics):
method flush (line 241) | def flush(self):
method finish (line 244) | def finish(self):
function _configure_logger (line 252) | def _configure_logger(
function setup_logging (line 323) | def setup_logging(
FILE: storm/utils/losses.py
function compute_depth_loss (line 8) | def compute_depth_loss(pred_depth, gt_depth, max_depth=None):
function compute_sky_depth_loss (line 41) | def compute_sky_depth_loss(pred_depth, gt_sky_mask, sky_depth: float = 1...
function compute_loss (line 77) | def compute_loss(output_dict, target_dict, args=None, lpips_loss=None):
function compute_scene_flow_metrics (line 161) | def compute_scene_flow_metrics(pred, labels):
FILE: storm/utils/lpips.py
function download (line 20) | def download(url, local_path, chunk_size=1024):
function md5_hash (line 32) | def md5_hash(path):
function get_ckpt_path (line 38) | def get_ckpt_path(name, root, check=False):
class LPIPS (line 49) | class LPIPS(nn.Module):
method __init__ (line 51) | def __init__(self, use_dropout=True):
method load_from_pretrained (line 65) | def load_from_pretrained(self, name="vgg_lpips"):
method from_pretrained (line 71) | def from_pretrained(cls, name="vgg_lpips"):
method forward (line 79) | def forward(self, input, target):
class ScalingLayer (line 97) | class ScalingLayer(nn.Module):
method __init__ (line 98) | def __init__(self):
method forward (line 103) | def forward(self, inp):
class NetLinLayer (line 107) | class NetLinLayer(nn.Module):
method __init__ (line 110) | def __init__(self, chn_in, chn_out=1, use_dropout=False):
class vgg16 (line 125) | class vgg16(torch.nn.Module):
method __init__ (line 126) | def __init__(self, requires_grad=False, pretrained=True):
method forward (line 149) | def forward(self, X):
function normalize_tensor (line 167) | def normalize_tensor(x, eps=1e-8):
function spatial_average (line 172) | def spatial_average(x, keepdim=True):
FILE: storm/utils/lpips_loss.py
class RGBLpipsLoss (line 8) | class RGBLpipsLoss(nn.Module):
method __init__ (line 18) | def __init__(
method set_perceptual_loss (line 36) | def set_perceptual_loss(self, enable=True):
method forward (line 45) | def forward(self, rgb, targets):
FILE: storm/utils/misc.py
function fix_random_seeds (line 18) | def fix_random_seeds(seed=31):
function _ntuple (line 25) | def _ntuple(n):
function cleanup_checkpoints (line 47) | def cleanup_checkpoints(ckpt_dir, keep_num=1):
function load_model (line 75) | def load_model(args, model_without_ddp, optimizer=None, loss_scaler=None):
function adjust_learning_rate (line 163) | def adjust_learning_rate(optimizer, iteration, args):
function unwrap_model (line 198) | def unwrap_model(model):
function get_grad_norm_ (line 202) | def get_grad_norm_(parameters, norm_type=2.0):
class NativeScalerWithGradNormCount (line 228) | class NativeScalerWithGradNormCount:
method __init__ (line 238) | def __init__(self, enabled=True):
method __call__ (line 241) | def __call__(
method state_dict (line 262) | def state_dict(self):
method load_state_dict (line 266) | def load_state_dict(self, state_dict):
FILE: storm/visualization/annotation.py
function draw_label (line 20) | def draw_label(
function add_label (line 42) | def add_label(
FILE: storm/visualization/layout.py
function _sanitize_color (line 34) | def _sanitize_color(color: Color) -> Float[Tensor, "#channel"]:
function _intersperse (line 48) | def _intersperse(iterable: Iterable, delimiter: Any) -> Generator[Any, N...
function _get_main_dim (line 56) | def _get_main_dim(main_axis: Axis) -> int:
function _get_cross_dim (line 63) | def _get_cross_dim(main_axis: Axis) -> int:
function _compute_offset (line 70) | def _compute_offset(base: int, overlay: int, align: Alignment) -> slice:
function overlay (line 80) | def overlay(
function cat (line 111) | def cat(
function hcat (line 151) | def hcat(
function vcat (line 173) | def vcat(
function add_border (line 195) | def add_border(
function resize (line 210) | def resize(
function prep_image (line 233) | def prep_image(image: torch.Tensor) -> UInt8[np.ndarray, "height width c...
FILE: storm/visualization/video_maker.py
function get_pca_map (line 25) | def get_pca_map(x):
function make_video (line 35) | def make_video(
function make_video_vis (line 353) | def make_video_vis(
function make_video_av2 (line 626) | def make_video_av2(
FILE: storm/visualization/visualization_tools.py
function to8b (line 38) | def to8b(x):
function sinebow (line 44) | def sinebow(h):
function matte (line 50) | def matte(vis, acc, dark=0.8, light=1.0, width=8):
function weighted_percentile (line 60) | def weighted_percentile(x, w, ps, assume_sorted=False):
function visualize_cmap (line 71) | def visualize_cmap(
function visualize_depth (line 134) | def visualize_depth(x, acc=None, lo=None, hi=None, depth_curve_fn=lambda...
function _make_colorwheel (line 147) | def _make_colorwheel(
function scene_flow_to_rgb (line 197) | def scene_flow_to_rgb(
function get_robust_pca (line 285) | def get_robust_pca(features: Tensor, m: float = 2, remove_first_componen...
function get_pca_map (line 319) | def get_pca_map(
function get_scale_map (line 359) | def get_scale_map(
function get_similarity_map (line 384) | def get_similarity_map(features: Tensor, img_size=(224, 224)):
FILE: third_party/depth_anything_v2/dinov2.py
function named_apply (line 27) | def named_apply(
class BlockChunk (line 42) | class BlockChunk(nn.ModuleList):
method forward (line 43) | def forward(self, x):
class DinoVisionTransformer (line 49) | class DinoVisionTransformer(nn.Module):
method __init__ (line 50) | def __init__(
method init_weights (line 185) | def init_weights(self):
method interpolate_pos_encoding (line 192) | def interpolate_pos_encoding(self, x, w, h):
method prepare_tokens_with_masks (line 225) | def prepare_tokens_with_masks(self, x, masks=None):
method forward_features_list (line 246) | def forward_features_list(self, x_list, masks_list):
method forward_features (line 266) | def forward_features(self, x, masks=None):
method _get_intermediate_layers_not_chunked (line 284) | def _get_intermediate_layers_not_chunked(self, x, n=1):
method _get_intermediate_layers_chunked (line 298) | def _get_intermediate_layers_chunked(self, x, n=1):
method get_intermediate_layers (line 314) | def get_intermediate_layers(
method forward (line 342) | def forward(self, *args, is_training=False, **kwargs):
function init_weights_vit_timm (line 350) | def init_weights_vit_timm(module: nn.Module, name: str = ""):
function vit_small (line 358) | def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
function vit_base (line 372) | def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
function vit_large (line 386) | def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
function vit_giant2 (line 400) | def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
function DINOv2 (line 417) | def DINOv2(model_name):
FILE: third_party/depth_anything_v2/dinov2_layers/attention.py
class Attention (line 27) | class Attention(nn.Module):
method __init__ (line 28) | def __init__(
method forward (line 47) | def forward(self, x: Tensor) -> Tensor:
class MemEffAttention (line 67) | class MemEffAttention(Attention):
method forward (line 68) | def forward(self, x: Tensor, attn_bias=None) -> Tensor:
FILE: third_party/depth_anything_v2/dinov2_layers/block.py
class Block (line 34) | class Block(nn.Module):
method __init__ (line 35) | def __init__(
method forward (line 80) | def forward(self, x: Tensor) -> Tensor:
function drop_add_residual_stochastic_depth (line 108) | def drop_add_residual_stochastic_depth(
function get_branges_scales (line 134) | def get_branges_scales(x, sample_drop_ratio=0.0):
function add_residual (line 142) | def add_residual(x, brange, residual, residual_scale_factor, scaling_vec...
function get_attn_bias_and_cat (line 163) | def get_attn_bias_and_cat(x_list, branges=None):
function drop_add_residual_stochastic_depth_list (line 191) | def drop_add_residual_stochastic_depth_list(
class NestedTensorBlock (line 218) | class NestedTensorBlock(Block):
method forward_nested (line 219) | def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
method forward (line 259) | def forward(self, x_or_x_list):
FILE: third_party/depth_anything_v2/dinov2_layers/drop_path.py
function drop_path (line 15) | def drop_path(x, drop_prob: float = 0.0, training: bool = False):
class DropPath (line 27) | class DropPath(nn.Module):
method __init__ (line 30) | def __init__(self, drop_prob=None):
method forward (line 34) | def forward(self, x):
FILE: third_party/depth_anything_v2/dinov2_layers/layer_scale.py
class LayerScale (line 15) | class LayerScale(nn.Module):
method __init__ (line 16) | def __init__(
method forward (line 26) | def forward(self, x: Tensor) -> Tensor:
FILE: third_party/depth_anything_v2/dinov2_layers/mlp.py
class Mlp (line 17) | class Mlp(nn.Module):
method __init__ (line 18) | def __init__(
method forward (line 35) | def forward(self, x: Tensor) -> Tensor:
FILE: third_party/depth_anything_v2/dinov2_layers/patch_embed.py
function make_2tuple (line 17) | def make_2tuple(x):
class PatchEmbed (line 26) | class PatchEmbed(nn.Module):
method __init__ (line 38) | def __init__(
method forward (line 69) | def forward(self, x: Tensor) -> Tensor:
method flops (line 88) | def flops(self) -> float:
FILE: third_party/depth_anything_v2/dinov2_layers/swiglu_ffn.py
class SwiGLUFFN (line 13) | class SwiGLUFFN(nn.Module):
method __init__ (line 14) | def __init__(
method forward (line 29) | def forward(self, x: Tensor) -> Tensor:
class SwiGLUFFNFused (line 45) | class SwiGLUFFNFused(SwiGLU):
method __init__ (line 46) | def __init__(
FILE: third_party/depth_anything_v2/dpt.py
function _make_fusion_block (line 12) | def _make_fusion_block(features, use_bn, size=None):
class ConvBlock (line 24) | class ConvBlock(nn.Module):
method __init__ (line 25) | def __init__(self, in_feature, out_feature):
method forward (line 34) | def forward(self, x):
class DPTHead (line 38) | class DPTHead(nn.Module):
method __init__ (line 39) | def __init__(
method forward (line 126) | def forward(self, out_features, patch_h, patch_w):
class DepthAnythingV2 (line 164) | class DepthAnythingV2(nn.Module):
method __init__ (line 165) | def __init__(
method forward (line 194) | def forward(self, x):
FILE: third_party/depth_anything_v2/util/blocks.py
function _make_scratch (line 4) | def _make_scratch(in_shape, out_shape, groups=1, expand=False):
class ResidualConvUnit (line 37) | class ResidualConvUnit(nn.Module):
method __init__ (line 40) | def __init__(self, features, activation, bn):
method forward (line 68) | def forward(self, x):
class FeatureFusionBlock (line 94) | class FeatureFusionBlock(nn.Module):
method __init__ (line 97) | def __init__(
method forward (line 135) | def forward(self, *xs, size=None):
FILE: third_party/depth_anything_v2/util/transform.py
class Resize (line 5) | class Resize(object):
method __init__ (line 8) | def __init__(
method constrain_to_multiple_of (line 50) | def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
method get_size (line 61) | def get_size(self, width, height):
method __call__ (line 108) | def __call__(self, sample):
class NormalizeImage (line 132) | class NormalizeImage(object):
method __init__ (line 135) | def __init__(self, mean, std):
method __call__ (line 139) | def __call__(self, sample):
class PrepareForNet (line 145) | class PrepareForNet(object):
method __init__ (line 148) | def __init__(self):
method __call__ (line 151) | def __call__(self, sample):
Condensed preview — 51 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (811K chars).
[
{
"path": ".gitignore",
"chars": 304,
"preview": "data/\ndata\nwork_dirs/\nlocal_scripts/\nsync_files.sh\n*.mp4\n*draft*\n*.zip\n*.tar\n*.gz\n\n# media\n*.mp4\n*.png\n*.jpg\n*.html\n\n\nck"
},
{
"path": ".vscode/settings.json",
"chars": 1009,
"preview": "{\n // Automatically format using Black on save.\n \"editor.formatOnSave\": true,\n // Draw a ruler at Black's colum"
},
{
"path": "LICENSE",
"chars": 3998,
"preview": "NVIDIA License\n\n1. Definitions\n\n“Licensor” means any person or entity that distributes its Work.\n“Work” means (a) the or"
},
{
"path": "README.md",
"chars": 4802,
"preview": "<div align=\"center\">\n\n# **STORM** \n### Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes \n\n[Project "
},
{
"path": "docs/WAYMO.md",
"chars": 6781,
"preview": "# Preparing Waymo Dataset\n> Note: This document is modified from [OmniRe](https://github.com/ziyc/drivestudio/blob/main/"
},
{
"path": "docs/example_data/segment-990914685337955114_980_000_1000_000_with_camera_labels.json",
"chars": 363355,
"preview": "{\"dataset\": \"waymo\", \"scene_id\": 796, \"scene_name\": \"segment-990914685337955114_980_000_1000_000_with_camera_labels\", \"n"
},
{
"path": "docs/example_data/waymo_train.txt",
"chars": 774,
"preview": "annotations/waymo/training/segment-10231929575853664160_1160_000_1180_000_with_camera_labels.json\nannotations/waymo/trai"
},
{
"path": "engine_storm.py",
"chars": 20859,
"preview": "import datetime\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom skimage.m"
},
{
"path": "extract_sky.py",
"chars": 5232,
"preview": "import argparse\nimport os\nfrom typing import Optional, Tuple\n\nimport numpy as np\nimport torch\nimport torch.utils.data\nim"
},
{
"path": "inference.py",
"chars": 7805,
"preview": "import argparse\nimport copy\nimport datetime\nimport json\nimport logging\nimport math\nimport os\nimport time\n\nimport numpy a"
},
{
"path": "main_storm.py",
"chars": 22742,
"preview": "import argparse\nimport copy\nimport datetime\nimport json\nimport logging\nimport math\nimport os\nimport sys\nimport time\n\nimp"
},
{
"path": "preproc/utils.py",
"chars": 4937,
"preview": "# To track process without installing mmcv\n# Code is modified from mmcv.utils\nimport sys\nimport time\nfrom collections.ab"
},
{
"path": "preproc/waymo_download.py",
"chars": 2504,
"preview": "import argparse\nimport os\nimport subprocess\nfrom concurrent.futures import ThreadPoolExecutor\nfrom typing import List\n\n\n"
},
{
"path": "preproc/waymo_preprocess.py",
"chars": 48613,
"preview": "import json\nimport os\nimport time\n\nimport imageio\nimport imageio_ffmpeg as imageio_ffmpeg # Ensure ffmpeg is installed\n"
},
{
"path": "preprocess.py",
"chars": 5317,
"preview": "import argparse\nimport os\n\nimport numpy as np\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(descripti"
},
{
"path": "requirements.txt",
"chars": 314,
"preview": "--extra-index-url https://download.pytorch.org/whl/cu121\ntorch==2.3.1\ntorchvision\ntimm\neinops\njaxtyping\nopencv-python\nma"
},
{
"path": "requirements_data_preprocess.txt",
"chars": 186,
"preview": "--extra-index-url https://download.pytorch.org/whl/cu112\ntorch\nwaymo-open-dataset-tf-2-11-0==1.6.1\ntensorflow-gpu==2.11."
},
{
"path": "storm/dataset/constants.py",
"chars": 3623,
"preview": "import numpy as np\n\nMEAN = [0.5, 0.5, 0.5]\nSTD = [0.5, 0.5, 0.5]\n\n\nIMGNET_MEAN = [0.485, 0.456, 0.406]\nIMGNET_STD = [0.2"
},
{
"path": "storm/dataset/data_utils.py",
"chars": 18031,
"preview": "from typing import List, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom scipy import"
},
{
"path": "storm/dataset/samplers.py",
"chars": 4493,
"preview": "import itertools\nfrom typing import Any, Optional\n\nimport torch\nfrom torch.utils.data.sampler import Sampler\n\nimport sto"
},
{
"path": "storm/dataset/storm_dataset.py",
"chars": 34557,
"preview": "import json\nimport logging\nimport os\nfrom typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n\nimpo"
},
{
"path": "storm/models/__init__.py",
"chars": 60,
"preview": "from .storm import STORM_models\nfrom .vit import ViT_models\n"
},
{
"path": "storm/models/decoder.py",
"chars": 8083,
"preview": "import logging\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange, repeat\nfrom tor"
},
{
"path": "storm/models/embedders.py",
"chars": 10050,
"preview": "# --------------------------------------------------------\n# References:\n# timm: https://github.com/rwightman/pytorch-im"
},
{
"path": "storm/models/layers.py",
"chars": 12035,
"preview": "import logging\nimport math\nfrom functools import partial\nfrom typing import List, Optional\n\nimport numpy as np\nimport to"
},
{
"path": "storm/models/storm.py",
"chars": 32727,
"preview": "import torch\nimport torch.nn as nn\nfrom einops import rearrange, repeat\nfrom gsplat.rendering import rasterization\nfrom "
},
{
"path": "storm/models/vit.py",
"chars": 4375,
"preview": "# --------------------------------------------------------\n# References:\n# timm: https://github.com/rwightman/pytorch-im"
},
{
"path": "storm/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "storm/utils/distributed.py",
"chars": 8673,
"preview": "import logging\nimport os\nimport random\nimport re\nimport socket\nimport sys\nfrom typing import Dict, List\n\nimport torch\nim"
},
{
"path": "storm/utils/logging.py",
"chars": 11576,
"preview": "import datetime\nimport functools\nimport json\nimport logging\nimport os\nimport sys\nimport time\nfrom collections import def"
},
{
"path": "storm/utils/losses.py",
"chars": 9636,
"preview": "import torch\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom storm.dataset.constants import MEAN, STD"
},
{
"path": "storm/utils/lpips.py",
"chars": 6246,
"preview": "\"\"\"Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models\"\"\"\n\nimport hashlib\nimport os"
},
{
"path": "storm/utils/lpips_loss.py",
"chars": 2283,
"preview": "import torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom .lpips import LPIPS\n\n\nclass RGB"
},
{
"path": "storm/utils/misc.py",
"chars": 9704,
"preview": "import collections.abc\nimport datetime\nimport logging\nimport math\nimport os\nimport random\nfrom collections import Ordere"
},
{
"path": "storm/visualization/annotation.py",
"chars": 1568,
"preview": "\"\"\"\nFrom https://github.com/dcharatan/pixelsplat/blob/main/src/visualization/annotation.py\n\"\"\"\n\nfrom pathlib import Path"
},
{
"path": "storm/visualization/layout.py",
"chars": 7512,
"preview": "\"\"\"\nFrom https://github.com/dcharatan/pixelsplat/blob/main/src/visualization/layout.py\nThis file contains useful layout "
},
{
"path": "storm/visualization/video_maker.py",
"chars": 38098,
"preview": "import logging\nimport time\n\nimport imageio\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom einops i"
},
{
"path": "storm/visualization/visualization_tools.py",
"chars": 14957,
"preview": "import logging\nimport os\nfrom collections import namedtuple\nfrom itertools import accumulate\nfrom typing import Optional"
},
{
"path": "third_party/depth_anything_v2/dinov2.py",
"chars": 15378,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the Apache License, Version "
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/__init__.py",
"chars": 382,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/attention.py",
"chars": 2378,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/block.py",
"chars": 9462,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/drop_path.py",
"chars": 1160,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/layer_scale.py",
"chars": 806,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/mlp.py",
"chars": 1272,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/patch_embed.py",
"chars": 2880,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dinov2_layers/swiglu_ffn.py",
"chars": 1859,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the l"
},
{
"path": "third_party/depth_anything_v2/dpt.py",
"chars": 6191,
"preview": "import cv2\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import Compose"
},
{
"path": "third_party/depth_anything_v2/util/blocks.py",
"chars": 4075,
"preview": "import torch.nn as nn\n\n\ndef _make_scratch(in_shape, out_shape, groups=1, expand=False):\n scratch = nn.Module()\n\n o"
},
{
"path": "third_party/depth_anything_v2/util/transform.py",
"chars": 6152,
"preview": "import cv2\nimport numpy as np\n\n\nclass Resize(object):\n \"\"\"Resize sample to given size (width, height).\"\"\"\n\n def __"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the NVlabs/GaussianSTORM GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 51 files (771.3 KB), approximately 276.9k tokens, and a symbol index with 374 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.