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a/deps/monodepth2/.gitignore b/deps/monodepth2/.gitignore new file mode 100644 index 0000000..2d58d4e --- /dev/null +++ b/deps/monodepth2/.gitignore @@ -0,0 +1,7 @@ +__pycache__ +*.pyc +*_disp.jpg +*_disp.npy +*.npz +kitti_data +models diff --git a/deps/monodepth2/LICENSE b/deps/monodepth2/LICENSE new file mode 100644 index 0000000..08db21d --- /dev/null +++ b/deps/monodepth2/LICENSE @@ -0,0 +1,181 @@ +Copyright © Niantic, Inc. 2018. Patent Pending. + +All rights reserved. + + + +================================================================================ + + + +This Software is licensed under the terms of the following Monodepth2 license +which allows for non-commercial use only. For any other use of the software not +covered by the terms of this license, please contact partnerships@nianticlabs.com + + + +================================================================================ + + + +Monodepth v2 License + + + This Agreement is made by and between the Licensor and the Licensee as +defined and identified below. + + +1. Definitions. + + In this Agreement (“the Agreement”) the following words shall have the +following meanings: + + "Authors" shall mean C. Godard, O. Mac Aodha, M. Firman, G. Brostow + "Licensee" Shall mean the person or organization agreeing to use the +Software in accordance with these terms and conditions. + "Licensor" shall mean Niantic Inc., a company organized and existing under +the laws of Delaware, whose principal place of business is at 1 Ferry Building, +Suite 200, San Francisco, 94111. + "Software" shall mean the MonoDepth v2 Software uploaded by Licensor to the +GitHub repository at [URL] on [DATE] in source code or object code form and any +accompanying documentation as well as any modifications or additions uploaded +to the same GitHub repository by Licensor. + + +2. License. + + 2.1 The Licensor has all necessary rights to grant a license under: (i) +copyright and rights in the nature of copyright subsisting in the Software; and +(ii) certain patent rights resulting from a patent application filed by the +Licensor in the United States in connection with the Software. The Licensor +grants the Licensee for the duration of this Agreement, a free of charge, +non-sublicenseable, non-exclusive, non-transferable copyright and patent +license (in consequence of said patent application) to use the Software for +non-commercial purpose only, including teaching and research at educational +institutions and research at not-for-profit research institutions in accordance +with the provisions of this Agreement. Non-commercial use expressly excludes +any profit-making or commercial activities, including without limitation sale, +license, manufacture or development of commercial products, use in +commercially-sponsored research, use at a laboratory or other facility owned or +controlled (whether in whole or in part) by a commercial entity, provision of +consulting service, use for or on behalf of any commercial entity, and use in +research where a commercial party obtains rights to research results or any +other benefit. Any use of the Software for any purpose other than +non-commercial research shall automatically terminate this License. + + + 2.2 The Licensee is permitted to make modifications to the Software +provided that any distribution of such modifications is in accordance with +Clause 3. + + 2.3 Except as expressly permitted by this Agreement and save to the +extent and in the circumstances expressly required to be permitted by law, the +Licensee is not permitted to rent, lease, sell, offer to sell, or loan the +Software or its associated documentation. + + +3. Redistribution and modifications + + 3.1 The Licensee may reproduce and distribute copies of the Software, with +or without modifications, in source format only and only to this same GitHub +repository , and provided that any and every distribution is accompanied by an +unmodified copy of this License and that the following copyright notice is +always displayed in an obvious manner: Copyright © Niantic, Inc. 2018. All +rights reserved. + + + 3.2 In the case where the Software has been modified, any distribution must +include prominent notices indicating which files have been changed. + + 3.3 The Licensee shall cause any work that it distributes or publishes, +that in whole or in part contains or is derived from the Software or any part +thereof (“Work based on the Software”), to be licensed as a whole at no charge +to all third parties entitled to a license to the Software under the terms of +this License and on the same terms provided in this License. + + +4. Duration. + + This Agreement is effective until the Licensee terminates it by destroying +the Software, any Work based on the Software, and its documentation together +with all copies. It will also terminate automatically if the Licensee fails to +abide by its terms. Upon automatic termination the Licensee agrees to destroy +all copies of the Software, Work based on the Software, and its documentation. + + +5. Disclaimer of Warranties. + + The Software is provided as is. To the maximum extent permitted by law, +Licensor provides no warranties or conditions of any kind, either express or +implied, including without limitation, any warranties or condition of title, +non-infringement or fitness for a particular purpose. + + +6. LIMITATION OF LIABILITY. + + IN NO EVENT SHALL THE LICENSOR AND/OR AUTHORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES (INCLUDING +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +7. Indemnity. + + The Licensee shall indemnify the Licensor and/or Authors against all third +party claims that may be asserted against or suffered by the Licensor and/or +Authors and which relate to use of the Software by the Licensee. + + +8. Intellectual Property. + + 8.1 As between the Licensee and Licensor, copyright and all other +intellectual property rights subsisting in or in connection with the Software +and supporting information shall remain at all times the property of the +Licensor. The Licensee shall acquire no rights in any such material except as +expressly provided in this Agreement. + + 8.2 No permission is granted to use the trademarks or product names of the +Licensor except as required for reasonable and customary use in describing the +origin of the Software and for the purposes of abiding by the terms of Clause +3.1. + + 8.3 The Licensee shall promptly notify the Licensor of any improvement or +new use of the Software (“Improvements”) in sufficient detail for Licensor to +evaluate the Improvements. The Licensee hereby grants the Licensor and its +affiliates a non-exclusive, fully paid-up, royalty-free, irrevocable and +perpetual license to all Improvements for non-commercial academic research and +teaching purposes upon creation of such improvements. + + 8.4 The Licensee grants an exclusive first option to the Licensor to be +exercised by the Licensor within three (3) years of the date of notification of +an Improvement under Clause 8.3 to use any the Improvement for commercial +purposes on terms to be negotiated and agreed by Licensee and Licensor in good +faith within a period of six (6) months from the date of exercise of the said +option (including without limitation any royalty share in net income from such +commercialization payable to the Licensee, as the case may be). + + +9. Acknowledgements. + + The Licensee shall acknowledge the Authors and use of the Software in the +publication of any work that uses, or results that are achieved through, the +use of the Software. The following citation shall be included in the +acknowledgement: “Digging Into Self-Supervised Monocular Depth Estimation, +by C. Godard, O. Mac Aodha, M. Firman, G. Brostow, arXiv:1806.01260”. + + +10. Governing Law. + + This Agreement shall be governed by, construed and interpreted in +accordance with English law and the parties submit to the exclusive +jurisdiction of the English courts. + + +11. Termination. + + Upon termination of this Agreement, the licenses granted hereunder will +terminate and Sections 5, 6, 7, 8, 9, 10 and 11 shall survive any termination +of this Agreement. diff --git a/deps/monodepth2/README.md b/deps/monodepth2/README.md new file mode 100644 index 0000000..7d09358 --- /dev/null +++ b/deps/monodepth2/README.md @@ -0,0 +1,245 @@ +# Monodepth2 + +This is the reference PyTorch implementation for training and testing depth estimation models using the method described in + +> **Digging into Self-Supervised Monocular Depth Prediction** +> +> [Clément Godard](http://www0.cs.ucl.ac.uk/staff/C.Godard/), [Oisin Mac Aodha](http://vision.caltech.edu/~macaodha/), [Michael Firman](http://www.michaelfirman.co.uk) and [Gabriel J. Brostow](http://www0.cs.ucl.ac.uk/staff/g.brostow/) +> +> [arXiv 2018](https://arxiv.org/abs/1806.01260) + +

+ example input output gif +

+ +This code is for non-commercial use; please see the [license file](LICENSE) for terms. + +If you find our work useful in your research please consider citing our paper: + +``` +@article{monodepth2, + title = {Digging into Self-Supervised Monocular Depth Prediction}, + author = {Cl{\'{e}}ment Godard and + Oisin {Mac Aodha} and + Michael Firman and + Gabriel J. Brostow}, + journal = {arXiv:1806.01260}, + year = {2018} +} +``` + + + +## ⚙️ Setup + +Assuming a fresh [Anaconda](https://www.anaconda.com/download/) distribution, you can install the dependencies with: +```shell +conda install pytorch=0.4.1 torchvision=0.2.1 -c pytorch +pip install tensorboardX==1.4 +conda install opencv=3.3.1 # just needed for evaluation +``` +We ran our experiments with PyTorch 0.4.1, CUDA 9.1, Python 3.6 and Ubuntu 18.04. +We have also successfully trained models with PyTorch 1.0, and our code is compatible with Python 2.7. + + + + +## 🖼️ Prediction for a single image + +You can predict depth for a single image with: +```shell +python test_simple.py --image_path assets/test_image.jpg --model_name mono+stereo_640x192 +``` + +On its first run this will download the `mono+stereo_640x192` pretrained model (99MB) into the `models/` folder. +We provide the following options for `--model_name`: + + +| `--model_name` | Training modality | Native model resolution | KITTI abs. rel. error | delta < 1.25 | +|-------------------------|-------------------|--------------------------|-----------------------|----------------| +| [`mono_640x192`](https://www.dropbox.com/s/vwqzo60c8lh76bp/mono_640x192.zip?dl=1) | Mono | 640 x 192 | 0.115 | 0.877 | +| [`stereo_640x192`](https://www.dropbox.com/s/677tw1sdbhj18qk/stereo_640x192.zip?dl=1) | Stereo | 640 x 192 | 0.109 | 0.864 | +| [`mono+stereo_640x192`](https://www.dropbox.com/s/fvrkipsvbtbk6bw/mono%2Bstereo_640x192.zip?dl=1) | Mono + Stereo | 640 x 192 | 0.106 | 0.874 | +| [`mono_1024x320`](https://www.dropbox.com/s/nt1sripjmvalivn/mono_1024x320.zip?dl=1) | Mono | 1024 x 320 | 0.115 | 0.879 | +| [`stereo_1024x320`](https://www.dropbox.com/s/waph78vw4ciw2b2/stereo_1024x320.zip?dl=1) | Stereo | 1024 x 320 | 0.107 | 0.874 | +| [`mono+stereo_1024x320`](https://www.dropbox.com/s/lfj7tw6k3wh22h4/mono%2Bstereo_1024x320.zip?dl=1) | Mono + Stereo | 1024 x 320 | 0.106 | 0.876 | + + +## 💾 KITTI training data + +You can download the entire [raw KITTI dataset](http://www.cvlibs.net/datasets/kitti/raw_data.php) by running: +```shell +wget -i splits/kitti_archives_to_download.txt -P kitti_data/ +``` +Then unzip with +```shell +cd kitti_data +unzip "*.zip" +cd .. +``` +**Warning:** it weighs about **175GB**, so make sure you have enough space to unzip too! + +Our default settings expect that you have converted the png images to jpeg with this command, **which also deletes the raw KITTI `.png` files**: +```shell +find kitti_data/ -name '*.png' | parallel 'convert -quality 92 -sampling-factor 2x2,1x1,1x1 {.}.png {.}.jpg && rm {}' +``` +**or** you can skip this conversion step and train from raw png files by adding the flag `--png` when training, at the expense of slower load times. + +The above conversion command creates images which match our experiments, where KITTI `.png` images were converted to `.jpg` on Ubuntu 16.04 with default chroma subsampling `2x2,1x1,1x1`. +We found that Ubuntu 18.04 defaults to `2x2,2x2,2x2`, which gives different results, hence the explicit parameter in the conversion command. + +You can also place the KITTI dataset wherever you like and point towards it with the `--data_path` flag during training and evaluation. + +**Splits** + +The train/test/validation splits are defined in the `splits/` folder. +By default, the code will train a depth model using [Zhou's subset](https://github.com/tinghuiz/SfMLearner) of the standard Eigen split of KITTI, which is designed for monocular training. +You can also train a model using the new [benchmark split](http://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction) or the [odometry split](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) by setting the `--split` flag. + + +**Custom dataset** + +You can train on a custom monocular or stereo dataset by writing a new dataloader class which inherits from `MonoDataset` – see the `KITTIDataset` class in `datasets/kitti_dataset.py` for an example. + + +## ⏳ Training + +By default models and tensorboard event files are saved to `~/tmp/`. +This can be changed with the `--log_dir` flag. + + +**Monocular training:** +```shell +python train.py --model_name mono_model +``` + +**Stereo training:** + +Our code defaults to using Zhou's subsampled Eigen training data. For stereo-only training we have to specify that we want to use the full Eigen training set – see paper for details. +```shell +python train.py --model_name stereo_model \ + --frame_ids 0 --use_stereo --split eigen_full +``` + +**Monocular + stereo training:** +```shell +python train.py --model_name mono+stereo_model \ + --frame_ids 0 -1 1 --use_stereo +``` + + +### GPUs + +The code can only be run on a single GPU. +You can specify which GPU to use with the `CUDA_VISIBLE_DEVICES` environment variable: +```shell +CUDA_VISIBLE_DEVICES=2 python train.py --model_name mono_model +``` + +All our experiments were performed on a single NVIDIA Titan Xp. + +| Training modality | Approximate GPU memory | Approximate training time | +|-------------------|-------------------------|-----------------------------| +| Mono | 9GB | 12 hours | +| Stereo | 6GB | 8 hours | +| Mono + Stereo | 11GB | 15 hours | + + + +### 💽 Finetuning a pretrained model + +Add the following to the training command to load an existing model for finetuning: +```shell +python train.py --model_name finetuned_mono --load_weights_folder ~/tmp/mono_model/models/weights_19 +``` + + +### 🔧 Other training options + +Run `python train.py -h` (or look at `options.py`) to see the range of other training options, such as learning rates and ablation settings. + + +## 📊 KITTI evaluation + +To prepare the ground truth depth maps run: +```shell +python export_gt_depth.py --data_path kitti_data --split eigen +python export_gt_depth.py --data_path kitti_data --split eigen_benchmark +``` +...assuming that you have placed the KITTI dataset in the default location of `./kitti_data/`. + +The following example command evaluates the epoch 19 weights of a model named `mono_model`: +```shell +python evaluate_depth.py --load_weights_folder ~/tmp/mono_model/models/weights_19/ --eval_mono +``` +For stereo models, you must use the `--eval_stereo` flag (see note below): +```shell +python evaluate_depth.py --load_weights_folder ~/tmp/stereo_model/models/weights_19/ --eval_stereo +``` +If you train your own model with our code you are likely to see slight differences to the publication results due to randomization in the weights initialization and data loading. + +An additional parameter `--eval_split` can be set. +The three different values possible for `eval_split` are explained here: + +| `--eval_split` | Test set size | For models trained with... | Description | +|-----------------------|---------------|----------------------------|--------------| +| **`eigen`** | 697 | `--split eigen_zhou` (default) or `--split eigen_full` | The standard Eigen test files | +| **`eigen_benchmark`** | 652 | `--split eigen_zhou` (default) or `--split eigen_full` | Evaluate with the improved ground truth from the [new KITTI depth benchmark](http://www.cvlibs.netasets/kitti/eval_depth.php?ark=depth_prediction) | +| **`benchmark`** | 500 | `--split benchmark` | The [new KITTI depth benchmark](http://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction) test files. | + +Because no ground truth is available for the new KITTI depth benchmark, no scores will be reported when `--eval_split benchmark` is set. +Instead, a set of `.png` images will be saved to disk ready for upload to the evaluation server. + + +**External disparities evaluation** + +Finally you can also use `evaluate_depth.py` to evaluate raw disparities (or inverse depth) from other methods by using the `--ext_disp_to_eval` flag: + +```shell +python evaluate_depth.py --ext_disp_to_eval ~/other_method_disp.npy +``` + + +**📷📷 Note on stereo evaluation** + +Our stereo models are trained with an effective baseline of `0.1` units, while the actual KITTI stereo rig has a baseline of `0.54m`. This means a scaling of `5.4` must be applied for evaluation. +In addition, for models trained with stereo supervision we disable median scaling. +Setting the `--eval_stereo` flag when evaluating will automatically disable median scaling and scale predicted depths by `5.4`. + + +**⤴️⤵️ Odometry evaluation** + +We include code for evaluating poses predicted by models trained with `--split odom`. + +For this evaluation, the [KITTI odometry dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) **(color, 65GB)** and **ground truth poses** zip files must be downloaded. +As above, we assume that the pngs have been converted to jpgs. + +If this data has been unzipped to folder `kitti_odom`, a model can be evaluated with: +```shell +python evaluate_pose.py --eval_split odom_9 --load_weights_folder ./odom_split.M/models/weights_29 --data_path kitti_odom/ +python evaluate_pose.py --eval_split odom_10 --load_weights_folder ./odom_split.M/models/weights_29 --data_path kitti_odom/ +``` + + +## 📦 Precomputed results + +You can download our precomputed disparity predictions from the following links: + + +| Training modality | Input size | `.npy` filesize | Eigen disparities | +|-------------------|-------------|-----------------|-----------------------------------------------------------------------------------------------| +| Mono | 640 x 192 | 343 MB | [Download 🔗](https://www.dropbox.com/s/fygn3n4xa6dcbch/mono_640x192_eigen.npy?dl=1) | +| Stereo | 640 x 192 | 343 MB | [Download 🔗](https://www.dropbox.com/s/u18u5kfwbz856e1/stereo_640x192_eigen.npy?dl=1) | +| Mono + Stereo | 640 x 192 | 343 MB | [Download 🔗](https://www.dropbox.com/s/ypom4fofkdrcswq/mono%2Bstereo_640x192_eigen.npy?dl=1) | +| Mono | 1024 x 320 | 914 MB | [Download 🔗](https://www.dropbox.com/s/g9ybl2tap75va40/mono_1024x320_eigen.npy?dl=1) | +| Stereo | 1024 x 320 | 914 MB | [Download 🔗](https://www.dropbox.com/s/p4m0i89jvdr2xz3/stereo_1024x320_eigen.npy?dl=1) | +| Mono + Stereo | 1024 x 320 | 914 MB | [Download 🔗](https://www.dropbox.com/s/kjri31d8bpiowxs/mono%2Bstereo_1024x320_eigen.npy?dl=1) | + + + +## 👩‍⚖️ License +Copyright © Niantic, Inc. 2019. Patent Pending. +All rights reserved. +Please see the [license file](LICENSE) for terms. diff --git a/deps/monodepth2/assets/copyright_notice.txt b/deps/monodepth2/assets/copyright_notice.txt new file mode 100644 index 0000000..d318392 --- /dev/null +++ b/deps/monodepth2/assets/copyright_notice.txt @@ -0,0 +1,5 @@ +The image test_image.jpg is a modified (cropped) version of the original image which can be found at: +https://www.flickr.com/photos/31176607@N05/44108158051/in/faves-41364765@N06/ + +This image was licensed under the creative commons 2.0 CC BY 2.0 licence. +https://creativecommons.org/licenses/by/2.0/ diff --git a/deps/monodepth2/assets/teaser.gif b/deps/monodepth2/assets/teaser.gif new file mode 100644 index 0000000000000000000000000000000000000000..5db15ac47891dbf02f365dab4e78860dee1286a9 GIT binary patch literal 9242452 zcmWhzc{~&TAKq>cb(&BFLV72tnB00006>;XU^0VqTg3WW(G1cl%# zNMRunQM9O-5K2g2OxO@DA%T`cic2X;ikeExNlMG>V-!?nPy~5VYXuZZK~Y9QNk>V+ 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