├── .gitignore
├── LICENSE
├── README.md
├── assets
├── 10min.png
├── 2min.png
├── generalization-plot.png
├── relations.png
├── relative-scale.png
├── scale_demo.png
├── teaser.png
└── world-space-measure.png
├── data
├── dataset_jsons
│ └── megadepth
│ │ ├── bigben.json
│ │ ├── florence.json
│ │ ├── notredame.json
│ │ └── venice.json
└── overlap_data
│ └── megadepth
│ ├── bigben
│ ├── images_with_depth.txt
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
│ ├── florence
│ ├── images_with_depth.txt
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
│ ├── notredame
│ ├── images_with_depth.txt
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
│ └── venice
│ ├── images_with_depth.txt
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
├── environment.yml
├── generate_dataset.sh
├── relative_scale_example.ipynb
├── src
├── __init__.py
├── datasets
│ ├── __init__.py
│ ├── dataset_generator
│ │ ├── __init__.py
│ │ ├── compute_normals.py
│ │ ├── compute_overlap.py
│ │ ├── options.py
│ │ └── utils.py
│ └── megadepth_loader.py
├── model.py
├── options.py
├── test.py
├── train.py
├── trainer.py
└── utils.py
├── test.sh
└── train.sh
/.gitignore:
--------------------------------------------------------------------------------
1 | *.pyc
2 | .DS_Store
3 | __pycache__/
4 | .mypy_cache
5 | *.egg-info
6 |
7 | # scripts for running experiments
8 | .idea/
9 |
10 | # testing
11 | .coverage
12 | .coverage.*
13 | *,cover
14 | .pytest_cache
15 |
16 | # Python related
17 | # Byte-compiled / optimized / DLL files
18 | __pycache__/
19 | *.py[cod]
20 | *$py.class
21 |
22 | # C extensions
23 | *.so
24 |
25 | # Distribution / packaging
26 | .gradle
27 | .Python
28 | build/
29 | develop-eggs/
30 | dist/
31 | downloads/
32 | eggs/
33 | .eggs/
34 | lib/
35 | lib64/
36 | parts/
37 | sdist/
38 | var/
39 | wheels/
40 | pip-wheel-metadata/
41 | share/python-wheels/
42 | *.egg-info/
43 | .installed.cfg
44 | *.egg
45 | MANIFEST
46 |
47 | # PyInstaller
48 | # Usually these files are written by a python script from a template
49 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
50 | *.manifest
51 | *.spec
52 |
53 | # Installer logs
54 | pip-log.txt
55 | pip-delete-this-directory.txt
56 |
57 | # Unit test / coverage reports
58 | htmlcov/
59 | .tox/
60 | .nox/
61 | .coverage
62 | .coverage.*
63 | .cache
64 | nosetests.xml
65 | coverage.xml
66 | *.cover
67 | .hypothesis/
68 | .pytest_cache/
69 |
70 | # Translations
71 | *.mo
72 | *.pot
73 |
74 | # Django stuff:
75 | *.log
76 | local_settings.py
77 | db.sqlite3
78 | db.sqlite3-journal
79 |
80 | # Flask stuff:
81 | instance/
82 | .webassets-cache
83 |
84 | # Scrapy stuff:
85 | .scrapy
86 |
87 | # Sphinx documentation
88 | docs/_build/
89 |
90 | # PyBuilder
91 | target/
92 |
93 | # Jupyter Notebook
94 | .ipynb_checkpoints
95 |
96 | # IPython
97 | profile_default/
98 | ipython_config.py
99 |
100 | # pyenv
101 | .python-version
102 |
103 | # pipenv
104 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
105 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
106 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
107 | # install all needed dependencies.
108 | #Pipfile.lock
109 |
110 | # celery beat schedule file
111 | celerybeat-schedule
112 |
113 | # SageMath parsed files
114 | *.sage.py
115 |
116 | # Environments
117 | .env
118 | .venv
119 | env/
120 | venv/
121 | ENV/
122 | env.bak/
123 | venv.bak/
124 |
125 | # Spyder project settings
126 | .spyderproject
127 | .spyproject
128 |
129 | # Rope project settings
130 | .ropeproject
131 |
132 | # mkdocs documentation
133 | /site
134 |
135 | # mypy
136 | .mypy_cache/
137 | .dmypy.json
138 | dmypy.json
139 |
140 | # Pyre type checker
141 | .pyre/
--------------------------------------------------------------------------------
/LICENSE:
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/README.md:
--------------------------------------------------------------------------------
1 | # [Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings](https://arxiv.org/abs/2008.05785)
2 |
3 | **[Anita Rau](https://anitarau.github.io), [Guillermo Garcia-Hernando](https://guiggh.github.io/), [Danail Stoyanov](https://scholar.google.co.uk/citations?user=pGfEK6UAAAAJ&hl=en), [Gabriel J. Brostow](http://www0.cs.ucl.ac.uk/staff/g.brostow/) and [Daniyar Turmukhambetov](http://dantkz.github.io/about) – ECCV 2020 (Spotlight presentation)**
4 |
5 | [Supplementary pdf](https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/Predicting_Visual_Overlap_Supplement.pdf)
6 |
7 |
8 |
9 | 
10 |
11 |
12 |
13 |
14 |
15 |
16 | 
17 |
18 |
19 |
20 |
21 | 
22 |
23 |
24 | To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically
25 | requires an expensive search across scale space, with matching and geometric verification of large sets of
26 | local features. This expense is further multiplied when a query image is evaluated against
27 | a gallery, e.g. in visual relocalization. While we don’t obviate the need for geometric
28 | verification, we propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup.
29 |
30 | Neural networks can be trained to predict a vector representations for images, such that the relative camera position between
31 | pairs of images is approximated by a distance in vector space. And there are a few versions of such relations, that
32 | unfortunately are not interpretable.
33 |
34 | 
35 |
36 |
37 |
38 | We propose to capture camera position relations through normalized surface overlap (NSO).
39 | NSO measure is not symmetric, but it is interpretable.
40 |
41 | 
42 |
43 |
44 | We propose to represent images as boxes, not vectors. Two boxes can intersect, and boxes can have different volumes.
45 | The ratio of intersection over volume can be used to approximate normalized surface overlap. So, box representation
46 | allows us to model non-symmetric (non-metric) relations between pairs of images. The result is that with box embeddings
47 | we can quickly identify, for example, which test image is a close-up version of another.
48 |
49 | Next we plot the predicted NSO relationship between a test query image and a set of test images. We say "enclosure" for NSO of query pixels visible in the retrieved image, and concentration for NSO of retrieved image pixels visible in the query image.
50 |
51 | 
52 |
53 | In the figure above, we show a query image from the test set (lower left corner) and the concentration and enclosure between randomly sampled test images from the same scene. The query image shows Big Ben from the view of the Westminster Bridge. (i) It can be observed that close-ups on the tower clock are clustered around the coordinates (80, 15). (ii) The images in the upper right corner show the waterfront side of Westminster Palace. These are crop-outs of the query image. In fact, the tower in the lower left corner of the query is one of the two towers that mark the corners of the water-front side of the palace. The retrievals in the upper right quadrant of the cluster therefore extends the view of the query. (iii) The images in the lower right area of the cluster clearly show zoom outs, with the pointy bell tower visible in all images. (iv) Lastly, one can observe that the images in the clone-like category are in fact similar views on Big Ben.
54 |
55 |
56 | Finally, the predicted normalized surface overlap can be used to derive relative scale factor between a pair of images.
57 |
58 |
59 | 
60 |
61 | Figure above illustrates several examples of how our method can estimate geometric relationships between images. For each pair the enclosure and concentration are calculated from which the relative estimated scaled can be derived. Based on that scale, the first image is resized and shown in the third position. The resized images match the scale of the scene in the first image to the scale in the second image. The two numbers below each image pair show the estimated enclosure and concentration. Note that although some scale estimates are inaccurate, overwhelmingly the rescaling does not increase the scale difference between the two images, but only reduces it.
62 |
63 | Subsequently, local features need only be detected at that scale. We validate our scene-specific
64 | model by showing how this embedding yields competitive image-matching results, while being simpler, faster,
65 | and also interpretable by humans.
66 |
67 | ## ⚙ Setup
68 |
69 | This codebase is a minimal implementation of the training data generation and the training function using [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning).
70 | A minimal working [Anaconda](https://www.anaconda.com/products/individual) environment is provided with the codebase: [environment.yml](environment.yml).
71 | You can install and activate a new conda environment from this file with:
72 |
73 | ```bash
74 | conda env create -f environment.yml -n boxes
75 | conda activate boxes
76 | ```
77 |
78 | ## 💾 MegaDepth data and splits
79 |
80 | To run the provided scripts the [MegaDepth](https://research.cs.cornell.edu/megadepth/) dataset needs to be downloaded.
81 | Once downloaded, update the fields `path_sfm` and `path_depth` with the correct paths of you machine in (each of) the dataset files on
82 | `data/dataset_jsons/megadepth/`.
83 |
84 | We provide training, validation and test splits for the image overlap prediction task on four scenes: Big Ben, Notre Dame
85 | Venice and Florence. You can find them on the folders
86 | `data/overlap_data/megadepth//`. Each file (`train.txt`, `val.txt` and `test.txt`) contains the filenames of
87 | pairs of images and their computed ground-truth overlap.
88 |
89 | If you wish to generate this date yourself, check the next section.
90 |
91 | ## 🌍 Generating normalized surface overlap datasets
92 |
93 | Code to generate normalized surface overlaps between pairs of MegaDepth images can be found in the Python package
94 | `src/datasets/dataset_generator`. The package has two main components i)`compute_normals.py` and ii)`compute_overlap.py`.
95 |
96 | i. `compute_normals.py` computes the surface normals using available depth images. The list of available for each scene
97 | depth images can be found in `data/overlap_data/megadepth//images_with_depth`. Don't forget to update the
98 | json paths as described above.
99 | ii. `compute_overlap.py` computes the normalized surface overlap between image pairs given the surface normals from
100 | the previous step.
101 |
102 | For convenience we provide an example bash script in `generate_dataset.sh`. NOTE: Normal data is stored uncompressed and
103 | it is about `50MB` per image in average, so storage size requirement can easily escalate.
104 |
105 |
106 | ## ⏳ Training
107 |
108 | To train a model run:
109 | ```
110 | python -m src.train \
111 | --name my_box_model \
112 | --dataset_json data/dataset_jsons/megadepth/bigben.json \
113 | --box_ndim 32 \
114 | --batch_size 32 \
115 | --model resnet50 \
116 | --num_gpus 1 \
117 | --backend dp
118 | ```
119 |
120 | where `box_ndim` are the dimensions of the embedding space. `backend` is the PyTorch Lightning [distributed backend](https://pytorch-lightning.readthedocs.io/en/latest/multi_gpu.html#data-parallel) which is flexible (we have only tested this implementation on `dp` and `ddp`) and can be used with different `num_gpus`.
121 | We also provide a training bash script `train.sh`. By default tensorboard logs and models are saved on a folder with the same name as the experiment `/`.
122 |
123 | ## 📊 MegaDepth evaluation
124 |
125 | To evaluate model on surface overlap prediction and reproduce the results on the paper (Table 1) you can run:
126 | ```
127 | python -m src.test \
128 | --model_scene bigben \
129 | --model resnet50 \
130 | --dataset_json data/dataset_jsons/megadepth/bigben.json
131 | ```
132 |
133 | or, alternatively, run the bash script provided `test.sh`.
134 |
135 | ## 🖼️ Estimating relative scale between two images estimation
136 |
137 | For an interactive example using our models to predict the relative scale of two images you can run the following [Jupyter
138 | Notebook](https://jupyter.org/) `relative_scale_example.ipynb`.
139 |
140 |
141 | 
142 |
143 |
144 | ## 📦 Trained models on MegaDepth
145 | | Scene | Input size and model | filesize | Link |
146 | |-------------------|-------------|-----------------|-----------------------------------------------------------------------------------------------|
147 | | Big Ben | 256 x 456 ResNet50 | 95 MB | [Download 🔗](https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/bigben.zip) |
148 | | Notre Dame | 256 x 456 ResNet50 | 95 MB | [Download 🔗](https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/notredame.zip) |
149 | | Venice | 256 x 456 ResNet50 | 95 MB | [Download 🔗](https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/venice.zip) |
150 | | Florence | 256 x 456 ResNet50| 95 MB | [Download 🔗](https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/florence.zip) |
151 |
152 | ## ✏️ 📄 Citation
153 |
154 | If you find our work useful or interesting, please consider citing [our paper](https://arxiv.org/abs/2008.05785):
155 |
156 | ```
157 | @inproceedings{rau-2020-image-box-overlap,
158 | title = {Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings},
159 | author = {Anita Rau and
160 | Guillermo Garcia-Hernando and
161 | Danail Stoyanov and
162 | Gabriel J. Brostow and
163 | Daniyar Turmukhambetov
164 | },
165 | booktitle = {European Conference on Computer Vision ({ECCV})},
166 | year = {2020}
167 | }
168 | ```
169 |
170 |
171 | # 👩⚖️ License
172 | Copyright © Niantic, Inc. 2020. Patent Pending. All rights reserved. Please see the license file for terms.
173 |
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1 | [
2 | {
3 | "scene": "0003",
4 | "path_sfm": "/path_to/megadepth/MegaDepth_v1_SfM/",
5 | "train_file": "data/overlap_data/megadepth/bigben/train.txt",
6 | "val_file": "data/overlap_data/megadepth/bigben/val.txt",
7 | "test_file": "data/overlap_data/megadepth/bigben/test.txt",
8 | "path_depth": "/path_to/phoenix/S6/zl548/MegaDepth_v1/",
9 | "list_images_with_depth": "data/overlap_data/megadepth/bigben/images_with_depth.txt"
10 |
11 | }
12 | ]
13 |
14 |
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/data/dataset_jsons/megadepth/florence.json:
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1 | [
2 | {
3 | "scene": "0032",
4 | "path_sfm": "/path_to/megadepth/MegaDepth_v1_SfM/",
5 | "train_file": "data/overlap_data/megadepth/florence/train.txt",
6 | "val_file": "data/overlap_data/megadepth/florence/val.txt",
7 | "test_file": "data/overlap_data/megadepth/florence/test.txt",
8 | "path_depth": "/path_to/phoenix/S6/zl548/MegaDepth_v1/",
9 | "list_images_with_depth": "data/overlap_data/megadepth/florence/images_with_depth.txt"
10 | }
11 | ]
12 |
13 |
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/data/dataset_jsons/megadepth/notredame.json:
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1 | [
2 | {
3 | "scene": "0004",
4 | "path_sfm": "/path_to/megadepth/MegaDepth_v1_SfM/",
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9 | "list_images_with_depth": "data/overlap_data/megadepth/notredame/images_with_depth.txt"
10 | }
11 | ]
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/data/dataset_jsons/megadepth/venice.json:
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1 | [
2 | {
3 | "scene": "0008",
4 | "path_sfm": "/path_to/megadepth/MegaDepth_v1_SfM/",
5 | "train_file": "data/overlap_data/megadepth/venice/train.txt",
6 | "val_file": "data/overlap_data/megadepth/venice/val.txt",
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8 | "path_depth": "/path_to/phoenix/S6/zl548/MegaDepth_v1/",
9 | "list_images_with_depth": "data/overlap_data/megadepth/venice/images_with_depth.txt"
10 | }
11 | ]
12 |
13 |
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/environment.yml:
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1 | name: boxes
2 | channels:
3 | - default
4 | - pytorch
5 | - conda-forge
6 | dependencies:
7 | - python=3.6.7
8 | - pytorch=1.3.1
9 | - torchvision=0.4.2
10 | - numpy=1.16.4
11 | - pillow=6.2.1
12 | - scipy=1.4.1
13 | - h5py
14 | - pip
15 | - pip:
16 | - pytorch-lightning==0.7.6
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/generate_dataset.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | dataset_output_path=data/overlap_data/megadepth/my_data
3 | dataset_json_path=data/dataset_jsons/megadepth/bigben.json
4 | python -m src.datasets.dataset_generator.compute_normals \
5 | --dataset_json $dataset_json_path \
6 | --output_folder $dataset_output_path
7 | python -m src.datasets.dataset_generator.compute_overlap \
8 | --dataset_json $dataset_json_path \
9 | --normals_folder $dataset_output_path \
10 | --num_sampled_points 5000 \
11 | --num_pairs 25 \
12 | --threshold 0.1
--------------------------------------------------------------------------------
/src/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/nianticlabs/image-box-overlap/af1fcb4e806b7685f976054daa5d73ad071ffcb2/src/__init__.py
--------------------------------------------------------------------------------
/src/datasets/__init__.py:
--------------------------------------------------------------------------------
1 | from .megadepth_loader import MegaDepthSurfacePairLoader, MegaDepthImageLoader
2 |
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/src/datasets/dataset_generator/__init__.py:
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1 | from .utils import pil_loader, params2matrix, quat2mat
2 |
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/src/datasets/dataset_generator/compute_normals.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Compute and store surface normals.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import os
14 | import numpy as np
15 | import torch
16 | import pickle
17 | from tqdm import tqdm
18 | from .options import NormalsComputeOptions
19 | from .utils import backproject_depthmap, get_normals, read_depth_megadepth
20 | from ..megadepth_loader import MegaDepthImageLoader
21 |
22 |
23 | def process_normals_batch(batch, batch_size=1):
24 | depth_images, k_mat, inv_k_mat, file_names_depth = [], [], [], []
25 | for i in range(batch_size):
26 | inv_k_mat.append(torch.tensor(np.linalg.inv(batch['camera_calib'][i])))
27 | depth_images.append(torch.tensor(read_depth_megadepth(batch['depth_path'][i])))
28 |
29 | depth_images = torch.stack(depth_images)
30 | inv_k_mat = torch.stack(inv_k_mat).float()
31 |
32 | if torch.cuda.is_available():
33 | inv_k_mat = inv_k_mat.cuda()
34 | depth_images = depth_images.cuda()
35 |
36 | with torch.no_grad():
37 | points3d = backproject_depthmap(depth_images, inv_k_mat)
38 | normals, plane_fit_error = get_normals(points3d)
39 |
40 | normals = normals.cpu().detach().numpy().astype(np.float32)
41 | points3d = points3d.cpu().detach().numpy().astype(np.float32)
42 | plane_fit_error = plane_fit_error.cpu().detach().numpy().astype(np.float32)
43 |
44 | return normals, points3d, plane_fit_error
45 |
46 |
47 | def main():
48 | opts = NormalsComputeOptions().parse()
49 | dataset = MegaDepthImageLoader(opts.dataset_json, mode='data_generation')
50 | data_loader = torch.utils.data.DataLoader(dataset,
51 | batch_size=opts.batch_size,
52 | shuffle=False,
53 | num_workers=opts.num_workers,
54 | pin_memory=True)
55 |
56 | if not os.path.exists(opts.output_folder):
57 | os.makedirs(opts.output_folder)
58 |
59 | print(f"Computing and saving normals of a total of {len(data_loader)} images...")
60 | for batch_idx, batch in enumerate(tqdm(data_loader)):
61 | normals, points3d, plane_fit_error = process_normals_batch(batch,
62 | opts.batch_size)
63 | for i in range(opts.batch_size):
64 | result_dict_i = {
65 | 'normals': normals[i],
66 | 'points3d': points3d[i],
67 | 'plane_fit_error': plane_fit_error[i]
68 | }
69 | result_path_i = os.path.join(opts.output_folder,
70 | f"{batch['image_id'][i]}.pickle")
71 |
72 | with open(result_path_i, 'wb') as f:
73 | pickle.dump(result_dict_i, f)
74 |
75 |
76 | if __name__ == '__main__':
77 | main()
78 |
--------------------------------------------------------------------------------
/src/datasets/dataset_generator/compute_overlap.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Normalized surface overlap dataset generator.
5 | It requires precomputed normals with compute_normals.py.
6 |
7 | Copyright Niantic 2020. Patent Pending. All rights reserved.
8 |
9 | This software is licensed under the terms of the Image-box-overlap licence
10 | which allows for non-commercial use only, the full terms of which are made
11 | available in the LICENSE file.
12 | """
13 |
14 | import os
15 | import torch
16 | from tqdm import tqdm
17 | from .options import OverlapComputeOptions
18 | from .utils import process_surface_overlap_batch
19 | from ..megadepth_loader import MegaDepthImageLoader
20 |
21 |
22 | def main():
23 | opts = OverlapComputeOptions().parse()
24 |
25 | dataset = MegaDepthImageLoader(opts.dataset_json, mode='data_generation')
26 | data_loader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True,
27 | num_workers=opts.num_workers, pin_memory=True)
28 |
29 | output_file = os.path.join(opts.normals_folder, 'computed_overlaps.txt')
30 | writer = open(output_file, 'w')
31 |
32 | print(f"Computing the surface overlap of {opts.num_pairs} image pairs from previously computed normals...")
33 | for _ in tqdm(range(opts.num_pairs)):
34 | batch = next(iter(data_loader))
35 | nso_batch = process_surface_overlap_batch(batch,
36 | opts.normals_folder,
37 | opts.threshold,
38 | opts.num_sampled_points)
39 | if nso_batch:
40 | writer.write(f"{batch['image_id'][0]} {batch['image_id'][1]} {str(nso_batch['x->y'])}\n")
41 | writer.write(f"{batch['image_id'][1]} {batch['image_id'][0]} {str(nso_batch['y->x'])}\n")
42 |
43 | writer.close()
44 |
45 |
46 | if __name__ == '__main__':
47 | main()
--------------------------------------------------------------------------------
/src/datasets/dataset_generator/options.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings
3 |
4 | Datasets options parser.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import argparse
14 |
15 |
16 | class NormalsComputeOptions:
17 | def __init__(self):
18 | self.options = None
19 | self.parser = argparse.ArgumentParser(description='Compute Normals MegaDepth')
20 | self.parser.add_argument('--dataset_json', default='data/dataset_jsons/megadepth/bigben.json',
21 | help="Path to dataset json files.")
22 | self.parser.add_argument('--output_folder',
23 | help="Path to save normals.")
24 | self.parser.add_argument('--batch_size', type=int, default=1,
25 | help="Number of images to compute normals in parallel."
26 | "Important! For MegaDepth it only supports one image per batch.")
27 | self.parser.add_argument('--num_workers', type=int, default=8,
28 | help="Number of workers for data loading (default: 8).")
29 |
30 | def parse(self, *args, **kwargs):
31 | self.options = self.parser.parse_args(*args, **kwargs)
32 | return self.options
33 |
34 |
35 | class OverlapComputeOptions:
36 | def __init__(self):
37 | self.options = None
38 | self.parser = argparse.ArgumentParser(description='Compute Overlap MegaDepth (Requires Normals)')
39 | self.parser.add_argument('--normals_folder', help="Path to store normal data.")
40 | self.parser.add_argument('--dataset_json', default='data/dataset_jsons/megadepth/bigben.json',
41 | help="Path to dataset json files.")
42 | self.parser.add_argument('--num_pairs', type=int, default=10000,
43 | help="Number of image pairs to compute surface (default: 10000).")
44 | self.parser.add_argument('--num_sampled_points', type=int, default=5000,
45 | help='Number of points to be sampled for overlap computation (default: 5000).')
46 | self.parser.add_argument('--threshold', type=float, default=0.1,
47 | help='Maximum distance for two points to be considered as overlapping (default: 0.1).')
48 | self.parser.add_argument('--num_workers', type=int, default=8,
49 | help="Number of workers for data loading (default: 8).")
50 |
51 | def parse(self, *args, **kwargs):
52 | self.options = self.parser.parse_args(*args, **kwargs)
53 | return self.options
54 |
--------------------------------------------------------------------------------
/src/datasets/dataset_generator/utils.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Datasets util functions.
5 | We thank Carl Toft for helping with normal computation code during his internship at Niantic.
6 |
7 | Copyright Niantic 2020. Patent Pending. All rights reserved.
8 |
9 | This software is licensed under the terms of the Image-box-overlap licence
10 | which allows for non-commercial use only, the full terms of which are made
11 | available in the LICENSE file.
12 | """
13 |
14 | import os
15 | import numpy as np
16 | import torch
17 | import PIL
18 | import h5py
19 | import pickle
20 | from scipy.spatial.transform import Rotation
21 |
22 |
23 | def backproject_depthmap(depths, inv_k):
24 | """ Projects depth maps into 3D space
25 | :param depths: Input depths with shape [batch_size x height x width]
26 | :param inv_k: Inverse intrinsics with shape [batch_size x 3 x 3]
27 | :return: 3D points for each pixel in a depth map with shape [batch_size x height x width x 3]
28 | """
29 |
30 | batch_size = depths.shape[0]
31 |
32 | yy, xx = torch.meshgrid([torch.arange(depths.shape[1], dtype=depths.dtype, device=depths.device),
33 | torch.arange(depths.shape[2], dtype=depths.dtype, device=depths.device)])
34 |
35 | coords = torch.cat([
36 | xx.reshape(-1, 1),
37 | yy.reshape(-1, 1),
38 | torch.ones([depths.shape[1] * depths.shape[2], 1], dtype=depths.dtype, device=depths.device)], 1)
39 |
40 | coords = coords.reshape(1, -1, 3, 1).repeat(batch_size, 1, 1, 1)
41 | inv_k = inv_k.reshape(-1, 1, 3, 3)
42 | point3d = torch.matmul(inv_k, coords)
43 | point3d = point3d * depths.reshape(batch_size, -1, 1, 1)
44 | point3d = point3d.reshape(batch_size, depths.shape[1], depths.shape[2], 3)
45 | return point3d
46 |
47 |
48 | def get_normals(points3d):
49 | """ Computes normal maps from 3D cloud
50 | :param points3d: Ordered 3D cloud with shape [batch_size x height x width x 3]
51 | :return: Normal map with shape [batch_size x height x width x 3]
52 | """
53 | batch_size = points3d.shape[0]
54 | dtype = points3d.dtype
55 | device = points3d.device
56 | pi = torch.tensor([np.pi], dtype=dtype, device=device)
57 | nan = torch.tensor([np.nan], dtype=dtype, device=device)
58 |
59 | # Create a huge tensor to keep the points, as well as the points shifted one step in all directions
60 | hugeTensor = torch.zeros(batch_size,
61 | points3d.shape[1] + 2, points3d.shape[2] + 2, points3d.shape[3], 9,
62 | dtype=points3d.dtype, device=device)
63 | hugeTensor[:, 1:points3d.shape[1] + 1, 1:points3d.shape[2] + 1, 0:3, 0] = points3d # original points in center
64 |
65 | # Now add them in this order: top-left, top-center, top-right
66 | hugeTensor[:, 0:points3d.shape[1], 0:points3d.shape[2], 0:3, 1] = points3d
67 | hugeTensor[:, 0:points3d.shape[1], 1:points3d.shape[2] + 1, 0:3, 2] = points3d
68 | hugeTensor[:, 0:points3d.shape[1], 2:points3d.shape[2] + 2, 0:3, 3] = points3d
69 |
70 | # Now center-left, center-right
71 | hugeTensor[:, 1:points3d.shape[1] + 1, 0:points3d.shape[2], 0:3, 4] = points3d
72 | hugeTensor[:, 1:points3d.shape[1] + 1, 2:points3d.shape[2] + 2, 0:3, 5] = points3d
73 |
74 | # Now bottom-left, bottom-cente1, bottom-right
75 | hugeTensor[:, 2:points3d.shape[1] + 2, 0:points3d.shape[2], 0:3, 6] = points3d
76 | hugeTensor[:, 2:points3d.shape[1] + 2, 1:points3d.shape[2] + 1, 0:3, 7] = points3d
77 | hugeTensor[:, 2:points3d.shape[1] + 2, 2:points3d.shape[2] + 2, 0:3, 8] = points3d
78 |
79 | # Done! Now compute the mean vector for each pixel.
80 | meanPoints = hugeTensor.mean(4)
81 | h = hugeTensor.shape[1]
82 | w = hugeTensor.shape[2]
83 |
84 | S = torch.zeros(batch_size, h, w, 3, 3, dtype=dtype, device=device)
85 | for k in range(9):
86 | hugeTensor[:, :, :, :, k] = hugeTensor[:, :, :, :, k] - meanPoints
87 | S = S + hugeTensor[:, :, :, :, k].reshape(batch_size, h, w, 1, 3) * hugeTensor[:, :, :, :, k].reshape(
88 | batch_size, h, w, 3, 1)
89 | S = S / 8
90 |
91 | eye3 = torch.eye(3, dtype=dtype, device=device).unsqueeze(0)
92 | # Now, compute the smallest eigenvector of each covariance matrix
93 | mat = torch.tensor(np.array([[0, 1, 1], [0, 0, 1], [0, 0, 0]]), dtype=dtype, device=device)
94 | mat = mat.unsqueeze(0)
95 | p1 = ((S * mat) ** 2).sum(4).sum(3)
96 | q = (S * eye3).sum(4).sum(3) / 3
97 | p2 = ((((S - q.reshape(batch_size, q.shape[1], q.shape[2], 1, 1)) * eye3) * eye3) ** 2).sum(4).sum(3)
98 | p2 = p2 + 2 * p1
99 | val_mask = p2 > 0
100 | p2[~val_mask] = 1
101 |
102 | p2 = torch.sqrt(p2 / 6.0)
103 |
104 | # No more division by zero
105 | B = (S - q.reshape(batch_size, q.shape[1], q.shape[2], 1, 1) * eye3) / p2.reshape(batch_size, p2.shape[1],
106 | p2.shape[2], 1, 1)
107 |
108 | detB = B[:, :, :, 0, 0] * B[:, :, :, 1, 1] * B[:, :, :, 2, 2] + \
109 | B[:, :, :, 0, 1] * B[:, :, :, 1, 2] * B[:, :, :, 2, 0] + \
110 | B[:, :, :, 0, 2] * B[:, :, :, 1, 0] * B[:, :, :, 2, 1] - \
111 | B[:, :, :, 0, 2] * B[:, :, :, 1, 1] * B[:, :, :, 2, 0] - \
112 | B[:, :, :, 0, 1] * B[:, :, :, 1, 0] * B[:, :, :, 2, 2] - \
113 | B[:, :, :, 0, 0] * B[:, :, :, 1, 2] * B[:, :, :, 2, 1]
114 |
115 | detB = detB / 2.0
116 | detB = detB.clamp(min=-1, max=1)
117 | lambda0 = 2.0 * p2 * torch.cos(torch.acos(detB) / 3.0 + 2 * pi / 3.0) + q
118 |
119 | detM = (S[:, :, :, 0, 0] - lambda0) * (S[:, :, :, 1, 1] - lambda0) - S[:, :, :, 0, 1] * S[:, :, :, 1, 0]
120 | normals = torch.ones(batch_size, lambda0.shape[1], lambda0.shape[2], 3, dtype=dtype, device=device)
121 |
122 | val_mask = val_mask & (detM != 0)
123 | detM[~val_mask] = 1
124 | normals[:, :, :, 0] = (S[:, :, :, 0, 2] * (lambda0 - S[:, :, :, 1, 1]) + S[:, :, :, 1, 2] * S[:, :, :, 0, 1]) / detM
125 | normals[:, :, :, 1] = (S[:, :, :, 1, 2] * (lambda0 - S[:, :, :, 0, 0]) + S[:, :, :, 0, 2] * S[:, :, :, 1, 0]) / detM
126 |
127 | normals_length = torch.sqrt((normals ** 2).sum(3))
128 | normals = normals / normals_length.reshape(batch_size, normals.shape[1], normals.shape[2], 1)
129 |
130 | neg_offsets = ((normals * meanPoints).sum(3)).reshape(batch_size, normals.shape[1], normals.shape[2], 1)
131 | normals = normals * torch.sign(neg_offsets)
132 |
133 | normals[~val_mask] = nan
134 | lambda0[~val_mask] = nan
135 |
136 | normals = normals[:, 1:normals.shape[1] - 1, 1:normals.shape[2] - 1, :]
137 | return normals, lambda0
138 |
139 |
140 | def compute_nso(subset_1, subset_2, threshold, normals_1, normals_2):
141 | """ Computes normalized surface overlap from two input point clouds and their normals.
142 | :param subset_1: 3D cloud from image 1 (or 2) with shape [3 x num_points]
143 | :param threshold: Maximum distance for two points to be considered as overlapping.
144 | :param normals_1: Normals for image 1 (or 2) with shape [3 x num_points]
145 | :return: enclosure and concentration
146 | """
147 |
148 | # Compute the distance between each pair of pixels and find smallest distance
149 | set_1 = subset_1.reshape((3, -1, 1))
150 | set_2 = subset_2.reshape((3, 1, -1))
151 |
152 | diff = set_1 - set_2
153 | dist = np.sqrt(np.sum(np.square(diff), 0))
154 |
155 | s1_nns = np.argmin(dist, 1)
156 | s2_nns = np.argmin(dist, 0)
157 |
158 | s1_nn_dist = np.min(dist, 1)
159 | s2_nn_dist = np.min(dist, 0)
160 |
161 | # Compute cosine between normals of each point from cloud 1 and its nearest neighbor from cloud 2
162 | # and scale to interval [0,1]. Then vice-versa.
163 | v1 = normals_1
164 | v2 = normals_2[:, s1_nns]
165 |
166 | weights_1 = 0.5 * (np.sum(np.multiply(v1, v2), 0) + 1.0)
167 | weights_1[np.isnan(weights_1)] = 0.
168 |
169 | v1 = normals_2
170 | v2 = normals_1[:, s2_nns]
171 |
172 | weights_2 = 0.5 * (np.sum(np.multiply(v1, v2), 0) + 1.0)
173 | weights_2[np.isnan(weights_2)] = 0.
174 |
175 | # Compute weighted ratio of pixels with neighbor in distance smaller than threshold
176 | weighted_s1_has_nn = np.matmul((s1_nn_dist < threshold), weights_1)
177 | weighted_s2_has_nn = np.matmul((s2_nn_dist < threshold), weights_2)
178 |
179 | nso_x2y = weighted_s1_has_nn / set_1.shape[1]
180 | nso_y2x = weighted_s2_has_nn / set_2.shape[2]
181 |
182 | nso_dict = {
183 | 'x->y': nso_x2y,
184 | 'y->x': nso_y2x,
185 | }
186 |
187 | return nso_dict
188 |
189 |
190 | def process_surface_overlap_batch(batch, normals_folder, threshold, num_points):
191 |
192 | # Load precomputed normal maps and 3D clouds (output from compute_normals.py)
193 | normals_path1 = os.path.join(normals_folder, batch['image_id'][0] + '.pickle')
194 | normals_path2 = os.path.join(normals_folder, batch['image_id'][1] + '.pickle')
195 |
196 | with open(normals_path1, 'rb') as f:
197 | pick1 = pickle.load(f)
198 | with open(normals_path2, 'rb') as f:
199 | pick2 = pickle.load(f)
200 |
201 | normals_1 = pick1['normals'].transpose(2, 0, 1).reshape((3, -1))
202 | normals_2 = pick2['normals'].transpose(2, 0, 1).reshape((3, -1))
203 |
204 | coords_1 = pick1['points3d'].transpose(2, 0, 1).reshape((3, -1))
205 | coords_2 = pick2['points3d'].transpose(2, 0, 1).reshape((3, -1))
206 |
207 | coords_1 = np.concatenate([coords_1, np.ones((1, coords_1.shape[1]))], 0)
208 | coords_2 = np.concatenate([coords_2, np.ones((1, coords_2.shape[1]))], 0)
209 |
210 | # Delete invalid pixels with zero depth
211 | valid_coords_1 = coords_1[:, coords_1[2, :] > 0]
212 | valid_coords_2 = coords_2[:, coords_2[2, :] > 0]
213 |
214 | valid_normals_1 = normals_1[:, coords_1[2, :] > 0]
215 | valid_normals_2 = normals_2[:, coords_2[2, :] > 0]
216 |
217 | world_points_1 = cam2world(valid_coords_1, np.linalg.inv(batch['camera_pose'][0]))
218 | world_points_2 = cam2world(valid_coords_2, np.linalg.inv(batch['camera_pose'][1]))
219 |
220 | # Sample points for efficiency (if needed)
221 | if world_points_1.shape[1] > num_points:
222 | indices1 = np.random.choice(world_points_1.shape[1], size=num_points, replace=False)
223 | indices2 = np.random.choice(world_points_2.shape[1], size=num_points, replace=False)
224 | else:
225 | indices1 = np.random.choice(world_points_1.shape[1], size=world_points_1.shape[1], replace=False)
226 | indices2 = np.random.choice(world_points_2.shape[1], size=world_points_2.shape[1], replace=False)
227 |
228 | subset_1 = world_points_1[:3, indices1]
229 | subset_2 = world_points_2[:3, indices2]
230 |
231 | normals_1 = valid_normals_1[:, indices1]
232 | normals_2 = valid_normals_2[:, indices2]
233 |
234 | nso_dict = compute_nso(subset_1, subset_2, threshold, normals_1, normals_2)
235 |
236 | return nso_dict
237 |
238 |
239 | def pil_loader(path):
240 | """
241 | Load image from path with PIL. PIL is used to avoid
242 | ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
243 | """
244 | with open(path, 'rb') as file_handler:
245 | with PIL.Image.open(file_handler) as img:
246 | return img.convert('RGB')
247 |
248 |
249 | def cam2world(coords, pose):
250 | return np.matmul(pose, coords)
251 |
252 |
253 | def params2matrix(camera_params, resized_size, orig_size):
254 | """ From MegaDepth annotated camera parameter format to intrisincs matrix.
255 | Specific for MegaDepth: it also adjusts parameters taking into account discrepancy in image size between
256 | original color image (annotated) and aligned to depth images. More details in supplementary material.
257 | """
258 | scale_x = resized_size[0] / orig_size[0]
259 | scale_y = resized_size[1] / orig_size[1]
260 |
261 | return np.array([[scale_x * camera_params[0], scale_x * camera_params[3], scale_x * camera_params[1]],
262 | [0., scale_y * camera_params[0], scale_y * camera_params[2]],
263 | [0., 0., 1.]])
264 |
265 |
266 | def quat2mat(q, t):
267 | """
268 | Quaternion to rotation and translation in matrix form
269 | """
270 | p_mat = np.zeros((4, 4))
271 | r_mat = Rotation.from_quat(q).as_matrix()
272 | p_mat[:3, :3] = r_mat
273 | p_mat[:3, -1] = t
274 | p_mat[-1, -1] = 1.0
275 | return p_mat
276 |
277 |
278 | def read_depth_megadepth(filepath):
279 | hdf5_file_read = h5py.File(filepath, 'r')
280 | depth = hdf5_file_read.get('/depth')
281 | depth = np.array(depth)
282 | hdf5_file_read.close()
283 | return depth
284 |
--------------------------------------------------------------------------------
/src/datasets/megadepth_loader.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | MegaDepth dataset dataloaders.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import os
14 | import numpy as np
15 | import torch.utils.data
16 | import json
17 | import errno
18 | from PIL import Image
19 | from torchvision import transforms
20 | from .dataset_generator.utils import pil_loader, params2matrix, quat2mat
21 |
22 |
23 | class MegaDepthSurfacePairLoader(torch.utils.data.Dataset):
24 | """
25 | Loader for pairs of images and their respective normalized surface overlap (NSO)
26 | """
27 |
28 | def __init__(self, dataset_json_path, mode, image_hw=None, loader=pil_loader):
29 | super(MegaDepthSurfacePairLoader, self).__init__()
30 | if image_hw is None:
31 | image_hw = [256, 456]
32 | self.height, self.width = image_hw
33 | self.resizer = transforms.Resize((self.height, self.width), interpolation=Image.ANTIALIAS)
34 | self.to_tensor = transforms.ToTensor()
35 | self.loader = loader
36 | image_0, image_1, surface_overlaps = [], [], []
37 | num_pairs = 0
38 |
39 | with open(dataset_json_path, 'r') as f:
40 | dataset_json = json.load(f)
41 |
42 | for entry in dataset_json:
43 | if mode == 'train':
44 | image_list = entry['train_file']
45 | elif mode == 'val':
46 | image_list = entry['val_file']
47 | elif mode == 'test':
48 | image_list = entry['test_file']
49 | else:
50 | raise NotImplementedError("No valid split chosen.")
51 |
52 | scene_path = os.path.join(entry['path_sfm'], entry['scene'], 'images')
53 |
54 | with open(image_list, 'r') as f:
55 | for line in f.readlines():
56 | image_0_path = os.path.join(scene_path, line.split()[0])
57 | image_1_path = os.path.join(scene_path, line.split()[1])
58 |
59 | if os.path.isfile(image_0_path):
60 | image_0.append(image_0_path)
61 | else:
62 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), image_0_path)
63 |
64 | if os.path.isfile(image_1_path):
65 | image_1.append(image_1_path)
66 | else:
67 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), image_1_path)
68 |
69 | surface_overlaps.append(np.array(line.split()[2]).astype(np.float))
70 | num_pairs += 1
71 |
72 | self.image_0_paths = image_0
73 | self.image_1_paths = image_1
74 | self.surface_overlaps = surface_overlaps
75 | self.num_pairs = num_pairs
76 |
77 | def __getitem__(self, index):
78 |
79 | image_0_original = self.loader(self.image_0_paths[index])
80 | image_1_original = self.loader(self.image_1_paths[index])
81 |
82 | image_0 = self.to_tensor(self.resizer(image_0_original))
83 | image_1 = self.to_tensor(self.resizer(image_1_original))
84 |
85 | batch = {
86 | 'images': np.stack((image_0, image_1), 0),
87 | 'surface_overlap': self.surface_overlaps[index]
88 | }
89 | return batch
90 |
91 | def __len__(self):
92 | return self.num_pairs
93 |
94 |
95 | class MegaDepthImageLoader(torch.utils.data.Dataset):
96 | """
97 | Loader for single MegaDepth images. Apart from color and depth images paths
98 | it also returns camera pose and camera calibration parameters.
99 | """
100 |
101 | def __init__(self, dataset_json_path, mode, image_hw=None,
102 | loader=pil_loader):
103 | super(MegaDepthImageLoader, self).__init__()
104 |
105 | if image_hw is None:
106 | image_hw = [256, 456]
107 |
108 | self.height, self.width = image_hw
109 | self.resizer = transforms.Resize((self.height, self.width))
110 | self.to_tensor = transforms.ToTensor()
111 | self.loader = loader
112 | self.mode = mode
113 |
114 | with open(dataset_json_path, 'r') as f:
115 | dataset_json = json.load(f)
116 |
117 | image_ids, image_paths, depth_paths, aligned_paths = [], [], [], []
118 | camera_poses, camera_calibs = [], []
119 |
120 | for entry in dataset_json:
121 | scene_path = os.path.join(entry['path_sfm'], entry['scene'], 'images')
122 | # if you wish to use this loader to load single images and input in the network, for example to perform
123 | # image retrieval, just add an additional mode with the appropriate json entry, everything else is ready.
124 | if self.mode == "data_generation":
125 | files_pointer = entry['list_images_with_depth']
126 | else:
127 | raise NotImplementedError("No valid mode chosen.")
128 |
129 | image_ids.extend([line.split()[0] for line in open(files_pointer)])
130 |
131 | image_paths.extend([os.path.join(scene_path, line.split()[0]) for line in open(files_pointer)])
132 | for path_i in image_paths:
133 | if not os.path.isfile(path_i):
134 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path_i)
135 |
136 | depth_paths.extend(
137 | [os.path.join(entry['path_depth'], entry['scene'], 'dense0', 'depths', f"{line.split('.')[0]}.h5") for
138 | line in open(files_pointer)])
139 |
140 | for path_d in depth_paths:
141 | if not os.path.isfile(path_d):
142 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path_d)
143 |
144 | aligned_paths.extend(
145 | [os.path.join(entry['path_depth'], entry['scene'], 'dense0', 'imgs', f"{line.split('.')[0]}.jpg") for
146 | line in open(files_pointer)])
147 |
148 | for path_a in image_paths:
149 | if not os.path.isfile(path_a):
150 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path_a)
151 |
152 | camera_params, annotated_sizes = {}, {}
153 | with open(os.path.join(entry['path_sfm'], entry['scene'], 'sparse', 'manhattan', '0', 'cameras.txt'),
154 | 'r') as f:
155 | lines = f.readlines()[3:]
156 | for line in lines:
157 | tmp_str = line.split(' ')
158 | # CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
159 | camera_id = tmp_str[0]
160 | camera_params[camera_id] = [float(params) for params in tmp_str[4:]]
161 | annotated_sizes[camera_id] = tuple([int(tmp_str[2]), int(tmp_str[3])])
162 |
163 | reader = open(os.path.join(entry['path_sfm'], entry['scene'], 'sparse', 'manhattan', '0', 'images.txt'),
164 | 'r')
165 |
166 | poses_dict, cameras_dict = {}, {}
167 | for i, line in enumerate(reader):
168 | # image list with two lines of data per image:
169 | # IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME
170 | # POINTS2D[] as (X, Y, POINT3D_ID)
171 | if i % 2 == 0 and i > 3:
172 | line = line.split()
173 | tmp_img_name = line[-1]
174 | tmp_camera_id = line[-2]
175 | tmp_pose = np.array(line[1:8]).astype(np.float)
176 | # rearrange quaternion from [qw qx qy qz] to [qx qy qz qw]
177 | tmp_quat = np.concatenate((tmp_pose[1:4], tmp_pose[0]), axis=None)
178 | tmp_t = tmp_pose[4:]
179 | absolute_path_to_image = os.path.join(entry['path_sfm'], entry['scene'], 'images', tmp_img_name)
180 | poses_dict[absolute_path_to_image] = quat2mat(tmp_quat, tmp_t)
181 | cameras_dict[absolute_path_to_image] = tmp_camera_id
182 | reader.close()
183 |
184 | for i in range(len(image_paths)):
185 | camera_poses.append(poses_dict[image_paths[i]])
186 | # color image aligned to depth may have different size as original color image. See supplementary.
187 | tmp_camera = cameras_dict[image_paths[i]]
188 | tmp_params = camera_params[tmp_camera]
189 | tmp_anno_size = annotated_sizes[tmp_camera]
190 | image_aligned = Image.open(os.path.join(aligned_paths[i]))
191 | adjusted_calib = params2matrix(tmp_params, image_aligned.size, tmp_anno_size)
192 | camera_calibs.append(adjusted_calib)
193 |
194 | self.image_ids = image_ids
195 | self.image_paths = image_paths
196 | self.depth_paths = depth_paths
197 | self.camera_poses = camera_poses
198 | self.camera_calibs = camera_calibs
199 |
200 | def __getitem__(self, index):
201 | if self.mode == 'data_generation':
202 | dict_batch = {
203 | 'image_id': self.image_ids[index],
204 | 'image_path': self.image_paths[index],
205 | 'depth_path': self.depth_paths[index],
206 | 'camera_calib': self.camera_calibs[index],
207 | 'camera_pose': self.camera_poses[index],
208 | }
209 | else:
210 | dict_batch = {
211 | 'image': self.to_tensor(self.resizer(self.loader(self.image_paths[index]))),
212 | 'image_id': self.image_ids[index],
213 | 'image_path': self.image_paths[index],
214 | 'depth_path': self.depth_paths[index],
215 | 'camera_calib': self.camera_calibs[index],
216 | 'camera_pose': self.camera_poses[index],
217 | }
218 | return dict_batch
219 |
220 | def __len__(self):
221 | return len(self.image_paths)
222 |
--------------------------------------------------------------------------------
/src/model.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Network model.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import numpy as np
14 | import torch
15 | import torch.nn as nn
16 | import torch.nn.functional as F
17 | import torchvision.models as models
18 |
19 |
20 | class ResnetEncoder(nn.Module):
21 | def __init__(self, num_layers, box_ndim):
22 | super(ResnetEncoder, self).__init__()
23 | self.num_ch_enc = np.array([64, 64, 128, 256, 512])
24 |
25 | self.last_layer = 512
26 | if num_layers == 50:
27 | self.last_layer = 2048
28 | elif num_layers == 101:
29 | self.last_layer = 2048
30 |
31 | resnets = {18: models.resnet18,
32 | 34: models.resnet34,
33 | 50: models.resnet50,
34 | 101: models.resnet101,
35 | 152: models.resnet152}
36 |
37 | encoder = resnets[num_layers](True)
38 | self.encoder = encoder
39 |
40 | self.layer0 = nn.Sequential(encoder.conv1, encoder.bn1, encoder.relu)
41 | self.layer1 = nn.Sequential(encoder.maxpool, encoder.layer1)
42 | self.layer2 = encoder.layer2
43 | self.layer3 = encoder.layer3
44 | self.layer4 = encoder.layer4
45 | self.fc1 = nn.Linear(self.last_layer // 4, box_ndim)
46 | self.fc2 = nn.Linear(self.last_layer // 4, box_ndim)
47 | self.fc = nn.Linear(self.last_layer, self.last_layer // 4)
48 | del encoder
49 |
50 | if num_layers > 34:
51 | self.num_ch_enc[1:] *= 4
52 |
53 | def forward(self, input_image):
54 | # Normalize the input colorspace
55 | x = (input_image - 0.45) / 0.225
56 |
57 | x = self.layer0(x)
58 | x = self.layer1(x)
59 | x = self.layer2(x)
60 | x = self.layer3(x)
61 | x = self.layer4(x)
62 | x = x.view(x.shape[0], x.shape[1], -1)
63 | x = torch.sum(x, 2) / x.shape[2]
64 | x = self.fc(x)
65 | x = F.relu(x)
66 | b1 = self.fc1(x)
67 | b2 = self.fc2(x)
68 | b2 = F.softplus(b2) + 0.1 # we want to avoid small boxes
69 |
70 | return b1, b2
71 |
--------------------------------------------------------------------------------
/src/options.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings
3 |
4 | Options parser.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import argparse
14 |
15 |
16 | class OptionsBoxesTrain:
17 | def __init__(self):
18 | self.options = None
19 | self.parser = argparse.ArgumentParser(description='Predicting Visual Overlap of Images: Training Loop.')
20 | self.parser.add_argument('--name', default='debug', type=str,
21 | help='Name of the experiment (default: debug).')
22 | self.parser.add_argument('--dataset', type=str, default='megadepth',
23 | choices=['megadepth'], help='Parent dataset for scene (default: megadepth).')
24 | self.parser.add_argument('--dataset_json', default='data/dataset_jsons/megadepth/bigben.json',
25 | help='Path to dataset json files.')
26 | self.parser.add_argument('--log_path', default=None, type=str,
27 | help='Path to model and log files. If not specified it saves in current directory.')
28 | self.parser.add_argument('--model', default='resnet50', type=str,
29 | choices=['resnet18', 'resnet50', 'resnet101'],
30 | help='Backbone to use. (default: resnet50).')
31 | self.parser.add_argument('--box_ndim', default=32, type=int,
32 | help='Box embedding dimension (default: 32).')
33 | self.parser.add_argument('--input_hw', type=int, nargs='+', default=(256, 456),
34 | help='Network input height and width (default: (256,456).')
35 | self.parser.add_argument('--batch_size', type=int, default=32,
36 | help='Input batch size for training (default: 32).')
37 | self.parser.add_argument('--learning_rate', type=float, default=0.0001,
38 | help='Learning rate (default: 0.0001)')
39 | self.parser.add_argument('--num_epochs', type=int, default=20,
40 | help='Number of epochs to train (default: 20).')
41 | self.parser.add_argument('--log_frequency', type=int, default=250,
42 | help='Number of batches to run validation and log (default: 250).')
43 | self.parser.add_argument('--save_frequency', default=250, type=int,
44 | help='Save the model every N steps (default: 250).')
45 | self.parser.add_argument('--num_workers', type=int, default=8,
46 | help='Number of workers for data loading (default: 8).')
47 | self.parser.add_argument('--seed', default=42, type=int,
48 | help='Random seed for reproducibility.')
49 | self.parser.add_argument('--num_gpus', default=1, type=int,
50 | help='Number of gpus used for training (default: 1)')
51 | self.parser.add_argument('--backend', default='dp', type=str,
52 | help='Lightning distributed backend (default: dp)')
53 |
54 | def parse(self, *args, **kwargs):
55 | self.options = self.parser.parse_args(*args, **kwargs)
56 | return self.options
57 |
58 |
59 | class OptionsBoxesTest:
60 | def __init__(self):
61 | self.options = None
62 | self.parser = argparse.ArgumentParser(description='Predicting Visual Overlap of Images: Test Loop')
63 | self.parser.add_argument('--dataset', type=str, default='megadepth',
64 | choices=['megadepth'], help='Parent dataset for scene.')
65 | self.parser.add_argument('--dataset_json', default='data/dataset_jsons/megadepth/bigben.json',
66 | help='Path to dataset json files.')
67 | self.parser.add_argument('--model_scene', default='bigben', type=str,
68 | choices=['bigben', 'notredame', 'florence', 'venice'], help='Choose the model to load')
69 | self.parser.add_argument('--model', default='resnet50', type=str,
70 | choices=['resnet18', 'resnet50', 'resnet101'], help='Backbone of saved model.')
71 | self.parser.add_argument('--box_ndim', default=32, type=int,
72 | help='Box embedding dimension (default: 32)')
73 | self.parser.add_argument('--input_hw', type=int, nargs='+', default=(256, 456),
74 | help='Network input height and width (default:(256,456)')
75 | self.parser.add_argument('--batch_size', type=int, default=8,
76 | help='Input batch size for training (default: 8)')
77 | self.parser.add_argument('--num_workers', type=int, default=8,
78 | help='Number of workers for data loading (default: 8)')
79 |
80 | def parse(self, *args, **kwargs):
81 | self.options = self.parser.parse_args(*args, **kwargs)
82 | return self.options
--------------------------------------------------------------------------------
/src/test.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Example of evaluating a trained model for surface overlap prediction.
5 | If provided models are used it reproduces results on Table 1 of the paper.
6 |
7 | Copyright Niantic 2020. Patent Pending. All rights reserved.
8 |
9 | This software is licensed under the terms of the Image-box-overlap licence
10 | which allows for non-commercial use only, the full terms of which are made
11 | available in the LICENSE file.
12 | """
13 |
14 | import torch
15 | import os
16 | from tqdm import tqdm
17 | from .options import OptionsBoxesTest
18 | from .model import ResnetEncoder
19 | from .datasets import MegaDepthSurfacePairLoader
20 | from .utils import box_overlap_soft, checkpoint_loader, download_model_if_doesnt_exist
21 |
22 |
23 | def main():
24 | # Parse command line arguments
25 | opts = OptionsBoxesTest().parse()
26 | # Download the trained model if it doesn't exist to reproduce results.
27 | download_model_if_doesnt_exist(opts.model_scene)
28 |
29 | # If you train your own models using train.py, just change the path and use the .cpkt extension instead
30 | state_dict = checkpoint_loader(os.path.join('models', opts.model_scene, f'{opts.model_scene}.pth.tar'))
31 | test_loader = torch.utils.data.DataLoader(MegaDepthSurfacePairLoader(opts.dataset_json, mode='test'),
32 | batch_size=opts.batch_size,
33 | shuffle=False,
34 | num_workers=opts.num_workers,
35 | pin_memory=True)
36 |
37 | # Initializes the model. Gets number of Resnet layers from name
38 | model = ResnetEncoder(int(opts.model[-2:]), opts.box_ndim)
39 | model.load_state_dict(state_dict)
40 |
41 | if torch.cuda.is_available():
42 | model.to(torch.device('cuda'))
43 | model.eval()
44 |
45 | overlap_preds, overlap_gts = [], []
46 |
47 | print("Processing batches...")
48 | with torch.no_grad():
49 | for batch_idx, batch in enumerate(tqdm(test_loader)):
50 | if torch.cuda.is_available():
51 | for key, ipt in batch.items():
52 | batch[key] = ipt.cuda()
53 | bx_c, bx_e = model(batch['images'][:, 0])
54 | by_c, by_e = model(batch['images'][:, 1])
55 | overlap_pred = box_overlap_soft(bx_c, bx_e, by_c, by_e)
56 | overlap_preds.append(overlap_pred)
57 | overlap_gts.append(batch['surface_overlap'].type_as(overlap_pred))
58 |
59 | overlap_gts = torch.cat(overlap_gts)
60 | overlap_preds = torch.cat(overlap_preds)
61 | diff = overlap_preds - overlap_gts
62 | diff_abs_pair = torch.abs(diff).reshape((int(len(diff) / 2), 2))
63 |
64 | print("Results:")
65 | print("RMSE: {:.3f}".format(torch.sqrt(torch.mean(torch.sum(diff_abs_pair ** 2, 1))).cpu().numpy()))
66 | print("L1 Norm: {:.3f}".format(torch.mean(torch.sum(diff_abs_pair, 1)).cpu().numpy()))
67 | print("Acc. < 0.1: {:.1f}%".format(100*torch.mean((torch.abs(diff) < 0.1).double()).cpu().numpy()))
68 |
69 |
70 | if __name__ == '__main__':
71 | main()
72 |
--------------------------------------------------------------------------------
/src/train.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Training loop.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import os
14 | import torch
15 | import pytorch_lightning as pl
16 | from .options import OptionsBoxesTrain
17 | from .datasets import MegaDepthSurfacePairLoader
18 | from .trainer import BoxSurfaceOverlap
19 |
20 |
21 | def main():
22 | # Parse command line arguments
23 | opts = OptionsBoxesTrain().parse()
24 | # Random seeds
25 | pl.seed_everything(opts.seed)
26 | # Setup logging
27 | path_logs = os.path.join(opts.log_path, opts.name) \
28 | if opts.log_path is not None else os.path.join(os.getcwd(), opts.name)
29 |
30 | if not os.path.exists(path_logs):
31 | os.makedirs(path_logs)
32 |
33 | logger = pl.loggers.TensorBoardLogger(path_logs, name=opts.name)
34 |
35 | # Dataloader initialization
36 | if opts.dataset.lower() == 'megadepth':
37 | train_dataloader = torch.utils.data.DataLoader(
38 | MegaDepthSurfacePairLoader(opts.dataset_json, mode='train'),
39 | batch_size=opts.batch_size,
40 | shuffle=True, num_workers=opts.num_workers,
41 | pin_memory=True)
42 | val_dataloader = torch.utils.data.DataLoader(
43 | MegaDepthSurfacePairLoader(opts.dataset_json, mode='val'),
44 | batch_size=opts.batch_size,
45 | shuffle=False, num_workers=opts.num_workers,
46 | pin_memory=True)
47 | else:
48 | raise NotImplemented(f"Dataset {opts.dataset} not implemented!")
49 |
50 | # Initialize network
51 | model = BoxSurfaceOverlap(opts)
52 |
53 | # Initialize training loop
54 | trainer = pl.Trainer(default_save_path=path_logs,
55 | logger=logger,
56 | gpus=opts.num_gpus,
57 | val_check_interval=opts.log_frequency,
58 | distributed_backend=opts.backend,
59 | deterministic=True,
60 | fast_dev_run=False,
61 | max_epochs=opts.num_epochs)
62 |
63 | # Train
64 | trainer.fit(model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader)
65 |
66 |
67 | if __name__ == '__main__':
68 | main()
69 |
--------------------------------------------------------------------------------
/src/trainer.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Trainer definition within PyTorch Lightning's framework.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | import torch
14 | import pytorch_lightning as pl
15 | from .utils import box_overlap_soft
16 | from .model import ResnetEncoder
17 |
18 |
19 | class BoxSurfaceOverlap(pl.LightningModule):
20 |
21 | def __init__(self, hparams):
22 | super(BoxSurfaceOverlap, self).__init__()
23 | self.hparams = hparams
24 | self.box_ndim = hparams.box_ndim
25 | # Get number of Resnet layers from name
26 | self.model = ResnetEncoder(int(self.hparams.model[-2:]), self.box_ndim)
27 |
28 | def forward(self, x):
29 | return self.model.forward(x)
30 |
31 | def _shared_eval(self, batch):
32 | bx_center, bx_extent = self.forward((batch['images'][:, 0]))
33 | by_center, by_extent = self.forward((batch['images'][:, 1]))
34 |
35 | overlap_pred = box_overlap_soft(bx_center, bx_extent, by_center, by_extent)
36 | # also moves tensor to overlap_pred's device
37 | overlap_gt = batch['surface_overlap'].type_as(overlap_pred)
38 |
39 | loss = torch.nn.MSELoss(reduction='mean')
40 | loss = loss(overlap_pred, overlap_gt)
41 |
42 | accuracy = (torch.abs((overlap_pred - overlap_gt)) < 0.1).sum().double() / len(overlap_gt)
43 |
44 | return loss, accuracy
45 |
46 | def training_step(self, batch, batch_idx):
47 | loss, accuracy = self._shared_eval(batch)
48 | return {'loss': loss,
49 | 'progress_bar': {'accuracy': accuracy},
50 | 'log': {'train_loss': loss, 'train_accuracy': accuracy}}
51 |
52 | def validation_step(self, batch, batch_idx):
53 | loss, accuracy = self._shared_eval(batch)
54 | return {'val_loss': loss, 'val_accuracy': accuracy}
55 |
56 | def validation_epoch_end(self, outputs):
57 | avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
58 | avg_acc = torch.stack([x['val_accuracy'] for x in outputs]).mean()
59 | return {'val_loss': avg_loss, 'log': {'val_loss': avg_loss, 'val_accuracy': avg_acc}}
60 |
61 | def configure_optimizers(self):
62 | optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
63 | scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.95)
64 | return [optimizer], [scheduler]
--------------------------------------------------------------------------------
/src/utils.py:
--------------------------------------------------------------------------------
1 | """
2 | Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings.
3 |
4 | Util functions. It includes important functions as box overlap computation.
5 |
6 | Copyright Niantic 2020. Patent Pending. All rights reserved.
7 |
8 | This software is licensed under the terms of the Image-box-overlap licence
9 | which allows for non-commercial use only, the full terms of which are made
10 | available in the LICENSE file.
11 | """
12 |
13 | from __future__ import absolute_import, division, print_function
14 | import os
15 | import numpy as np
16 | import torch
17 | import hashlib
18 | import zipfile
19 | from six.moves import urllib
20 | from torch.nn import functional
21 |
22 |
23 | def box_overlap_soft(bx_center, bx_extent, by_center, by_extent, box_rho=5):
24 | """
25 | Computes soft box overlap of two boxes in D-dimensional space
26 | :param bx_center: D-dimensional vector with box x center coordinates
27 | :param bx_extent: D-dimensional vector with box x size
28 | :param by_center: D-dimensional vector with box y center coordinates
29 | :param by_extent: D-dimensional vector with box y size
30 | :param box_rho: temperature parameter, $\rho$ in the paper
31 | :return: box overlap of bx and by
32 | """
33 | bx_min, by_min = bx_center - 0.5 * bx_extent, by_center - 0.5 * by_extent
34 | bx_max, by_max = bx_min + bx_extent, by_min + by_extent
35 |
36 | lower_upper_bound = torch.min(torch.stack([bx_max, by_max], dim=-1), dim=-1)[0]
37 | upper_lower_bound = torch.max(torch.stack([bx_min, by_min], dim=-1), dim=-1)[0]
38 |
39 | flat_overlap = lower_upper_bound - upper_lower_bound
40 |
41 | area_x_exp = torch.exp(
42 | torch.sum(torch.log(functional.softplus((bx_max - bx_min) * box_rho) / box_rho + 1e-10), 1))
43 | intersect_exp = torch.exp(
44 | torch.sum(torch.log(functional.softplus(flat_overlap * box_rho) / box_rho + 1e-10), 1))
45 |
46 | return intersect_exp / area_x_exp
47 |
48 |
49 | def box_overlap(bx_center, bx_extent, by_center, by_extent):
50 | """
51 | Computes box overlap of two boxes in D-dimensional space
52 | :param bx_center: D-dimensional vector with box x center coordinates
53 | :param bx_extent: D-dimensional vector with box x size
54 | :param by_center: D-dimensional vector with box y center coordinates
55 | :param by_extent: D-dimensional vector with box y size
56 | :return: box overlap of bx and by
57 | """
58 | bx_min, by_min = bx_center - 0.5 * bx_extent, by_center - 0.5 * by_extent
59 | bx_max, by_max = bx_min + bx_extent, by_min + by_extent
60 |
61 | lower_upper_bound = torch.min(torch.stack([bx_max, by_max], dim=-1), dim=-1)[0]
62 | upper_lower_bound = torch.max(torch.stack([bx_min, by_min], dim=-1), dim=-1)[0]
63 |
64 | intersection = torch.prod(lower_upper_bound - upper_lower_bound, dim=-1)
65 | area_x = torch.prod(bx_max - bx_min, dim=-1)
66 |
67 | zeros_tensor = torch.zeros(intersection.shape).type_as(intersection)
68 |
69 | return torch.max(torch.stack([zeros_tensor, intersection / (area_x + 1e-10)]), dim=0)[0]
70 |
71 |
72 | def compute_relative_scale(enclosure, concentration, im_x_size, im_y_size):
73 | """
74 | Computes relative scale between two images using predicted enclosure and concentration
75 | """
76 | x_width, x_height = im_x_size
77 | y_width, y_height = im_y_size
78 |
79 | x_area = x_width * x_height
80 | y_area = y_width * y_height
81 |
82 | x_scale, y_scale = 1., 1.
83 |
84 | ratio = enclosure / concentration
85 |
86 | y_scale = np.sqrt(ratio * x_area / y_area)
87 |
88 | if y_scale > 1.: # we want to downsample, not upsample
89 | x_scale = 1. / y_scale
90 | y_scale = 1.
91 |
92 | return x_scale, y_scale
93 |
94 |
95 | def download_model_if_doesnt_exist(model_name):
96 | """
97 | If pretrained MegaDepth model doesn't exist, download and unzip it
98 | """
99 | # values are tuples of (, )
100 | download_paths = {
101 | 'bigben':
102 | ('https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/bigben.zip',
103 | 'df43091df376c70e9701ee4db4f903ef'),
104 | 'notredame':
105 | ('https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/notredame'
106 | '.zip',
107 | '2c228d48cac1f911b53b44de190633b6'),
108 | 'venice':
109 | ('https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/venice.zip',
110 | '2996a6e846cb94333ccd83a77445b913'),
111 | 'florence':
112 | ('https://storage.googleapis.com/niantic-lon-static/research/image-box-overlap/models/megadepth/florence'
113 | '.zip',
114 | '084194eb90a7b3e49b542e7b8e11451e'),
115 | }
116 |
117 | if not os.path.exists('models'):
118 | os.makedirs('models')
119 |
120 | model_path = os.path.join('models', model_name)
121 |
122 | def check_file_matches_md5(checksum, fpath):
123 | if not os.path.exists(fpath):
124 | return False
125 | with open(fpath, 'rb') as f:
126 | current_md5checksum = hashlib.md5(f.read()).hexdigest()
127 | return current_md5checksum == checksum
128 |
129 | # See if we have the model already downloaded...
130 | if not os.path.exists(os.path.join(model_path, f'{model_name}.pth.tar')):
131 |
132 | model_url, required_md5checksum = download_paths[model_name]
133 |
134 | if not check_file_matches_md5(required_md5checksum, f'{model_path}.zip'):
135 | print(f"-> Downloading pretrained model to {model_path}.zip")
136 | urllib.request.urlretrieve(model_url, f'{model_path}.zip')
137 |
138 | if not check_file_matches_md5(required_md5checksum, f'{model_path}.zip'):
139 | print(" Failed to download a file which matches the checksum - quitting")
140 | quit()
141 |
142 | print(" Unzipping model...")
143 | with zipfile.ZipFile(f'{model_path}.zip', 'r') as f:
144 | f.extractall(model_path)
145 |
146 | print(f" Model unzipped to {model_path}")
147 |
148 |
149 | def checkpoint_loader(ckpt_path):
150 | """
151 | Returns saved weights providing compatibility with saved models to reproduce paper results.
152 | """
153 | checkpoint = torch.load(ckpt_path)
154 | # trained models for ECCV paper using PyTorch (.pth.tar extension)
155 | if 'model_state_dict' in checkpoint:
156 | state_dict = checkpoint['model_state_dict']
157 | # trained models using new implementation with Lightning (.cpkt extension)
158 | elif 'state_dict' in checkpoint:
159 | state_dict = checkpoint['state_dict']
160 | state_dict_new = {}
161 | for key, value in state_dict.items():
162 | # lightning adds the prefix "model" to the module name layers, removing that
163 | new_key = key[6:]
164 | state_dict_new[new_key] = value
165 | state_dict = state_dict_new
166 | else:
167 | raise ModelVersionException
168 | return state_dict
169 |
170 |
171 | class ModelVersionException(Exception):
172 | def __init__(self, msg="Incompatible model version. Use provided ones or the ones trained with the provided code",
173 | *args, **kwargs):
174 | super().__init__(msg, *args, **kwargs)
175 |
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/test.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | python -m src.test \
3 | --model_scene venice \
4 | --model resnet50 \
5 | --dataset_json data/dataset_jsons/megadepth/venice.json
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/train.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | python -m src.train \
3 | --name my_model \
4 | --dataset_json data/dataset_jsons/megadepth/bigben.json \
5 | --box_ndim 32 \
6 | --batch_size 32 \
7 | --model resnet50 \
8 | --num_gpus 1 \
9 | --backend dp
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