Initial commit

Author: lihaipeng

.gitignore

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+gifs
+*.npy
+__pycache__
+/work/
+*.pyc
+*.pkl
+*.jpg
+*.jpeg
+*.png
+*.json
+train_log
+*.swp
+*.tar
+*best-epoch-*
+*epoch-*
+.DS_Store
+.nfs*
+.ipynb_checkpoints
+*.model
+*.gz
+*.mp4
+test.sh
+others
+images
+*.nori
+tmp*.py
+*.dlc
+*.ipynb*
+snpe/
+.idea/
+*.new
+tmp*
+result*
+data/
+experiments/
+.env/
+gyroFlow
+GOF_Clean
+GOF_Final

LICENSE

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+This repo is licensed under the Apache License, Version 2.0 (the "License")
+
+Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+Apache License
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+END OF TERMS AND CONDITIONS

README.md

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+# [ICCV 2021] GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning
+
+This is the official implementation of our ICCV2021 paper [GyroFlow](https://openaccess.thecvf.com/content/ICCV2021/html/Li_GyroFlow_Gyroscope-Guided_Unsupervised_Optical_Flow_Learning_ICCV_2021_paper.html).
+
+Our presentation video: [[Youtube](https://www.youtube.com/watch?v=6gh40PyWdHM)][[Bilibili](https://www.bilibili.com/video/BV1Tr4y127kd/)].
+
+## Our Poster
+
+![image](gyroflow_poster.jpg)
+
+## Dependencies
+
+* MegEngine==1.6.0
+* Other requirements please refer to`requirements.txt`.
+
+## Data Preparation
+
+### GOF-Train
+
+2021.11.15: We release the GOF_Train V1 that contains 2000 samples.
+
+The download link is [GoogleDrive](https://drive.google.com/file/d/1eG9W-AlKrQ_fsxT4As6wzGaewCksYxnK/view?usp=sharing). Put the data into `./dataset/GOF_Train`, and the contents of directories are as follows:
+
+```
+./dataset/GOF_Train
+├── sample_0
+│   ├── img1.png
+│   ├── img2.png
+│   ├── gyro_homo.npy
+├── sample_1
+│   ├── img1.png
+│   ├── img2.png
+│   ├── gyro_homo.npy
+.....................
+├── sample_1999
+│   ├── img1.png
+│   ├── img2.png
+│   ├── gyro_homo.npy
+
+```
+
+### GOF-Clean
+
+For quantitative evaluation, including input frames and the corresponding gyro readings, a ground-truth optical flow is required for each pair.
+
+The download link is [GoogleDrive](https://drive.google.com/file/d/1X9V_DT1JHJti6BeWnWnqAfR4QEzvFQoE/view?usp=sharing). Move the file to `./dataset/GOF_Clean.npy`.
+
+### GOF-Final
+
+The most difficult cases are collected in GOF-Final.
+
+The download link is [GoogleDrive](https://drive.google.com/file/d/1BMuTNygl_W0xWMoHlck_h60inyu7BE1x/view?usp=sharing). Move the file to `./dataset/GOF_Final.npy`.
+
+## Training and Evaluation
+
+### Training
+
+To train the model, you can just run:
+
+```
+python train.py --model_dir experiments
+```
+
+### Evaluation
+
+Load the pretrained checkpoint and run:
+
+```
+python evaluate.py --model_dir experiments --restore_file experiments/val_model_best.pth
+```
+
+We've updated the GOF (both trainset and testset), so the performance is a little bit different from the results reported in our paper.
+
+MegEngine checkpoint can be download via [Google Drive]().
+
+## Citation
+
+If you think this work is useful for your research, please kindly cite:
+
+```
+@InProceedings{Li_2021_ICCV,
+    author    = {Li, Haipeng and Luo, Kunming and Liu, Shuaicheng},
+    title     = {GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    month     = {October},
+    year      = {2021},
+    pages     = {12869-12878}
+}
+```
+
+## Acknowledgments
+
+In this project we use (parts of) the official implementations of the following works:
+
+* [ARFlow](https://github.com/lliuz/ARFlow)
+* [UpFlow](https://github.com/coolbeam/UPFlow_pytorch)
+* [RAFT](https://github.com/princeton-vl/RAFT)
+* [DeepOIS](https://github.com/lhaippp/DeepOIS)
+
+We thank the respective authors for open sourcing their methods.

common/__init__.py

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+# -*- coding: utf-8 -*-
+# This repo is licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import glob
+import json
+import os
+import time
+
+from prettytable import PrettyTable
+
+
+def monitor():
+    total_table = []
+    for dataset in ['val']:
+        table = PrettyTable(['exp_name', 'exp_id', 'accuracy'],
+                            sortby='accuracy',
+                            header_style='upper',
+                            valign='m',
+                            title='{} Result'.format(dataset),
+                            reversesort=True)
+
+        valid_dirs = [
+            'experiment_learning_rate',
+        ]
+        exp_dirs = glob.glob('./experiments/*/exp_*')
+        exp_dirs = [i for i in exp_dirs if i.split('/')[-2] in valid_dirs]
+
+        for exp_dir in exp_dirs:
+            params_json_path = os.path.join(exp_dir, 'params.json')
+            results_json_path = os.path.join(exp_dir, 'val_metrics_best.json')
+            logs_txt_path = os.path.join(exp_dir, 'log.txt')
+            if not os.path.exists(params_json_path) or not os.path.exists(
+                    results_json_path):
+                continue
+
+            params = json.load(open(params_json_path, 'r'))
+            results = json.load(open(results_json_path, 'r'))
+            # exp info
+            exp_name = exp_dir.split('/')[-2]
+            exp_id = exp_dir.split('_')[-1]
+            # model = params['model']
+            # results
+            accuracy = '{:>8.4f}'.format(results['accuracy'])
+            cur_row = [exp_name, exp_id, accuracy]
+            table.add_row(cur_row)
+        print(table)
+        total_table.append(str(table))
+
+
+def run(interval):
+    while True:
+        monitor()
+        time.sleep(interval)
+
+
+if __name__ == '__main__':
+    interval = 10 * 60
+    run(interval)