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- DiffIR2VR_fps_10.mp4 +0 -0
- GMFlow/LICENSE +201 -0
- GMFlow/README.md +239 -0
- GMFlow/data/__init__.py +7 -0
- GMFlow/data/chairs_split.txt +22872 -0
- GMFlow/data/datasets.py +312 -0
- GMFlow/data/transforms.py +284 -0
- GMFlow/environment.yml +162 -0
- GMFlow/evaluate.py +689 -0
- GMFlow/gmflow/__init__.py +0 -0
- GMFlow/gmflow/__pycache__/__init__.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/__init__.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/__init__.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/backbone.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/backbone.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/backbone.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/geometry.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/geometry.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/geometry.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/gmflow.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/gmflow.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/gmflow.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/matching.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/matching.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/matching.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/position.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/position.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/position.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/transformer.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/transformer.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/transformer.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/trident_conv.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/trident_conv.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/trident_conv.cpython-39.pyc +0 -0
- GMFlow/gmflow/__pycache__/utils.cpython-310.pyc +0 -0
- GMFlow/gmflow/__pycache__/utils.cpython-38.pyc +0 -0
- GMFlow/gmflow/__pycache__/utils.cpython-39.pyc +0 -0
- GMFlow/gmflow/backbone.py +117 -0
- GMFlow/gmflow/geometry.py +96 -0
- GMFlow/gmflow/gmflow.py +171 -0
- GMFlow/gmflow/matching.py +83 -0
- GMFlow/gmflow/position.py +46 -0
- GMFlow/gmflow/transformer.py +409 -0
- GMFlow/gmflow/trident_conv.py +90 -0
- GMFlow/gmflow/utils.py +97 -0
- GMFlow/loss.py +37 -0
- GMFlow/main.py +557 -0
- GMFlow/scripts/demo.sh +63 -0
- GMFlow/scripts/evaluate.sh +83 -0
- GMFlow/scripts/submission.sh +67 -0
DiffIR2VR_fps_10.mp4
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GMFlow/LICENSE
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GMFlow/README.md
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|
| 1 |
+
# GMFlow
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
Official PyTorch implementation of paper:
|
| 5 |
+
|
| 6 |
+
[**GMFlow: Learning Optical Flow via Global Matching**](https://arxiv.org/abs/2111.13680), **CVPR 2022, Oral**
|
| 7 |
+
|
| 8 |
+
Authors: [Haofei Xu](https://haofeixu.github.io/), [Jing Zhang](https://scholar.google.com.hk/citations?user=9jH5v74AAAAJ), [Jianfei Cai](https://jianfei-cai.github.io/), [Hamid Rezatofighi](https://scholar.google.com/citations?user=VxAuxMwAAAAJ), [Dacheng Tao](https://scholar.google.com/citations?user=RwlJNLcAAAAJ)
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
**11/15/2022 Update: Check out our new work: [Unifying Flow, Stereo and Depth Estimation](https://haofeixu.github.io/unimatch/) and code: [unimatch](https://github.com/autonomousvision/unimatch) for extending GMFlow to stereo and depth tasks. [More pretrained GMFlow models](https://github.com/autonomousvision/unimatch/blob/master/MODEL_ZOO.md) with different speed-accuracy trade-offs are also released. Check out our [Colab](https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing) and [HuggingFace](https://huggingface.co/spaces/haofeixu/unimatch) demo to play with GMFlow in your browser!**
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
**A [video introduction](https://www.bilibili.com/video/BV18A4y1R7PL) (in Chinese) of GMFlow is available at bilibili!**
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
https://user-images.githubusercontent.com/19343475/174446408-520b8a6c-9714-4ff3-978c-98e23ab29c1f.mp4
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
We streamline the optical flow estimation pipeline by reformulating optical flow as a **global matching** problem.
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
<p align="center"><img width=90% src="assets/gmflow.png"></p>
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
## Highlights
|
| 37 |
+
|
| 38 |
+
- **Flexible & Modular design**
|
| 39 |
+
|
| 40 |
+
We decompose the end-to-end optical flow framework into five components:
|
| 41 |
+
|
| 42 |
+
feature extraction, feature enhancement, feature matching, flow propagation and flow refinement.
|
| 43 |
+
|
| 44 |
+
One can easily construct a customized optical flow model by combining different components.
|
| 45 |
+
|
| 46 |
+
- **High accuracy**
|
| 47 |
+
|
| 48 |
+
With only one refinement, GMFlow outperforms 31-refinements RAFT on the challenging Sintel benchmark.
|
| 49 |
+
|
| 50 |
+
- **High efficiency**
|
| 51 |
+
|
| 52 |
+
A basic GMFlow model (without refinement) runs at 57ms (V100) or 26ms (A100) for Sintel data (436x1024).
|
| 53 |
+
|
| 54 |
+
GMFlow gains more speedup than RAFT on high-end GPUs (e.g., A100) since GMFlow doesn't require a large number of sequential computation.
|
| 55 |
+
|
| 56 |
+
GMFlow also simplifies backward flow computation without requiring to forward the network twice. The bidirectional flow can be used for occlusion detection with forward-backward consistency check.
|
| 57 |
+
|
| 58 |
+
<p align="center"><img width=90% src="assets/bidir_flow_occ.png"></p>
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
## Installation
|
| 64 |
+
|
| 65 |
+
Our code is based on pytorch 1.9.0, CUDA 10.2 and python 3.8. Higher version pytorch should also work well.
|
| 66 |
+
|
| 67 |
+
We recommend using [conda](https://www.anaconda.com/distribution/) for installation:
|
| 68 |
+
|
| 69 |
+
```
|
| 70 |
+
conda env create -f environment.yml
|
| 71 |
+
conda activate gmflow
|
| 72 |
+
```
|
| 73 |
+
|
| 74 |
+
## Demos
|
| 75 |
+
|
| 76 |
+
All pretrained models can be downloaded from [google drive](https://drive.google.com/file/d/1d5C5cgHIxWGsFR1vYs5XrQbbUiZl9TX2/view?usp=sharing).
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
You can run a trained model on a sequence of images and visualize the results:
|
| 81 |
+
|
| 82 |
+
```
|
| 83 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 84 |
+
--inference_dir demo/sintel_market_1 \
|
| 85 |
+
--output_path output/gmflow-norefine-sintel_market_1 \
|
| 86 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 87 |
+
```
|
| 88 |
+
|
| 89 |
+
You can also predict bidirectional flow with `--pred_bidir_flow` enabled and use `--fwd_bwd_consistency_check` for forward-backward consistency check. More examples can be found in [scripts/demo.sh](scripts/demo.sh).
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
## Datasets
|
| 94 |
+
|
| 95 |
+
The datasets used to train and evaluate GMFlow are as follows:
|
| 96 |
+
|
| 97 |
+
* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
|
| 98 |
+
* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
|
| 99 |
+
* [Sintel](http://sintel.is.tue.mpg.de/)
|
| 100 |
+
* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow)
|
| 101 |
+
* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/)
|
| 102 |
+
|
| 103 |
+
By default the dataloader [datasets.py](data/datasets.py) assumes the datasets are located in folder `datasets` and are organized as follows:
|
| 104 |
+
|
| 105 |
+
```
|
| 106 |
+
datasets
|
| 107 |
+
├── FlyingChairs_release
|
| 108 |
+
│ └── data
|
| 109 |
+
├── FlyingThings3D
|
| 110 |
+
│ ├── frames_cleanpass
|
| 111 |
+
│ ├── frames_finalpass
|
| 112 |
+
│ └── optical_flow
|
| 113 |
+
├── HD1K
|
| 114 |
+
│ ├── hd1k_challenge
|
| 115 |
+
│ ├── hd1k_flow_gt
|
| 116 |
+
│ ├── hd1k_flow_uncertainty
|
| 117 |
+
│ └── hd1k_input
|
| 118 |
+
├── KITTI
|
| 119 |
+
│ ├── testing
|
| 120 |
+
│ └── training
|
| 121 |
+
├── Sintel
|
| 122 |
+
│ ├── test
|
| 123 |
+
│ └── training
|
| 124 |
+
```
|
| 125 |
+
|
| 126 |
+
It is recommended to symlink your dataset root to `datasets`:
|
| 127 |
+
|
| 128 |
+
```shell
|
| 129 |
+
ln -s $YOUR_DATASET_ROOT datasets
|
| 130 |
+
```
|
| 131 |
+
|
| 132 |
+
Otherwise, you may need to change the corresponding paths in [datasets.py](data/datasets.py).
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
## Evaluation
|
| 137 |
+
|
| 138 |
+
You can evaluate a trained GMFlow model by running:
|
| 139 |
+
|
| 140 |
+
```
|
| 141 |
+
CUDA_VISIBLE_DEVICES=0 python main.py --eval --val_dataset things sintel --resume pretrained/gmflow_things-e9887eda.pth
|
| 142 |
+
```
|
| 143 |
+
|
| 144 |
+
More evaluation scripts can be found in [scripts/evaluate.sh](scripts/evaluate.sh).
|
| 145 |
+
|
| 146 |
+
|
| 147 |
+
|
| 148 |
+
For submission to Sintel and KITTI online test sets, you can run [scripts/submission.sh](scripts/submission.sh).
|
| 149 |
+
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
## Training
|
| 153 |
+
|
| 154 |
+
All training scripts on FlyingChairs, FlyingThings3D, Sintel and KITTI datasets can be found in [scripts/train_gmflow.sh](scripts/train_gmflow.sh) and [scripts/train_gmflow_with_refine.sh](scripts/train_gmflow_with_refine.sh).
|
| 155 |
+
|
| 156 |
+
Note that the basic GMFlow model (without refinement) can be trained on 4x 16GB V100 GPUs. For training GMFlow with refinement, 8x 16GB V100 or 4x 32GB V100 or 4x 40GB A100 GPUs are required by default. You may need to tune the batch size and training iterations according to your hardware.
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
We support using tensorboard to monitor and visualize the training process. You can first start a tensorboard session with
|
| 161 |
+
|
| 162 |
+
```shell
|
| 163 |
+
tensorboard --logdir checkpoints
|
| 164 |
+
```
|
| 165 |
+
|
| 166 |
+
and then access [http://localhost:6006](http://localhost:6006) in your browser.
|
| 167 |
+
|
| 168 |
+
|
| 169 |
+
|
| 170 |
+
## Citation
|
| 171 |
+
|
| 172 |
+
If you find our work useful in your research, please consider citing our paper:
|
| 173 |
+
|
| 174 |
+
```
|
| 175 |
+
@inproceedings{xu2022gmflow,
|
| 176 |
+
title={GMFlow: Learning Optical Flow via Global Matching},
|
| 177 |
+
author={Xu, Haofei and Zhang, Jing and Cai, Jianfei and Rezatofighi, Hamid and Tao, Dacheng},
|
| 178 |
+
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
| 179 |
+
pages={8121-8130},
|
| 180 |
+
year={2022}
|
| 181 |
+
}
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
|
| 185 |
+
|
| 186 |
+
## Acknowledgements
|
| 187 |
+
|
| 188 |
+
This project would not have been possible without relying on some awesome repos : [RAFT](https://github.com/princeton-vl/RAFT), [LoFTR](https://github.com/zju3dv/LoFTR), [DETR](https://github.com/facebookresearch/detr), [Swin](https://github.com/microsoft/Swin-Transformer), [mmdetection](https://github.com/open-mmlab/mmdetection) and [Detectron2](https://github.com/facebookresearch/detectron2/blob/main/projects/TridentNet/tridentnet/trident_conv.py). We thank the original authors for their excellent work.
|
| 189 |
+
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
|
| 199 |
+
|
| 200 |
+
|
| 201 |
+
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
|
| 205 |
+
|
| 206 |
+
|
| 207 |
+
|
| 208 |
+
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
|
| 216 |
+
|
| 217 |
+
|
| 218 |
+
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
|
| 224 |
+
|
| 225 |
+
|
| 226 |
+
|
| 227 |
+
|
| 228 |
+
|
| 229 |
+
|
| 230 |
+
|
| 231 |
+
|
| 232 |
+
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
|
| 237 |
+
|
| 238 |
+
|
| 239 |
+
|
GMFlow/data/__init__.py
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from .datasets import build_train_dataset
|
| 2 |
+
from .datasets import (FlyingChairs,
|
| 3 |
+
FlyingThings3D,
|
| 4 |
+
MpiSintel,
|
| 5 |
+
KITTI,
|
| 6 |
+
HD1K,
|
| 7 |
+
)
|
GMFlow/data/chairs_split.txt
ADDED
|
@@ -0,0 +1,22872 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
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|
|
|
|
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|
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2
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+
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2
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2
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1
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1
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1
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1
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| 2010 |
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1
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| 2011 |
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1
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| 2012 |
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1
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| 2013 |
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1
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| 2014 |
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1
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| 2015 |
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1
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| 2016 |
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1
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| 2017 |
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1
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| 2018 |
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1
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| 2019 |
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2
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| 2020 |
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1
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| 2021 |
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1
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| 2022 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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2
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1
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1
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1
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2
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1
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2
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1
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2
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1
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1
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1
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1
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1
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1
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1
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1
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| 2111 |
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1
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1
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2
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1
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1
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1
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2
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1
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1
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| 2210 |
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1
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1
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1
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1
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| 2214 |
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1
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1
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1
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2
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1
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1
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1
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1
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1
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1
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1
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1
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1
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2
|
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1
|
| 2239 |
+
1
|
| 2240 |
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1
|
| 2241 |
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1
|
| 2242 |
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1
|
| 2243 |
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1
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2
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2
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1
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1
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1
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1
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1
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1
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1
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2
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1
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1
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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1
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|
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|
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|
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|
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|
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|
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|
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|
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1
|
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|
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1
|
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|
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|
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|
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|
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|
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|
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|
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|
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1
|
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1
|
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1
|
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1
|
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2
|
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1
|
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1
|
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1
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1
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| 9934 |
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1
|
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1
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|
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|
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|
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+
1
|
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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2
|
| 10440 |
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1
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1
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1
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1
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1
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2
|
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1
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1
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1
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1
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| 10464 |
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2
|
| 10465 |
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1
|
| 10466 |
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2
|
| 10467 |
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1
|
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1
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1
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1
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| 10471 |
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2
|
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1
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1
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1
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1
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1
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2
|
| 10480 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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2
|
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1
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1
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1
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1
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2
|
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2
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
|
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1
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1
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1
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1
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1
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1
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
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1
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1
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1
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1
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
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1
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1
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1
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1
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1
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1
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1
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1
|
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1
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1
|
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1
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1
|
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1
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1
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 10667 |
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1
|
| 10668 |
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1
|
| 10669 |
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1
|
| 10670 |
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1
|
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1
|
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1
|
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1
|
| 10674 |
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1
|
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1
|
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1
|
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1
|
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1
|
| 10679 |
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1
|
| 10680 |
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1
|
| 10681 |
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1
|
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1
|
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1
|
| 10684 |
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1
|
| 10685 |
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1
|
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1
|
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1
|
| 10688 |
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1
|
| 10689 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 10699 |
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1
|
| 10700 |
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1
|
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1
|
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1
|
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1
|
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1
|
| 10705 |
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1
|
| 10706 |
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1
|
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1
|
| 10708 |
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1
|
| 10709 |
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1
|
| 10710 |
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1
|
| 10711 |
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1
|
| 10712 |
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1
|
| 10713 |
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1
|
| 10714 |
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1
|
| 10715 |
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1
|
| 10716 |
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1
|
| 10717 |
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1
|
| 10718 |
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1
|
| 10719 |
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1
|
| 10720 |
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1
|
| 10721 |
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1
|
| 10722 |
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1
|
| 10723 |
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1
|
| 10724 |
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1
|
| 10725 |
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1
|
| 10726 |
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1
|
| 10727 |
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1
|
| 10728 |
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1
|
| 10729 |
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1
|
| 10730 |
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1
|
| 10731 |
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1
|
| 10732 |
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1
|
| 10733 |
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1
|
| 10734 |
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1
|
| 10735 |
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1
|
| 10736 |
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1
|
| 10737 |
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1
|
| 10738 |
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1
|
| 10739 |
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1
|
| 10740 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 10760 |
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1
|
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1
|
| 10762 |
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1
|
| 10763 |
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1
|
| 10764 |
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1
|
| 10765 |
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1
|
| 10766 |
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1
|
| 10767 |
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1
|
| 10768 |
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1
|
| 10769 |
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1
|
| 10770 |
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1
|
| 10771 |
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1
|
| 10772 |
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1
|
| 10773 |
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1
|
| 10774 |
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1
|
| 10775 |
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1
|
| 10776 |
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1
|
| 10777 |
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1
|
| 10778 |
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1
|
| 10779 |
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1
|
| 10780 |
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1
|
| 10781 |
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1
|
| 10782 |
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1
|
| 10783 |
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1
|
| 10784 |
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1
|
| 10785 |
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1
|
| 10786 |
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1
|
| 10787 |
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1
|
| 10788 |
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1
|
| 10789 |
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1
|
| 10790 |
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1
|
| 10791 |
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1
|
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1
|
| 10793 |
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1
|
| 10794 |
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1
|
| 10795 |
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1
|
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1
|
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1
|
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1
|
| 10799 |
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1
|
| 10800 |
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1
|
| 10801 |
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1
|
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1
|
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1
|
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1
|
| 10805 |
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1
|
| 10806 |
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1
|
| 10807 |
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1
|
| 10808 |
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1
|
| 10809 |
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1
|
| 10810 |
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2
|
| 10811 |
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1
|
| 10812 |
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1
|
| 10813 |
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1
|
| 10814 |
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1
|
| 10815 |
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1
|
| 10816 |
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1
|
| 10817 |
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1
|
| 10818 |
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1
|
| 10819 |
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1
|
| 10820 |
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1
|
| 10821 |
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1
|
| 10822 |
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1
|
| 10823 |
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1
|
| 10824 |
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1
|
| 10825 |
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1
|
| 10826 |
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1
|
| 10827 |
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1
|
| 10828 |
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1
|
| 10829 |
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1
|
| 10830 |
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1
|
| 10831 |
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1
|
| 10832 |
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1
|
| 10833 |
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1
|
| 10834 |
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1
|
| 10835 |
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1
|
| 10836 |
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1
|
| 10837 |
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1
|
| 10838 |
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1
|
| 10839 |
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1
|
| 10840 |
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1
|
| 10841 |
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1
|
| 10842 |
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1
|
| 10843 |
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1
|
| 10844 |
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1
|
| 10845 |
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1
|
| 10846 |
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1
|
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1
|
| 10848 |
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1
|
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1
|
| 10850 |
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1
|
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1
|
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1
|
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1
|
| 10854 |
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1
|
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1
|
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1
|
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1
|
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1
|
| 10859 |
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1
|
| 10860 |
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1
|
| 10861 |
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1
|
| 10862 |
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1
|
| 10863 |
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1
|
| 10864 |
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1
|
| 10865 |
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1
|
| 10866 |
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1
|
| 10867 |
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1
|
| 10868 |
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1
|
| 10869 |
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1
|
| 10870 |
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1
|
| 10871 |
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1
|
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1
|
| 10873 |
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1
|
| 10874 |
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1
|
| 10875 |
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1
|
| 10876 |
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1
|
| 10877 |
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1
|
| 10878 |
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1
|
| 10879 |
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1
|
| 10880 |
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1
|
| 10881 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 10890 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 10898 |
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1
|
| 10899 |
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1
|
| 10900 |
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1
|
| 10901 |
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1
|
| 10902 |
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1
|
| 10903 |
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1
|
| 10904 |
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1
|
| 10905 |
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1
|
| 10906 |
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1
|
| 10907 |
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1
|
| 10908 |
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1
|
| 10909 |
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1
|
| 10910 |
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1
|
| 10911 |
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1
|
| 10912 |
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1
|
| 10913 |
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1
|
| 10914 |
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1
|
| 10915 |
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1
|
| 10916 |
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1
|
| 10917 |
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1
|
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1
|
| 10919 |
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1
|
| 10920 |
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1
|
| 10921 |
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1
|
| 10922 |
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1
|
| 10923 |
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1
|
| 10924 |
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1
|
| 10925 |
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1
|
| 10926 |
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1
|
| 10927 |
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1
|
| 10928 |
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1
|
| 10929 |
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1
|
| 10930 |
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1
|
| 10931 |
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1
|
| 10932 |
+
1
|
| 10933 |
+
1
|
| 10934 |
+
1
|
| 10935 |
+
1
|
| 10936 |
+
1
|
| 10937 |
+
1
|
| 10938 |
+
1
|
| 10939 |
+
1
|
| 10940 |
+
1
|
| 10941 |
+
1
|
| 10942 |
+
1
|
| 10943 |
+
1
|
| 10944 |
+
1
|
| 10945 |
+
1
|
| 10946 |
+
1
|
| 10947 |
+
1
|
| 10948 |
+
1
|
| 10949 |
+
1
|
| 10950 |
+
1
|
| 10951 |
+
1
|
| 10952 |
+
1
|
| 10953 |
+
1
|
| 10954 |
+
1
|
| 10955 |
+
1
|
| 10956 |
+
1
|
| 10957 |
+
1
|
| 10958 |
+
1
|
| 10959 |
+
1
|
| 10960 |
+
1
|
| 10961 |
+
1
|
| 10962 |
+
1
|
| 10963 |
+
1
|
| 10964 |
+
1
|
| 10965 |
+
1
|
| 10966 |
+
1
|
| 10967 |
+
1
|
| 10968 |
+
1
|
| 10969 |
+
1
|
| 10970 |
+
1
|
| 10971 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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| 11865 |
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2
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| 11866 |
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1
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| 11867 |
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1
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| 11868 |
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1
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| 11869 |
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1
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| 11870 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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| 11900 |
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1
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| 11901 |
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1
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1
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1
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1
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1
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| 11906 |
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1
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| 11907 |
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1
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| 11908 |
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1
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| 11909 |
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1
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| 11910 |
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1
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| 11911 |
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1
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| 11912 |
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1
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| 11913 |
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1
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| 11914 |
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1
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| 11915 |
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1
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| 11916 |
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1
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1
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1
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1
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| 11920 |
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1
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| 11921 |
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1
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| 11922 |
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1
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| 11923 |
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1
|
| 11924 |
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1
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| 11925 |
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1
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| 11926 |
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1
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| 11927 |
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1
|
| 11928 |
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1
|
| 11929 |
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1
|
| 11930 |
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1
|
| 11931 |
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1
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1
|
| 11933 |
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1
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| 11934 |
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1
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| 11935 |
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1
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| 11936 |
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1
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| 11937 |
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1
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| 11938 |
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1
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| 11939 |
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1
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| 11940 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
|
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1
|
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1
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| 11967 |
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1
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| 11968 |
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1
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| 11969 |
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1
|
| 11970 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
|
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1
|
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1
|
| 11982 |
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1
|
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1
|
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1
|
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1
|
| 11986 |
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1
|
| 11987 |
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1
|
| 11988 |
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1
|
| 11989 |
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1
|
| 11990 |
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1
|
| 11991 |
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1
|
| 11992 |
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1
|
| 11993 |
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1
|
| 11994 |
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1
|
| 11995 |
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1
|
| 11996 |
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1
|
| 11997 |
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1
|
| 11998 |
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1
|
| 11999 |
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1
|
| 12000 |
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1
|
| 12001 |
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1
|
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1
|
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1
|
| 12004 |
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1
|
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1
|
| 12006 |
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1
|
| 12007 |
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1
|
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1
|
| 12009 |
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1
|
| 12010 |
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1
|
| 12011 |
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1
|
| 12012 |
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1
|
| 12013 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 12023 |
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1
|
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|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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|
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1
|
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|
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1
|
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|
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1
|
| 12039 |
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1
|
| 12040 |
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1
|
| 12041 |
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1
|
| 12042 |
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1
|
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1
|
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|
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|
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|
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|
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|
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|
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|
| 12051 |
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|
| 12052 |
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1
|
| 12053 |
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1
|
| 12054 |
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1
|
| 12055 |
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1
|
| 12056 |
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1
|
| 12057 |
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1
|
| 12058 |
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1
|
| 12059 |
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1
|
| 12060 |
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1
|
| 12061 |
+
1
|
| 12062 |
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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1
|
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1
|
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1
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1
|
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+
1
|
| 19700 |
+
1
|
| 19701 |
+
1
|
| 19702 |
+
1
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| 19703 |
+
1
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| 19704 |
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1
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2
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|
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|
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| 22627 |
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|
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|
| 22630 |
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1
|
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1
|
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|
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1
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 22664 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 22689 |
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|
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|
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|
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|
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|
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|
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|
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|
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1
|
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1
|
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1
|
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1
|
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|
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|
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|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
| 22712 |
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1
|
| 22713 |
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1
|
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1
|
| 22715 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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|
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1
|
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1
|
| 22728 |
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1
|
| 22729 |
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1
|
| 22730 |
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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|
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1
|
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1
|
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|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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1
|
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+
1
|
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+
1
|
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1
|
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+
1
|
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1
|
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+
1
|
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+
1
|
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+
1
|
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+
1
|
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1
|
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1
|
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|
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1
|
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1
|
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|
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1
|
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1
|
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+
1
|
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1
|
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+
1
|
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1
|
| 22784 |
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1
|
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+
1
|
| 22786 |
+
1
|
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+
1
|
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+
1
|
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+
1
|
| 22790 |
+
1
|
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+
1
|
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+
1
|
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+
1
|
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+
1
|
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+
1
|
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+
1
|
| 22797 |
+
1
|
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+
1
|
| 22799 |
+
1
|
| 22800 |
+
1
|
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+
1
|
| 22802 |
+
1
|
| 22803 |
+
1
|
| 22804 |
+
1
|
| 22805 |
+
1
|
| 22806 |
+
1
|
| 22807 |
+
1
|
| 22808 |
+
1
|
| 22809 |
+
1
|
| 22810 |
+
1
|
| 22811 |
+
1
|
| 22812 |
+
1
|
| 22813 |
+
1
|
| 22814 |
+
1
|
| 22815 |
+
1
|
| 22816 |
+
1
|
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+
1
|
| 22818 |
+
1
|
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+
1
|
| 22820 |
+
1
|
| 22821 |
+
1
|
| 22822 |
+
1
|
| 22823 |
+
1
|
| 22824 |
+
1
|
| 22825 |
+
1
|
| 22826 |
+
1
|
| 22827 |
+
1
|
| 22828 |
+
1
|
| 22829 |
+
1
|
| 22830 |
+
1
|
| 22831 |
+
1
|
| 22832 |
+
1
|
| 22833 |
+
1
|
| 22834 |
+
1
|
| 22835 |
+
1
|
| 22836 |
+
1
|
| 22837 |
+
1
|
| 22838 |
+
1
|
| 22839 |
+
1
|
| 22840 |
+
1
|
| 22841 |
+
1
|
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+
1
|
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+
1
|
| 22844 |
+
1
|
| 22845 |
+
1
|
| 22846 |
+
1
|
| 22847 |
+
1
|
| 22848 |
+
1
|
| 22849 |
+
1
|
| 22850 |
+
1
|
| 22851 |
+
1
|
| 22852 |
+
1
|
| 22853 |
+
1
|
| 22854 |
+
1
|
| 22855 |
+
1
|
| 22856 |
+
1
|
| 22857 |
+
1
|
| 22858 |
+
2
|
| 22859 |
+
2
|
| 22860 |
+
1
|
| 22861 |
+
1
|
| 22862 |
+
1
|
| 22863 |
+
1
|
| 22864 |
+
1
|
| 22865 |
+
1
|
| 22866 |
+
1
|
| 22867 |
+
2
|
| 22868 |
+
1
|
| 22869 |
+
1
|
| 22870 |
+
1
|
| 22871 |
+
1
|
| 22872 |
+
1
|
GMFlow/data/datasets.py
ADDED
|
@@ -0,0 +1,312 @@
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|
| 1 |
+
# Data loading based on https://github.com/NVIDIA/flownet2-pytorch
|
| 2 |
+
|
| 3 |
+
import numpy as np
|
| 4 |
+
import torch
|
| 5 |
+
import torch.utils.data as data
|
| 6 |
+
|
| 7 |
+
import os
|
| 8 |
+
import random
|
| 9 |
+
from glob import glob
|
| 10 |
+
import os.path as osp
|
| 11 |
+
|
| 12 |
+
from utils import frame_utils
|
| 13 |
+
from data.transforms import FlowAugmentor, SparseFlowAugmentor
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
class FlowDataset(data.Dataset):
|
| 17 |
+
def __init__(self, aug_params=None, sparse=False,
|
| 18 |
+
load_occlusion=False,
|
| 19 |
+
):
|
| 20 |
+
self.augmentor = None
|
| 21 |
+
self.sparse = sparse
|
| 22 |
+
|
| 23 |
+
if aug_params is not None:
|
| 24 |
+
if sparse:
|
| 25 |
+
self.augmentor = SparseFlowAugmentor(**aug_params)
|
| 26 |
+
else:
|
| 27 |
+
self.augmentor = FlowAugmentor(**aug_params)
|
| 28 |
+
|
| 29 |
+
self.is_test = False
|
| 30 |
+
self.init_seed = False
|
| 31 |
+
self.flow_list = []
|
| 32 |
+
self.image_list = []
|
| 33 |
+
self.extra_info = []
|
| 34 |
+
|
| 35 |
+
self.load_occlusion = load_occlusion
|
| 36 |
+
self.occ_list = []
|
| 37 |
+
|
| 38 |
+
def __getitem__(self, index):
|
| 39 |
+
|
| 40 |
+
if self.is_test:
|
| 41 |
+
img1 = frame_utils.read_gen(self.image_list[index][0])
|
| 42 |
+
img2 = frame_utils.read_gen(self.image_list[index][1])
|
| 43 |
+
|
| 44 |
+
img1 = np.array(img1).astype(np.uint8)[..., :3]
|
| 45 |
+
img2 = np.array(img2).astype(np.uint8)[..., :3]
|
| 46 |
+
|
| 47 |
+
img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
|
| 48 |
+
img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
|
| 49 |
+
|
| 50 |
+
return img1, img2, self.extra_info[index]
|
| 51 |
+
|
| 52 |
+
if not self.init_seed:
|
| 53 |
+
worker_info = torch.utils.data.get_worker_info()
|
| 54 |
+
if worker_info is not None:
|
| 55 |
+
torch.manual_seed(worker_info.id)
|
| 56 |
+
np.random.seed(worker_info.id)
|
| 57 |
+
random.seed(worker_info.id)
|
| 58 |
+
self.init_seed = True
|
| 59 |
+
|
| 60 |
+
index = index % len(self.image_list)
|
| 61 |
+
valid = None
|
| 62 |
+
|
| 63 |
+
if self.sparse:
|
| 64 |
+
flow, valid = frame_utils.readFlowKITTI(self.flow_list[index]) # [H, W, 2], [H, W]
|
| 65 |
+
else:
|
| 66 |
+
flow = frame_utils.read_gen(self.flow_list[index])
|
| 67 |
+
|
| 68 |
+
if self.load_occlusion:
|
| 69 |
+
occlusion = frame_utils.read_gen(self.occ_list[index]) # [H, W], 0 or 255 (occluded)
|
| 70 |
+
|
| 71 |
+
img1 = frame_utils.read_gen(self.image_list[index][0])
|
| 72 |
+
img2 = frame_utils.read_gen(self.image_list[index][1])
|
| 73 |
+
|
| 74 |
+
flow = np.array(flow).astype(np.float32)
|
| 75 |
+
img1 = np.array(img1).astype(np.uint8)
|
| 76 |
+
img2 = np.array(img2).astype(np.uint8)
|
| 77 |
+
|
| 78 |
+
if self.load_occlusion:
|
| 79 |
+
occlusion = np.array(occlusion).astype(np.float32)
|
| 80 |
+
|
| 81 |
+
# grayscale images
|
| 82 |
+
if len(img1.shape) == 2:
|
| 83 |
+
img1 = np.tile(img1[..., None], (1, 1, 3))
|
| 84 |
+
img2 = np.tile(img2[..., None], (1, 1, 3))
|
| 85 |
+
else:
|
| 86 |
+
img1 = img1[..., :3]
|
| 87 |
+
img2 = img2[..., :3]
|
| 88 |
+
|
| 89 |
+
if self.augmentor is not None:
|
| 90 |
+
if self.sparse:
|
| 91 |
+
img1, img2, flow, valid = self.augmentor(img1, img2, flow, valid)
|
| 92 |
+
else:
|
| 93 |
+
if self.load_occlusion:
|
| 94 |
+
img1, img2, flow, occlusion = self.augmentor(img1, img2, flow, occlusion=occlusion)
|
| 95 |
+
else:
|
| 96 |
+
img1, img2, flow = self.augmentor(img1, img2, flow)
|
| 97 |
+
|
| 98 |
+
img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
|
| 99 |
+
img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
|
| 100 |
+
flow = torch.from_numpy(flow).permute(2, 0, 1).float()
|
| 101 |
+
|
| 102 |
+
if self.load_occlusion:
|
| 103 |
+
occlusion = torch.from_numpy(occlusion) # [H, W]
|
| 104 |
+
|
| 105 |
+
if valid is not None:
|
| 106 |
+
valid = torch.from_numpy(valid)
|
| 107 |
+
else:
|
| 108 |
+
valid = (flow[0].abs() < 1000) & (flow[1].abs() < 1000)
|
| 109 |
+
|
| 110 |
+
# mask out occluded pixels
|
| 111 |
+
if self.load_occlusion:
|
| 112 |
+
# non-occlusion: 0, occlusion: 255
|
| 113 |
+
noc_valid = 1 - occlusion / 255. # 0 or 1
|
| 114 |
+
|
| 115 |
+
return img1, img2, flow, valid.float(), noc_valid.float()
|
| 116 |
+
|
| 117 |
+
return img1, img2, flow, valid.float()
|
| 118 |
+
|
| 119 |
+
def __rmul__(self, v):
|
| 120 |
+
self.flow_list = v * self.flow_list
|
| 121 |
+
self.image_list = v * self.image_list
|
| 122 |
+
|
| 123 |
+
return self
|
| 124 |
+
|
| 125 |
+
def __len__(self):
|
| 126 |
+
return len(self.image_list)
|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
class MpiSintel(FlowDataset):
|
| 130 |
+
def __init__(self, aug_params=None, split='training',
|
| 131 |
+
root='datasets/Sintel',
|
| 132 |
+
dstype='clean',
|
| 133 |
+
load_occlusion=False,
|
| 134 |
+
):
|
| 135 |
+
super(MpiSintel, self).__init__(aug_params,
|
| 136 |
+
load_occlusion=load_occlusion,
|
| 137 |
+
)
|
| 138 |
+
|
| 139 |
+
flow_root = osp.join(root, split, 'flow')
|
| 140 |
+
image_root = osp.join(root, split, dstype)
|
| 141 |
+
|
| 142 |
+
if load_occlusion:
|
| 143 |
+
occlusion_root = osp.join(root, split, 'occlusions')
|
| 144 |
+
|
| 145 |
+
if split == 'test':
|
| 146 |
+
self.is_test = True
|
| 147 |
+
|
| 148 |
+
for scene in os.listdir(image_root):
|
| 149 |
+
image_list = sorted(glob(osp.join(image_root, scene, '*.png')))
|
| 150 |
+
for i in range(len(image_list) - 1):
|
| 151 |
+
self.image_list += [[image_list[i], image_list[i + 1]]]
|
| 152 |
+
self.extra_info += [(scene, i)] # scene and frame_id
|
| 153 |
+
|
| 154 |
+
if split != 'test':
|
| 155 |
+
self.flow_list += sorted(glob(osp.join(flow_root, scene, '*.flo')))
|
| 156 |
+
|
| 157 |
+
if load_occlusion:
|
| 158 |
+
self.occ_list += sorted(glob(osp.join(occlusion_root, scene, '*.png')))
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
class FlyingChairs(FlowDataset):
|
| 162 |
+
def __init__(self, aug_params=None, split='train',
|
| 163 |
+
root='datasets/FlyingChairs_release/data',
|
| 164 |
+
):
|
| 165 |
+
super(FlyingChairs, self).__init__(aug_params)
|
| 166 |
+
|
| 167 |
+
images = sorted(glob(osp.join(root, '*.ppm')))
|
| 168 |
+
flows = sorted(glob(osp.join(root, '*.flo')))
|
| 169 |
+
assert (len(images) // 2 == len(flows))
|
| 170 |
+
|
| 171 |
+
split_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'chairs_split.txt')
|
| 172 |
+
split_list = np.loadtxt(split_file, dtype=np.int32)
|
| 173 |
+
for i in range(len(flows)):
|
| 174 |
+
xid = split_list[i]
|
| 175 |
+
if (split == 'training' and xid == 1) or (split == 'validation' and xid == 2):
|
| 176 |
+
self.flow_list += [flows[i]]
|
| 177 |
+
self.image_list += [[images[2 * i], images[2 * i + 1]]]
|
| 178 |
+
|
| 179 |
+
|
| 180 |
+
class FlyingThings3D(FlowDataset):
|
| 181 |
+
def __init__(self, aug_params=None,
|
| 182 |
+
root='datasets/FlyingThings3D',
|
| 183 |
+
dstype='frames_cleanpass',
|
| 184 |
+
test_set=False,
|
| 185 |
+
validate_subset=True,
|
| 186 |
+
):
|
| 187 |
+
super(FlyingThings3D, self).__init__(aug_params)
|
| 188 |
+
|
| 189 |
+
img_dir = root
|
| 190 |
+
flow_dir = root
|
| 191 |
+
|
| 192 |
+
for cam in ['left']:
|
| 193 |
+
for direction in ['into_future', 'into_past']:
|
| 194 |
+
if test_set:
|
| 195 |
+
image_dirs = sorted(glob(osp.join(img_dir, dstype, 'TEST/*/*')))
|
| 196 |
+
else:
|
| 197 |
+
image_dirs = sorted(glob(osp.join(img_dir, dstype, 'TRAIN/*/*')))
|
| 198 |
+
image_dirs = sorted([osp.join(f, cam) for f in image_dirs])
|
| 199 |
+
|
| 200 |
+
if test_set:
|
| 201 |
+
flow_dirs = sorted(glob(osp.join(flow_dir, 'optical_flow/TEST/*/*')))
|
| 202 |
+
else:
|
| 203 |
+
flow_dirs = sorted(glob(osp.join(flow_dir, 'optical_flow/TRAIN/*/*')))
|
| 204 |
+
flow_dirs = sorted([osp.join(f, direction, cam) for f in flow_dirs])
|
| 205 |
+
|
| 206 |
+
for idir, fdir in zip(image_dirs, flow_dirs):
|
| 207 |
+
images = sorted(glob(osp.join(idir, '*.png')))
|
| 208 |
+
flows = sorted(glob(osp.join(fdir, '*.pfm')))
|
| 209 |
+
for i in range(len(flows) - 1):
|
| 210 |
+
if direction == 'into_future':
|
| 211 |
+
self.image_list += [[images[i], images[i + 1]]]
|
| 212 |
+
self.flow_list += [flows[i]]
|
| 213 |
+
elif direction == 'into_past':
|
| 214 |
+
self.image_list += [[images[i + 1], images[i]]]
|
| 215 |
+
self.flow_list += [flows[i + 1]]
|
| 216 |
+
|
| 217 |
+
# validate on 1024 subset of test set for fast speed
|
| 218 |
+
if test_set and validate_subset:
|
| 219 |
+
num_val_samples = 1024
|
| 220 |
+
all_test_samples = len(self.image_list) # 7866
|
| 221 |
+
|
| 222 |
+
stride = all_test_samples // num_val_samples
|
| 223 |
+
remove = all_test_samples % num_val_samples
|
| 224 |
+
|
| 225 |
+
# uniformly sample a subset
|
| 226 |
+
self.image_list = self.image_list[:-remove][::stride]
|
| 227 |
+
self.flow_list = self.flow_list[:-remove][::stride]
|
| 228 |
+
|
| 229 |
+
|
| 230 |
+
class KITTI(FlowDataset):
|
| 231 |
+
def __init__(self, aug_params=None, split='training',
|
| 232 |
+
root='datasets/KITTI',
|
| 233 |
+
):
|
| 234 |
+
super(KITTI, self).__init__(aug_params, sparse=True,
|
| 235 |
+
)
|
| 236 |
+
if split == 'testing':
|
| 237 |
+
self.is_test = True
|
| 238 |
+
|
| 239 |
+
root = osp.join(root, split)
|
| 240 |
+
images1 = sorted(glob(osp.join(root, 'image_2/*_10.png')))
|
| 241 |
+
images2 = sorted(glob(osp.join(root, 'image_2/*_11.png')))
|
| 242 |
+
|
| 243 |
+
for img1, img2 in zip(images1, images2):
|
| 244 |
+
frame_id = img1.split('/')[-1]
|
| 245 |
+
self.extra_info += [[frame_id]]
|
| 246 |
+
self.image_list += [[img1, img2]]
|
| 247 |
+
|
| 248 |
+
if split == 'training':
|
| 249 |
+
self.flow_list = sorted(glob(osp.join(root, 'flow_occ/*_10.png')))
|
| 250 |
+
|
| 251 |
+
|
| 252 |
+
class HD1K(FlowDataset):
|
| 253 |
+
def __init__(self, aug_params=None, root='datasets/HD1K'):
|
| 254 |
+
super(HD1K, self).__init__(aug_params, sparse=True)
|
| 255 |
+
|
| 256 |
+
seq_ix = 0
|
| 257 |
+
while 1:
|
| 258 |
+
flows = sorted(glob(os.path.join(root, 'hd1k_flow_gt', 'flow_occ/%06d_*.png' % seq_ix)))
|
| 259 |
+
images = sorted(glob(os.path.join(root, 'hd1k_input', 'image_2/%06d_*.png' % seq_ix)))
|
| 260 |
+
|
| 261 |
+
if len(flows) == 0:
|
| 262 |
+
break
|
| 263 |
+
|
| 264 |
+
for i in range(len(flows) - 1):
|
| 265 |
+
self.flow_list += [flows[i]]
|
| 266 |
+
self.image_list += [[images[i], images[i + 1]]]
|
| 267 |
+
|
| 268 |
+
seq_ix += 1
|
| 269 |
+
|
| 270 |
+
|
| 271 |
+
def build_train_dataset(args):
|
| 272 |
+
""" Create the data loader for the corresponding training set """
|
| 273 |
+
if args.stage == 'chairs':
|
| 274 |
+
aug_params = {'crop_size': args.image_size, 'min_scale': -0.1, 'max_scale': 1.0, 'do_flip': True}
|
| 275 |
+
|
| 276 |
+
train_dataset = FlyingChairs(aug_params, split='training')
|
| 277 |
+
|
| 278 |
+
elif args.stage == 'things':
|
| 279 |
+
aug_params = {'crop_size': args.image_size, 'min_scale': -0.4, 'max_scale': 0.8, 'do_flip': True}
|
| 280 |
+
|
| 281 |
+
clean_dataset = FlyingThings3D(aug_params, dstype='frames_cleanpass')
|
| 282 |
+
final_dataset = FlyingThings3D(aug_params, dstype='frames_finalpass')
|
| 283 |
+
train_dataset = clean_dataset + final_dataset
|
| 284 |
+
|
| 285 |
+
elif args.stage == 'sintel':
|
| 286 |
+
# 1041 pairs for clean and final each
|
| 287 |
+
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True}
|
| 288 |
+
|
| 289 |
+
things = FlyingThings3D(aug_params, dstype='frames_cleanpass') # 40302
|
| 290 |
+
|
| 291 |
+
sintel_clean = MpiSintel(aug_params, split='training', dstype='clean')
|
| 292 |
+
sintel_final = MpiSintel(aug_params, split='training', dstype='final')
|
| 293 |
+
|
| 294 |
+
aug_params = {'crop_size': args.image_size, 'min_scale': -0.3, 'max_scale': 0.5, 'do_flip': True}
|
| 295 |
+
|
| 296 |
+
kitti = KITTI(aug_params=aug_params) # 200
|
| 297 |
+
|
| 298 |
+
aug_params = {'crop_size': args.image_size, 'min_scale': -0.5, 'max_scale': 0.2, 'do_flip': True}
|
| 299 |
+
|
| 300 |
+
hd1k = HD1K(aug_params=aug_params) # 1047
|
| 301 |
+
|
| 302 |
+
train_dataset = 100 * sintel_clean + 100 * sintel_final + 200 * kitti + 5 * hd1k + things
|
| 303 |
+
|
| 304 |
+
elif args.stage == 'kitti':
|
| 305 |
+
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.4, 'do_flip': False}
|
| 306 |
+
|
| 307 |
+
train_dataset = KITTI(aug_params, split='training',
|
| 308 |
+
)
|
| 309 |
+
else:
|
| 310 |
+
raise ValueError(f'stage {args.stage} is not supported')
|
| 311 |
+
|
| 312 |
+
return train_dataset
|
GMFlow/data/transforms.py
ADDED
|
@@ -0,0 +1,284 @@
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|
|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import cv2
|
| 3 |
+
from PIL import Image
|
| 4 |
+
from torchvision.transforms import ColorJitter
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
class FlowAugmentor:
|
| 8 |
+
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True,
|
| 9 |
+
no_eraser_aug=True,
|
| 10 |
+
):
|
| 11 |
+
# spatial augmentation params
|
| 12 |
+
self.crop_size = crop_size
|
| 13 |
+
self.min_scale = min_scale
|
| 14 |
+
self.max_scale = max_scale
|
| 15 |
+
self.spatial_aug_prob = 0.8
|
| 16 |
+
self.stretch_prob = 0.8
|
| 17 |
+
self.max_stretch = 0.2
|
| 18 |
+
|
| 19 |
+
# flip augmentation params
|
| 20 |
+
self.do_flip = do_flip
|
| 21 |
+
self.h_flip_prob = 0.5
|
| 22 |
+
self.v_flip_prob = 0.1
|
| 23 |
+
|
| 24 |
+
# photometric augmentation params
|
| 25 |
+
self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5 / 3.14)
|
| 26 |
+
|
| 27 |
+
self.asymmetric_color_aug_prob = 0.2
|
| 28 |
+
|
| 29 |
+
if no_eraser_aug:
|
| 30 |
+
# we disable eraser aug since no obvious improvement is observed in our experiments
|
| 31 |
+
self.eraser_aug_prob = -1
|
| 32 |
+
else:
|
| 33 |
+
self.eraser_aug_prob = 0.5
|
| 34 |
+
|
| 35 |
+
def color_transform(self, img1, img2):
|
| 36 |
+
""" Photometric augmentation """
|
| 37 |
+
|
| 38 |
+
# asymmetric
|
| 39 |
+
if np.random.rand() < self.asymmetric_color_aug_prob:
|
| 40 |
+
img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
|
| 41 |
+
img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
|
| 42 |
+
|
| 43 |
+
# symmetric
|
| 44 |
+
else:
|
| 45 |
+
image_stack = np.concatenate([img1, img2], axis=0)
|
| 46 |
+
image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
|
| 47 |
+
img1, img2 = np.split(image_stack, 2, axis=0)
|
| 48 |
+
|
| 49 |
+
return img1, img2
|
| 50 |
+
|
| 51 |
+
def eraser_transform(self, img1, img2, bounds=[50, 100]):
|
| 52 |
+
""" Occlusion augmentation """
|
| 53 |
+
|
| 54 |
+
ht, wd = img1.shape[:2]
|
| 55 |
+
if np.random.rand() < self.eraser_aug_prob:
|
| 56 |
+
mean_color = np.mean(img2.reshape(-1, 3), axis=0)
|
| 57 |
+
for _ in range(np.random.randint(1, 3)):
|
| 58 |
+
x0 = np.random.randint(0, wd)
|
| 59 |
+
y0 = np.random.randint(0, ht)
|
| 60 |
+
dx = np.random.randint(bounds[0], bounds[1])
|
| 61 |
+
dy = np.random.randint(bounds[0], bounds[1])
|
| 62 |
+
img2[y0:y0 + dy, x0:x0 + dx, :] = mean_color
|
| 63 |
+
|
| 64 |
+
return img1, img2
|
| 65 |
+
|
| 66 |
+
def spatial_transform(self, img1, img2, flow, occlusion=None):
|
| 67 |
+
# randomly sample scale
|
| 68 |
+
ht, wd = img1.shape[:2]
|
| 69 |
+
|
| 70 |
+
min_scale = np.maximum(
|
| 71 |
+
(self.crop_size[0] + 8) / float(ht),
|
| 72 |
+
(self.crop_size[1] + 8) / float(wd))
|
| 73 |
+
|
| 74 |
+
scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
|
| 75 |
+
scale_x = scale
|
| 76 |
+
scale_y = scale
|
| 77 |
+
if np.random.rand() < self.stretch_prob:
|
| 78 |
+
scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
|
| 79 |
+
scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
|
| 80 |
+
|
| 81 |
+
scale_x = np.clip(scale_x, min_scale, None)
|
| 82 |
+
scale_y = np.clip(scale_y, min_scale, None)
|
| 83 |
+
|
| 84 |
+
if np.random.rand() < self.spatial_aug_prob:
|
| 85 |
+
# rescale the images
|
| 86 |
+
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
| 87 |
+
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
| 88 |
+
flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
| 89 |
+
flow = flow * [scale_x, scale_y]
|
| 90 |
+
|
| 91 |
+
if occlusion is not None:
|
| 92 |
+
occlusion = cv2.resize(occlusion, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
| 93 |
+
|
| 94 |
+
if self.do_flip:
|
| 95 |
+
if np.random.rand() < self.h_flip_prob: # h-flip
|
| 96 |
+
img1 = img1[:, ::-1]
|
| 97 |
+
img2 = img2[:, ::-1]
|
| 98 |
+
flow = flow[:, ::-1] * [-1.0, 1.0]
|
| 99 |
+
|
| 100 |
+
if occlusion is not None:
|
| 101 |
+
occlusion = occlusion[:, ::-1]
|
| 102 |
+
|
| 103 |
+
if np.random.rand() < self.v_flip_prob: # v-flip
|
| 104 |
+
img1 = img1[::-1, :]
|
| 105 |
+
img2 = img2[::-1, :]
|
| 106 |
+
flow = flow[::-1, :] * [1.0, -1.0]
|
| 107 |
+
|
| 108 |
+
if occlusion is not None:
|
| 109 |
+
occlusion = occlusion[::-1, :]
|
| 110 |
+
|
| 111 |
+
# In case no cropping
|
| 112 |
+
if img1.shape[0] - self.crop_size[0] > 0:
|
| 113 |
+
y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
|
| 114 |
+
else:
|
| 115 |
+
y0 = 0
|
| 116 |
+
if img1.shape[1] - self.crop_size[1] > 0:
|
| 117 |
+
x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
|
| 118 |
+
else:
|
| 119 |
+
x0 = 0
|
| 120 |
+
|
| 121 |
+
img1 = img1[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 122 |
+
img2 = img2[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 123 |
+
flow = flow[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 124 |
+
|
| 125 |
+
if occlusion is not None:
|
| 126 |
+
occlusion = occlusion[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 127 |
+
return img1, img2, flow, occlusion
|
| 128 |
+
|
| 129 |
+
return img1, img2, flow
|
| 130 |
+
|
| 131 |
+
def __call__(self, img1, img2, flow, occlusion=None):
|
| 132 |
+
img1, img2 = self.color_transform(img1, img2)
|
| 133 |
+
img1, img2 = self.eraser_transform(img1, img2)
|
| 134 |
+
|
| 135 |
+
if occlusion is not None:
|
| 136 |
+
img1, img2, flow, occlusion = self.spatial_transform(
|
| 137 |
+
img1, img2, flow, occlusion)
|
| 138 |
+
else:
|
| 139 |
+
img1, img2, flow = self.spatial_transform(img1, img2, flow)
|
| 140 |
+
|
| 141 |
+
img1 = np.ascontiguousarray(img1)
|
| 142 |
+
img2 = np.ascontiguousarray(img2)
|
| 143 |
+
flow = np.ascontiguousarray(flow)
|
| 144 |
+
|
| 145 |
+
if occlusion is not None:
|
| 146 |
+
occlusion = np.ascontiguousarray(occlusion)
|
| 147 |
+
return img1, img2, flow, occlusion
|
| 148 |
+
|
| 149 |
+
return img1, img2, flow
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
class SparseFlowAugmentor:
|
| 153 |
+
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False,
|
| 154 |
+
no_eraser_aug=True,
|
| 155 |
+
):
|
| 156 |
+
# spatial augmentation params
|
| 157 |
+
self.crop_size = crop_size
|
| 158 |
+
self.min_scale = min_scale
|
| 159 |
+
self.max_scale = max_scale
|
| 160 |
+
self.spatial_aug_prob = 0.8
|
| 161 |
+
self.stretch_prob = 0.8
|
| 162 |
+
self.max_stretch = 0.2
|
| 163 |
+
|
| 164 |
+
# flip augmentation params
|
| 165 |
+
self.do_flip = do_flip
|
| 166 |
+
self.h_flip_prob = 0.5
|
| 167 |
+
self.v_flip_prob = 0.1
|
| 168 |
+
|
| 169 |
+
# photometric augmentation params
|
| 170 |
+
self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3 / 3.14)
|
| 171 |
+
self.asymmetric_color_aug_prob = 0.2
|
| 172 |
+
|
| 173 |
+
if no_eraser_aug:
|
| 174 |
+
# we disable eraser aug since no obvious improvement is observed in our experiments
|
| 175 |
+
self.eraser_aug_prob = -1
|
| 176 |
+
else:
|
| 177 |
+
self.eraser_aug_prob = 0.5
|
| 178 |
+
|
| 179 |
+
def color_transform(self, img1, img2):
|
| 180 |
+
image_stack = np.concatenate([img1, img2], axis=0)
|
| 181 |
+
image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
|
| 182 |
+
img1, img2 = np.split(image_stack, 2, axis=0)
|
| 183 |
+
return img1, img2
|
| 184 |
+
|
| 185 |
+
def eraser_transform(self, img1, img2):
|
| 186 |
+
ht, wd = img1.shape[:2]
|
| 187 |
+
if np.random.rand() < self.eraser_aug_prob:
|
| 188 |
+
mean_color = np.mean(img2.reshape(-1, 3), axis=0)
|
| 189 |
+
for _ in range(np.random.randint(1, 3)):
|
| 190 |
+
x0 = np.random.randint(0, wd)
|
| 191 |
+
y0 = np.random.randint(0, ht)
|
| 192 |
+
dx = np.random.randint(50, 100)
|
| 193 |
+
dy = np.random.randint(50, 100)
|
| 194 |
+
img2[y0:y0 + dy, x0:x0 + dx, :] = mean_color
|
| 195 |
+
|
| 196 |
+
return img1, img2
|
| 197 |
+
|
| 198 |
+
def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
|
| 199 |
+
ht, wd = flow.shape[:2]
|
| 200 |
+
coords = np.meshgrid(np.arange(wd), np.arange(ht))
|
| 201 |
+
coords = np.stack(coords, axis=-1)
|
| 202 |
+
|
| 203 |
+
coords = coords.reshape(-1, 2).astype(np.float32)
|
| 204 |
+
flow = flow.reshape(-1, 2).astype(np.float32)
|
| 205 |
+
valid = valid.reshape(-1).astype(np.float32)
|
| 206 |
+
|
| 207 |
+
coords0 = coords[valid >= 1]
|
| 208 |
+
flow0 = flow[valid >= 1]
|
| 209 |
+
|
| 210 |
+
ht1 = int(round(ht * fy))
|
| 211 |
+
wd1 = int(round(wd * fx))
|
| 212 |
+
|
| 213 |
+
coords1 = coords0 * [fx, fy]
|
| 214 |
+
flow1 = flow0 * [fx, fy]
|
| 215 |
+
|
| 216 |
+
xx = np.round(coords1[:, 0]).astype(np.int32)
|
| 217 |
+
yy = np.round(coords1[:, 1]).astype(np.int32)
|
| 218 |
+
|
| 219 |
+
v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
|
| 220 |
+
xx = xx[v]
|
| 221 |
+
yy = yy[v]
|
| 222 |
+
flow1 = flow1[v]
|
| 223 |
+
|
| 224 |
+
flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
|
| 225 |
+
valid_img = np.zeros([ht1, wd1], dtype=np.int32)
|
| 226 |
+
|
| 227 |
+
flow_img[yy, xx] = flow1
|
| 228 |
+
valid_img[yy, xx] = 1
|
| 229 |
+
|
| 230 |
+
return flow_img, valid_img
|
| 231 |
+
|
| 232 |
+
def spatial_transform(self, img1, img2, flow, valid):
|
| 233 |
+
# randomly sample scale
|
| 234 |
+
|
| 235 |
+
ht, wd = img1.shape[:2]
|
| 236 |
+
min_scale = np.maximum(
|
| 237 |
+
(self.crop_size[0] + 1) / float(ht),
|
| 238 |
+
(self.crop_size[1] + 1) / float(wd))
|
| 239 |
+
|
| 240 |
+
scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
|
| 241 |
+
scale_x = np.clip(scale, min_scale, None)
|
| 242 |
+
scale_y = np.clip(scale, min_scale, None)
|
| 243 |
+
|
| 244 |
+
if np.random.rand() < self.spatial_aug_prob:
|
| 245 |
+
# rescale the images
|
| 246 |
+
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
| 247 |
+
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
|
| 248 |
+
|
| 249 |
+
flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
|
| 250 |
+
|
| 251 |
+
if self.do_flip:
|
| 252 |
+
if np.random.rand() < 0.5: # h-flip
|
| 253 |
+
img1 = img1[:, ::-1]
|
| 254 |
+
img2 = img2[:, ::-1]
|
| 255 |
+
flow = flow[:, ::-1] * [-1.0, 1.0]
|
| 256 |
+
valid = valid[:, ::-1]
|
| 257 |
+
|
| 258 |
+
margin_y = 20
|
| 259 |
+
margin_x = 50
|
| 260 |
+
|
| 261 |
+
y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
|
| 262 |
+
x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
|
| 263 |
+
|
| 264 |
+
y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
|
| 265 |
+
x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
|
| 266 |
+
|
| 267 |
+
img1 = img1[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 268 |
+
img2 = img2[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 269 |
+
flow = flow[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 270 |
+
valid = valid[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
|
| 271 |
+
return img1, img2, flow, valid
|
| 272 |
+
|
| 273 |
+
def __call__(self, img1, img2, flow, valid):
|
| 274 |
+
img1, img2 = self.color_transform(img1, img2)
|
| 275 |
+
img1, img2 = self.eraser_transform(img1, img2)
|
| 276 |
+
|
| 277 |
+
img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
|
| 278 |
+
|
| 279 |
+
img1 = np.ascontiguousarray(img1)
|
| 280 |
+
img2 = np.ascontiguousarray(img2)
|
| 281 |
+
flow = np.ascontiguousarray(flow)
|
| 282 |
+
valid = np.ascontiguousarray(valid)
|
| 283 |
+
|
| 284 |
+
return img1, img2, flow, valid
|
GMFlow/environment.yml
ADDED
|
@@ -0,0 +1,162 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: gmflow
|
| 2 |
+
channels:
|
| 3 |
+
- pytorch
|
| 4 |
+
- defaults
|
| 5 |
+
dependencies:
|
| 6 |
+
- _libgcc_mutex=0.1=main
|
| 7 |
+
- _openmp_mutex=4.5=1_gnu
|
| 8 |
+
- blas=1.0=mkl
|
| 9 |
+
- bottleneck=1.3.2=py38heb32a55_1
|
| 10 |
+
- brotli=1.0.9=he6710b0_2
|
| 11 |
+
- bzip2=1.0.8=h7b6447c_0
|
| 12 |
+
- ca-certificates=2021.10.26=h06a4308_2
|
| 13 |
+
- certifi=2021.10.8=py38h06a4308_2
|
| 14 |
+
- cudatoolkit=10.2.89=hfd86e86_1
|
| 15 |
+
- cycler=0.10.0=py38_0
|
| 16 |
+
- dbus=1.13.18=hb2f20db_0
|
| 17 |
+
- expat=2.4.1=h2531618_2
|
| 18 |
+
- ffmpeg=4.3=hf484d3e_0
|
| 19 |
+
- fontconfig=2.13.1=h6c09931_0
|
| 20 |
+
- fonttools=4.25.0=pyhd3eb1b0_0
|
| 21 |
+
- freetype=2.10.4=h5ab3b9f_0
|
| 22 |
+
- glib=2.69.0=h5202010_0
|
| 23 |
+
- gmp=6.2.1=h2531618_2
|
| 24 |
+
- gnutls=3.6.15=he1e5248_0
|
| 25 |
+
- gst-plugins-base=1.14.0=h8213a91_2
|
| 26 |
+
- gstreamer=1.14.0=h28cd5cc_2
|
| 27 |
+
- icu=58.2=he6710b0_3
|
| 28 |
+
- imageio=2.9.0=pyhd3eb1b0_0
|
| 29 |
+
- intel-openmp=2021.3.0=h06a4308_3350
|
| 30 |
+
- jpeg=9b=h024ee3a_2
|
| 31 |
+
- kiwisolver=1.3.1=py38h2531618_0
|
| 32 |
+
- lame=3.100=h7b6447c_0
|
| 33 |
+
- lcms2=2.12=h3be6417_0
|
| 34 |
+
- ld_impl_linux-64=2.35.1=h7274673_9
|
| 35 |
+
- libffi=3.3=he6710b0_2
|
| 36 |
+
- libgcc-ng=9.3.0=h5101ec6_17
|
| 37 |
+
- libgfortran-ng=7.5.0=ha8ba4b0_17
|
| 38 |
+
- libgfortran4=7.5.0=ha8ba4b0_17
|
| 39 |
+
- libgomp=9.3.0=h5101ec6_17
|
| 40 |
+
- libiconv=1.15=h63c8f33_5
|
| 41 |
+
- libidn2=2.3.2=h7f8727e_0
|
| 42 |
+
- libpng=1.6.37=hbc83047_0
|
| 43 |
+
- libstdcxx-ng=9.3.0=hd4cf53a_17
|
| 44 |
+
- libtasn1=4.16.0=h27cfd23_0
|
| 45 |
+
- libtiff=4.2.0=h85742a9_0
|
| 46 |
+
- libunistring=0.9.10=h27cfd23_0
|
| 47 |
+
- libuuid=1.0.3=h1bed415_2
|
| 48 |
+
- libuv=1.40.0=h7b6447c_0
|
| 49 |
+
- libwebp-base=1.2.0=h27cfd23_0
|
| 50 |
+
- libxcb=1.14=h7b6447c_0
|
| 51 |
+
- libxml2=2.9.12=h03d6c58_0
|
| 52 |
+
- lz4-c=1.9.3=h2531618_0
|
| 53 |
+
- matplotlib=3.4.2=py38h06a4308_0
|
| 54 |
+
- matplotlib-base=3.4.2=py38hab158f2_0
|
| 55 |
+
- mkl=2021.3.0=h06a4308_520
|
| 56 |
+
- mkl-service=2.4.0=py38h7f8727e_0
|
| 57 |
+
- mkl_fft=1.3.0=py38h42c9631_2
|
| 58 |
+
- mkl_random=1.2.2=py38h51133e4_0
|
| 59 |
+
- munkres=1.1.4=py_0
|
| 60 |
+
- ncurses=6.2=he6710b0_1
|
| 61 |
+
- nettle=3.7.3=hbbd107a_1
|
| 62 |
+
- ninja=1.10.2=hff7bd54_1
|
| 63 |
+
- numexpr=2.7.3=py38h22e1b3c_1
|
| 64 |
+
- numpy=1.20.3=py38hf144106_0
|
| 65 |
+
- numpy-base=1.20.3=py38h74d4b33_0
|
| 66 |
+
- olefile=0.46=py_0
|
| 67 |
+
- openh264=2.1.0=hd408876_0
|
| 68 |
+
- openjpeg=2.3.0=h05c96fa_1
|
| 69 |
+
- openssl=1.1.1m=h7f8727e_0
|
| 70 |
+
- pandas=1.3.2=py38h8c16a72_0
|
| 71 |
+
- pcre=8.45=h295c915_0
|
| 72 |
+
- pillow=8.3.1=py38h2c7a002_0
|
| 73 |
+
- pip=21.2.2=py38h06a4308_0
|
| 74 |
+
- pyparsing=2.4.7=pyhd3eb1b0_0
|
| 75 |
+
- pyqt=5.9.2=py38h05f1152_4
|
| 76 |
+
- python=3.8.11=h12debd9_0_cpython
|
| 77 |
+
- python-dateutil=2.8.2=pyhd3eb1b0_0
|
| 78 |
+
- pytorch=1.9.0=py3.8_cuda10.2_cudnn7.6.5_0
|
| 79 |
+
- pytz=2021.1=pyhd3eb1b0_0
|
| 80 |
+
- qt=5.9.7=h5867ecd_1
|
| 81 |
+
- readline=8.1=h27cfd23_0
|
| 82 |
+
- scipy=1.6.2=py38had2a1c9_1
|
| 83 |
+
- seaborn=0.11.2=pyhd3eb1b0_0
|
| 84 |
+
- setuptools=52.0.0=py38h06a4308_0
|
| 85 |
+
- sip=4.19.13=py38he6710b0_0
|
| 86 |
+
- six=1.16.0=pyhd3eb1b0_0
|
| 87 |
+
- sqlite=3.36.0=hc218d9a_0
|
| 88 |
+
- tk=8.6.10=hbc83047_0
|
| 89 |
+
- torchaudio=0.9.0=py38
|
| 90 |
+
- torchvision=0.10.0=py38_cu102
|
| 91 |
+
- tornado=6.1=py38h27cfd23_0
|
| 92 |
+
- typing_extensions=3.10.0.0=pyh06a4308_0
|
| 93 |
+
- wheel=0.36.2=pyhd3eb1b0_0
|
| 94 |
+
- xz=5.2.5=h7b6447c_0
|
| 95 |
+
- zlib=1.2.11=h7b6447c_3
|
| 96 |
+
- zstd=1.4.9=haebb681_0
|
| 97 |
+
- pip:
|
| 98 |
+
- absl-py==0.13.0
|
| 99 |
+
- argon2-cffi==21.1.0
|
| 100 |
+
- attrs==21.2.0
|
| 101 |
+
- backcall==0.2.0
|
| 102 |
+
- bleach==4.1.0
|
| 103 |
+
- cachetools==4.2.2
|
| 104 |
+
- cffi==1.14.6
|
| 105 |
+
- charset-normalizer==2.0.4
|
| 106 |
+
- debugpy==1.4.3
|
| 107 |
+
- decorator==5.1.0
|
| 108 |
+
- defusedxml==0.7.1
|
| 109 |
+
- einops==0.3.2
|
| 110 |
+
- entrypoints==0.3
|
| 111 |
+
- google-auth==1.34.0
|
| 112 |
+
- google-auth-oauthlib==0.4.5
|
| 113 |
+
- grpcio==1.39.0
|
| 114 |
+
- idna==3.2
|
| 115 |
+
- ipykernel==6.4.1
|
| 116 |
+
- ipython==7.27.0
|
| 117 |
+
- ipython-genutils==0.2.0
|
| 118 |
+
- jedi==0.18.0
|
| 119 |
+
- jinja2==3.0.1
|
| 120 |
+
- jsonschema==3.2.0
|
| 121 |
+
- jupyter-client==7.0.3
|
| 122 |
+
- jupyter-core==4.8.1
|
| 123 |
+
- jupyterlab-pygments==0.1.2
|
| 124 |
+
- markdown==3.3.4
|
| 125 |
+
- markupsafe==2.0.1
|
| 126 |
+
- matplotlib-inline==0.1.3
|
| 127 |
+
- mistune==0.8.4
|
| 128 |
+
- nbclient==0.5.4
|
| 129 |
+
- nbconvert==6.1.0
|
| 130 |
+
- nbformat==5.1.3
|
| 131 |
+
- nest-asyncio==1.5.1
|
| 132 |
+
- oauthlib==3.1.1
|
| 133 |
+
- opencv-python==4.5.3.56
|
| 134 |
+
- packaging==21.0
|
| 135 |
+
- pandocfilters==1.5.0
|
| 136 |
+
- parso==0.8.2
|
| 137 |
+
- pexpect==4.8.0
|
| 138 |
+
- pickleshare==0.7.5
|
| 139 |
+
- prometheus-client==0.11.0
|
| 140 |
+
- prompt-toolkit==3.0.20
|
| 141 |
+
- protobuf==3.17.3
|
| 142 |
+
- ptyprocess==0.7.0
|
| 143 |
+
- pyasn1==0.4.8
|
| 144 |
+
- pyasn1-modules==0.2.8
|
| 145 |
+
- pycparser==2.20
|
| 146 |
+
- pygments==2.10.0
|
| 147 |
+
- pyrsistent==0.18.0
|
| 148 |
+
- pyzmq==22.3.0
|
| 149 |
+
- requests==2.26.0
|
| 150 |
+
- requests-oauthlib==1.3.0
|
| 151 |
+
- rsa==4.7.2
|
| 152 |
+
- send2trash==1.8.0
|
| 153 |
+
- tensorboard==2.5.0
|
| 154 |
+
- tensorboard-data-server==0.6.1
|
| 155 |
+
- tensorboard-plugin-wit==1.8.0
|
| 156 |
+
- terminado==0.12.1
|
| 157 |
+
- testpath==0.5.0
|
| 158 |
+
- traitlets==5.1.0
|
| 159 |
+
- urllib3==1.26.6
|
| 160 |
+
- wcwidth==0.2.5
|
| 161 |
+
- webencodings==0.5.1
|
| 162 |
+
- werkzeug==2.0.1
|
GMFlow/evaluate.py
ADDED
|
@@ -0,0 +1,689 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
from PIL import Image
|
| 2 |
+
import os
|
| 3 |
+
import time
|
| 4 |
+
import numpy as np
|
| 5 |
+
import torch
|
| 6 |
+
import torch.nn.functional as F
|
| 7 |
+
|
| 8 |
+
import data
|
| 9 |
+
from utils import frame_utils
|
| 10 |
+
from utils.flow_viz import save_vis_flow_tofile
|
| 11 |
+
|
| 12 |
+
from utils.utils import InputPadder, compute_out_of_boundary_mask
|
| 13 |
+
from glob import glob
|
| 14 |
+
from gmflow.geometry import forward_backward_consistency_check
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
@torch.no_grad()
|
| 18 |
+
def create_sintel_submission(model,
|
| 19 |
+
output_path='sintel_submission',
|
| 20 |
+
padding_factor=8,
|
| 21 |
+
save_vis_flow=False,
|
| 22 |
+
no_save_flo=False,
|
| 23 |
+
attn_splits_list=None,
|
| 24 |
+
corr_radius_list=None,
|
| 25 |
+
prop_radius_list=None,
|
| 26 |
+
):
|
| 27 |
+
""" Create submission for the Sintel leaderboard """
|
| 28 |
+
model.eval()
|
| 29 |
+
for dstype in ['clean', 'final']:
|
| 30 |
+
test_dataset = data.MpiSintel(split='test', aug_params=None, dstype=dstype)
|
| 31 |
+
|
| 32 |
+
flow_prev, sequence_prev = None, None
|
| 33 |
+
for test_id in range(len(test_dataset)):
|
| 34 |
+
image1, image2, (sequence, frame) = test_dataset[test_id]
|
| 35 |
+
if sequence != sequence_prev:
|
| 36 |
+
flow_prev = None
|
| 37 |
+
|
| 38 |
+
padder = InputPadder(image1.shape, padding_factor=padding_factor)
|
| 39 |
+
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
|
| 40 |
+
|
| 41 |
+
results_dict = model(image1, image2,
|
| 42 |
+
attn_splits_list=attn_splits_list,
|
| 43 |
+
corr_radius_list=corr_radius_list,
|
| 44 |
+
prop_radius_list=prop_radius_list,
|
| 45 |
+
)
|
| 46 |
+
|
| 47 |
+
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
|
| 48 |
+
|
| 49 |
+
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
|
| 50 |
+
|
| 51 |
+
output_dir = os.path.join(output_path, dstype, sequence)
|
| 52 |
+
output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame + 1))
|
| 53 |
+
|
| 54 |
+
if not os.path.exists(output_dir):
|
| 55 |
+
os.makedirs(output_dir)
|
| 56 |
+
|
| 57 |
+
if not no_save_flo:
|
| 58 |
+
frame_utils.writeFlow(output_file, flow)
|
| 59 |
+
sequence_prev = sequence
|
| 60 |
+
|
| 61 |
+
# Save vis flow
|
| 62 |
+
if save_vis_flow:
|
| 63 |
+
vis_flow_file = output_file.replace('.flo', '.png')
|
| 64 |
+
save_vis_flow_tofile(flow, vis_flow_file)
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
@torch.no_grad()
|
| 68 |
+
def create_kitti_submission(model,
|
| 69 |
+
output_path='kitti_submission',
|
| 70 |
+
padding_factor=8,
|
| 71 |
+
save_vis_flow=False,
|
| 72 |
+
attn_splits_list=None,
|
| 73 |
+
corr_radius_list=None,
|
| 74 |
+
prop_radius_list=None,
|
| 75 |
+
):
|
| 76 |
+
""" Create submission for the Sintel leaderboard """
|
| 77 |
+
model.eval()
|
| 78 |
+
test_dataset = data.KITTI(split='testing', aug_params=None)
|
| 79 |
+
|
| 80 |
+
if not os.path.exists(output_path):
|
| 81 |
+
os.makedirs(output_path)
|
| 82 |
+
|
| 83 |
+
for test_id in range(len(test_dataset)):
|
| 84 |
+
image1, image2, (frame_id,) = test_dataset[test_id]
|
| 85 |
+
padder = InputPadder(image1.shape, mode='kitti', padding_factor=padding_factor)
|
| 86 |
+
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
|
| 87 |
+
|
| 88 |
+
results_dict = model(image1, image2,
|
| 89 |
+
attn_splits_list=attn_splits_list,
|
| 90 |
+
corr_radius_list=corr_radius_list,
|
| 91 |
+
prop_radius_list=prop_radius_list,
|
| 92 |
+
)
|
| 93 |
+
|
| 94 |
+
flow_pr = results_dict['flow_preds'][-1]
|
| 95 |
+
|
| 96 |
+
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
|
| 97 |
+
|
| 98 |
+
output_filename = os.path.join(output_path, frame_id)
|
| 99 |
+
|
| 100 |
+
if save_vis_flow:
|
| 101 |
+
vis_flow_file = output_filename
|
| 102 |
+
save_vis_flow_tofile(flow, vis_flow_file)
|
| 103 |
+
else:
|
| 104 |
+
frame_utils.writeFlowKITTI(output_filename, flow)
|
| 105 |
+
|
| 106 |
+
|
| 107 |
+
@torch.no_grad()
|
| 108 |
+
def validate_chairs(model,
|
| 109 |
+
with_speed_metric=False,
|
| 110 |
+
attn_splits_list=False,
|
| 111 |
+
corr_radius_list=False,
|
| 112 |
+
prop_radius_list=False,
|
| 113 |
+
):
|
| 114 |
+
""" Perform evaluation on the FlyingChairs (test) split """
|
| 115 |
+
model.eval()
|
| 116 |
+
epe_list = []
|
| 117 |
+
results = {}
|
| 118 |
+
|
| 119 |
+
if with_speed_metric:
|
| 120 |
+
s0_10_list = []
|
| 121 |
+
s10_40_list = []
|
| 122 |
+
s40plus_list = []
|
| 123 |
+
|
| 124 |
+
val_dataset = data.FlyingChairs(split='validation')
|
| 125 |
+
|
| 126 |
+
print('Number of validation image pairs: %d' % len(val_dataset))
|
| 127 |
+
|
| 128 |
+
for val_id in range(len(val_dataset)):
|
| 129 |
+
image1, image2, flow_gt, _ = val_dataset[val_id]
|
| 130 |
+
|
| 131 |
+
image1 = image1[None].cuda()
|
| 132 |
+
image2 = image2[None].cuda()
|
| 133 |
+
|
| 134 |
+
results_dict = model(image1, image2,
|
| 135 |
+
attn_splits_list=attn_splits_list,
|
| 136 |
+
corr_radius_list=corr_radius_list,
|
| 137 |
+
prop_radius_list=prop_radius_list,
|
| 138 |
+
)
|
| 139 |
+
|
| 140 |
+
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
|
| 141 |
+
|
| 142 |
+
assert flow_pr.size()[-2:] == flow_gt.size()[-2:]
|
| 143 |
+
|
| 144 |
+
epe = torch.sum((flow_pr[0].cpu() - flow_gt) ** 2, dim=0).sqrt()
|
| 145 |
+
epe_list.append(epe.view(-1).numpy())
|
| 146 |
+
|
| 147 |
+
if with_speed_metric:
|
| 148 |
+
flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
|
| 149 |
+
valid_mask = (flow_gt_speed < 10)
|
| 150 |
+
if valid_mask.max() > 0:
|
| 151 |
+
s0_10_list.append(epe[valid_mask].cpu().numpy())
|
| 152 |
+
|
| 153 |
+
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40)
|
| 154 |
+
if valid_mask.max() > 0:
|
| 155 |
+
s10_40_list.append(epe[valid_mask].cpu().numpy())
|
| 156 |
+
|
| 157 |
+
valid_mask = (flow_gt_speed > 40)
|
| 158 |
+
if valid_mask.max() > 0:
|
| 159 |
+
s40plus_list.append(epe[valid_mask].cpu().numpy())
|
| 160 |
+
|
| 161 |
+
epe_all = np.concatenate(epe_list)
|
| 162 |
+
epe = np.mean(epe_all)
|
| 163 |
+
px1 = np.mean(epe_all > 1)
|
| 164 |
+
px3 = np.mean(epe_all > 3)
|
| 165 |
+
px5 = np.mean(epe_all > 5)
|
| 166 |
+
print("Validation Chairs EPE: %.3f, 1px: %.3f, 3px: %.3f, 5px: %.3f" % (epe, px1, px3, px5))
|
| 167 |
+
results['chairs_epe'] = epe
|
| 168 |
+
results['chairs_1px'] = px1
|
| 169 |
+
results['chairs_3px'] = px3
|
| 170 |
+
results['chairs_5px'] = px5
|
| 171 |
+
|
| 172 |
+
if with_speed_metric:
|
| 173 |
+
s0_10 = np.mean(np.concatenate(s0_10_list))
|
| 174 |
+
s10_40 = np.mean(np.concatenate(s10_40_list))
|
| 175 |
+
s40plus = np.mean(np.concatenate(s40plus_list))
|
| 176 |
+
|
| 177 |
+
print("Validation Chairs s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
|
| 178 |
+
s0_10,
|
| 179 |
+
s10_40,
|
| 180 |
+
s40plus))
|
| 181 |
+
|
| 182 |
+
results['chairs_s0_10'] = s0_10
|
| 183 |
+
results['chairs_s10_40'] = s10_40
|
| 184 |
+
results['chairs_s40+'] = s40plus
|
| 185 |
+
|
| 186 |
+
return results
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
@torch.no_grad()
|
| 190 |
+
def validate_things(model,
|
| 191 |
+
padding_factor=8,
|
| 192 |
+
with_speed_metric=False,
|
| 193 |
+
max_val_flow=400,
|
| 194 |
+
val_things_clean_only=True,
|
| 195 |
+
attn_splits_list=False,
|
| 196 |
+
corr_radius_list=False,
|
| 197 |
+
prop_radius_list=False,
|
| 198 |
+
):
|
| 199 |
+
""" Peform validation using the Things (test) split """
|
| 200 |
+
model.eval()
|
| 201 |
+
results = {}
|
| 202 |
+
|
| 203 |
+
for dstype in ['frames_cleanpass', 'frames_finalpass']:
|
| 204 |
+
if val_things_clean_only:
|
| 205 |
+
if dstype == 'frames_finalpass':
|
| 206 |
+
continue
|
| 207 |
+
|
| 208 |
+
val_dataset = data.FlyingThings3D(dstype=dstype, test_set=True, validate_subset=True,
|
| 209 |
+
)
|
| 210 |
+
print('Number of validation image pairs: %d' % len(val_dataset))
|
| 211 |
+
epe_list = []
|
| 212 |
+
|
| 213 |
+
if with_speed_metric:
|
| 214 |
+
s0_10_list = []
|
| 215 |
+
s10_40_list = []
|
| 216 |
+
s40plus_list = []
|
| 217 |
+
|
| 218 |
+
for val_id in range(len(val_dataset)):
|
| 219 |
+
image1, image2, flow_gt, valid_gt = val_dataset[val_id]
|
| 220 |
+
image1 = image1[None].cuda()
|
| 221 |
+
image2 = image2[None].cuda()
|
| 222 |
+
|
| 223 |
+
padder = InputPadder(image1.shape, padding_factor=padding_factor)
|
| 224 |
+
image1, image2 = padder.pad(image1, image2)
|
| 225 |
+
|
| 226 |
+
results_dict = model(image1, image2,
|
| 227 |
+
attn_splits_list=attn_splits_list,
|
| 228 |
+
corr_radius_list=corr_radius_list,
|
| 229 |
+
prop_radius_list=prop_radius_list,
|
| 230 |
+
)
|
| 231 |
+
flow_pr = results_dict['flow_preds'][-1]
|
| 232 |
+
|
| 233 |
+
flow = padder.unpad(flow_pr[0]).cpu()
|
| 234 |
+
|
| 235 |
+
# Evaluation on flow <= max_val_flow
|
| 236 |
+
flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
|
| 237 |
+
valid_gt = valid_gt * (flow_gt_speed < max_val_flow)
|
| 238 |
+
valid_gt = valid_gt.contiguous()
|
| 239 |
+
|
| 240 |
+
epe = torch.sum((flow - flow_gt) ** 2, dim=0).sqrt()
|
| 241 |
+
val = valid_gt >= 0.5
|
| 242 |
+
epe_list.append(epe[val].cpu().numpy())
|
| 243 |
+
|
| 244 |
+
if with_speed_metric:
|
| 245 |
+
valid_mask = (flow_gt_speed < 10) * (valid_gt >= 0.5)
|
| 246 |
+
if valid_mask.max() > 0:
|
| 247 |
+
s0_10_list.append(epe[valid_mask].cpu().numpy())
|
| 248 |
+
|
| 249 |
+
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40) * (valid_gt >= 0.5)
|
| 250 |
+
if valid_mask.max() > 0:
|
| 251 |
+
s10_40_list.append(epe[valid_mask].cpu().numpy())
|
| 252 |
+
|
| 253 |
+
valid_mask = (flow_gt_speed > 40) * (valid_gt >= 0.5)
|
| 254 |
+
if valid_mask.max() > 0:
|
| 255 |
+
s40plus_list.append(epe[valid_mask].cpu().numpy())
|
| 256 |
+
|
| 257 |
+
epe_list = np.mean(np.concatenate(epe_list))
|
| 258 |
+
|
| 259 |
+
epe = np.mean(epe_list)
|
| 260 |
+
|
| 261 |
+
if dstype == 'frames_cleanpass':
|
| 262 |
+
dstype = 'things_clean'
|
| 263 |
+
if dstype == 'frames_finalpass':
|
| 264 |
+
dstype = 'things_final'
|
| 265 |
+
|
| 266 |
+
print("Validation Things test set (%s) EPE: %.3f" % (dstype, epe))
|
| 267 |
+
results[dstype + '_epe'] = epe
|
| 268 |
+
|
| 269 |
+
if with_speed_metric:
|
| 270 |
+
s0_10 = np.mean(np.concatenate(s0_10_list))
|
| 271 |
+
s10_40 = np.mean(np.concatenate(s10_40_list))
|
| 272 |
+
s40plus = np.mean(np.concatenate(s40plus_list))
|
| 273 |
+
|
| 274 |
+
print("Validation Things test (%s) s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
|
| 275 |
+
dstype, s0_10,
|
| 276 |
+
s10_40,
|
| 277 |
+
s40plus))
|
| 278 |
+
|
| 279 |
+
results[dstype + '_s0_10'] = s0_10
|
| 280 |
+
results[dstype + '_s10_40'] = s10_40
|
| 281 |
+
results[dstype + '_s40+'] = s40plus
|
| 282 |
+
|
| 283 |
+
return results
|
| 284 |
+
|
| 285 |
+
|
| 286 |
+
@torch.no_grad()
|
| 287 |
+
def validate_sintel(model,
|
| 288 |
+
count_time=False,
|
| 289 |
+
padding_factor=8,
|
| 290 |
+
with_speed_metric=False,
|
| 291 |
+
evaluate_matched_unmatched=False,
|
| 292 |
+
attn_splits_list=False,
|
| 293 |
+
corr_radius_list=False,
|
| 294 |
+
prop_radius_list=False,
|
| 295 |
+
):
|
| 296 |
+
""" Peform validation using the Sintel (train) split """
|
| 297 |
+
model.eval()
|
| 298 |
+
results = {}
|
| 299 |
+
|
| 300 |
+
if count_time:
|
| 301 |
+
total_time = 0
|
| 302 |
+
num_runs = 100
|
| 303 |
+
|
| 304 |
+
for dstype in ['clean', 'final']:
|
| 305 |
+
val_dataset = data.MpiSintel(split='training', dstype=dstype,
|
| 306 |
+
load_occlusion=evaluate_matched_unmatched,
|
| 307 |
+
)
|
| 308 |
+
|
| 309 |
+
print('Number of validation image pairs: %d' % len(val_dataset))
|
| 310 |
+
epe_list = []
|
| 311 |
+
|
| 312 |
+
if evaluate_matched_unmatched:
|
| 313 |
+
matched_epe_list = []
|
| 314 |
+
unmatched_epe_list = []
|
| 315 |
+
|
| 316 |
+
if with_speed_metric:
|
| 317 |
+
s0_10_list = []
|
| 318 |
+
s10_40_list = []
|
| 319 |
+
s40plus_list = []
|
| 320 |
+
|
| 321 |
+
for val_id in range(len(val_dataset)):
|
| 322 |
+
if evaluate_matched_unmatched:
|
| 323 |
+
image1, image2, flow_gt, valid, noc_valid = val_dataset[val_id]
|
| 324 |
+
|
| 325 |
+
# compuate in-image-plane valid mask
|
| 326 |
+
in_image_valid = compute_out_of_boundary_mask(flow_gt.unsqueeze(0)).squeeze(0) # [H, W]
|
| 327 |
+
|
| 328 |
+
else:
|
| 329 |
+
image1, image2, flow_gt, _ = val_dataset[val_id]
|
| 330 |
+
|
| 331 |
+
image1 = image1[None].cuda()
|
| 332 |
+
image2 = image2[None].cuda()
|
| 333 |
+
|
| 334 |
+
padder = InputPadder(image1.shape, padding_factor=padding_factor)
|
| 335 |
+
image1, image2 = padder.pad(image1, image2)
|
| 336 |
+
|
| 337 |
+
if count_time and val_id >= 5: # 5 warmup
|
| 338 |
+
torch.cuda.synchronize()
|
| 339 |
+
time_start = time.perf_counter()
|
| 340 |
+
|
| 341 |
+
results_dict = model(image1, image2,
|
| 342 |
+
attn_splits_list=attn_splits_list,
|
| 343 |
+
corr_radius_list=corr_radius_list,
|
| 344 |
+
prop_radius_list=prop_radius_list,
|
| 345 |
+
)
|
| 346 |
+
|
| 347 |
+
# useful when using parallel branches
|
| 348 |
+
flow_pr = results_dict['flow_preds'][-1]
|
| 349 |
+
|
| 350 |
+
if count_time and val_id >= 5:
|
| 351 |
+
torch.cuda.synchronize()
|
| 352 |
+
total_time += time.perf_counter() - time_start
|
| 353 |
+
|
| 354 |
+
if val_id >= num_runs + 4:
|
| 355 |
+
break
|
| 356 |
+
|
| 357 |
+
flow = padder.unpad(flow_pr[0]).cpu()
|
| 358 |
+
|
| 359 |
+
epe = torch.sum((flow - flow_gt) ** 2, dim=0).sqrt()
|
| 360 |
+
epe_list.append(epe.view(-1).numpy())
|
| 361 |
+
|
| 362 |
+
if evaluate_matched_unmatched:
|
| 363 |
+
matched_valid_mask = (noc_valid > 0.5) & (in_image_valid > 0.5)
|
| 364 |
+
|
| 365 |
+
if matched_valid_mask.max() > 0:
|
| 366 |
+
matched_epe_list.append(epe[matched_valid_mask].cpu().numpy())
|
| 367 |
+
unmatched_epe_list.append(epe[~matched_valid_mask].cpu().numpy())
|
| 368 |
+
|
| 369 |
+
if with_speed_metric:
|
| 370 |
+
flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
|
| 371 |
+
valid_mask = (flow_gt_speed < 10)
|
| 372 |
+
if valid_mask.max() > 0:
|
| 373 |
+
s0_10_list.append(epe[valid_mask].cpu().numpy())
|
| 374 |
+
|
| 375 |
+
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40)
|
| 376 |
+
if valid_mask.max() > 0:
|
| 377 |
+
s10_40_list.append(epe[valid_mask].cpu().numpy())
|
| 378 |
+
|
| 379 |
+
valid_mask = (flow_gt_speed > 40)
|
| 380 |
+
if valid_mask.max() > 0:
|
| 381 |
+
s40plus_list.append(epe[valid_mask].cpu().numpy())
|
| 382 |
+
|
| 383 |
+
epe_all = np.concatenate(epe_list)
|
| 384 |
+
epe = np.mean(epe_all)
|
| 385 |
+
px1 = np.mean(epe_all > 1)
|
| 386 |
+
px3 = np.mean(epe_all > 3)
|
| 387 |
+
px5 = np.mean(epe_all > 5)
|
| 388 |
+
|
| 389 |
+
dstype_ori = dstype
|
| 390 |
+
|
| 391 |
+
print("Validation Sintel (%s) EPE: %.3f, 1px: %.3f, 3px: %.3f, 5px: %.3f" % (dstype_ori, epe, px1, px3, px5))
|
| 392 |
+
|
| 393 |
+
dstype = 'sintel_' + dstype
|
| 394 |
+
|
| 395 |
+
results[dstype + '_epe'] = np.mean(epe_list)
|
| 396 |
+
results[dstype + '_1px'] = px1
|
| 397 |
+
results[dstype + '_3px'] = px3
|
| 398 |
+
results[dstype + '_5px'] = px5
|
| 399 |
+
|
| 400 |
+
if with_speed_metric:
|
| 401 |
+
s0_10 = np.mean(np.concatenate(s0_10_list))
|
| 402 |
+
s10_40 = np.mean(np.concatenate(s10_40_list))
|
| 403 |
+
s40plus = np.mean(np.concatenate(s40plus_list))
|
| 404 |
+
|
| 405 |
+
print("Validation Sintel (%s) s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
|
| 406 |
+
dstype_ori, s0_10,
|
| 407 |
+
s10_40,
|
| 408 |
+
s40plus))
|
| 409 |
+
|
| 410 |
+
results[dstype + '_s0_10'] = s0_10
|
| 411 |
+
results[dstype + '_s10_40'] = s10_40
|
| 412 |
+
results[dstype + '_s40+'] = s40plus
|
| 413 |
+
|
| 414 |
+
if count_time:
|
| 415 |
+
print('Time: %.6fs' % (total_time / num_runs))
|
| 416 |
+
break # only the clean pass when counting time
|
| 417 |
+
|
| 418 |
+
if evaluate_matched_unmatched:
|
| 419 |
+
matched_epe = np.mean(np.concatenate(matched_epe_list))
|
| 420 |
+
unmatched_epe = np.mean(np.concatenate(unmatched_epe_list))
|
| 421 |
+
|
| 422 |
+
print('Validatation Sintel (%s) matched epe: %.3f, unmatched epe: %.3f' % (
|
| 423 |
+
dstype_ori, matched_epe, unmatched_epe))
|
| 424 |
+
|
| 425 |
+
results[dstype + '_matched'] = matched_epe
|
| 426 |
+
results[dstype + '_unmatched'] = unmatched_epe
|
| 427 |
+
|
| 428 |
+
return results
|
| 429 |
+
|
| 430 |
+
|
| 431 |
+
@torch.no_grad()
|
| 432 |
+
def validate_kitti(model,
|
| 433 |
+
padding_factor=8,
|
| 434 |
+
with_speed_metric=False,
|
| 435 |
+
average_over_pixels=True,
|
| 436 |
+
attn_splits_list=False,
|
| 437 |
+
corr_radius_list=False,
|
| 438 |
+
prop_radius_list=False,
|
| 439 |
+
):
|
| 440 |
+
""" Peform validation using the KITTI-2015 (train) split """
|
| 441 |
+
model.eval()
|
| 442 |
+
|
| 443 |
+
val_dataset = data.KITTI(split='training')
|
| 444 |
+
print('Number of validation image pairs: %d' % len(val_dataset))
|
| 445 |
+
|
| 446 |
+
out_list, epe_list = [], []
|
| 447 |
+
results = {}
|
| 448 |
+
|
| 449 |
+
if with_speed_metric:
|
| 450 |
+
if average_over_pixels:
|
| 451 |
+
s0_10_list = []
|
| 452 |
+
s10_40_list = []
|
| 453 |
+
s40plus_list = []
|
| 454 |
+
else:
|
| 455 |
+
s0_10_epe_sum = 0
|
| 456 |
+
s0_10_valid_samples = 0
|
| 457 |
+
s10_40_epe_sum = 0
|
| 458 |
+
s10_40_valid_samples = 0
|
| 459 |
+
s40plus_epe_sum = 0
|
| 460 |
+
s40plus_valid_samples = 0
|
| 461 |
+
|
| 462 |
+
for val_id in range(len(val_dataset)):
|
| 463 |
+
image1, image2, flow_gt, valid_gt = val_dataset[val_id]
|
| 464 |
+
image1 = image1[None].cuda()
|
| 465 |
+
image2 = image2[None].cuda()
|
| 466 |
+
|
| 467 |
+
padder = InputPadder(image1.shape, mode='kitti', padding_factor=padding_factor)
|
| 468 |
+
image1, image2 = padder.pad(image1, image2)
|
| 469 |
+
|
| 470 |
+
results_dict = model(image1, image2,
|
| 471 |
+
attn_splits_list=attn_splits_list,
|
| 472 |
+
corr_radius_list=corr_radius_list,
|
| 473 |
+
prop_radius_list=prop_radius_list,
|
| 474 |
+
)
|
| 475 |
+
|
| 476 |
+
# useful when using parallel branches
|
| 477 |
+
flow_pr = results_dict['flow_preds'][-1]
|
| 478 |
+
|
| 479 |
+
flow = padder.unpad(flow_pr[0]).cpu()
|
| 480 |
+
|
| 481 |
+
epe = torch.sum((flow - flow_gt) ** 2, dim=0).sqrt()
|
| 482 |
+
mag = torch.sum(flow_gt ** 2, dim=0).sqrt()
|
| 483 |
+
|
| 484 |
+
if with_speed_metric:
|
| 485 |
+
# flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
|
| 486 |
+
flow_gt_speed = mag
|
| 487 |
+
|
| 488 |
+
if average_over_pixels:
|
| 489 |
+
valid_mask = (flow_gt_speed < 10) * (valid_gt >= 0.5) # note KITTI GT is sparse
|
| 490 |
+
if valid_mask.max() > 0:
|
| 491 |
+
s0_10_list.append(epe[valid_mask].cpu().numpy())
|
| 492 |
+
|
| 493 |
+
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40) * (valid_gt >= 0.5)
|
| 494 |
+
if valid_mask.max() > 0:
|
| 495 |
+
s10_40_list.append(epe[valid_mask].cpu().numpy())
|
| 496 |
+
|
| 497 |
+
valid_mask = (flow_gt_speed > 40) * (valid_gt >= 0.5)
|
| 498 |
+
if valid_mask.max() > 0:
|
| 499 |
+
s40plus_list.append(epe[valid_mask].cpu().numpy())
|
| 500 |
+
|
| 501 |
+
else:
|
| 502 |
+
valid_mask = (flow_gt_speed < 10) * (valid_gt >= 0.5) # note KITTI GT is sparse
|
| 503 |
+
if valid_mask.max() > 0:
|
| 504 |
+
s0_10_epe_sum += (epe * valid_mask).sum() / valid_mask.sum()
|
| 505 |
+
s0_10_valid_samples += 1
|
| 506 |
+
|
| 507 |
+
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40) * (valid_gt >= 0.5)
|
| 508 |
+
if valid_mask.max() > 0:
|
| 509 |
+
s10_40_epe_sum += (epe * valid_mask).sum() / valid_mask.sum()
|
| 510 |
+
s10_40_valid_samples += 1
|
| 511 |
+
|
| 512 |
+
valid_mask = (flow_gt_speed > 40) * (valid_gt >= 0.5)
|
| 513 |
+
if valid_mask.max() > 0:
|
| 514 |
+
s40plus_epe_sum += (epe * valid_mask).sum() / valid_mask.sum()
|
| 515 |
+
s40plus_valid_samples += 1
|
| 516 |
+
|
| 517 |
+
epe = epe.view(-1)
|
| 518 |
+
mag = mag.view(-1)
|
| 519 |
+
val = valid_gt.view(-1) >= 0.5
|
| 520 |
+
|
| 521 |
+
out = ((epe > 3.0) & ((epe / mag) > 0.05)).float()
|
| 522 |
+
|
| 523 |
+
if average_over_pixels:
|
| 524 |
+
epe_list.append(epe[val].cpu().numpy())
|
| 525 |
+
else:
|
| 526 |
+
epe_list.append(epe[val].mean().item())
|
| 527 |
+
|
| 528 |
+
out_list.append(out[val].cpu().numpy())
|
| 529 |
+
|
| 530 |
+
if average_over_pixels:
|
| 531 |
+
epe_list = np.concatenate(epe_list)
|
| 532 |
+
else:
|
| 533 |
+
epe_list = np.array(epe_list)
|
| 534 |
+
out_list = np.concatenate(out_list)
|
| 535 |
+
|
| 536 |
+
epe = np.mean(epe_list)
|
| 537 |
+
f1 = 100 * np.mean(out_list)
|
| 538 |
+
|
| 539 |
+
print("Validation KITTI EPE: %.3f, F1-all: %.3f" % (epe, f1))
|
| 540 |
+
results['kitti_epe'] = epe
|
| 541 |
+
results['kitti_f1'] = f1
|
| 542 |
+
|
| 543 |
+
if with_speed_metric:
|
| 544 |
+
if average_over_pixels:
|
| 545 |
+
s0_10 = np.mean(np.concatenate(s0_10_list))
|
| 546 |
+
s10_40 = np.mean(np.concatenate(s10_40_list))
|
| 547 |
+
s40plus = np.mean(np.concatenate(s40plus_list))
|
| 548 |
+
else:
|
| 549 |
+
s0_10 = s0_10_epe_sum / s0_10_valid_samples
|
| 550 |
+
s10_40 = s10_40_epe_sum / s10_40_valid_samples
|
| 551 |
+
s40plus = s40plus_epe_sum / s40plus_valid_samples
|
| 552 |
+
|
| 553 |
+
print("Validation KITTI s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
|
| 554 |
+
s0_10,
|
| 555 |
+
s10_40,
|
| 556 |
+
s40plus))
|
| 557 |
+
|
| 558 |
+
results['kitti_s0_10'] = s0_10
|
| 559 |
+
results['kitti_s10_40'] = s10_40
|
| 560 |
+
results['kitti_s40+'] = s40plus
|
| 561 |
+
|
| 562 |
+
return results
|
| 563 |
+
|
| 564 |
+
|
| 565 |
+
@torch.no_grad()
|
| 566 |
+
def inference_on_dir(model,
|
| 567 |
+
inference_dir,
|
| 568 |
+
output_path='output',
|
| 569 |
+
padding_factor=8,
|
| 570 |
+
inference_size=None,
|
| 571 |
+
paired_data=False, # dir of paired testdata instead of a sequence
|
| 572 |
+
save_flo_flow=False, # save as .flo for quantative evaluation
|
| 573 |
+
attn_splits_list=None,
|
| 574 |
+
corr_radius_list=None,
|
| 575 |
+
prop_radius_list=None,
|
| 576 |
+
pred_bidir_flow=False,
|
| 577 |
+
fwd_bwd_consistency_check=False,
|
| 578 |
+
):
|
| 579 |
+
""" Inference on a directory """
|
| 580 |
+
model.eval()
|
| 581 |
+
|
| 582 |
+
if fwd_bwd_consistency_check:
|
| 583 |
+
assert pred_bidir_flow
|
| 584 |
+
|
| 585 |
+
if not os.path.exists(output_path):
|
| 586 |
+
os.makedirs(output_path)
|
| 587 |
+
|
| 588 |
+
filenames = sorted(glob(inference_dir + '/*'))
|
| 589 |
+
print('%d images found' % len(filenames))
|
| 590 |
+
|
| 591 |
+
stride = 2 if paired_data else 1
|
| 592 |
+
|
| 593 |
+
if paired_data:
|
| 594 |
+
assert len(filenames) % 2 == 0
|
| 595 |
+
|
| 596 |
+
for test_id in range(0, len(filenames) - 1, stride):
|
| 597 |
+
|
| 598 |
+
image1 = frame_utils.read_gen(filenames[test_id])
|
| 599 |
+
image2 = frame_utils.read_gen(filenames[test_id + 1])
|
| 600 |
+
|
| 601 |
+
image1 = np.array(image1).astype(np.uint8)
|
| 602 |
+
image2 = np.array(image2).astype(np.uint8)
|
| 603 |
+
|
| 604 |
+
if len(image1.shape) == 2: # gray image, for example, HD1K
|
| 605 |
+
image1 = np.tile(image1[..., None], (1, 1, 3))
|
| 606 |
+
image2 = np.tile(image2[..., None], (1, 1, 3))
|
| 607 |
+
else:
|
| 608 |
+
image1 = image1[..., :3]
|
| 609 |
+
image2 = image2[..., :3]
|
| 610 |
+
|
| 611 |
+
image1 = torch.from_numpy(image1).permute(2, 0, 1).float()
|
| 612 |
+
image2 = torch.from_numpy(image2).permute(2, 0, 1).float()
|
| 613 |
+
|
| 614 |
+
if inference_size is None:
|
| 615 |
+
padder = InputPadder(image1.shape, padding_factor=padding_factor)
|
| 616 |
+
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
|
| 617 |
+
else:
|
| 618 |
+
image1, image2 = image1[None].cuda(), image2[None].cuda()
|
| 619 |
+
|
| 620 |
+
# resize before inference
|
| 621 |
+
if inference_size is not None:
|
| 622 |
+
assert isinstance(inference_size, list) or isinstance(inference_size, tuple)
|
| 623 |
+
ori_size = image1.shape[-2:]
|
| 624 |
+
image1 = F.interpolate(image1, size=inference_size, mode='bilinear',
|
| 625 |
+
align_corners=True)
|
| 626 |
+
image2 = F.interpolate(image2, size=inference_size, mode='bilinear',
|
| 627 |
+
align_corners=True)
|
| 628 |
+
|
| 629 |
+
results_dict = model(image1, image2,
|
| 630 |
+
attn_splits_list=attn_splits_list,
|
| 631 |
+
corr_radius_list=corr_radius_list,
|
| 632 |
+
prop_radius_list=prop_radius_list,
|
| 633 |
+
pred_bidir_flow=pred_bidir_flow,
|
| 634 |
+
)
|
| 635 |
+
|
| 636 |
+
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
|
| 637 |
+
|
| 638 |
+
# resize back
|
| 639 |
+
if inference_size is not None:
|
| 640 |
+
flow_pr = F.interpolate(flow_pr, size=ori_size, mode='bilinear',
|
| 641 |
+
align_corners=True)
|
| 642 |
+
flow_pr[:, 0] = flow_pr[:, 0] * ori_size[-1] / inference_size[-1]
|
| 643 |
+
flow_pr[:, 1] = flow_pr[:, 1] * ori_size[-2] / inference_size[-2]
|
| 644 |
+
|
| 645 |
+
if inference_size is None:
|
| 646 |
+
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy() # [H, W, 2]
|
| 647 |
+
else:
|
| 648 |
+
flow = flow_pr[0].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
|
| 649 |
+
|
| 650 |
+
output_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_flow.png')
|
| 651 |
+
|
| 652 |
+
# save vis flow
|
| 653 |
+
save_vis_flow_tofile(flow, output_file)
|
| 654 |
+
|
| 655 |
+
# also predict backward flow
|
| 656 |
+
if pred_bidir_flow:
|
| 657 |
+
assert flow_pr.size(0) == 2 # [2, H, W, 2]
|
| 658 |
+
|
| 659 |
+
if inference_size is None:
|
| 660 |
+
flow_bwd = padder.unpad(flow_pr[1]).permute(1, 2, 0).cpu().numpy() # [H, W, 2]
|
| 661 |
+
else:
|
| 662 |
+
flow_bwd = flow_pr[1].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
|
| 663 |
+
|
| 664 |
+
output_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_flow_bwd.png')
|
| 665 |
+
|
| 666 |
+
# save vis flow
|
| 667 |
+
save_vis_flow_tofile(flow_bwd, output_file)
|
| 668 |
+
|
| 669 |
+
# forward-backward consistency check
|
| 670 |
+
# occlusion is 1
|
| 671 |
+
if fwd_bwd_consistency_check:
|
| 672 |
+
if inference_size is None:
|
| 673 |
+
fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0) # [1, 2, H, W]
|
| 674 |
+
bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0) # [1, 2, H, W]
|
| 675 |
+
else:
|
| 676 |
+
fwd_flow = flow_pr[0].unsqueeze(0)
|
| 677 |
+
bwd_flow = flow_pr[1].unsqueeze(0)
|
| 678 |
+
|
| 679 |
+
fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow) # [1, H, W] float
|
| 680 |
+
|
| 681 |
+
fwd_occ_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_occ.png')
|
| 682 |
+
bwd_occ_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_occ_bwd.png')
|
| 683 |
+
|
| 684 |
+
Image.fromarray((fwd_occ[0].cpu().numpy() * 255.).astype(np.uint8)).save(fwd_occ_file)
|
| 685 |
+
Image.fromarray((bwd_occ[0].cpu().numpy() * 255.).astype(np.uint8)).save(bwd_occ_file)
|
| 686 |
+
|
| 687 |
+
if save_flo_flow:
|
| 688 |
+
output_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_pred.flo')
|
| 689 |
+
frame_utils.writeFlow(output_file, flow)
|
GMFlow/gmflow/__init__.py
ADDED
|
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GMFlow/gmflow/__pycache__/__init__.cpython-310.pyc
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GMFlow/gmflow/backbone.py
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|
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|
|
|
| 1 |
+
import torch.nn as nn
|
| 2 |
+
|
| 3 |
+
from .trident_conv import MultiScaleTridentConv
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class ResidualBlock(nn.Module):
|
| 7 |
+
def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d, stride=1, dilation=1,
|
| 8 |
+
):
|
| 9 |
+
super(ResidualBlock, self).__init__()
|
| 10 |
+
|
| 11 |
+
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
|
| 12 |
+
dilation=dilation, padding=dilation, stride=stride, bias=False)
|
| 13 |
+
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
|
| 14 |
+
dilation=dilation, padding=dilation, bias=False)
|
| 15 |
+
self.relu = nn.ReLU(inplace=True)
|
| 16 |
+
|
| 17 |
+
self.norm1 = norm_layer(planes)
|
| 18 |
+
self.norm2 = norm_layer(planes)
|
| 19 |
+
if not stride == 1 or in_planes != planes:
|
| 20 |
+
self.norm3 = norm_layer(planes)
|
| 21 |
+
|
| 22 |
+
if stride == 1 and in_planes == planes:
|
| 23 |
+
self.downsample = None
|
| 24 |
+
else:
|
| 25 |
+
self.downsample = nn.Sequential(
|
| 26 |
+
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
|
| 27 |
+
|
| 28 |
+
def forward(self, x):
|
| 29 |
+
y = x
|
| 30 |
+
y = self.relu(self.norm1(self.conv1(y)))
|
| 31 |
+
y = self.relu(self.norm2(self.conv2(y)))
|
| 32 |
+
|
| 33 |
+
if self.downsample is not None:
|
| 34 |
+
x = self.downsample(x)
|
| 35 |
+
|
| 36 |
+
return self.relu(x + y)
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
class CNNEncoder(nn.Module):
|
| 40 |
+
def __init__(self, output_dim=128,
|
| 41 |
+
norm_layer=nn.InstanceNorm2d,
|
| 42 |
+
num_output_scales=1,
|
| 43 |
+
**kwargs,
|
| 44 |
+
):
|
| 45 |
+
super(CNNEncoder, self).__init__()
|
| 46 |
+
self.num_branch = num_output_scales
|
| 47 |
+
|
| 48 |
+
feature_dims = [64, 96, 128]
|
| 49 |
+
|
| 50 |
+
self.conv1 = nn.Conv2d(3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False) # 1/2
|
| 51 |
+
self.norm1 = norm_layer(feature_dims[0])
|
| 52 |
+
self.relu1 = nn.ReLU(inplace=True)
|
| 53 |
+
|
| 54 |
+
self.in_planes = feature_dims[0]
|
| 55 |
+
self.layer1 = self._make_layer(feature_dims[0], stride=1, norm_layer=norm_layer) # 1/2
|
| 56 |
+
self.layer2 = self._make_layer(feature_dims[1], stride=2, norm_layer=norm_layer) # 1/4
|
| 57 |
+
|
| 58 |
+
# highest resolution 1/4 or 1/8
|
| 59 |
+
stride = 2 if num_output_scales == 1 else 1
|
| 60 |
+
self.layer3 = self._make_layer(feature_dims[2], stride=stride,
|
| 61 |
+
norm_layer=norm_layer,
|
| 62 |
+
) # 1/4 or 1/8
|
| 63 |
+
|
| 64 |
+
self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0)
|
| 65 |
+
|
| 66 |
+
if self.num_branch > 1:
|
| 67 |
+
if self.num_branch == 4:
|
| 68 |
+
strides = (1, 2, 4, 8)
|
| 69 |
+
elif self.num_branch == 3:
|
| 70 |
+
strides = (1, 2, 4)
|
| 71 |
+
elif self.num_branch == 2:
|
| 72 |
+
strides = (1, 2)
|
| 73 |
+
else:
|
| 74 |
+
raise ValueError
|
| 75 |
+
|
| 76 |
+
self.trident_conv = MultiScaleTridentConv(output_dim, output_dim,
|
| 77 |
+
kernel_size=3,
|
| 78 |
+
strides=strides,
|
| 79 |
+
paddings=1,
|
| 80 |
+
num_branch=self.num_branch,
|
| 81 |
+
)
|
| 82 |
+
|
| 83 |
+
for m in self.modules():
|
| 84 |
+
if isinstance(m, nn.Conv2d):
|
| 85 |
+
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
| 86 |
+
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
|
| 87 |
+
if m.weight is not None:
|
| 88 |
+
nn.init.constant_(m.weight, 1)
|
| 89 |
+
if m.bias is not None:
|
| 90 |
+
nn.init.constant_(m.bias, 0)
|
| 91 |
+
|
| 92 |
+
def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d):
|
| 93 |
+
layer1 = ResidualBlock(self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation)
|
| 94 |
+
layer2 = ResidualBlock(dim, dim, norm_layer=norm_layer, stride=1, dilation=dilation)
|
| 95 |
+
|
| 96 |
+
layers = (layer1, layer2)
|
| 97 |
+
|
| 98 |
+
self.in_planes = dim
|
| 99 |
+
return nn.Sequential(*layers)
|
| 100 |
+
|
| 101 |
+
def forward(self, x):
|
| 102 |
+
x = self.conv1(x)
|
| 103 |
+
x = self.norm1(x)
|
| 104 |
+
x = self.relu1(x)
|
| 105 |
+
|
| 106 |
+
x = self.layer1(x) # 1/2
|
| 107 |
+
x = self.layer2(x) # 1/4
|
| 108 |
+
x = self.layer3(x) # 1/8 or 1/4
|
| 109 |
+
|
| 110 |
+
x = self.conv2(x)
|
| 111 |
+
|
| 112 |
+
if self.num_branch > 1:
|
| 113 |
+
out = self.trident_conv([x] * self.num_branch) # high to low res
|
| 114 |
+
else:
|
| 115 |
+
out = [x]
|
| 116 |
+
|
| 117 |
+
return out
|
GMFlow/gmflow/geometry.py
ADDED
|
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn.functional as F
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
def coords_grid(b, h, w, homogeneous=False, device=None):
|
| 6 |
+
y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W]
|
| 7 |
+
|
| 8 |
+
stacks = [x, y]
|
| 9 |
+
|
| 10 |
+
if homogeneous:
|
| 11 |
+
ones = torch.ones_like(x) # [H, W]
|
| 12 |
+
stacks.append(ones)
|
| 13 |
+
|
| 14 |
+
grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W]
|
| 15 |
+
|
| 16 |
+
grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W]
|
| 17 |
+
|
| 18 |
+
if device is not None:
|
| 19 |
+
grid = grid.to(device)
|
| 20 |
+
|
| 21 |
+
return grid
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
def generate_window_grid(h_min, h_max, w_min, w_max, len_h, len_w, device=None):
|
| 25 |
+
assert device is not None
|
| 26 |
+
|
| 27 |
+
x, y = torch.meshgrid([torch.linspace(w_min, w_max, len_w, device=device),
|
| 28 |
+
torch.linspace(h_min, h_max, len_h, device=device)],
|
| 29 |
+
)
|
| 30 |
+
grid = torch.stack((x, y), -1).transpose(0, 1).float() # [H, W, 2]
|
| 31 |
+
|
| 32 |
+
return grid
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def normalize_coords(coords, h, w):
|
| 36 |
+
# coords: [B, H, W, 2]
|
| 37 |
+
c = torch.Tensor([(w - 1) / 2., (h - 1) / 2.]).float().to(coords.device)
|
| 38 |
+
return (coords - c) / c # [-1, 1]
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def bilinear_sample(img, sample_coords, mode='bilinear', padding_mode='zeros', return_mask=False):
|
| 42 |
+
# img: [B, C, H, W]
|
| 43 |
+
# sample_coords: [B, 2, H, W] in image scale
|
| 44 |
+
if sample_coords.size(1) != 2: # [B, H, W, 2]
|
| 45 |
+
sample_coords = sample_coords.permute(0, 3, 1, 2)
|
| 46 |
+
|
| 47 |
+
b, _, h, w = sample_coords.shape
|
| 48 |
+
|
| 49 |
+
# Normalize to [-1, 1]
|
| 50 |
+
x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1
|
| 51 |
+
y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1
|
| 52 |
+
|
| 53 |
+
grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2]
|
| 54 |
+
|
| 55 |
+
img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)
|
| 56 |
+
|
| 57 |
+
if return_mask:
|
| 58 |
+
mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W]
|
| 59 |
+
|
| 60 |
+
return img, mask
|
| 61 |
+
|
| 62 |
+
return img
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def flow_warp(feature, flow, mask=False, padding_mode='zeros'):
|
| 66 |
+
b, c, h, w = feature.size()
|
| 67 |
+
assert flow.size(1) == 2
|
| 68 |
+
|
| 69 |
+
grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W]
|
| 70 |
+
|
| 71 |
+
return bilinear_sample(feature, grid, padding_mode=padding_mode,
|
| 72 |
+
return_mask=mask)
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
def forward_backward_consistency_check(fwd_flow, bwd_flow,
|
| 76 |
+
alpha=0.01,
|
| 77 |
+
beta=0.5
|
| 78 |
+
):
|
| 79 |
+
# fwd_flow, bwd_flow: [B, 2, H, W]
|
| 80 |
+
# alpha and beta values are following UnFlow (https://arxiv.org/abs/1711.07837)
|
| 81 |
+
assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4
|
| 82 |
+
assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2
|
| 83 |
+
flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W]
|
| 84 |
+
|
| 85 |
+
warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W]
|
| 86 |
+
warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W]
|
| 87 |
+
|
| 88 |
+
diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W]
|
| 89 |
+
diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)
|
| 90 |
+
|
| 91 |
+
threshold = alpha * flow_mag + beta
|
| 92 |
+
|
| 93 |
+
fwd_occ = (diff_fwd > threshold).float() # [B, H, W]
|
| 94 |
+
bwd_occ = (diff_bwd > threshold).float()
|
| 95 |
+
|
| 96 |
+
return fwd_occ, bwd_occ
|
GMFlow/gmflow/gmflow.py
ADDED
|
@@ -0,0 +1,171 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn as nn
|
| 3 |
+
import torch.nn.functional as F
|
| 4 |
+
|
| 5 |
+
from .backbone import CNNEncoder
|
| 6 |
+
from .transformer import FeatureTransformer, FeatureFlowAttention
|
| 7 |
+
from .matching import global_correlation_softmax, local_correlation_softmax
|
| 8 |
+
from .geometry import flow_warp
|
| 9 |
+
from .utils import normalize_img, feature_add_position
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
class GMFlow(nn.Module):
|
| 13 |
+
def __init__(self,
|
| 14 |
+
num_scales=1,
|
| 15 |
+
upsample_factor=8,
|
| 16 |
+
feature_channels=128,
|
| 17 |
+
attention_type='swin',
|
| 18 |
+
num_transformer_layers=6,
|
| 19 |
+
ffn_dim_expansion=4,
|
| 20 |
+
num_head=1,
|
| 21 |
+
**kwargs,
|
| 22 |
+
):
|
| 23 |
+
super(GMFlow, self).__init__()
|
| 24 |
+
|
| 25 |
+
self.num_scales = num_scales
|
| 26 |
+
self.feature_channels = feature_channels
|
| 27 |
+
self.upsample_factor = upsample_factor
|
| 28 |
+
self.attention_type = attention_type
|
| 29 |
+
self.num_transformer_layers = num_transformer_layers
|
| 30 |
+
|
| 31 |
+
# CNN backbone
|
| 32 |
+
self.backbone = CNNEncoder(output_dim=feature_channels, num_output_scales=num_scales)
|
| 33 |
+
|
| 34 |
+
# Transformer
|
| 35 |
+
self.transformer = FeatureTransformer(num_layers=num_transformer_layers,
|
| 36 |
+
d_model=feature_channels,
|
| 37 |
+
nhead=num_head,
|
| 38 |
+
attention_type=attention_type,
|
| 39 |
+
ffn_dim_expansion=ffn_dim_expansion,
|
| 40 |
+
)
|
| 41 |
+
|
| 42 |
+
# flow propagation with self-attn
|
| 43 |
+
self.feature_flow_attn = FeatureFlowAttention(in_channels=feature_channels)
|
| 44 |
+
|
| 45 |
+
# convex upsampling: concat feature0 and flow as input
|
| 46 |
+
self.upsampler = nn.Sequential(nn.Conv2d(2 + feature_channels, 256, 3, 1, 1),
|
| 47 |
+
nn.ReLU(inplace=True),
|
| 48 |
+
nn.Conv2d(256, upsample_factor ** 2 * 9, 1, 1, 0))
|
| 49 |
+
|
| 50 |
+
def extract_feature(self, img0, img1):
|
| 51 |
+
concat = torch.cat((img0, img1), dim=0) # [2B, C, H, W]
|
| 52 |
+
features = self.backbone(concat) # list of [2B, C, H, W], resolution from high to low
|
| 53 |
+
|
| 54 |
+
# reverse: resolution from low to high
|
| 55 |
+
features = features[::-1]
|
| 56 |
+
|
| 57 |
+
feature0, feature1 = [], []
|
| 58 |
+
|
| 59 |
+
for i in range(len(features)):
|
| 60 |
+
feature = features[i]
|
| 61 |
+
chunks = torch.chunk(feature, 2, 0) # tuple
|
| 62 |
+
feature0.append(chunks[0])
|
| 63 |
+
feature1.append(chunks[1])
|
| 64 |
+
|
| 65 |
+
return feature0, feature1
|
| 66 |
+
|
| 67 |
+
def upsample_flow(self, flow, feature, bilinear=False, upsample_factor=8,
|
| 68 |
+
):
|
| 69 |
+
if bilinear:
|
| 70 |
+
up_flow = F.interpolate(flow, scale_factor=upsample_factor,
|
| 71 |
+
mode='bilinear', align_corners=True) * upsample_factor
|
| 72 |
+
|
| 73 |
+
else:
|
| 74 |
+
# convex upsampling
|
| 75 |
+
concat = torch.cat((flow, feature), dim=1)
|
| 76 |
+
|
| 77 |
+
mask = self.upsampler(concat)
|
| 78 |
+
b, flow_channel, h, w = flow.shape
|
| 79 |
+
mask = mask.view(b, 1, 9, self.upsample_factor, self.upsample_factor, h, w) # [B, 1, 9, K, K, H, W]
|
| 80 |
+
mask = torch.softmax(mask, dim=2)
|
| 81 |
+
|
| 82 |
+
up_flow = F.unfold(self.upsample_factor * flow, [3, 3], padding=1)
|
| 83 |
+
up_flow = up_flow.view(b, flow_channel, 9, 1, 1, h, w) # [B, 2, 9, 1, 1, H, W]
|
| 84 |
+
|
| 85 |
+
up_flow = torch.sum(mask * up_flow, dim=2) # [B, 2, K, K, H, W]
|
| 86 |
+
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) # [B, 2, K, H, K, W]
|
| 87 |
+
up_flow = up_flow.reshape(b, flow_channel, self.upsample_factor * h,
|
| 88 |
+
self.upsample_factor * w) # [B, 2, K*H, K*W]
|
| 89 |
+
|
| 90 |
+
return up_flow
|
| 91 |
+
|
| 92 |
+
def forward(self, img0, img1,
|
| 93 |
+
attn_splits_list=None,
|
| 94 |
+
corr_radius_list=None,
|
| 95 |
+
prop_radius_list=None,
|
| 96 |
+
pred_bidir_flow=False,
|
| 97 |
+
**kwargs,
|
| 98 |
+
):
|
| 99 |
+
|
| 100 |
+
results_dict = {}
|
| 101 |
+
flow_preds = []
|
| 102 |
+
|
| 103 |
+
img0, img1 = normalize_img(img0, img1) # [B, 3, H, W]
|
| 104 |
+
|
| 105 |
+
# import ipdb; ipdb.set_trace()
|
| 106 |
+
# resolution low to high
|
| 107 |
+
feature0_list, feature1_list = self.extract_feature(img0, img1) # list of features
|
| 108 |
+
|
| 109 |
+
flow = None
|
| 110 |
+
|
| 111 |
+
assert len(attn_splits_list) == len(corr_radius_list) == len(prop_radius_list) == self.num_scales
|
| 112 |
+
|
| 113 |
+
for scale_idx in range(self.num_scales):
|
| 114 |
+
feature0, feature1 = feature0_list[scale_idx], feature1_list[scale_idx]
|
| 115 |
+
|
| 116 |
+
if pred_bidir_flow and scale_idx > 0:
|
| 117 |
+
# predicting bidirectional flow with refinement
|
| 118 |
+
feature0, feature1 = torch.cat((feature0, feature1), dim=0), torch.cat((feature1, feature0), dim=0)
|
| 119 |
+
|
| 120 |
+
upsample_factor = self.upsample_factor * (2 ** (self.num_scales - 1 - scale_idx))
|
| 121 |
+
|
| 122 |
+
if scale_idx > 0:
|
| 123 |
+
flow = F.interpolate(flow, scale_factor=2, mode='bilinear', align_corners=True) * 2
|
| 124 |
+
|
| 125 |
+
if flow is not None:
|
| 126 |
+
flow = flow.detach()
|
| 127 |
+
feature1 = flow_warp(feature1, flow) # [B, C, H, W]
|
| 128 |
+
|
| 129 |
+
attn_splits = attn_splits_list[scale_idx]
|
| 130 |
+
corr_radius = corr_radius_list[scale_idx]
|
| 131 |
+
prop_radius = prop_radius_list[scale_idx]
|
| 132 |
+
|
| 133 |
+
# add position to features
|
| 134 |
+
feature0, feature1 = feature_add_position(feature0, feature1, attn_splits, self.feature_channels)
|
| 135 |
+
|
| 136 |
+
# Transformer
|
| 137 |
+
feature0, feature1 = self.transformer(feature0, feature1, attn_num_splits=attn_splits)
|
| 138 |
+
|
| 139 |
+
# correlation and softmax
|
| 140 |
+
if corr_radius == -1: # global matching
|
| 141 |
+
flow_pred = global_correlation_softmax(feature0, feature1, pred_bidir_flow)[0]
|
| 142 |
+
else: # local matching
|
| 143 |
+
flow_pred = local_correlation_softmax(feature0, feature1, corr_radius)[0]
|
| 144 |
+
|
| 145 |
+
# flow or residual flow
|
| 146 |
+
flow = flow + flow_pred if flow is not None else flow_pred
|
| 147 |
+
|
| 148 |
+
# upsample to the original resolution for supervison
|
| 149 |
+
if self.training: # only need to upsample intermediate flow predictions at training time
|
| 150 |
+
flow_bilinear = self.upsample_flow(flow, None, bilinear=True, upsample_factor=upsample_factor)
|
| 151 |
+
flow_preds.append(flow_bilinear)
|
| 152 |
+
|
| 153 |
+
# flow propagation with self-attn
|
| 154 |
+
if pred_bidir_flow and scale_idx == 0:
|
| 155 |
+
feature0 = torch.cat((feature0, feature1), dim=0) # [2*B, C, H, W] for propagation
|
| 156 |
+
flow = self.feature_flow_attn(feature0, flow.detach(),
|
| 157 |
+
local_window_attn=prop_radius > 0,
|
| 158 |
+
local_window_radius=prop_radius)
|
| 159 |
+
|
| 160 |
+
# bilinear upsampling at training time except the last one
|
| 161 |
+
if self.training and scale_idx < self.num_scales - 1:
|
| 162 |
+
flow_up = self.upsample_flow(flow, feature0, bilinear=True, upsample_factor=upsample_factor)
|
| 163 |
+
flow_preds.append(flow_up)
|
| 164 |
+
|
| 165 |
+
if scale_idx == self.num_scales - 1:
|
| 166 |
+
flow_up = self.upsample_flow(flow, feature0)
|
| 167 |
+
flow_preds.append(flow_up)
|
| 168 |
+
|
| 169 |
+
results_dict.update({'flow_preds': flow_preds})
|
| 170 |
+
|
| 171 |
+
return results_dict
|
GMFlow/gmflow/matching.py
ADDED
|
@@ -0,0 +1,83 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn.functional as F
|
| 3 |
+
|
| 4 |
+
from .geometry import coords_grid, generate_window_grid, normalize_coords
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
def global_correlation_softmax(feature0, feature1,
|
| 8 |
+
pred_bidir_flow=False,
|
| 9 |
+
):
|
| 10 |
+
# global correlation
|
| 11 |
+
b, c, h, w = feature0.shape
|
| 12 |
+
feature0 = feature0.view(b, c, -1).permute(0, 2, 1) # [B, H*W, C]
|
| 13 |
+
feature1 = feature1.view(b, c, -1) # [B, C, H*W]
|
| 14 |
+
|
| 15 |
+
correlation = torch.matmul(feature0, feature1).view(b, h, w, h, w) / (c ** 0.5) # [B, H, W, H, W]
|
| 16 |
+
|
| 17 |
+
# flow from softmax
|
| 18 |
+
init_grid = coords_grid(b, h, w).to(correlation.device) # [B, 2, H, W]
|
| 19 |
+
grid = init_grid.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2]
|
| 20 |
+
|
| 21 |
+
correlation = correlation.view(b, h * w, h * w) # [B, H*W, H*W]
|
| 22 |
+
|
| 23 |
+
if pred_bidir_flow:
|
| 24 |
+
correlation = torch.cat((correlation, correlation.permute(0, 2, 1)), dim=0) # [2*B, H*W, H*W]
|
| 25 |
+
init_grid = init_grid.repeat(2, 1, 1, 1) # [2*B, 2, H, W]
|
| 26 |
+
grid = grid.repeat(2, 1, 1) # [2*B, H*W, 2]
|
| 27 |
+
b = b * 2
|
| 28 |
+
|
| 29 |
+
prob = F.softmax(correlation, dim=-1) # [B, H*W, H*W]
|
| 30 |
+
|
| 31 |
+
correspondence = torch.matmul(prob, grid).view(b, h, w, 2).permute(0, 3, 1, 2) # [B, 2, H, W]
|
| 32 |
+
|
| 33 |
+
# when predicting bidirectional flow, flow is the concatenation of forward flow and backward flow
|
| 34 |
+
flow = correspondence - init_grid
|
| 35 |
+
|
| 36 |
+
return flow, prob
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
def local_correlation_softmax(feature0, feature1, local_radius,
|
| 40 |
+
padding_mode='zeros',
|
| 41 |
+
):
|
| 42 |
+
b, c, h, w = feature0.size()
|
| 43 |
+
coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W]
|
| 44 |
+
coords = coords_init.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2]
|
| 45 |
+
|
| 46 |
+
local_h = 2 * local_radius + 1
|
| 47 |
+
local_w = 2 * local_radius + 1
|
| 48 |
+
|
| 49 |
+
window_grid = generate_window_grid(-local_radius, local_radius,
|
| 50 |
+
-local_radius, local_radius,
|
| 51 |
+
local_h, local_w, device=feature0.device) # [2R+1, 2R+1, 2]
|
| 52 |
+
window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1)^2, 2]
|
| 53 |
+
sample_coords = coords.unsqueeze(-2) + window_grid # [B, H*W, (2R+1)^2, 2]
|
| 54 |
+
|
| 55 |
+
sample_coords_softmax = sample_coords
|
| 56 |
+
|
| 57 |
+
# exclude coords that are out of image space
|
| 58 |
+
valid_x = (sample_coords[:, :, :, 0] >= 0) & (sample_coords[:, :, :, 0] < w) # [B, H*W, (2R+1)^2]
|
| 59 |
+
valid_y = (sample_coords[:, :, :, 1] >= 0) & (sample_coords[:, :, :, 1] < h) # [B, H*W, (2R+1)^2]
|
| 60 |
+
|
| 61 |
+
valid = valid_x & valid_y # [B, H*W, (2R+1)^2], used to mask out invalid values when softmax
|
| 62 |
+
|
| 63 |
+
# normalize coordinates to [-1, 1]
|
| 64 |
+
sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1]
|
| 65 |
+
window_feature = F.grid_sample(feature1, sample_coords_norm,
|
| 66 |
+
padding_mode=padding_mode, align_corners=True
|
| 67 |
+
).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)^2]
|
| 68 |
+
feature0_view = feature0.permute(0, 2, 3, 1).view(b, h * w, 1, c) # [B, H*W, 1, C]
|
| 69 |
+
|
| 70 |
+
corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)^2]
|
| 71 |
+
|
| 72 |
+
# mask invalid locations
|
| 73 |
+
corr[~valid] = -1e9
|
| 74 |
+
|
| 75 |
+
prob = F.softmax(corr, -1) # [B, H*W, (2R+1)^2]
|
| 76 |
+
|
| 77 |
+
correspondence = torch.matmul(prob.unsqueeze(-2), sample_coords_softmax).squeeze(-2).view(
|
| 78 |
+
b, h, w, 2).permute(0, 3, 1, 2) # [B, 2, H, W]
|
| 79 |
+
|
| 80 |
+
flow = correspondence - coords_init
|
| 81 |
+
match_prob = prob
|
| 82 |
+
|
| 83 |
+
return flow, match_prob
|
GMFlow/gmflow/position.py
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 2 |
+
# https://github.com/facebookresearch/detr/blob/main/models/position_encoding.py
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
import torch.nn as nn
|
| 6 |
+
import math
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
class PositionEmbeddingSine(nn.Module):
|
| 10 |
+
"""
|
| 11 |
+
This is a more standard version of the position embedding, very similar to the one
|
| 12 |
+
used by the Attention is all you need paper, generalized to work on images.
|
| 13 |
+
"""
|
| 14 |
+
|
| 15 |
+
def __init__(self, num_pos_feats=64, temperature=10000, normalize=True, scale=None):
|
| 16 |
+
super().__init__()
|
| 17 |
+
self.num_pos_feats = num_pos_feats
|
| 18 |
+
self.temperature = temperature
|
| 19 |
+
self.normalize = normalize
|
| 20 |
+
if scale is not None and normalize is False:
|
| 21 |
+
raise ValueError("normalize should be True if scale is passed")
|
| 22 |
+
if scale is None:
|
| 23 |
+
scale = 2 * math.pi
|
| 24 |
+
self.scale = scale
|
| 25 |
+
|
| 26 |
+
def forward(self, x):
|
| 27 |
+
# x = tensor_list.tensors # [B, C, H, W]
|
| 28 |
+
# mask = tensor_list.mask # [B, H, W], input with padding, valid as 0
|
| 29 |
+
b, c, h, w = x.size()
|
| 30 |
+
mask = torch.ones((b, h, w), device=x.device) # [B, H, W]
|
| 31 |
+
y_embed = mask.cumsum(1, dtype=torch.float32)
|
| 32 |
+
x_embed = mask.cumsum(2, dtype=torch.float32)
|
| 33 |
+
if self.normalize:
|
| 34 |
+
eps = 1e-6
|
| 35 |
+
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
| 36 |
+
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
| 37 |
+
|
| 38 |
+
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
| 39 |
+
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
| 40 |
+
|
| 41 |
+
pos_x = x_embed[:, :, :, None] / dim_t
|
| 42 |
+
pos_y = y_embed[:, :, :, None] / dim_t
|
| 43 |
+
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
|
| 44 |
+
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
|
| 45 |
+
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
| 46 |
+
return pos
|
GMFlow/gmflow/transformer.py
ADDED
|
@@ -0,0 +1,409 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
|
|
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|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn as nn
|
| 3 |
+
import torch.nn.functional as F
|
| 4 |
+
|
| 5 |
+
from .utils import split_feature, merge_splits
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
def single_head_full_attention(q, k, v):
|
| 9 |
+
# q, k, v: [B, L, C]
|
| 10 |
+
assert q.dim() == k.dim() == v.dim() == 3
|
| 11 |
+
|
| 12 |
+
scores = torch.matmul(q, k.permute(0, 2, 1)) / (q.size(2) ** .5) # [B, L, L]
|
| 13 |
+
attn = torch.softmax(scores, dim=2) # [B, L, L]
|
| 14 |
+
out = torch.matmul(attn, v) # [B, L, C]
|
| 15 |
+
|
| 16 |
+
return out
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def generate_shift_window_attn_mask(input_resolution, window_size_h, window_size_w,
|
| 20 |
+
shift_size_h, shift_size_w, device=torch.device('cuda')):
|
| 21 |
+
# Ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
|
| 22 |
+
# calculate attention mask for SW-MSA
|
| 23 |
+
h, w = input_resolution
|
| 24 |
+
img_mask = torch.zeros((1, h, w, 1)).to(device) # 1 H W 1
|
| 25 |
+
h_slices = (slice(0, -window_size_h),
|
| 26 |
+
slice(-window_size_h, -shift_size_h),
|
| 27 |
+
slice(-shift_size_h, None))
|
| 28 |
+
w_slices = (slice(0, -window_size_w),
|
| 29 |
+
slice(-window_size_w, -shift_size_w),
|
| 30 |
+
slice(-shift_size_w, None))
|
| 31 |
+
cnt = 0
|
| 32 |
+
for h in h_slices:
|
| 33 |
+
for w in w_slices:
|
| 34 |
+
img_mask[:, h, w, :] = cnt
|
| 35 |
+
cnt += 1
|
| 36 |
+
|
| 37 |
+
mask_windows = split_feature(img_mask, num_splits=input_resolution[-1] // window_size_w, channel_last=True)
|
| 38 |
+
|
| 39 |
+
mask_windows = mask_windows.view(-1, window_size_h * window_size_w)
|
| 40 |
+
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
| 41 |
+
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
| 42 |
+
|
| 43 |
+
return attn_mask
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def single_head_split_window_attention(q, k, v,
|
| 47 |
+
num_splits=1,
|
| 48 |
+
with_shift=False,
|
| 49 |
+
h=None,
|
| 50 |
+
w=None,
|
| 51 |
+
attn_mask=None,
|
| 52 |
+
):
|
| 53 |
+
# Ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
|
| 54 |
+
# q, k, v: [B, L, C]
|
| 55 |
+
assert q.dim() == k.dim() == v.dim() == 3
|
| 56 |
+
|
| 57 |
+
assert h is not None and w is not None
|
| 58 |
+
assert q.size(1) == h * w
|
| 59 |
+
|
| 60 |
+
b, _, c = q.size()
|
| 61 |
+
|
| 62 |
+
b_new = b * num_splits * num_splits
|
| 63 |
+
|
| 64 |
+
window_size_h = h // num_splits
|
| 65 |
+
window_size_w = w // num_splits
|
| 66 |
+
|
| 67 |
+
q = q.view(b, h, w, c) # [B, H, W, C]
|
| 68 |
+
k = k.view(b, h, w, c)
|
| 69 |
+
v = v.view(b, h, w, c)
|
| 70 |
+
|
| 71 |
+
scale_factor = c ** 0.5
|
| 72 |
+
|
| 73 |
+
if with_shift:
|
| 74 |
+
assert attn_mask is not None # compute once
|
| 75 |
+
shift_size_h = window_size_h // 2
|
| 76 |
+
shift_size_w = window_size_w // 2
|
| 77 |
+
|
| 78 |
+
q = torch.roll(q, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2))
|
| 79 |
+
k = torch.roll(k, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2))
|
| 80 |
+
v = torch.roll(v, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2))
|
| 81 |
+
|
| 82 |
+
q = split_feature(q, num_splits=num_splits, channel_last=True) # [B*K*K, H/K, W/K, C]
|
| 83 |
+
k = split_feature(k, num_splits=num_splits, channel_last=True)
|
| 84 |
+
v = split_feature(v, num_splits=num_splits, channel_last=True)
|
| 85 |
+
|
| 86 |
+
scores = torch.matmul(q.view(b_new, -1, c), k.view(b_new, -1, c).permute(0, 2, 1)
|
| 87 |
+
) / scale_factor # [B*K*K, H/K*W/K, H/K*W/K]
|
| 88 |
+
|
| 89 |
+
if with_shift:
|
| 90 |
+
scores += attn_mask.repeat(b, 1, 1)
|
| 91 |
+
|
| 92 |
+
attn = torch.softmax(scores, dim=-1)
|
| 93 |
+
|
| 94 |
+
out = torch.matmul(attn, v.view(b_new, -1, c)) # [B*K*K, H/K*W/K, C]
|
| 95 |
+
|
| 96 |
+
out = merge_splits(out.view(b_new, h // num_splits, w // num_splits, c),
|
| 97 |
+
num_splits=num_splits, channel_last=True) # [B, H, W, C]
|
| 98 |
+
|
| 99 |
+
# shift back
|
| 100 |
+
if with_shift:
|
| 101 |
+
out = torch.roll(out, shifts=(shift_size_h, shift_size_w), dims=(1, 2))
|
| 102 |
+
|
| 103 |
+
out = out.view(b, -1, c)
|
| 104 |
+
|
| 105 |
+
return out
|
| 106 |
+
|
| 107 |
+
|
| 108 |
+
class TransformerLayer(nn.Module):
|
| 109 |
+
def __init__(self,
|
| 110 |
+
d_model=256,
|
| 111 |
+
nhead=1,
|
| 112 |
+
attention_type='swin',
|
| 113 |
+
no_ffn=False,
|
| 114 |
+
ffn_dim_expansion=4,
|
| 115 |
+
with_shift=False,
|
| 116 |
+
**kwargs,
|
| 117 |
+
):
|
| 118 |
+
super(TransformerLayer, self).__init__()
|
| 119 |
+
|
| 120 |
+
self.dim = d_model
|
| 121 |
+
self.nhead = nhead
|
| 122 |
+
self.attention_type = attention_type
|
| 123 |
+
self.no_ffn = no_ffn
|
| 124 |
+
|
| 125 |
+
self.with_shift = with_shift
|
| 126 |
+
|
| 127 |
+
# multi-head attention
|
| 128 |
+
self.q_proj = nn.Linear(d_model, d_model, bias=False)
|
| 129 |
+
self.k_proj = nn.Linear(d_model, d_model, bias=False)
|
| 130 |
+
self.v_proj = nn.Linear(d_model, d_model, bias=False)
|
| 131 |
+
|
| 132 |
+
self.merge = nn.Linear(d_model, d_model, bias=False)
|
| 133 |
+
|
| 134 |
+
self.norm1 = nn.LayerNorm(d_model)
|
| 135 |
+
|
| 136 |
+
# no ffn after self-attn, with ffn after cross-attn
|
| 137 |
+
if not self.no_ffn:
|
| 138 |
+
in_channels = d_model * 2
|
| 139 |
+
self.mlp = nn.Sequential(
|
| 140 |
+
nn.Linear(in_channels, in_channels * ffn_dim_expansion, bias=False),
|
| 141 |
+
nn.GELU(),
|
| 142 |
+
nn.Linear(in_channels * ffn_dim_expansion, d_model, bias=False),
|
| 143 |
+
)
|
| 144 |
+
|
| 145 |
+
self.norm2 = nn.LayerNorm(d_model)
|
| 146 |
+
|
| 147 |
+
def forward(self, source, target,
|
| 148 |
+
height=None,
|
| 149 |
+
width=None,
|
| 150 |
+
shifted_window_attn_mask=None,
|
| 151 |
+
attn_num_splits=None,
|
| 152 |
+
**kwargs,
|
| 153 |
+
):
|
| 154 |
+
# source, target: [B, L, C]
|
| 155 |
+
query, key, value = source, target, target
|
| 156 |
+
|
| 157 |
+
# single-head attention
|
| 158 |
+
query = self.q_proj(query) # [B, L, C]
|
| 159 |
+
key = self.k_proj(key) # [B, L, C]
|
| 160 |
+
value = self.v_proj(value) # [B, L, C]
|
| 161 |
+
|
| 162 |
+
if self.attention_type == 'swin' and attn_num_splits > 1:
|
| 163 |
+
if self.nhead > 1:
|
| 164 |
+
# we observe that multihead attention slows down the speed and increases the memory consumption
|
| 165 |
+
# without bringing obvious performance gains and thus the implementation is removed
|
| 166 |
+
raise NotImplementedError
|
| 167 |
+
else:
|
| 168 |
+
message = single_head_split_window_attention(query, key, value,
|
| 169 |
+
num_splits=attn_num_splits,
|
| 170 |
+
with_shift=self.with_shift,
|
| 171 |
+
h=height,
|
| 172 |
+
w=width,
|
| 173 |
+
attn_mask=shifted_window_attn_mask,
|
| 174 |
+
)
|
| 175 |
+
else:
|
| 176 |
+
message = single_head_full_attention(query, key, value) # [B, L, C]
|
| 177 |
+
|
| 178 |
+
message = self.merge(message) # [B, L, C]
|
| 179 |
+
message = self.norm1(message)
|
| 180 |
+
|
| 181 |
+
if not self.no_ffn:
|
| 182 |
+
message = self.mlp(torch.cat([source, message], dim=-1))
|
| 183 |
+
message = self.norm2(message)
|
| 184 |
+
|
| 185 |
+
return source + message
|
| 186 |
+
|
| 187 |
+
|
| 188 |
+
class TransformerBlock(nn.Module):
|
| 189 |
+
"""self attention + cross attention + FFN"""
|
| 190 |
+
|
| 191 |
+
def __init__(self,
|
| 192 |
+
d_model=256,
|
| 193 |
+
nhead=1,
|
| 194 |
+
attention_type='swin',
|
| 195 |
+
ffn_dim_expansion=4,
|
| 196 |
+
with_shift=False,
|
| 197 |
+
**kwargs,
|
| 198 |
+
):
|
| 199 |
+
super(TransformerBlock, self).__init__()
|
| 200 |
+
|
| 201 |
+
self.self_attn = TransformerLayer(d_model=d_model,
|
| 202 |
+
nhead=nhead,
|
| 203 |
+
attention_type=attention_type,
|
| 204 |
+
no_ffn=True,
|
| 205 |
+
ffn_dim_expansion=ffn_dim_expansion,
|
| 206 |
+
with_shift=with_shift,
|
| 207 |
+
)
|
| 208 |
+
|
| 209 |
+
self.cross_attn_ffn = TransformerLayer(d_model=d_model,
|
| 210 |
+
nhead=nhead,
|
| 211 |
+
attention_type=attention_type,
|
| 212 |
+
ffn_dim_expansion=ffn_dim_expansion,
|
| 213 |
+
with_shift=with_shift,
|
| 214 |
+
)
|
| 215 |
+
|
| 216 |
+
def forward(self, source, target,
|
| 217 |
+
height=None,
|
| 218 |
+
width=None,
|
| 219 |
+
shifted_window_attn_mask=None,
|
| 220 |
+
attn_num_splits=None,
|
| 221 |
+
**kwargs,
|
| 222 |
+
):
|
| 223 |
+
# source, target: [B, L, C]
|
| 224 |
+
|
| 225 |
+
# self attention
|
| 226 |
+
source = self.self_attn(source, source,
|
| 227 |
+
height=height,
|
| 228 |
+
width=width,
|
| 229 |
+
shifted_window_attn_mask=shifted_window_attn_mask,
|
| 230 |
+
attn_num_splits=attn_num_splits,
|
| 231 |
+
)
|
| 232 |
+
|
| 233 |
+
# cross attention and ffn
|
| 234 |
+
source = self.cross_attn_ffn(source, target,
|
| 235 |
+
height=height,
|
| 236 |
+
width=width,
|
| 237 |
+
shifted_window_attn_mask=shifted_window_attn_mask,
|
| 238 |
+
attn_num_splits=attn_num_splits,
|
| 239 |
+
)
|
| 240 |
+
|
| 241 |
+
return source
|
| 242 |
+
|
| 243 |
+
|
| 244 |
+
class FeatureTransformer(nn.Module):
|
| 245 |
+
def __init__(self,
|
| 246 |
+
num_layers=6,
|
| 247 |
+
d_model=128,
|
| 248 |
+
nhead=1,
|
| 249 |
+
attention_type='swin',
|
| 250 |
+
ffn_dim_expansion=4,
|
| 251 |
+
**kwargs,
|
| 252 |
+
):
|
| 253 |
+
super(FeatureTransformer, self).__init__()
|
| 254 |
+
|
| 255 |
+
self.attention_type = attention_type
|
| 256 |
+
|
| 257 |
+
self.d_model = d_model
|
| 258 |
+
self.nhead = nhead
|
| 259 |
+
|
| 260 |
+
self.layers = nn.ModuleList([
|
| 261 |
+
TransformerBlock(d_model=d_model,
|
| 262 |
+
nhead=nhead,
|
| 263 |
+
attention_type=attention_type,
|
| 264 |
+
ffn_dim_expansion=ffn_dim_expansion,
|
| 265 |
+
with_shift=True if attention_type == 'swin' and i % 2 == 1 else False,
|
| 266 |
+
)
|
| 267 |
+
for i in range(num_layers)])
|
| 268 |
+
|
| 269 |
+
for p in self.parameters():
|
| 270 |
+
if p.dim() > 1:
|
| 271 |
+
nn.init.xavier_uniform_(p)
|
| 272 |
+
|
| 273 |
+
def forward(self, feature0, feature1,
|
| 274 |
+
attn_num_splits=None,
|
| 275 |
+
**kwargs,
|
| 276 |
+
):
|
| 277 |
+
|
| 278 |
+
b, c, h, w = feature0.shape
|
| 279 |
+
assert self.d_model == c
|
| 280 |
+
|
| 281 |
+
feature0 = feature0.flatten(-2).permute(0, 2, 1) # [B, H*W, C]
|
| 282 |
+
feature1 = feature1.flatten(-2).permute(0, 2, 1) # [B, H*W, C]
|
| 283 |
+
|
| 284 |
+
if self.attention_type == 'swin' and attn_num_splits > 1:
|
| 285 |
+
# global and refine use different number of splits
|
| 286 |
+
window_size_h = h // attn_num_splits
|
| 287 |
+
window_size_w = w // attn_num_splits
|
| 288 |
+
|
| 289 |
+
# compute attn mask once
|
| 290 |
+
shifted_window_attn_mask = generate_shift_window_attn_mask(
|
| 291 |
+
input_resolution=(h, w),
|
| 292 |
+
window_size_h=window_size_h,
|
| 293 |
+
window_size_w=window_size_w,
|
| 294 |
+
shift_size_h=window_size_h // 2,
|
| 295 |
+
shift_size_w=window_size_w // 2,
|
| 296 |
+
device=feature0.device,
|
| 297 |
+
) # [K*K, H/K*W/K, H/K*W/K]
|
| 298 |
+
else:
|
| 299 |
+
shifted_window_attn_mask = None
|
| 300 |
+
|
| 301 |
+
# concat feature0 and feature1 in batch dimension to compute in parallel
|
| 302 |
+
concat0 = torch.cat((feature0, feature1), dim=0) # [2B, H*W, C]
|
| 303 |
+
concat1 = torch.cat((feature1, feature0), dim=0) # [2B, H*W, C]
|
| 304 |
+
|
| 305 |
+
for layer in self.layers:
|
| 306 |
+
concat0 = layer(concat0, concat1,
|
| 307 |
+
height=h,
|
| 308 |
+
width=w,
|
| 309 |
+
shifted_window_attn_mask=shifted_window_attn_mask,
|
| 310 |
+
attn_num_splits=attn_num_splits,
|
| 311 |
+
)
|
| 312 |
+
|
| 313 |
+
# update feature1
|
| 314 |
+
concat1 = torch.cat(concat0.chunk(chunks=2, dim=0)[::-1], dim=0)
|
| 315 |
+
|
| 316 |
+
feature0, feature1 = concat0.chunk(chunks=2, dim=0) # [B, H*W, C]
|
| 317 |
+
|
| 318 |
+
# reshape back
|
| 319 |
+
feature0 = feature0.view(b, h, w, c).permute(0, 3, 1, 2).contiguous() # [B, C, H, W]
|
| 320 |
+
feature1 = feature1.view(b, h, w, c).permute(0, 3, 1, 2).contiguous() # [B, C, H, W]
|
| 321 |
+
|
| 322 |
+
return feature0, feature1
|
| 323 |
+
|
| 324 |
+
|
| 325 |
+
class FeatureFlowAttention(nn.Module):
|
| 326 |
+
"""
|
| 327 |
+
flow propagation with self-attention on feature
|
| 328 |
+
query: feature0, key: feature0, value: flow
|
| 329 |
+
"""
|
| 330 |
+
|
| 331 |
+
def __init__(self, in_channels,
|
| 332 |
+
**kwargs,
|
| 333 |
+
):
|
| 334 |
+
super(FeatureFlowAttention, self).__init__()
|
| 335 |
+
|
| 336 |
+
self.q_proj = nn.Linear(in_channels, in_channels)
|
| 337 |
+
self.k_proj = nn.Linear(in_channels, in_channels)
|
| 338 |
+
|
| 339 |
+
for p in self.parameters():
|
| 340 |
+
if p.dim() > 1:
|
| 341 |
+
nn.init.xavier_uniform_(p)
|
| 342 |
+
|
| 343 |
+
def forward(self, feature0, flow,
|
| 344 |
+
local_window_attn=False,
|
| 345 |
+
local_window_radius=1,
|
| 346 |
+
**kwargs,
|
| 347 |
+
):
|
| 348 |
+
# q, k: feature [B, C, H, W], v: flow [B, 2, H, W]
|
| 349 |
+
if local_window_attn:
|
| 350 |
+
return self.forward_local_window_attn(feature0, flow,
|
| 351 |
+
local_window_radius=local_window_radius)
|
| 352 |
+
|
| 353 |
+
b, c, h, w = feature0.size()
|
| 354 |
+
|
| 355 |
+
query = feature0.view(b, c, h * w).permute(0, 2, 1) # [B, H*W, C]
|
| 356 |
+
|
| 357 |
+
# a note: the ``correct'' implementation should be:
|
| 358 |
+
# ``query = self.q_proj(query), key = self.k_proj(query)''
|
| 359 |
+
# this problem is observed while cleaning up the code
|
| 360 |
+
# however, this doesn't affect the performance since the projection is a linear operation,
|
| 361 |
+
# thus the two projection matrices for key can be merged
|
| 362 |
+
# so I just leave it as is in order to not re-train all models :)
|
| 363 |
+
query = self.q_proj(query) # [B, H*W, C]
|
| 364 |
+
key = self.k_proj(query) # [B, H*W, C]
|
| 365 |
+
|
| 366 |
+
value = flow.view(b, flow.size(1), h * w).permute(0, 2, 1) # [B, H*W, 2]
|
| 367 |
+
|
| 368 |
+
scores = torch.matmul(query, key.permute(0, 2, 1)) / (c ** 0.5) # [B, H*W, H*W]
|
| 369 |
+
prob = torch.softmax(scores, dim=-1)
|
| 370 |
+
|
| 371 |
+
out = torch.matmul(prob, value) # [B, H*W, 2]
|
| 372 |
+
out = out.view(b, h, w, value.size(-1)).permute(0, 3, 1, 2) # [B, 2, H, W]
|
| 373 |
+
|
| 374 |
+
return out
|
| 375 |
+
|
| 376 |
+
def forward_local_window_attn(self, feature0, flow,
|
| 377 |
+
local_window_radius=1,
|
| 378 |
+
):
|
| 379 |
+
assert flow.size(1) == 2
|
| 380 |
+
assert local_window_radius > 0
|
| 381 |
+
|
| 382 |
+
b, c, h, w = feature0.size()
|
| 383 |
+
|
| 384 |
+
feature0_reshape = self.q_proj(feature0.view(b, c, -1).permute(0, 2, 1)
|
| 385 |
+
).reshape(b * h * w, 1, c) # [B*H*W, 1, C]
|
| 386 |
+
|
| 387 |
+
kernel_size = 2 * local_window_radius + 1
|
| 388 |
+
|
| 389 |
+
feature0_proj = self.k_proj(feature0.view(b, c, -1).permute(0, 2, 1)).permute(0, 2, 1).reshape(b, c, h, w)
|
| 390 |
+
|
| 391 |
+
feature0_window = F.unfold(feature0_proj, kernel_size=kernel_size,
|
| 392 |
+
padding=local_window_radius) # [B, C*(2R+1)^2), H*W]
|
| 393 |
+
|
| 394 |
+
feature0_window = feature0_window.view(b, c, kernel_size ** 2, h, w).permute(
|
| 395 |
+
0, 3, 4, 1, 2).reshape(b * h * w, c, kernel_size ** 2) # [B*H*W, C, (2R+1)^2]
|
| 396 |
+
|
| 397 |
+
flow_window = F.unfold(flow, kernel_size=kernel_size,
|
| 398 |
+
padding=local_window_radius) # [B, 2*(2R+1)^2), H*W]
|
| 399 |
+
|
| 400 |
+
flow_window = flow_window.view(b, 2, kernel_size ** 2, h, w).permute(
|
| 401 |
+
0, 3, 4, 2, 1).reshape(b * h * w, kernel_size ** 2, 2) # [B*H*W, (2R+1)^2, 2]
|
| 402 |
+
|
| 403 |
+
scores = torch.matmul(feature0_reshape, feature0_window) / (c ** 0.5) # [B*H*W, 1, (2R+1)^2]
|
| 404 |
+
|
| 405 |
+
prob = torch.softmax(scores, dim=-1)
|
| 406 |
+
|
| 407 |
+
out = torch.matmul(prob, flow_window).view(b, h, w, 2).permute(0, 3, 1, 2).contiguous() # [B, 2, H, W]
|
| 408 |
+
|
| 409 |
+
return out
|
GMFlow/gmflow/trident_conv.py
ADDED
|
@@ -0,0 +1,90 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
+
# https://github.com/facebookresearch/detectron2/blob/main/projects/TridentNet/tridentnet/trident_conv.py
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
from torch import nn
|
| 6 |
+
from torch.nn import functional as F
|
| 7 |
+
from torch.nn.modules.utils import _pair
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
class MultiScaleTridentConv(nn.Module):
|
| 11 |
+
def __init__(
|
| 12 |
+
self,
|
| 13 |
+
in_channels,
|
| 14 |
+
out_channels,
|
| 15 |
+
kernel_size,
|
| 16 |
+
stride=1,
|
| 17 |
+
strides=1,
|
| 18 |
+
paddings=0,
|
| 19 |
+
dilations=1,
|
| 20 |
+
dilation=1,
|
| 21 |
+
groups=1,
|
| 22 |
+
num_branch=1,
|
| 23 |
+
test_branch_idx=-1,
|
| 24 |
+
bias=False,
|
| 25 |
+
norm=None,
|
| 26 |
+
activation=None,
|
| 27 |
+
):
|
| 28 |
+
super(MultiScaleTridentConv, self).__init__()
|
| 29 |
+
self.in_channels = in_channels
|
| 30 |
+
self.out_channels = out_channels
|
| 31 |
+
self.kernel_size = _pair(kernel_size)
|
| 32 |
+
self.num_branch = num_branch
|
| 33 |
+
self.stride = _pair(stride)
|
| 34 |
+
self.groups = groups
|
| 35 |
+
self.with_bias = bias
|
| 36 |
+
self.dilation = dilation
|
| 37 |
+
if isinstance(paddings, int):
|
| 38 |
+
paddings = [paddings] * self.num_branch
|
| 39 |
+
if isinstance(dilations, int):
|
| 40 |
+
dilations = [dilations] * self.num_branch
|
| 41 |
+
if isinstance(strides, int):
|
| 42 |
+
strides = [strides] * self.num_branch
|
| 43 |
+
self.paddings = [_pair(padding) for padding in paddings]
|
| 44 |
+
self.dilations = [_pair(dilation) for dilation in dilations]
|
| 45 |
+
self.strides = [_pair(stride) for stride in strides]
|
| 46 |
+
self.test_branch_idx = test_branch_idx
|
| 47 |
+
self.norm = norm
|
| 48 |
+
self.activation = activation
|
| 49 |
+
|
| 50 |
+
assert len({self.num_branch, len(self.paddings), len(self.strides)}) == 1
|
| 51 |
+
|
| 52 |
+
self.weight = nn.Parameter(
|
| 53 |
+
torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)
|
| 54 |
+
)
|
| 55 |
+
if bias:
|
| 56 |
+
self.bias = nn.Parameter(torch.Tensor(out_channels))
|
| 57 |
+
else:
|
| 58 |
+
self.bias = None
|
| 59 |
+
|
| 60 |
+
nn.init.kaiming_uniform_(self.weight, nonlinearity="relu")
|
| 61 |
+
if self.bias is not None:
|
| 62 |
+
nn.init.constant_(self.bias, 0)
|
| 63 |
+
|
| 64 |
+
def forward(self, inputs):
|
| 65 |
+
num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1
|
| 66 |
+
assert len(inputs) == num_branch
|
| 67 |
+
|
| 68 |
+
if self.training or self.test_branch_idx == -1:
|
| 69 |
+
outputs = [
|
| 70 |
+
F.conv2d(input, self.weight, self.bias, stride, padding, self.dilation, self.groups)
|
| 71 |
+
for input, stride, padding in zip(inputs, self.strides, self.paddings)
|
| 72 |
+
]
|
| 73 |
+
else:
|
| 74 |
+
outputs = [
|
| 75 |
+
F.conv2d(
|
| 76 |
+
inputs[0],
|
| 77 |
+
self.weight,
|
| 78 |
+
self.bias,
|
| 79 |
+
self.strides[self.test_branch_idx] if self.test_branch_idx == -1 else self.strides[-1],
|
| 80 |
+
self.paddings[self.test_branch_idx] if self.test_branch_idx == -1 else self.paddings[-1],
|
| 81 |
+
self.dilation,
|
| 82 |
+
self.groups,
|
| 83 |
+
)
|
| 84 |
+
]
|
| 85 |
+
|
| 86 |
+
if self.norm is not None:
|
| 87 |
+
outputs = [self.norm(x) for x in outputs]
|
| 88 |
+
if self.activation is not None:
|
| 89 |
+
outputs = [self.activation(x) for x in outputs]
|
| 90 |
+
return outputs
|
GMFlow/gmflow/utils.py
ADDED
|
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
from .position import PositionEmbeddingSine
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
def split_feature(feature,
|
| 6 |
+
num_splits=2,
|
| 7 |
+
channel_last=False,
|
| 8 |
+
):
|
| 9 |
+
if channel_last: # [B, H, W, C]
|
| 10 |
+
b, h, w, c = feature.size()
|
| 11 |
+
# if h % num_splits:
|
| 12 |
+
# feature = feature[:, :, :-1, :]
|
| 13 |
+
# if w % num_splits:
|
| 14 |
+
# feature = feature[:, :, :, :-1]
|
| 15 |
+
# b, h, w, c = feature.size()
|
| 16 |
+
assert h % num_splits == 0 and w % num_splits == 0
|
| 17 |
+
|
| 18 |
+
b_new = b * num_splits * num_splits
|
| 19 |
+
h_new = h // num_splits
|
| 20 |
+
w_new = w // num_splits
|
| 21 |
+
|
| 22 |
+
feature = feature.view(b, num_splits, h // num_splits, num_splits, w // num_splits, c
|
| 23 |
+
).permute(0, 1, 3, 2, 4, 5).reshape(b_new, h_new, w_new, c) # [B*K*K, H/K, W/K, C]
|
| 24 |
+
else: # [B, C, H, W]
|
| 25 |
+
b, c, h, w = feature.size()
|
| 26 |
+
# if h % num_splits:
|
| 27 |
+
# feature = feature[:, :, :-1, :]
|
| 28 |
+
# if w % num_splits:
|
| 29 |
+
# feature = feature[:, :, :, :-1]
|
| 30 |
+
# b, c, h, w = feature.size()
|
| 31 |
+
assert h % num_splits == 0 and w % num_splits == 0
|
| 32 |
+
|
| 33 |
+
b_new = b * num_splits * num_splits
|
| 34 |
+
h_new = h // num_splits
|
| 35 |
+
w_new = w // num_splits
|
| 36 |
+
|
| 37 |
+
feature = feature.view(b, c, num_splits, h // num_splits, num_splits, w // num_splits
|
| 38 |
+
).permute(0, 2, 4, 1, 3, 5).reshape(b_new, c, h_new, w_new) # [B*K*K, C, H/K, W/K]
|
| 39 |
+
|
| 40 |
+
return feature
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
def merge_splits(splits,
|
| 44 |
+
num_splits=2,
|
| 45 |
+
channel_last=False,
|
| 46 |
+
):
|
| 47 |
+
if channel_last: # [B*K*K, H/K, W/K, C]
|
| 48 |
+
b, h, w, c = splits.size()
|
| 49 |
+
new_b = b // num_splits // num_splits
|
| 50 |
+
|
| 51 |
+
splits = splits.view(new_b, num_splits, num_splits, h, w, c)
|
| 52 |
+
merge = splits.permute(0, 1, 3, 2, 4, 5).contiguous().view(
|
| 53 |
+
new_b, num_splits * h, num_splits * w, c) # [B, H, W, C]
|
| 54 |
+
else: # [B*K*K, C, H/K, W/K]
|
| 55 |
+
b, c, h, w = splits.size()
|
| 56 |
+
new_b = b // num_splits // num_splits
|
| 57 |
+
|
| 58 |
+
splits = splits.view(new_b, num_splits, num_splits, c, h, w)
|
| 59 |
+
merge = splits.permute(0, 3, 1, 4, 2, 5).contiguous().view(
|
| 60 |
+
new_b, c, num_splits * h, num_splits * w) # [B, C, H, W]
|
| 61 |
+
|
| 62 |
+
return merge
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def normalize_img(img0, img1):
|
| 66 |
+
# loaded images are in [0, 255]
|
| 67 |
+
# normalize by ImageNet mean and std
|
| 68 |
+
mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(img1.device)
|
| 69 |
+
std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(img1.device)
|
| 70 |
+
img0 = (img0 / 255. - mean) / std
|
| 71 |
+
img1 = (img1 / 255. - mean) / std
|
| 72 |
+
|
| 73 |
+
return img0, img1
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def feature_add_position(feature0, feature1, attn_splits, feature_channels):
|
| 77 |
+
pos_enc = PositionEmbeddingSine(num_pos_feats=feature_channels // 2)
|
| 78 |
+
|
| 79 |
+
if attn_splits > 1: # add position in splited window
|
| 80 |
+
# import ipdb; ipdb.set_trace()
|
| 81 |
+
feature0_splits = split_feature(feature0, num_splits=attn_splits)
|
| 82 |
+
feature1_splits = split_feature(feature1, num_splits=attn_splits)
|
| 83 |
+
|
| 84 |
+
position = pos_enc(feature0_splits)
|
| 85 |
+
|
| 86 |
+
feature0_splits = feature0_splits + position
|
| 87 |
+
feature1_splits = feature1_splits + position
|
| 88 |
+
|
| 89 |
+
feature0 = merge_splits(feature0_splits, num_splits=attn_splits)
|
| 90 |
+
feature1 = merge_splits(feature1_splits, num_splits=attn_splits)
|
| 91 |
+
else:
|
| 92 |
+
position = pos_enc(feature0)
|
| 93 |
+
|
| 94 |
+
feature0 = feature0 + position
|
| 95 |
+
feature1 = feature1 + position
|
| 96 |
+
|
| 97 |
+
return feature0, feature1
|
GMFlow/loss.py
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
def flow_loss_func(flow_preds, flow_gt, valid,
|
| 5 |
+
gamma=0.9,
|
| 6 |
+
max_flow=400,
|
| 7 |
+
**kwargs,
|
| 8 |
+
):
|
| 9 |
+
n_predictions = len(flow_preds)
|
| 10 |
+
flow_loss = 0.0
|
| 11 |
+
|
| 12 |
+
# exlude invalid pixels and extremely large diplacements
|
| 13 |
+
mag = torch.sum(flow_gt ** 2, dim=1).sqrt() # [B, H, W]
|
| 14 |
+
valid = (valid >= 0.5) & (mag < max_flow)
|
| 15 |
+
|
| 16 |
+
for i in range(n_predictions):
|
| 17 |
+
i_weight = gamma ** (n_predictions - i - 1)
|
| 18 |
+
|
| 19 |
+
i_loss = (flow_preds[i] - flow_gt).abs()
|
| 20 |
+
|
| 21 |
+
flow_loss += i_weight * (valid[:, None] * i_loss).mean()
|
| 22 |
+
|
| 23 |
+
epe = torch.sum((flow_preds[-1] - flow_gt) ** 2, dim=1).sqrt()
|
| 24 |
+
|
| 25 |
+
if valid.max() < 0.5:
|
| 26 |
+
pass
|
| 27 |
+
|
| 28 |
+
epe = epe.view(-1)[valid.view(-1)]
|
| 29 |
+
|
| 30 |
+
metrics = {
|
| 31 |
+
'epe': epe.mean().item(),
|
| 32 |
+
'1px': (epe > 1).float().mean().item(),
|
| 33 |
+
'3px': (epe > 3).float().mean().item(),
|
| 34 |
+
'5px': (epe > 5).float().mean().item(),
|
| 35 |
+
}
|
| 36 |
+
|
| 37 |
+
return flow_loss, metrics
|
GMFlow/main.py
ADDED
|
@@ -0,0 +1,557 @@
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|
|
|
| 1 |
+
import torch
|
| 2 |
+
from torch.utils.data import DataLoader
|
| 3 |
+
from torch.utils.tensorboard import SummaryWriter
|
| 4 |
+
|
| 5 |
+
import argparse
|
| 6 |
+
import numpy as np
|
| 7 |
+
import os
|
| 8 |
+
|
| 9 |
+
from data import build_train_dataset
|
| 10 |
+
from gmflow.gmflow import GMFlow
|
| 11 |
+
from loss import flow_loss_func
|
| 12 |
+
from evaluate import (validate_chairs, validate_things, validate_sintel, validate_kitti,
|
| 13 |
+
create_sintel_submission, create_kitti_submission, inference_on_dir)
|
| 14 |
+
|
| 15 |
+
from utils.logger import Logger
|
| 16 |
+
from utils import misc
|
| 17 |
+
from utils.dist_utils import get_dist_info, init_dist, setup_for_distributed
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
def get_args_parser():
|
| 21 |
+
parser = argparse.ArgumentParser()
|
| 22 |
+
|
| 23 |
+
# dataset
|
| 24 |
+
parser.add_argument('--checkpoint_dir', default='tmp', type=str,
|
| 25 |
+
help='where to save the training log and models')
|
| 26 |
+
parser.add_argument('--stage', default='chairs', type=str,
|
| 27 |
+
help='training stage')
|
| 28 |
+
parser.add_argument('--image_size', default=[384, 512], type=int, nargs='+',
|
| 29 |
+
help='image size for training')
|
| 30 |
+
parser.add_argument('--padding_factor', default=16, type=int,
|
| 31 |
+
help='the input should be divisible by padding_factor, otherwise do padding')
|
| 32 |
+
|
| 33 |
+
parser.add_argument('--max_flow', default=400, type=int,
|
| 34 |
+
help='exclude very large motions during training')
|
| 35 |
+
parser.add_argument('--val_dataset', default=['chairs'], type=str, nargs='+',
|
| 36 |
+
help='validation dataset')
|
| 37 |
+
parser.add_argument('--with_speed_metric', action='store_true',
|
| 38 |
+
help='with speed metric when evaluation')
|
| 39 |
+
|
| 40 |
+
# training
|
| 41 |
+
parser.add_argument('--lr', default=4e-4, type=float)
|
| 42 |
+
parser.add_argument('--batch_size', default=12, type=int)
|
| 43 |
+
parser.add_argument('--num_workers', default=4, type=int)
|
| 44 |
+
parser.add_argument('--weight_decay', default=1e-4, type=float)
|
| 45 |
+
parser.add_argument('--grad_clip', default=1.0, type=float)
|
| 46 |
+
parser.add_argument('--num_steps', default=100000, type=int)
|
| 47 |
+
parser.add_argument('--seed', default=326, type=int)
|
| 48 |
+
parser.add_argument('--summary_freq', default=100, type=int)
|
| 49 |
+
parser.add_argument('--val_freq', default=10000, type=int)
|
| 50 |
+
parser.add_argument('--save_ckpt_freq', default=10000, type=int)
|
| 51 |
+
parser.add_argument('--save_latest_ckpt_freq', default=1000, type=int)
|
| 52 |
+
|
| 53 |
+
# resume pretrained model or resume training
|
| 54 |
+
parser.add_argument('--resume', default=None, type=str,
|
| 55 |
+
help='resume from pretrain model for finetuing or resume from terminated training')
|
| 56 |
+
parser.add_argument('--strict_resume', action='store_true')
|
| 57 |
+
parser.add_argument('--no_resume_optimizer', action='store_true')
|
| 58 |
+
|
| 59 |
+
# GMFlow model
|
| 60 |
+
parser.add_argument('--num_scales', default=1, type=int,
|
| 61 |
+
help='basic gmflow model uses a single 1/8 feature, the refinement uses 1/4 feature')
|
| 62 |
+
parser.add_argument('--feature_channels', default=128, type=int)
|
| 63 |
+
parser.add_argument('--upsample_factor', default=8, type=int)
|
| 64 |
+
parser.add_argument('--num_transformer_layers', default=6, type=int)
|
| 65 |
+
parser.add_argument('--num_head', default=1, type=int)
|
| 66 |
+
parser.add_argument('--attention_type', default='swin', type=str)
|
| 67 |
+
parser.add_argument('--ffn_dim_expansion', default=4, type=int)
|
| 68 |
+
|
| 69 |
+
parser.add_argument('--attn_splits_list', default=[2], type=int, nargs='+',
|
| 70 |
+
help='number of splits in attention')
|
| 71 |
+
parser.add_argument('--corr_radius_list', default=[-1], type=int, nargs='+',
|
| 72 |
+
help='correlation radius for matching, -1 indicates global matching')
|
| 73 |
+
parser.add_argument('--prop_radius_list', default=[-1], type=int, nargs='+',
|
| 74 |
+
help='self-attention radius for flow propagation, -1 indicates global attention')
|
| 75 |
+
|
| 76 |
+
# loss
|
| 77 |
+
parser.add_argument('--gamma', default=0.9, type=float,
|
| 78 |
+
help='loss weight')
|
| 79 |
+
|
| 80 |
+
# evaluation
|
| 81 |
+
parser.add_argument('--eval', action='store_true')
|
| 82 |
+
parser.add_argument('--save_eval_to_file', action='store_true')
|
| 83 |
+
parser.add_argument('--evaluate_matched_unmatched', action='store_true')
|
| 84 |
+
|
| 85 |
+
# inference on a directory
|
| 86 |
+
parser.add_argument('--inference_dir', default=None, type=str)
|
| 87 |
+
parser.add_argument('--inference_size', default=None, type=int, nargs='+',
|
| 88 |
+
help='can specify the inference size')
|
| 89 |
+
parser.add_argument('--dir_paired_data', action='store_true',
|
| 90 |
+
help='Paired data in a dir instead of a sequence')
|
| 91 |
+
parser.add_argument('--save_flo_flow', action='store_true')
|
| 92 |
+
parser.add_argument('--pred_bidir_flow', action='store_true',
|
| 93 |
+
help='predict bidirectional flow')
|
| 94 |
+
parser.add_argument('--fwd_bwd_consistency_check', action='store_true',
|
| 95 |
+
help='forward backward consistency check with bidirection flow')
|
| 96 |
+
|
| 97 |
+
# predict on sintel and kitti test set for submission
|
| 98 |
+
parser.add_argument('--submission', action='store_true',
|
| 99 |
+
help='submission to sintel or kitti test sets')
|
| 100 |
+
parser.add_argument('--output_path', default='output', type=str,
|
| 101 |
+
help='where to save the prediction results')
|
| 102 |
+
parser.add_argument('--save_vis_flow', action='store_true',
|
| 103 |
+
help='visualize flow prediction as .png image')
|
| 104 |
+
parser.add_argument('--no_save_flo', action='store_true',
|
| 105 |
+
help='not save flow as .flo')
|
| 106 |
+
|
| 107 |
+
# distributed training
|
| 108 |
+
parser.add_argument('--local_rank', default=0, type=int)
|
| 109 |
+
parser.add_argument('--distributed', action='store_true')
|
| 110 |
+
parser.add_argument('--launcher', default='none', type=str, choices=['none', 'pytorch'])
|
| 111 |
+
parser.add_argument('--gpu_ids', default=0, type=int, nargs='+')
|
| 112 |
+
|
| 113 |
+
parser.add_argument('--count_time', action='store_true',
|
| 114 |
+
help='measure the inference time on sintel')
|
| 115 |
+
|
| 116 |
+
return parser
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
def main(args):
|
| 120 |
+
if not args.eval and not args.submission and args.inference_dir is None:
|
| 121 |
+
if args.local_rank == 0:
|
| 122 |
+
print('pytorch version:', torch.__version__)
|
| 123 |
+
print(args)
|
| 124 |
+
misc.save_args(args)
|
| 125 |
+
misc.check_path(args.checkpoint_dir)
|
| 126 |
+
misc.save_command(args.checkpoint_dir)
|
| 127 |
+
|
| 128 |
+
seed = args.seed
|
| 129 |
+
torch.manual_seed(seed)
|
| 130 |
+
np.random.seed(seed)
|
| 131 |
+
|
| 132 |
+
torch.backends.cudnn.benchmark = True
|
| 133 |
+
|
| 134 |
+
if args.launcher == 'none':
|
| 135 |
+
args.distributed = False
|
| 136 |
+
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
| 137 |
+
else:
|
| 138 |
+
args.distributed = True
|
| 139 |
+
|
| 140 |
+
# adjust batch size for each gpu
|
| 141 |
+
assert args.batch_size % torch.cuda.device_count() == 0
|
| 142 |
+
args.batch_size = args.batch_size // torch.cuda.device_count()
|
| 143 |
+
|
| 144 |
+
dist_params = dict(backend='nccl')
|
| 145 |
+
init_dist(args.launcher, **dist_params)
|
| 146 |
+
# re-set gpu_ids with distributed training mode
|
| 147 |
+
_, world_size = get_dist_info()
|
| 148 |
+
args.gpu_ids = range(world_size)
|
| 149 |
+
device = torch.device('cuda:{}'.format(args.local_rank))
|
| 150 |
+
|
| 151 |
+
setup_for_distributed(args.local_rank == 0)
|
| 152 |
+
|
| 153 |
+
# model
|
| 154 |
+
model = GMFlow(feature_channels=args.feature_channels,
|
| 155 |
+
num_scales=args.num_scales,
|
| 156 |
+
upsample_factor=args.upsample_factor,
|
| 157 |
+
num_head=args.num_head,
|
| 158 |
+
attention_type=args.attention_type,
|
| 159 |
+
ffn_dim_expansion=args.ffn_dim_expansion,
|
| 160 |
+
num_transformer_layers=args.num_transformer_layers,
|
| 161 |
+
).to(device)
|
| 162 |
+
|
| 163 |
+
if not args.eval and not args.submission and not args.inference_dir:
|
| 164 |
+
print('Model definition:')
|
| 165 |
+
print(model)
|
| 166 |
+
|
| 167 |
+
if args.distributed:
|
| 168 |
+
model = torch.nn.parallel.DistributedDataParallel(
|
| 169 |
+
model.to(device),
|
| 170 |
+
device_ids=[args.local_rank],
|
| 171 |
+
output_device=args.local_rank)
|
| 172 |
+
model_without_ddp = model.module
|
| 173 |
+
else:
|
| 174 |
+
if torch.cuda.device_count() > 1:
|
| 175 |
+
print('Use %d GPUs' % torch.cuda.device_count())
|
| 176 |
+
model = torch.nn.DataParallel(model)
|
| 177 |
+
|
| 178 |
+
model_without_ddp = model.module
|
| 179 |
+
else:
|
| 180 |
+
model_without_ddp = model
|
| 181 |
+
|
| 182 |
+
num_params = sum(p.numel() for p in model.parameters())
|
| 183 |
+
print('Number of params:', num_params)
|
| 184 |
+
if not args.eval and not args.submission and args.inference_dir is None:
|
| 185 |
+
save_name = '%d_parameters' % num_params
|
| 186 |
+
open(os.path.join(args.checkpoint_dir, save_name), 'a').close()
|
| 187 |
+
|
| 188 |
+
optimizer = torch.optim.AdamW(model_without_ddp.parameters(), lr=args.lr,
|
| 189 |
+
weight_decay=args.weight_decay)
|
| 190 |
+
|
| 191 |
+
start_epoch = 0
|
| 192 |
+
start_step = 0
|
| 193 |
+
# resume checkpoints
|
| 194 |
+
if args.resume:
|
| 195 |
+
print('Load checkpoint: %s' % args.resume)
|
| 196 |
+
|
| 197 |
+
loc = 'cuda:{}'.format(args.local_rank)
|
| 198 |
+
checkpoint = torch.load(args.resume, map_location=loc)
|
| 199 |
+
|
| 200 |
+
weights = checkpoint['model'] if 'model' in checkpoint else checkpoint
|
| 201 |
+
|
| 202 |
+
model_without_ddp.load_state_dict(weights, strict=args.strict_resume)
|
| 203 |
+
|
| 204 |
+
if 'optimizer' in checkpoint and 'step' in checkpoint and 'epoch' in checkpoint and not \
|
| 205 |
+
args.no_resume_optimizer:
|
| 206 |
+
print('Load optimizer')
|
| 207 |
+
optimizer.load_state_dict(checkpoint['optimizer'])
|
| 208 |
+
start_epoch = checkpoint['epoch']
|
| 209 |
+
start_step = checkpoint['step']
|
| 210 |
+
|
| 211 |
+
print('start_epoch: %d, start_step: %d' % (start_epoch, start_step))
|
| 212 |
+
|
| 213 |
+
# evaluate
|
| 214 |
+
if args.eval:
|
| 215 |
+
val_results = {}
|
| 216 |
+
|
| 217 |
+
if 'chairs' in args.val_dataset:
|
| 218 |
+
results_dict = validate_chairs(model_without_ddp,
|
| 219 |
+
with_speed_metric=args.with_speed_metric,
|
| 220 |
+
attn_splits_list=args.attn_splits_list,
|
| 221 |
+
corr_radius_list=args.corr_radius_list,
|
| 222 |
+
prop_radius_list=args.prop_radius_list,
|
| 223 |
+
)
|
| 224 |
+
|
| 225 |
+
val_results.update(results_dict)
|
| 226 |
+
|
| 227 |
+
if 'things' in args.val_dataset:
|
| 228 |
+
results_dict = validate_things(model_without_ddp,
|
| 229 |
+
padding_factor=args.padding_factor,
|
| 230 |
+
with_speed_metric=args.with_speed_metric,
|
| 231 |
+
attn_splits_list=args.attn_splits_list,
|
| 232 |
+
corr_radius_list=args.corr_radius_list,
|
| 233 |
+
prop_radius_list=args.prop_radius_list,
|
| 234 |
+
)
|
| 235 |
+
val_results.update(results_dict)
|
| 236 |
+
|
| 237 |
+
if 'sintel' in args.val_dataset:
|
| 238 |
+
results_dict = validate_sintel(model_without_ddp,
|
| 239 |
+
count_time=args.count_time,
|
| 240 |
+
padding_factor=args.padding_factor,
|
| 241 |
+
with_speed_metric=args.with_speed_metric,
|
| 242 |
+
evaluate_matched_unmatched=args.evaluate_matched_unmatched,
|
| 243 |
+
attn_splits_list=args.attn_splits_list,
|
| 244 |
+
corr_radius_list=args.corr_radius_list,
|
| 245 |
+
prop_radius_list=args.prop_radius_list,
|
| 246 |
+
)
|
| 247 |
+
val_results.update(results_dict)
|
| 248 |
+
|
| 249 |
+
if 'kitti' in args.val_dataset:
|
| 250 |
+
results_dict = validate_kitti(model_without_ddp,
|
| 251 |
+
padding_factor=args.padding_factor,
|
| 252 |
+
with_speed_metric=args.with_speed_metric,
|
| 253 |
+
attn_splits_list=args.attn_splits_list,
|
| 254 |
+
corr_radius_list=args.corr_radius_list,
|
| 255 |
+
prop_radius_list=args.prop_radius_list,
|
| 256 |
+
)
|
| 257 |
+
val_results.update(results_dict)
|
| 258 |
+
|
| 259 |
+
if args.save_eval_to_file:
|
| 260 |
+
misc.check_path(args.checkpoint_dir)
|
| 261 |
+
val_file = os.path.join(args.checkpoint_dir, 'val_results.txt')
|
| 262 |
+
with open(val_file, 'a') as f:
|
| 263 |
+
f.write('\neval results after training done\n\n')
|
| 264 |
+
metrics = ['chairs_epe', 'chairs_s0_10', 'chairs_s10_40', 'chairs_s40+',
|
| 265 |
+
'things_clean_epe', 'things_clean_s0_10', 'things_clean_s10_40', 'things_clean_s40+',
|
| 266 |
+
'things_final_epe', 'things_final_s0_10', 'things_final_s10_40', 'things_final_s40+',
|
| 267 |
+
'sintel_clean_epe', 'sintel_clean_s0_10', 'sintel_clean_s10_40', 'sintel_clean_s40+',
|
| 268 |
+
'sintel_final_epe', 'sintel_final_s0_10', 'sintel_final_s10_40', 'sintel_final_s40+',
|
| 269 |
+
'kitti_epe', 'kitti_f1', 'kitti_s0_10', 'kitti_s10_40', 'kitti_s40+',
|
| 270 |
+
]
|
| 271 |
+
eval_metrics = []
|
| 272 |
+
for metric in metrics:
|
| 273 |
+
if metric in val_results.keys():
|
| 274 |
+
eval_metrics.append(metric)
|
| 275 |
+
|
| 276 |
+
metrics_values = [val_results[metric] for metric in eval_metrics]
|
| 277 |
+
|
| 278 |
+
num_metrics = len(eval_metrics)
|
| 279 |
+
|
| 280 |
+
# save as markdown format
|
| 281 |
+
f.write(("| {:>20} " * num_metrics + '\n').format(*eval_metrics))
|
| 282 |
+
f.write(("| {:20.3f} " * num_metrics).format(*metrics_values))
|
| 283 |
+
|
| 284 |
+
f.write('\n\n')
|
| 285 |
+
|
| 286 |
+
return
|
| 287 |
+
|
| 288 |
+
# Sintel and KITTI submission
|
| 289 |
+
if args.submission:
|
| 290 |
+
# NOTE: args.val_dataset is a list
|
| 291 |
+
if args.val_dataset[0] == 'sintel':
|
| 292 |
+
create_sintel_submission(model_without_ddp,
|
| 293 |
+
output_path=args.output_path,
|
| 294 |
+
padding_factor=args.padding_factor,
|
| 295 |
+
save_vis_flow=args.save_vis_flow,
|
| 296 |
+
no_save_flo=args.no_save_flo,
|
| 297 |
+
attn_splits_list=args.attn_splits_list,
|
| 298 |
+
corr_radius_list=args.corr_radius_list,
|
| 299 |
+
prop_radius_list=args.prop_radius_list,
|
| 300 |
+
)
|
| 301 |
+
elif args.val_dataset[0] == 'kitti':
|
| 302 |
+
create_kitti_submission(model_without_ddp,
|
| 303 |
+
output_path=args.output_path,
|
| 304 |
+
padding_factor=args.padding_factor,
|
| 305 |
+
save_vis_flow=args.save_vis_flow,
|
| 306 |
+
attn_splits_list=args.attn_splits_list,
|
| 307 |
+
corr_radius_list=args.corr_radius_list,
|
| 308 |
+
prop_radius_list=args.prop_radius_list,
|
| 309 |
+
)
|
| 310 |
+
else:
|
| 311 |
+
raise ValueError(f'Not supported dataset for submission')
|
| 312 |
+
|
| 313 |
+
return
|
| 314 |
+
|
| 315 |
+
# inferece on a dir
|
| 316 |
+
if args.inference_dir is not None:
|
| 317 |
+
inference_on_dir(model_without_ddp,
|
| 318 |
+
inference_dir=args.inference_dir,
|
| 319 |
+
output_path=args.output_path,
|
| 320 |
+
padding_factor=args.padding_factor,
|
| 321 |
+
inference_size=args.inference_size,
|
| 322 |
+
paired_data=args.dir_paired_data,
|
| 323 |
+
save_flo_flow=args.save_flo_flow,
|
| 324 |
+
attn_splits_list=args.attn_splits_list,
|
| 325 |
+
corr_radius_list=args.corr_radius_list,
|
| 326 |
+
prop_radius_list=args.prop_radius_list,
|
| 327 |
+
pred_bidir_flow=args.pred_bidir_flow,
|
| 328 |
+
fwd_bwd_consistency_check=args.fwd_bwd_consistency_check,
|
| 329 |
+
)
|
| 330 |
+
|
| 331 |
+
return
|
| 332 |
+
|
| 333 |
+
# training datset
|
| 334 |
+
train_dataset = build_train_dataset(args)
|
| 335 |
+
print('Number of training images:', len(train_dataset))
|
| 336 |
+
|
| 337 |
+
# Multi-processing
|
| 338 |
+
if args.distributed:
|
| 339 |
+
train_sampler = torch.utils.data.distributed.DistributedSampler(
|
| 340 |
+
train_dataset,
|
| 341 |
+
num_replicas=torch.cuda.device_count(),
|
| 342 |
+
rank=args.local_rank)
|
| 343 |
+
else:
|
| 344 |
+
train_sampler = None
|
| 345 |
+
|
| 346 |
+
shuffle = False if args.distributed else True
|
| 347 |
+
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size,
|
| 348 |
+
shuffle=shuffle, num_workers=args.num_workers,
|
| 349 |
+
pin_memory=True, drop_last=True,
|
| 350 |
+
sampler=train_sampler)
|
| 351 |
+
|
| 352 |
+
last_epoch = start_step if args.resume and start_step > 0 else -1
|
| 353 |
+
lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(
|
| 354 |
+
optimizer, args.lr,
|
| 355 |
+
args.num_steps + 10,
|
| 356 |
+
pct_start=0.05,
|
| 357 |
+
cycle_momentum=False,
|
| 358 |
+
anneal_strategy='cos',
|
| 359 |
+
last_epoch=last_epoch,
|
| 360 |
+
)
|
| 361 |
+
|
| 362 |
+
if args.local_rank == 0:
|
| 363 |
+
summary_writer = SummaryWriter(args.checkpoint_dir)
|
| 364 |
+
logger = Logger(lr_scheduler, summary_writer, args.summary_freq,
|
| 365 |
+
start_step=start_step)
|
| 366 |
+
|
| 367 |
+
total_steps = start_step
|
| 368 |
+
epoch = start_epoch
|
| 369 |
+
print('Start training')
|
| 370 |
+
|
| 371 |
+
while total_steps < args.num_steps:
|
| 372 |
+
model.train()
|
| 373 |
+
|
| 374 |
+
# mannual change random seed for shuffling every epoch
|
| 375 |
+
if args.distributed:
|
| 376 |
+
train_sampler.set_epoch(epoch)
|
| 377 |
+
|
| 378 |
+
for i, sample in enumerate(train_loader):
|
| 379 |
+
img1, img2, flow_gt, valid = [x.to(device) for x in sample]
|
| 380 |
+
|
| 381 |
+
results_dict = model(img1, img2,
|
| 382 |
+
attn_splits_list=args.attn_splits_list,
|
| 383 |
+
corr_radius_list=args.corr_radius_list,
|
| 384 |
+
prop_radius_list=args.prop_radius_list,
|
| 385 |
+
)
|
| 386 |
+
|
| 387 |
+
flow_preds = results_dict['flow_preds']
|
| 388 |
+
|
| 389 |
+
loss, metrics = flow_loss_func(flow_preds, flow_gt, valid,
|
| 390 |
+
gamma=args.gamma,
|
| 391 |
+
max_flow=args.max_flow,
|
| 392 |
+
)
|
| 393 |
+
|
| 394 |
+
if isinstance(loss, float):
|
| 395 |
+
continue
|
| 396 |
+
|
| 397 |
+
if torch.isnan(loss):
|
| 398 |
+
continue
|
| 399 |
+
|
| 400 |
+
metrics.update({'total_loss': loss.item()})
|
| 401 |
+
|
| 402 |
+
# more efficient zero_grad
|
| 403 |
+
for param in model_without_ddp.parameters():
|
| 404 |
+
param.grad = None
|
| 405 |
+
|
| 406 |
+
loss.backward()
|
| 407 |
+
|
| 408 |
+
# Gradient clipping
|
| 409 |
+
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
|
| 410 |
+
|
| 411 |
+
optimizer.step()
|
| 412 |
+
|
| 413 |
+
lr_scheduler.step()
|
| 414 |
+
|
| 415 |
+
if args.local_rank == 0:
|
| 416 |
+
logger.push(metrics)
|
| 417 |
+
|
| 418 |
+
logger.add_image_summary(img1, img2, flow_preds, flow_gt)
|
| 419 |
+
|
| 420 |
+
total_steps += 1
|
| 421 |
+
|
| 422 |
+
if total_steps % args.save_ckpt_freq == 0 or total_steps == args.num_steps:
|
| 423 |
+
if args.local_rank == 0:
|
| 424 |
+
checkpoint_path = os.path.join(args.checkpoint_dir, 'step_%06d.pth' % total_steps)
|
| 425 |
+
torch.save({
|
| 426 |
+
'model': model_without_ddp.state_dict()
|
| 427 |
+
}, checkpoint_path)
|
| 428 |
+
|
| 429 |
+
if total_steps % args.save_latest_ckpt_freq == 0:
|
| 430 |
+
checkpoint_path = os.path.join(args.checkpoint_dir, 'checkpoint_latest.pth')
|
| 431 |
+
|
| 432 |
+
if args.local_rank == 0:
|
| 433 |
+
torch.save({
|
| 434 |
+
'model': model_without_ddp.state_dict(),
|
| 435 |
+
'optimizer': optimizer.state_dict(),
|
| 436 |
+
'step': total_steps,
|
| 437 |
+
'epoch': epoch,
|
| 438 |
+
}, checkpoint_path)
|
| 439 |
+
|
| 440 |
+
if total_steps % args.val_freq == 0:
|
| 441 |
+
print('Start validation')
|
| 442 |
+
|
| 443 |
+
val_results = {}
|
| 444 |
+
# support validation on multiple datasets
|
| 445 |
+
if 'chairs' in args.val_dataset:
|
| 446 |
+
results_dict = validate_chairs(model_without_ddp,
|
| 447 |
+
with_speed_metric=args.with_speed_metric,
|
| 448 |
+
attn_splits_list=args.attn_splits_list,
|
| 449 |
+
corr_radius_list=args.corr_radius_list,
|
| 450 |
+
prop_radius_list=args.prop_radius_list,
|
| 451 |
+
)
|
| 452 |
+
if args.local_rank == 0:
|
| 453 |
+
val_results.update(results_dict)
|
| 454 |
+
|
| 455 |
+
if 'things' in args.val_dataset:
|
| 456 |
+
results_dict = validate_things(model_without_ddp,
|
| 457 |
+
padding_factor=args.padding_factor,
|
| 458 |
+
with_speed_metric=args.with_speed_metric,
|
| 459 |
+
attn_splits_list=args.attn_splits_list,
|
| 460 |
+
corr_radius_list=args.corr_radius_list,
|
| 461 |
+
prop_radius_list=args.prop_radius_list,
|
| 462 |
+
)
|
| 463 |
+
if args.local_rank == 0:
|
| 464 |
+
val_results.update(results_dict)
|
| 465 |
+
|
| 466 |
+
if 'sintel' in args.val_dataset:
|
| 467 |
+
results_dict = validate_sintel(model_without_ddp,
|
| 468 |
+
count_time=args.count_time,
|
| 469 |
+
padding_factor=args.padding_factor,
|
| 470 |
+
with_speed_metric=args.with_speed_metric,
|
| 471 |
+
evaluate_matched_unmatched=args.evaluate_matched_unmatched,
|
| 472 |
+
attn_splits_list=args.attn_splits_list,
|
| 473 |
+
corr_radius_list=args.corr_radius_list,
|
| 474 |
+
prop_radius_list=args.prop_radius_list,
|
| 475 |
+
)
|
| 476 |
+
if args.local_rank == 0:
|
| 477 |
+
val_results.update(results_dict)
|
| 478 |
+
|
| 479 |
+
if 'kitti' in args.val_dataset:
|
| 480 |
+
results_dict = validate_kitti(model_without_ddp,
|
| 481 |
+
padding_factor=args.padding_factor,
|
| 482 |
+
with_speed_metric=args.with_speed_metric,
|
| 483 |
+
attn_splits_list=args.attn_splits_list,
|
| 484 |
+
corr_radius_list=args.corr_radius_list,
|
| 485 |
+
prop_radius_list=args.prop_radius_list,
|
| 486 |
+
)
|
| 487 |
+
if args.local_rank == 0:
|
| 488 |
+
val_results.update(results_dict)
|
| 489 |
+
|
| 490 |
+
if args.local_rank == 0:
|
| 491 |
+
logger.write_dict(val_results)
|
| 492 |
+
|
| 493 |
+
# Save validation results
|
| 494 |
+
val_file = os.path.join(args.checkpoint_dir, 'val_results.txt')
|
| 495 |
+
with open(val_file, 'a') as f:
|
| 496 |
+
f.write('step: %06d\n' % total_steps)
|
| 497 |
+
if args.evaluate_matched_unmatched:
|
| 498 |
+
metrics = ['chairs_epe',
|
| 499 |
+
'chairs_s0_10', 'chairs_s10_40', 'chairs_s40+',
|
| 500 |
+
'things_clean_epe', 'things_clean_s0_10', 'things_clean_s10_40',
|
| 501 |
+
'things_clean_s40+',
|
| 502 |
+
'sintel_clean_epe', 'sintel_clean_matched', 'sintel_clean_unmatched',
|
| 503 |
+
'sintel_clean_s0_10', 'sintel_clean_s10_40',
|
| 504 |
+
'sintel_clean_s40+',
|
| 505 |
+
'sintel_final_epe', 'sintel_final_matched', 'sintel_final_unmatched',
|
| 506 |
+
'sintel_final_s0_10', 'sintel_final_s10_40',
|
| 507 |
+
'sintel_final_s40+',
|
| 508 |
+
'kitti_epe', 'kitti_f1', 'kitti_s0_10', 'kitti_s10_40', 'kitti_s40+',
|
| 509 |
+
]
|
| 510 |
+
else:
|
| 511 |
+
metrics = ['chairs_epe', 'chairs_s0_10', 'chairs_s10_40', 'chairs_s40+',
|
| 512 |
+
'things_clean_epe', 'things_clean_s0_10', 'things_clean_s10_40',
|
| 513 |
+
'things_clean_s40+',
|
| 514 |
+
'sintel_clean_epe', 'sintel_clean_s0_10', 'sintel_clean_s10_40',
|
| 515 |
+
'sintel_clean_s40+',
|
| 516 |
+
'sintel_final_epe', 'sintel_final_s0_10', 'sintel_final_s10_40',
|
| 517 |
+
'sintel_final_s40+',
|
| 518 |
+
'kitti_epe', 'kitti_f1', 'kitti_s0_10', 'kitti_s10_40', 'kitti_s40+',
|
| 519 |
+
]
|
| 520 |
+
|
| 521 |
+
eval_metrics = []
|
| 522 |
+
for metric in metrics:
|
| 523 |
+
if metric in val_results.keys():
|
| 524 |
+
eval_metrics.append(metric)
|
| 525 |
+
|
| 526 |
+
metrics_values = [val_results[metric] for metric in eval_metrics]
|
| 527 |
+
|
| 528 |
+
num_metrics = len(eval_metrics)
|
| 529 |
+
|
| 530 |
+
# save as markdown format
|
| 531 |
+
if args.evaluate_matched_unmatched:
|
| 532 |
+
f.write(("| {:>25} " * num_metrics + '\n').format(*eval_metrics))
|
| 533 |
+
f.write(("| {:25.3f} " * num_metrics).format(*metrics_values))
|
| 534 |
+
else:
|
| 535 |
+
f.write(("| {:>20} " * num_metrics + '\n').format(*eval_metrics))
|
| 536 |
+
f.write(("| {:20.3f} " * num_metrics).format(*metrics_values))
|
| 537 |
+
|
| 538 |
+
f.write('\n\n')
|
| 539 |
+
|
| 540 |
+
model.train()
|
| 541 |
+
|
| 542 |
+
if total_steps >= args.num_steps:
|
| 543 |
+
print('Training done')
|
| 544 |
+
|
| 545 |
+
return
|
| 546 |
+
|
| 547 |
+
epoch += 1
|
| 548 |
+
|
| 549 |
+
|
| 550 |
+
if __name__ == '__main__':
|
| 551 |
+
parser = get_args_parser()
|
| 552 |
+
args = parser.parse_args()
|
| 553 |
+
|
| 554 |
+
if 'LOCAL_RANK' not in os.environ:
|
| 555 |
+
os.environ['LOCAL_RANK'] = str(args.local_rank)
|
| 556 |
+
|
| 557 |
+
main(args)
|
GMFlow/scripts/demo.sh
ADDED
|
@@ -0,0 +1,63 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
|
| 3 |
+
# inference GMFlow without refinement
|
| 4 |
+
|
| 5 |
+
# sintel
|
| 6 |
+
|
| 7 |
+
# only predict forward flow
|
| 8 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 9 |
+
--inference_dir demo/sintel_market_1 \
|
| 10 |
+
--output_path output/gmflow-norefine-sintel_market_1 \
|
| 11 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 12 |
+
|
| 13 |
+
# predict forward & backward flow
|
| 14 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 15 |
+
--inference_dir demo/sintel_market_1 \
|
| 16 |
+
--output_path output/gmflow-norefine-sintel_market_1 \
|
| 17 |
+
--pred_bidir_flow \
|
| 18 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
# predict forward & backward flow with forward-backward consistency check
|
| 22 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 23 |
+
--inference_dir demo/sintel_market_1 \
|
| 24 |
+
--output_path output/gmflow-norefine-sintel_market_1 \
|
| 25 |
+
--pred_bidir_flow \
|
| 26 |
+
--fwd_bwd_consistency_check \
|
| 27 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
# davis
|
| 31 |
+
|
| 32 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 33 |
+
--inference_dir demo/davis_breakdance-flare \
|
| 34 |
+
--output_path output/gmflow-norefine-davis_breakdance-flare \
|
| 35 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
# inference GMFlow with refinement
|
| 41 |
+
|
| 42 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 43 |
+
--inference_dir demo/davis_breakdance-flare \
|
| 44 |
+
--output_path output/gmflow-withrefine-davis_breakdance-flare \
|
| 45 |
+
--resume pretrained/gmflow_with_refine_sintel-3ed1cf48.pth \
|
| 46 |
+
--padding_factor 32 \
|
| 47 |
+
--upsample_factor 4 \
|
| 48 |
+
--num_scales 2 \
|
| 49 |
+
--attn_splits_list 2 8 \
|
| 50 |
+
--corr_radius_list -1 4 \
|
| 51 |
+
--prop_radius_list -1 1
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 57 |
+
--inference_dir demo/sintel_test_clean_market_1 \
|
| 58 |
+
--output_path output/gmflow-norefine-sintel_test_clean_market_1 \
|
| 59 |
+
--pred_bidir_flow \
|
| 60 |
+
--fwd_bwd_consistency_check \
|
| 61 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 62 |
+
|
| 63 |
+
|
GMFlow/scripts/evaluate.sh
ADDED
|
@@ -0,0 +1,83 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
|
| 3 |
+
# evaluate GMFlow without refinement
|
| 4 |
+
|
| 5 |
+
# evaluate chairs & things trained model on things and sintel (Table 3 of GMFlow paper)
|
| 6 |
+
# the output should be:
|
| 7 |
+
# Number of validation image pairs: 1024
|
| 8 |
+
# Validation Things test set (things_clean) EPE: 3.475
|
| 9 |
+
# Validation Things test (things_clean) s0_10: 0.666, s10_40: 1.310, s40+: 8.968
|
| 10 |
+
# Number of validation image pairs: 1041
|
| 11 |
+
# Validation Sintel (clean) EPE: 1.495, 1px: 0.161, 3px: 0.059, 5px: 0.040
|
| 12 |
+
# Validation Sintel (clean) s0_10: 0.457, s10_40: 1.770, s40+: 8.257
|
| 13 |
+
# Number of validation image pairs: 1041
|
| 14 |
+
# Validation Sintel (final) EPE: 2.955, 1px: 0.209, 3px: 0.098, 5px: 0.071
|
| 15 |
+
# Validation Sintel (final) s0_10: 0.725, s10_40: 3.446, s40+: 17.701
|
| 16 |
+
|
| 17 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 18 |
+
--eval \
|
| 19 |
+
--resume pretrained/gmflow_things-e9887eda.pth \
|
| 20 |
+
--val_dataset things sintel \
|
| 21 |
+
--with_speed_metric
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
# evaluate GMFlow with refinement
|
| 26 |
+
|
| 27 |
+
# evaluate chairs & things trained model on things and sintel (Table 3 of GMFlow paper)
|
| 28 |
+
# the output should be:
|
| 29 |
+
# Validation Things test set (things_clean) EPE: 2.804
|
| 30 |
+
# Validation Things test (things_clean) s0_10: 0.527, s10_40: 1.009, s40+: 7.314
|
| 31 |
+
# Number of validation image pairs: 1041
|
| 32 |
+
# Validation Sintel (clean) EPE: 1.084, 1px: 0.092, 3px: 0.040, 5px: 0.028
|
| 33 |
+
# Validation Sintel (clean) s0_10: 0.303, s10_40: 1.252, s40+: 6.261
|
| 34 |
+
# Number of validation image pairs: 1041
|
| 35 |
+
# Validation Sintel (final) EPE: 2.475, 1px: 0.147, 3px: 0.077, 5px: 0.058
|
| 36 |
+
# Validation Sintel (final) s0_10: 0.511, s10_40: 2.810, s40+: 15.669
|
| 37 |
+
|
| 38 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 39 |
+
--eval \
|
| 40 |
+
--resume pretrained/gmflow_with_refine_things-36579974.pth \
|
| 41 |
+
--val_dataset things sintel \
|
| 42 |
+
--with_speed_metric \
|
| 43 |
+
--padding_factor 32 \
|
| 44 |
+
--upsample_factor 4 \
|
| 45 |
+
--num_scales 2 \
|
| 46 |
+
--attn_splits_list 2 8 \
|
| 47 |
+
--corr_radius_list -1 4 \
|
| 48 |
+
--prop_radius_list -1 1
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
# evaluate matched & matched on sintel
|
| 53 |
+
|
| 54 |
+
# evaluate GMFlow without refinement
|
| 55 |
+
|
| 56 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 57 |
+
--eval \
|
| 58 |
+
--evaluate_matched_unmatched \
|
| 59 |
+
--resume pretrained/gmflow_things-e9887eda.pth \
|
| 60 |
+
--val_dataset sintel
|
| 61 |
+
|
| 62 |
+
# evaluate GMFlow with refinement
|
| 63 |
+
|
| 64 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 65 |
+
--eval \
|
| 66 |
+
--evaluate_matched_unmatched \
|
| 67 |
+
--resume pretrained/gmflow_with_refine_things-36579974.pth \
|
| 68 |
+
--val_dataset sintel \
|
| 69 |
+
--with_speed_metric \
|
| 70 |
+
--padding_factor 32 \
|
| 71 |
+
--upsample_factor 4 \
|
| 72 |
+
--num_scales 2 \
|
| 73 |
+
--attn_splits_list 2 8 \
|
| 74 |
+
--corr_radius_list -1 4 \
|
| 75 |
+
--prop_radius_list -1 1
|
| 76 |
+
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
|
GMFlow/scripts/submission.sh
ADDED
|
@@ -0,0 +1,67 @@
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
# generate prediction results for submission on sintel and kitti online servers
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
# GMFlow without refinement
|
| 8 |
+
|
| 9 |
+
# submission to sintel
|
| 10 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 11 |
+
--submission \
|
| 12 |
+
--output_path submission/sintel-gmflow-norefine \
|
| 13 |
+
--val_dataset sintel \
|
| 14 |
+
--resume pretrained/gmflow_sintel-0c07dcb3.pth
|
| 15 |
+
|
| 16 |
+
# submission to kitti
|
| 17 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 18 |
+
--submission \
|
| 19 |
+
--output_path submission/kitti-gmflow-norefine \
|
| 20 |
+
--val_dataset kitti \
|
| 21 |
+
--resume pretrained/gmflow_kitti-285701a8.pth
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
# you can also visualize the predictions before submission
|
| 25 |
+
# CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 26 |
+
# --submission \
|
| 27 |
+
# --output_path submission/sintel-gmflow-norefine-vis \
|
| 28 |
+
# --save_vis_flow \
|
| 29 |
+
# --no_save_flo \
|
| 30 |
+
# --val_dataset sintel \
|
| 31 |
+
# --resume pretrained/gmflow_sintel.pth
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
# GMFlow with refinement
|
| 37 |
+
|
| 38 |
+
# submission to sintel
|
| 39 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 40 |
+
--submission \
|
| 41 |
+
--output_path submission/sintel-gmflow-withrefine \
|
| 42 |
+
--val_dataset sintel \
|
| 43 |
+
--resume pretrained/gmflow_with_refine_sintel-3ed1cf48.pth \
|
| 44 |
+
--padding_factor 32 \
|
| 45 |
+
--upsample_factor 4 \
|
| 46 |
+
--num_scales 2 \
|
| 47 |
+
--attn_splits_list 2 8 \
|
| 48 |
+
--corr_radius_list -1 4 \
|
| 49 |
+
--prop_radius_list -1 1
|
| 50 |
+
|
| 51 |
+
# submission to kitti
|
| 52 |
+
CUDA_VISIBLE_DEVICES=0 python main.py \
|
| 53 |
+
--submission \
|
| 54 |
+
--output_path submission/kitti-gmflow-withrefine \
|
| 55 |
+
--val_dataset kitti \
|
| 56 |
+
--resume pretrained/gmflow_with_refine_kitti-8d3b9786.pth \
|
| 57 |
+
--padding_factor 32 \
|
| 58 |
+
--upsample_factor 4 \
|
| 59 |
+
--num_scales 2 \
|
| 60 |
+
--attn_splits_list 2 8 \
|
| 61 |
+
--corr_radius_list -1 4 \
|
| 62 |
+
--prop_radius_list -1 1
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
|