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Repository: baegwangbin/MaGNet
Branch: master
Commit: d916ec69db4c
Files: 50
Total size: 8.4 MB
Directory structure:
gitextract_ko94q_er/
├── LICENSE
├── README.md
├── ckpts/
│ └── download.py
├── data/
│ ├── dataloader_7scenes.py
│ ├── dataloader_7scenes_D.py
│ ├── dataloader_kitti.py
│ ├── dataloader_kitti_D.py
│ ├── dataloader_scannet.py
│ ├── dataloader_scannet_D.py
│ └── scannet_raw_WH.json
├── data_split/
│ ├── kitti_eigen_test.txt
│ ├── kitti_eigen_train.txt
│ ├── kitti_eigen_val.txt
│ ├── kitti_official_test.txt
│ ├── kitti_official_train.txt
│ ├── scannet_long_test.txt
│ ├── scannet_rob_test.txt
│ ├── scannet_train.txt
│ └── sevenscenes_long_test.txt
├── models/
│ ├── DNET.py
│ ├── FNET.py
│ ├── MAGNET.py
│ └── submodules/
│ ├── D_dense_depth.py
│ ├── F_psmnet.py
│ └── homography.py
├── requirements.txt
├── test_DNet.py
├── test_MaGNet.py
├── test_scripts/
│ ├── dnet/
│ │ ├── 7scenes.txt
│ │ ├── kitti_eigen.txt
│ │ ├── kitti_official.txt
│ │ └── scannet.txt
│ └── magnet/
│ ├── 7scenes.txt
│ ├── kitti_eigen.txt
│ ├── kitti_official.txt
│ └── scannet.txt
├── train_DNet.py
├── train_FNet.py
├── train_MaGNet.py
├── train_scripts/
│ ├── dnet/
│ │ ├── kitti_eigen.txt
│ │ ├── kitti_official.txt
│ │ └── scannet.txt
│ ├── fnet/
│ │ ├── kitti_eigen.txt
│ │ ├── kitti_official.txt
│ │ └── scannet.txt
│ └── magnet/
│ ├── kitti_eigen.txt
│ ├── kitti_official.txt
│ └── scannet.txt
└── utils/
├── losses.py
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2021 Gwangbin Bae
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
Official implementation of the paper
> **Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry**
>
> CVPR 2022 [oral]
>
> [Gwangbin Bae](https://baegwangbin.com), [Ignas Budvytis](https://mi.eng.cam.ac.uk/~ib255/), and [Roberto Cipolla](https://mi.eng.cam.ac.uk/~cipolla/)
>
> [[arXiv]](https://arxiv.org/abs/2112.08177) [[openaccess]](https://openaccess.thecvf.com/content/CVPR2022/html/Bae_Multi-View_Depth_Estimation_by_Fusing_Single-View_Depth_Probability_With_Multi-View_CVPR_2022_paper.html) [[oral presentation]](https://www.youtube.com/watch?v=LM113ibJVmQ)
<p align="center">
<img width=100% src="https://github.com/baegwangbin/MaGNet/blob/master/figs/method.png?raw=true?raw=true">
</p>
*We present **MaGNet** (**M**onocular **a**nd **G**eometric **Net**work), a novel framework for fusing single-view depth probability with multi-view geometry, to improve the accuracy, robustness and efficiency of multi-view depth estimation. For each frame, MaGNet estimates a single-view depth probability distribution, parameterized as a pixel-wise Gaussian. The distribution estimated for the reference frame is then used to sample per-pixel depth candidates. Such probabilistic sampling enables the network to achieve higher accuracy while evaluating fewer depth candidates. We also propose depth consistency weighting for the multi-view matching score, to ensure that the multi-view depth is consistent with the single-view predictions. The proposed method achieves state-of-the-art performance on ScanNet, 7-Scenes and KITTI. Qualitative evaluation demonstrates that our method is more robust against challenging artifacts such as texture-less/reflective surfaces and moving objects.*
## Datasets
We evaluated MaGNet on ScanNet, 7-Scenes and KITTI
### ScanNet
* In order to download ScanNet, you should submit an agreement to the Terms of Use. Please follow the instructions in [this link](http://kaldir.vc.in.tum.de/scannet_benchmark/documentation).
* The folder should be organized as
>`/path/to/ScanNet` \
>`/path/to/ScanNet/scans` \
>`/path/to/ScanNet/scans/scene0000_00 ...` \
>`/path/to/ScanNet/scans_test` \
>`/path/to/ScanNet/scans_test/scene0707_00 ...`
### 7-Scenes
* Download all seven scenes (Chess, Fire, Heads, Office, Pumpkin, RedKitchen, Stairs) from [this link](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/).
* The folder should be organized as:
>`/path/to/SevenScenes` \
>`/path/to/SevenScenes/chess ...`
### KITTI
* Download raw data from [this link](https://www.cvlibs.net/datasets/kitti/raw_data.php).
* Download depth maps from [this link](https://www.cvlibs.net/datasets/kitti/eval_depth_all.php)
* The folder should be organized as:
>`/path/to/KITTI` \
>`/path/to/KITTI/rawdata` \
>`/path/to/KITTI/rawdata/2011_09_26 ...` \
>`/path/to/KITTI/train` \
>`/path/to/KITTI/train/2011_09_26_drive_0001_sync ...` \
>`/path/to/KITTI/val` \
>`/path/to/KITTI/val/2011_09_26_drive_0002_sync ...`
## Download model weights
Download model weights by
```python
python ckpts/download.py
```
If some files are not downloaded properly, download them manually from [this link](https://drive.google.com/drive/u/0/folders/1O4yng6XFe8Wy2fHZp_xjO9KDTfYE7yDB) and place the files under `./ckpts`.
## Install dependencies
We recommend using a virtual environment.
```
python3.6 -m venv --system-site-packages ./venv
source ./venv/bin/activate
```
Install the necessary dependencies by
```
python3.6 -m pip install -r requirements.txt
```
## Test scripts
If you wish to evaluate the accuracy of our D-Net (single-view), run
```python
python test_DNet.py ./test_scripts/dnet/scannet.txt
python test_DNet.py ./test_scripts/dnet/7scenes.txt
python test_DNet.py ./test_scripts/dnet/kitti_eigen.txt
python test_DNet.py ./test_scripts/dnet/kitti_official.txt
```
You should get the following results:
|Dataset|abs_rel|abs_diff|sq_rel|rmse|rmse_log|irmse|log_10|silog|a1|a2|a3|NLL|
|-|-|-|-|-|-|-|-|-|-|-|-|-|
|ScanNet|0.1186|0.2070|0.0493|0.2708|0.1461|0.1086|0.0515|10.0098|0.8546|0.9703|0.9928|2.2352|
|7-Scenes|0.1339|0.2209|0.0549|0.2932|0.1677|0.1165|0.0566|12.8807|0.8308|0.9716|0.9948|2.7941|
|KITTI (eigen)|0.0605|1.1331|0.2086|2.4215|0.0921|0.0075|0.0261|8.4312|0.9602|0.9946|0.9989|2.6443|
|KITTI (official)|0.0629|1.1682|0.2541|2.4708|0.1021|0.0080|0.0270|9.5752|0.9581|0.9905|0.9971|1.7810|
In order to evaluate the accuracy of the full pipeline (multi-view), run
```python
python test_MaGNet.py ./test_scripts/magnet/scannet.txt
python test_MaGNet.py ./test_scripts/magnet/7scenes.txt
python test_MaGNet.py ./test_scripts/magnet/kitti_eigen.txt
python test_MaGNet.py ./test_scripts/magnet/kitti_official.txt
```
You should get the following results:
|Dataset|abs_rel|abs_diff|sq_rel|rmse|rmse_log|irmse|log_10|silog|a1|a2|a3|NLL|
|-|-|-|-|-|-|-|-|-|-|-|-|-|
|ScanNet|0.0810|0.1466|0.0302|0.2098|0.1101|0.1055|0.0351|8.7686|0.9298|0.9835|0.9946|0.1454|
|7-Scenes|0.1257|0.2133|0.0552|0.2957|0.1639|0.1782|0.0527|13.6210|0.8552|0.9715|0.9935|1.5605|
|KITTI (eigen)|0.0535|0.9995|0.1623|2.1584|0.0826|0.0566|0.0235|7.4645|0.9714|0.9958|0.9990|1.8053|
|KITTI (official)|0.0503|0.9135|0.1667|1.9707|0.0848|0.2423|0.0219|7.9451|0.9769|0.9941|0.9979|1.4750|
## Training scripts
If you wish to train the models, run
```python
python train_DNet.py ./test_scripts/dnet/{scannet, kitti_eigen, kitti_official}.txt
python train_FNet.py ./test_scripts/dnet/{scannet, kitti_eigen, kitti_official}.txt
python train_MaGNet.py ./test_scripts/dnet/{scannet, kitti_eigen, kitti_official}.txt
```
Note that the `dataset_path` argument in the script `.txt` files should be modified
## Citation
If you find our work useful in your research please consider citing our paper:
```
@InProceedings{Bae2022,
title = {Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry}
author = {Gwangbin Bae and Ignas Budvytis and Roberto Cipolla},
booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
```
================================================
FILE: ckpts/download.py
================================================
# Download network weights
# Source: https://stackoverflow.com/a/39225039
import requests
def download_file_from_google_drive(id, destination):
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
def save_response_content(response, destination):
CHUNK_SIZE = 32768
with open(destination, "wb") as f:
for chunk in response.iter_content(CHUNK_SIZE):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
URL = "https://docs.google.com/uc?export=download"
session = requests.Session()
response = session.get(URL, params={'id': id}, stream=True)
token = get_confirm_token(response)
if token:
params = {'id': id,
'confirm': token}
response = session.get(URL, params=params, stream=True)
save_response_content(response, destination)
if __name__ == "__main__":
# AdaBins encoder ... used when training on KITTI eigen split
download_file_from_google_drive('1wNMVvZmaLVUflIM_yFLj9vQBD7jBmT0N', './ckpts/AdaBins_kitti_encoder.pt')
# DNET weights
download_file_from_google_drive('1eRQtf9MJNPXmn1UDr2RjEqbQfY4NQ7jT', './ckpts/DNET_kitti_eigen.pt')
download_file_from_google_drive('1z_3zz-hPxSfiUKsN1TIBeZv6YRvZGtfP', './ckpts/DNET_kitti_official.pt')
download_file_from_google_drive('1bbzfboj6XkfFhoJ54Iiqc5Ylj95A015M', './ckpts/DNET_scannet.pt')
# FNET weights
download_file_from_google_drive('1_mcielHqddp9p9ua7by77JG55h_5S9tT', './ckpts/FNET_kitti_eigen.pt')
download_file_from_google_drive('1raQGaE5HrciulIZmNn5TNGp87AgyYp4Y', './ckpts/FNET_kitti_official.pt')
download_file_from_google_drive('1ugDr67UOanpQZMlPopiM8OihUexhPql4', './ckpts/FNET_scannet.pt')
# MAGNET weights
download_file_from_google_drive('1MmqunqAr1mGqYUGBNUUmaJHAO7fYgiYn', './ckpts/MAGNET_kitti_eigen.pt')
download_file_from_google_drive('1mKspc_p3yXp-zd1sZDeau9qrl82pJyGG', './ckpts/MAGNET_kitti_official.pt')
download_file_from_google_drive('1Zuy_8P97OT9Of5PtyNc22DzhXQlD2OE-', './ckpts/MAGNET_scannet.pt')
================================================
FILE: data/dataloader_7scenes.py
================================================
# dataloader for 7-Scenes / when testing F-Net and MaGNet
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
# read camera pose
def _read_ExtM_from_txt(fpath_txt):
ExtM = np.eye(4)
with open(fpath_txt, 'r') as f:
content = f.readlines()
content = [x.strip() for x in content]
for ir, row in enumerate(ExtM):
row_content = content[ir].split()
row = np.asarray([float(x) for x in row_content])
ExtM[ir, :] = row
ExtM = np.linalg.inv(ExtM)
return ExtM
class SevenScenesLoader(object):
def __init__(self, args, mode):
self.t_samples = SevenScenesLoadPreprocess(args, mode)
self.data = DataLoader(self.t_samples, 1, shuffle=False, num_workers=1)
class SevenScenesLoadPreprocess(Dataset):
def __init__(self, args, mode):
self.args = args
# Test set by Long et al. (CVPR 21)
with open("./data_split/sevenscenes_long_test.txt", 'r') as f:
self.filenames = f.readlines()
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.dataset_path = args.dataset_path
# local window
self.window_radius = args.MAGNET_window_radius
self.n_views = args.MAGNET_num_source_views
self.frame_interval = self.window_radius // (self.n_views // 2)
self.img_idx_center = self.n_views // 2
# window_idx_list
self.window_idx_list = list(range(-self.n_views // 2, (self.n_views // 2) + 1))
self.window_idx_list = [i * self.frame_interval for i in self.window_idx_list]
# image resolution
self.img_H = args.input_height
self.img_W = args.input_width
self.dpv_H = args.dpv_height
self.dpv_W = args.dpv_width
# ray array
self.ray_array = self.get_ray_array()
self.cam_intrins = self.get_cam_intrinsics()
def __len__(self):
return len(self.filenames)
# ray array used to back-project depth-map into camera-centered coordinates
def get_ray_array(self):
ray_array = np.ones((self.dpv_H, self.dpv_W, 3))
x_range = np.arange(self.dpv_W)
y_range = np.arange(self.dpv_H)
x_range = np.concatenate([x_range.reshape(1, self.dpv_W)] * self.dpv_H, axis=0)
y_range = np.concatenate([y_range.reshape(self.dpv_H, 1)] * self.dpv_W, axis=1)
ray_array[:, :, 0] = x_range + 0.5
ray_array[:, :, 1] = y_range + 0.5
return ray_array
# get camera intrinscs
def get_cam_intrinsics(self):
IntM_ = np.eye(3)
raw_W, raw_H = self.img_W, self.img_H
# use the parameters in :
# https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/
IntM_[0, 0] = 585.
IntM_[1, 1] = 585.
IntM_[0, 2] = 320.
IntM_[1, 2] = 240.
# updated intrinsic matrix
IntM = np.zeros((3, 3))
IntM[2, 2] = 1.
IntM[0, 0] = IntM_[0, 0] * (self.dpv_W / raw_W)
IntM[1, 1] = IntM_[1, 1] * (self.dpv_H / raw_H)
IntM[0, 2] = IntM_[0, 2] * (self.dpv_W / raw_W)
IntM[1, 2] = IntM_[1, 2] * (self.dpv_H / raw_H)
# pixel to ray array
pixel_to_ray_array = np.copy(self.ray_array)
pixel_to_ray_array[:, :, 0] = ((pixel_to_ray_array[:, :, 0] * (raw_W / self.dpv_W))
- IntM_[0, 2]) / IntM_[0, 0]
pixel_to_ray_array[:, :, 1] = ((pixel_to_ray_array[:, :, 1] * (raw_H / self.dpv_H))
- IntM_[1, 2]) / IntM_[1, 1]
pixel_to_ray_array_2D = np.reshape(np.transpose(pixel_to_ray_array, axes=[2, 0, 1]), [3, -1]) # (3, H*W)
pixel_to_ray_array_2D = torch.from_numpy(pixel_to_ray_array_2D.astype(np.float32))
cam_intrinsics = {
'unit_ray_array_2D': pixel_to_ray_array_2D,
'intM': torch.from_numpy(IntM.astype(np.float32)),
}
return cam_intrinsics
def __getitem__(self, idx):
scene_name, seq_id, img_idx = self.filenames[idx].split(' ')
seq_id = int(seq_id)
img_idx = int(img_idx)
scene_dir = self.dataset_path + '/{}/seq-%02d/'.format(scene_name) % seq_id
# identify the neighbor views
img_idx_list = []
for i in self.window_idx_list:
if os.path.exists(scene_dir + '/frame-%06d.color.png' % (img_idx + i)):
img_idx_list.append(img_idx + i)
else:
img_idx_list.append(img_idx - i - np.sign(i) * int(self.frame_interval * 0.5))
# data array
data_array = []
for i in range(self.n_views + 1):
cur_idx = img_idx_list[i]
img_path = scene_dir + '/frame-%06d.color.png' % cur_idx
dmap_path = scene_dir + '/frame-%06d.depth.png' % cur_idx
pose_path = scene_dir + '/frame-%06d.pose.txt' % cur_idx
# read img
img = Image.open(img_path).convert("RGB").resize(size=(self.img_W, self.img_H), resample=Image.BILINEAR)
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
img = torch.from_numpy(img).permute(2, 0, 1) # (3, H, W)
img = self.normalize(img)
# read dmap (only for the ref img)
if i == self.img_idx_center:
gt_dmap = Image.open(dmap_path).resize(size=(self.img_W, self.img_H), resample=Image.NEAREST)
gt_dmap = np.array(gt_dmap)[:, :, np.newaxis]
gt_dmap[gt_dmap == 65535] = 0
gt_dmap = gt_dmap.astype(np.float32) / 1000.0
gt_dmap = torch.from_numpy(gt_dmap).permute(2, 0, 1) # (1, H, W)
else:
gt_dmap = 0.0
# read pose
extM = _read_ExtM_from_txt(pose_path)
data_dict = {
'img': img,
'gt_dmap': gt_dmap,
'extM': extM,
'scene_name': '%s_seq-%02d' % (scene_name, seq_id),
'img_idx': cur_idx
}
data_array.append(data_dict)
return data_array, self.cam_intrins
================================================
FILE: data/dataloader_7scenes_D.py
================================================
# dataloader for 7-Scenes / when testing D-Net
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import torchvision.transforms.functional as TF
class SevenScenesLoader(object):
def __init__(self, args, mode):
self.t_samples = SevenScenesLoadPreprocess(args, mode)
self.data = DataLoader(self.t_samples, 1, shuffle=False, num_workers=1)
class SevenScenesLoadPreprocess(Dataset):
def __init__(self, args, mode):
self.args = args
# Test set by Long et al. (CVPR 21)
with open("./data_split/sevenscenes_long_test.txt", 'r') as f:
self.filenames = f.readlines()
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.dataset_path = args.dataset_path
# img resolution
self.img_H = args.input_height # 480
self.img_W = args.input_width # 640
def __len__(self):
return len(self.filenames)
def __getitem__(self, idx):
scene_name, seq_id, img_idx = self.filenames[idx].split(' ')
seq_id = int(seq_id)
img_idx = int(img_idx)
scene_dir = self.dataset_path + '/{}/seq-%02d/'.format(scene_name) % seq_id
# img path and depth path
img_path = scene_dir + '/frame-%06d.color.png' % img_idx
depth_path = scene_dir + '/frame-%06d.depth.png' % img_idx
# read img and depth
img = Image.open(img_path).convert("RGB").resize(size=(self.img_W, self.img_H), resample=Image.BILINEAR)
depth_gt = Image.open(depth_path).resize(size=(self.img_W, self.img_H), resample=Image.NEAREST)
# img to tensor
img = np.array(img).astype(np.float32) / 255.0
img = torch.from_numpy(img).permute(2, 0, 1) # (3, H, W)
img = self.normalize(img)
depth_gt = np.array(depth_gt)[:, :, np.newaxis]
depth_gt[depth_gt == 65535] = 0.0 # filter out invalid depth
depth_gt = depth_gt.astype(np.float32) / 1000.0 # from mm to m
depth_gt = torch.from_numpy(depth_gt).permute(2, 0, 1) # (1, H, W)
sample = {'img': img,
'depth': depth_gt,
'scene_name': '%s_seq-%02d' % (scene_name, seq_id),
'img_idx': str(img_idx)}
return sample
================================================
FILE: data/dataloader_kitti.py
================================================
# dataloader for KITTI / when training & testing F-Net and MaGNet
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import pykitti
class KittiLoader(object):
def __init__(self, args, mode):
self.t_samples = KittiLoadPreprocess(args, mode)
if mode == 'eigen_train' or mode == 'official_train':
if args.distributed:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(self.t_samples)
else:
self.train_sampler = None
self.data = DataLoader(self.t_samples, args.batch_size,
shuffle=(self.train_sampler is None),
num_workers=args.num_threads,
pin_memory=True,
drop_last=True,
sampler=self.train_sampler)
else:
self.data = DataLoader(self.t_samples, 1, shuffle=False, num_workers=1)
class KittiLoadPreprocess(Dataset):
def __init__(self, args, mode):
self.args = args
if mode == 'eigen_train':
with open("./data_split/kitti_eigen_train.txt", 'r') as f:
self.filenames = f.readlines()
elif mode == 'eigen_test':
with open("./data_split/kitti_eigen_test.txt", 'r') as f:
self.filenames = f.readlines()
elif mode == 'official_train':
with open("./data_split/kitti_official_train.txt", 'r') as f:
self.filenames = f.readlines()
elif mode == 'official_test':
with open("./data_split/kitti_official_test.txt", 'r') as f:
self.filenames = f.readlines()
else:
raise Exception('mode not recognized')
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.dataset_path = args.dataset_path
# local window
self.window_radius = args.MAGNET_window_radius
self.n_views = args.MAGNET_num_source_views
self.frame_interval = self.window_radius // (self.n_views // 2)
self.img_idx_center = self.n_views // 2
# window_idx_list
self.window_idx_list = list(range(-self.n_views // 2, (self.n_views // 2) + 1))
self.window_idx_list = [i * self.frame_interval for i in self.window_idx_list]
# image resolution
self.img_H = args.input_height # 352
self.img_W = args.input_width # 1216
self.dpv_H = args.dpv_height # 88
self.dpv_W = args.dpv_width # 304
# ray array
self.ray_array = self.get_ray_array()
def __len__(self):
return len(self.filenames)
# ray array used to back-project depth-map into camera-centered coordinates
def get_ray_array(self):
ray_array = np.ones((self.dpv_H, self.dpv_W, 3))
x_range = np.arange(self.dpv_W)
y_range = np.arange(self.dpv_H)
x_range = np.concatenate([x_range.reshape(1, self.dpv_W)] * self.dpv_H, axis=0)
y_range = np.concatenate([y_range.reshape(self.dpv_H, 1)] * self.dpv_W, axis=1)
ray_array[:, :, 0] = x_range + 0.5
ray_array[:, :, 1] = y_range + 0.5
return ray_array
# get camera intrinscs
def get_cam_intrinsics(self, p_data):
raw_img_size = p_data.get_cam2(0).size
raw_W = int(raw_img_size[0])
raw_H = int(raw_img_size[1])
top_margin = int(raw_H - 352)
left_margin = int((raw_W - 1216) / 2)
# original intrinsic matrix (4X4)
IntM_ = p_data.calib.K_cam2
# updated intrinsic matrix
IntM = np.zeros((3, 3))
IntM[2, 2] = 1.
IntM[0, 0] = IntM_[0, 0] * (self.dpv_W / float(self.img_W))
IntM[1, 1] = IntM_[1, 1] * (self.dpv_H / float(self.img_H))
IntM[0, 2] = (IntM_[0, 2] - left_margin) * (self.dpv_W / float(self.img_W))
IntM[1, 2] = (IntM_[1, 2] - top_margin) * (self.dpv_H / float(self.img_H))
# pixel to ray array
pixel_to_ray_array = np.copy(self.ray_array)
pixel_to_ray_array[:, :, 0] = ((pixel_to_ray_array[:, :, 0] * (self.img_W / float(self.dpv_W)))
- IntM_[0, 2] + left_margin) / IntM_[0, 0]
pixel_to_ray_array[:, :, 1] = ((pixel_to_ray_array[:, :, 1] * (self.img_H / float(self.dpv_H)))
- IntM_[1, 2] + top_margin) / IntM_[1, 1]
pixel_to_ray_array_2D = np.reshape(np.transpose(pixel_to_ray_array, axes=[2, 0, 1]), [3, -1])
pixel_to_ray_array_2D = torch.from_numpy(pixel_to_ray_array_2D.astype(np.float32))
cam_intrinsics = {
'unit_ray_array_2D': pixel_to_ray_array_2D,
'intM': torch.from_numpy(IntM.astype(np.float32))
}
return cam_intrinsics
def __getitem__(self, idx):
date, drive, mode, img_idx = self.filenames[idx].split(' ')
img_idx = int(img_idx)
scene_name = '%s_drive_%s_sync' % (date, drive)
# identify the neighbor views
img_idx_list = [img_idx + i for i in self.window_idx_list]
p_data = pykitti.raw(self.dataset_path + '/rawdata', date, drive, frames=img_idx_list)
# cam intrinsics
cam_intrins = self.get_cam_intrinsics(p_data)
# color augmentation
color_aug = False
if 'train' in self.mode and self.args.data_augmentation_color:
if random.random() > 0.5:
color_aug = True
aug_gamma = random.uniform(0.9, 1.1)
aug_brightness = random.uniform(0.9, 1.1)
aug_colors = np.random.uniform(0.9, 1.1, size=3)
# data array
data_array = []
for i in range(self.n_views + 1):
cur_idx = img_idx_list[i]
# read img
img_name = '%010d.png' % cur_idx
img_path = self.dataset_path + '/rawdata/{}/{}/image_02/data/{}'.format(date, scene_name, img_name)
img = Image.open(img_path).convert("RGB")
# kitti benchmark crop
height = img.height
width = img.width
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
img = img.crop((left_margin, top_margin, left_margin + 1216, top_margin + 352))
# to tensor
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
if color_aug:
img = self.augment_image(img, aug_gamma, aug_brightness, aug_colors)
img = torch.from_numpy(img).permute(2, 0, 1) # (3, H, W)
img = self.normalize(img)
# read dmap (only for the ref img)
if i == self.img_idx_center:
dmap_path = self.dataset_path + '/{}/{}/proj_depth/groundtruth/image_02/{}'.format(mode, scene_name,
img_name)
gt_dmap = Image.open(dmap_path).crop((left_margin, top_margin, left_margin + 1216, top_margin + 352))
gt_dmap = np.array(gt_dmap)[:, :, np.newaxis].astype(np.float32) # (H, W, 1)
gt_dmap = gt_dmap / 256.0
gt_dmap = torch.from_numpy(gt_dmap).permute(2, 0, 1) # (1, H, W)
else:
gt_dmap = 0.0
# read extM
pose = p_data.oxts[i].T_w_imu
M_imu2cam = p_data.calib.T_cam2_imu
extM = np.matmul(M_imu2cam, np.linalg.inv(pose))
data_dict = {
'img': img,
'gt_dmap': gt_dmap,
'extM': extM,
'scene_name': scene_name,
'img_idx': str(img_idx),
}
data_array.append(data_dict)
return data_array, cam_intrins
def augment_image(self, image, gamma, brightness, colors):
# gamma augmentation
image_aug = image ** gamma
# brightness augmentation
image_aug = image_aug * brightness
# color augmentation
white = np.ones((image.shape[0], image.shape[1]))
color_image = np.stack([white * colors[i] for i in range(3)], axis=2)
image_aug *= color_image
image_aug = np.clip(image_aug, 0, 1)
return image_aug
================================================
FILE: data/dataloader_kitti_D.py
================================================
# dataloader for KITTI / when training & testing D-Net
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import torchvision.transforms.functional as TF
class KittiLoader(object):
def __init__(self, args, mode):
self.t_samples = KittiLoadPreprocess(args, mode)
if mode == 'eigen_train' or mode == 'official_train':
if args.distributed:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(self.t_samples)
else:
self.train_sampler = None
self.data = DataLoader(self.t_samples, args.batch_size,
shuffle=(self.train_sampler is None),
num_workers=args.num_threads,
pin_memory=True,
drop_last=True,
sampler=self.train_sampler)
else:
self.data = DataLoader(self.t_samples, 1, shuffle=False, num_workers=1)
class KittiLoadPreprocess(Dataset):
def __init__(self, args, mode):
self.args = args
if mode == 'eigen_train':
with open("./data_split/kitti_eigen_train.txt", 'r') as f:
self.filenames = f.readlines()
elif mode == 'eigen_test':
with open("./data_split/kitti_eigen_test.txt", 'r') as f:
self.filenames = f.readlines()
elif mode == 'official_train':
with open("./data_split/kitti_official_train.txt", 'r') as f:
self.filenames = f.readlines()
elif mode == 'official_test':
with open("./data_split/kitti_official_test.txt", 'r') as f:
self.filenames = f.readlines()
else:
raise Exception('mode not recognized')
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.data_augmentation_rotate_degree = 1.0
self.dataset_path = args.dataset_path
# image resolution
self.img_H = args.input_height # not-used!
self.img_W = args.input_width # not-used!
self.crop_H = args.crop_height # 352
self.crop_W = args.crop_width # 704
def __len__(self):
return len(self.filenames)
def __getitem__(self, idx):
date, drive, mode, img_idx = self.filenames[idx].split(' ')
img_name = '%010d.png' % int(img_idx)
scene_name = '%s_drive_%s_sync' % (date, drive)
img_path = self.dataset_path + '/rawdata/{}/{}/image_02/data/{}'.format(date, scene_name, img_name)
depth_path = self.dataset_path + '/{}/{}/proj_depth/groundtruth/image_02/{}'.format(mode, scene_name, img_name)
# read img and depth
img = Image.open(img_path).convert("RGB")
depth_gt = Image.open(depth_path)
# kitti benchmark crop -> (352 X 1216)
if self.args.do_kb_crop is True:
height = img.height
width = img.width
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
img = img.crop((left_margin, top_margin, left_margin + 1216, top_margin + 352))
depth_gt = depth_gt.crop((left_margin, top_margin, left_margin + 1216, top_margin + 352))
if self.mode == 'eigen_train' or mode == 'official_train':
# data augmentation - rotate
if self.args.data_augmentation_rotate:
random_angle = (random.random() - 0.5) * 2 * self.data_augmentation_rotate_degree
img = img.rotate(random_angle, resample=Image.BILINEAR)
depth_gt = depth_gt.rotate(random_angle, resample=Image.NEAREST)
# data augmentation - flip
if self.args.data_augmentation_flip:
if random.random() > 0.5:
img = TF.hflip(img)
depth_gt = TF.hflip(depth_gt)
# img and depth to array
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
depth_gt = np.array(depth_gt)[:, :, np.newaxis].astype(np.float32) # (H, W, 1)
depth_gt = depth_gt / 256.0
# data augmentation - random crop
if self.args.data_augmentation_crop:
img, depth_gt = self.random_crop(img, depth_gt, self.crop_H, self.crop_W)
# data augmentation - color
if self.args.data_augmentation_color:
if random.random() > 0.5:
img = self.augment_image(img)
else:
# img and depth to array
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
depth_gt = np.array(depth_gt)[:, :, np.newaxis].astype(np.float32) # (H, W, 1)
depth_gt = depth_gt / 256.0
# img and depth to tensor
img = torch.from_numpy(img).permute(2, 0, 1) # (3, H, W)
img = self.normalize(img)
depth_gt = torch.from_numpy(depth_gt).permute(2, 0, 1) # (1, H, W)
sample = {'img': img,
'depth': depth_gt,
'scene_name': scene_name,
'img_idx': str(img_idx)}
return sample
def random_crop(self, img, depth, height, width):
assert img.shape[0] >= height
assert img.shape[1] >= width
assert img.shape[0] == depth.shape[0]
assert img.shape[1] == depth.shape[1]
x = random.randint(0, img.shape[1] - width)
y = random.randint(0, img.shape[0] - height)
img = img[y:y + height, x:x + width, :]
depth = depth[y:y + height, x:x + width, :]
return img, depth
def augment_image(self, image):
# gamma augmentation
gamma = random.uniform(0.9, 1.1)
image_aug = image ** gamma
# brightness augmentation
brightness = random.uniform(0.9, 1.1)
image_aug = image_aug * brightness
# color augmentation
colors = np.random.uniform(0.9, 1.1, size=3)
white = np.ones((image.shape[0], image.shape[1]))
color_image = np.stack([white * colors[i] for i in range(3)], axis=2)
image_aug *= color_image
image_aug = np.clip(image_aug, 0, 1)
return image_aug
================================================
FILE: data/dataloader_scannet.py
================================================
# dataloader for ScanNet / when training & testing F-Net MaGNet
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import json
def _read_ExtM_from_txt(fpath_txt):
ExtM = np.eye(4)
with open(fpath_txt, 'r') as f:
content = f.readlines()
content = [x.strip() for x in content]
for ir, row in enumerate(ExtM):
row_content = content[ir].split()
row = np.asarray([float(x) for x in row_content])
ExtM[ir, :] = row
ExtM = np.linalg.inv(ExtM) #cam2world -> world2cam
return ExtM
def _read_IntM_from_txt(fpath_txt):
IntM = np.eye(4)
with open(fpath_txt, 'r') as f:
content = f.readlines()
content = [x.strip() for x in content]
for ir, row in enumerate(IntM):
row_content = content[ir].split()
row = np.asarray([float(x) for x in row_content])
IntM[ir, :] = row
return IntM
class ScannetLoader(object):
def __init__(self, args, mode):
self.t_samples = ScannetLoadPreprocess(args, mode)
if mode == 'train':
if args.distributed:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(self.t_samples)
else:
self.train_sampler = None
self.data = DataLoader(self.t_samples, args.batch_size,
shuffle=(self.train_sampler is None),
num_workers=args.num_threads,
pin_memory=True,
drop_last=True,
sampler=self.train_sampler)
else:
self.data = DataLoader(self.t_samples, 1, shuffle=False, num_workers=1)
class ScannetLoadPreprocess(Dataset):
def __init__(self, args, mode):
self.args = args
if mode == 'train':
with open("./data_split/scannet_train.txt", 'r') as f:
self.filenames = f.readlines()
self.scans = 'scans'
elif mode == 'rob_test':
with open("./data_split/scannet_rob_test.txt", 'r') as f:
self.filenames = f.readlines()
self.scans = 'scans_test'
elif mode == 'long_test':
with open("./data_split/scannet_long_test.txt", 'r') as f:
self.filenames = f.readlines()
self.scans = 'scans_test'
else:
raise Exception('mode not recognized')
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.dataset_path = args.dataset_path
# local window
self.window_radius = args.MAGNET_window_radius
self.n_views = args.MAGNET_num_source_views
self.frame_interval = self.window_radius // (self.n_views // 2)
self.img_idx_center = self.n_views // 2
# window_idx_list
self.window_idx_list = list(range(-self.n_views // 2, (self.n_views // 2) + 1))
self.window_idx_list = [i * self.frame_interval for i in self.window_idx_list]
# image resolution
self.img_H = args.input_height # 480
self.img_W = args.input_width # 640
self.dpv_H = args.dpv_height # 120
self.dpv_W = args.dpv_width # 160
# ray array
self.ray_array = self.get_ray_array()
# dict containing the raw width and height of each scene
with open('./data/scannet_raw_WH.json', 'r') as f:
self.raw_WH_dict = json.load(f)
def __len__(self):
return len(self.filenames)
# ray array used to back-project depth-map into camera-centered coordinates
def get_ray_array(self):
ray_array = np.ones((self.dpv_H, self.dpv_W, 3))
x_range = np.arange(self.dpv_W)
y_range = np.arange(self.dpv_H)
x_range = np.concatenate([x_range.reshape(1, self.dpv_W)] * self.dpv_H, axis=0)
y_range = np.concatenate([y_range.reshape(self.dpv_H, 1)] * self.dpv_W, axis=1)
ray_array[:, :, 0] = x_range + 0.5
ray_array[:, :, 1] = y_range + 0.5
return ray_array
# get camera intrinscs
def get_cam_intrinsics(self, scene_dir, scene_name):
intrins_path = scene_dir + '/intrinsic/intrinsic_color.txt'
raw_W, raw_H = self.raw_WH_dict[scene_name]
# original intrinsic matrix (4X4)
IntM_ = _read_IntM_from_txt(intrins_path)
# updated intrinsic matrix
IntM = np.zeros((3, 3))
IntM[2, 2] = 1.
IntM[0, 0] = IntM_[0, 0] * (self.dpv_W / raw_W)
IntM[1, 1] = IntM_[1, 1] * (self.dpv_H / raw_H)
IntM[0, 2] = IntM_[0, 2] * (self.dpv_W / raw_W)
IntM[1, 2] = IntM_[1, 2] * (self.dpv_H / raw_H)
# pixel to ray array
pixel_to_ray_array = np.copy(self.ray_array)
pixel_to_ray_array[:, :, 0] = ((pixel_to_ray_array[:, :, 0] * (raw_W / self.dpv_W))
- IntM_[0, 2]) / IntM_[0, 0]
pixel_to_ray_array[:, :, 1] = ((pixel_to_ray_array[:, :, 1] * (raw_H / self.dpv_H))
- IntM_[1, 2]) / IntM_[1, 1]
pixel_to_ray_array_2D = np.reshape(np.transpose(pixel_to_ray_array, axes=[2, 0, 1]), [3, -1]) # (3, H*W)
pixel_to_ray_array_2D = torch.from_numpy(pixel_to_ray_array_2D.astype(np.float32))
cam_intrinsics = {
'unit_ray_array_2D': pixel_to_ray_array_2D,
'intM': torch.from_numpy(IntM.astype(np.float32)),
}
return cam_intrinsics
def __getitem__(self, idx):
scene_name, img_idx = self.filenames[idx].split(' ')
img_idx = int(img_idx)
scene_dir = self.dataset_path + '/{}/{}/'.format(self.scans, scene_name)
# identify the neighbor views
img_idx_list = []
for i in self.window_idx_list:
if os.path.exists(scene_dir + '/color/{}.jpg'.format(img_idx + i)):
img_idx_list.append(img_idx + i)
else:
img_idx_list.append(img_idx - i - np.sign(i) * int(self.frame_interval * 0.5))
# cam intrinsics
cam_intrins = self.get_cam_intrinsics(scene_dir, scene_name)
# color augmentation
color_aug = False
if 'train' in self.mode and self.args.data_augmentation_color:
if random.random() > 0.5:
color_aug = True
aug_gamma = random.uniform(0.9, 1.1)
aug_brightness = random.uniform(0.75, 1.25)
aug_colors = np.random.uniform(0.9, 1.1, size=3)
# data array
data_array = []
for i in range(self.n_views + 1):
cur_idx = str(img_idx_list[i])
img_path = scene_dir + '/color/{}.jpg'.format(cur_idx)
dmap_path = scene_dir + '/depth/{}.png'.format(cur_idx)
pose_path = scene_dir + '/pose/{}.txt'.format(cur_idx)
# read img
img = Image.open(img_path).convert("RGB").resize(size=(self.img_W, self.img_H), resample=Image.BILINEAR)
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
if color_aug:
img = self.augment_image(img, aug_gamma, aug_brightness, aug_colors)
img = torch.from_numpy(img).permute(2, 0, 1) # (3, H, W)
img = self.normalize(img)
# read dmap (only for the ref img)
if i == self.img_idx_center:
gt_dmap = Image.open(dmap_path).resize(size=(self.img_W, self.img_H), resample=Image.NEAREST)
gt_dmap = np.array(gt_dmap)[:, :, np.newaxis].astype(np.float32) # (H, W, 1)
gt_dmap = gt_dmap / 1000.0
gt_dmap = torch.from_numpy(gt_dmap).permute(2, 0, 1) # (1, H, W)
else:
gt_dmap = 0.0
# read pose
extM = _read_ExtM_from_txt(pose_path)
data_dict = {
'img': img,
'gt_dmap': gt_dmap,
'extM': extM,
'scene_name': scene_name,
'img_idx': cur_idx
}
data_array.append(data_dict)
return data_array, cam_intrins
def augment_image(self, image, gamma, brightness, colors):
# gamma augmentation
image_aug = image ** gamma
# brightness augmentation
image_aug = image_aug * brightness
# color augmentation
white = np.ones((image.shape[0], image.shape[1]))
color_image = np.stack([white * colors[i] for i in range(3)], axis=2)
image_aug *= color_image
image_aug = np.clip(image_aug, 0, 1)
return image_aug
================================================
FILE: data/dataloader_scannet_D.py
================================================
# dataloader for ScanNet / when training & testing DNet
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import torchvision.transforms.functional as TF
class ScannetLoader(object):
def __init__(self, args, mode):
self.t_samples = ScannetLoadPreprocess(args, mode)
if mode == 'train':
if args.distributed:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(self.t_samples)
else:
self.train_sampler = None
self.data = DataLoader(self.t_samples, args.batch_size,
shuffle=(self.train_sampler is None),
num_workers=args.num_threads,
pin_memory=True,
drop_last=True,
sampler=self.train_sampler)
else:
self.data = DataLoader(self.t_samples, 1, shuffle=False, num_workers=1)
class ScannetLoadPreprocess(Dataset):
def __init__(self, args, mode):
self.args = args
if mode == 'train':
with open("./data_split/scannet_train.txt", 'r') as f:
self.filenames = f.readlines()
self.scans = 'scans'
# Rob test set by DORN (CVPR 18)
elif mode == 'rob_test':
with open("./data_split/scannet_rob_test.txt", 'r') as f:
self.filenames = f.readlines()
self.scans = 'scans_test'
# Test set by Long et al. (CVPR 21)
elif mode == 'long_test':
with open("./data_split/scannet_long_test.txt", 'r') as f:
self.filenames = f.readlines()
self.scans = 'scans_test'
else:
raise Exception('mode not recognized')
self.mode = mode
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.data_augmentation_rotate_degree = 2.5
self.dataset_path = args.dataset_path
# img resolution
self.img_H = args.input_height # 480
self.img_W = args.input_width # 640
self.crop_H = args.crop_height # 416
self.crop_W = args.crop_width # 544
def __len__(self):
return len(self.filenames)
def __getitem__(self, idx):
scene_name, img_idx = self.filenames[idx].split(' ')
img_idx = int(img_idx)
scene_dir = self.dataset_path + '/{}/{}/'.format(self.scans, scene_name)
img_path = scene_dir + '/color/{}.jpg'.format(img_idx)
depth_path = scene_dir + '/depth/{}.png'.format(img_idx)
# read img and depth
img = Image.open(img_path).convert("RGB").resize(size=(self.img_W, self.img_H), resample=Image.BILINEAR)
depth_gt = Image.open(depth_path).resize(size=(self.img_W, self.img_H), resample=Image.NEAREST)
if self.mode == 'train':
# data augmentation - rotate
if self.args.data_augmentation_rotate:
random_angle = (random.random() - 0.5) * 2 * self.data_augmentation_rotate_degree
img = img.rotate(random_angle, resample=Image.BILINEAR)
depth_gt = depth_gt.rotate(random_angle, resample=Image.NEAREST)
# data augmentation - flip
if self.args.data_augmentation_flip:
if random.random() > 0.5:
img = TF.hflip(img)
depth_gt = TF.hflip(depth_gt)
# img and depth to array
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
depth_gt = np.array(depth_gt)[:, :, np.newaxis].astype(np.float32) # (H, W, 1)
depth_gt = depth_gt / 1000.0
# data augmentation - random crop
if self.args.data_augmentation_crop:
img, depth_gt = self.random_crop(img, depth_gt, self.crop_H, self.crop_W)
# data augmentation - color
if self.args.data_augmentation_color:
if random.random() > 0.5:
img = self.augment_image(img)
else:
# img and depth to array
img = np.array(img).astype(np.float32) / 255.0 # (H, W, 3)
depth_gt = np.array(depth_gt)[:, :, np.newaxis].astype(np.float32) # (H, W, 1)
depth_gt = depth_gt / 1000.0
# img and depth to tensor
img = torch.from_numpy(img).permute(2, 0, 1) # (3, H, W)
img = self.normalize(img)
depth_gt = torch.from_numpy(depth_gt).permute(2, 0, 1) # (1, H, W)
sample = {'img': img,
'depth': depth_gt,
'scene_name': scene_name,
'img_idx': str(img_idx)}
return sample
def random_crop(self, img, depth, height, width):
assert img.shape[0] >= height
assert img.shape[1] >= width
assert img.shape[0] == depth.shape[0]
assert img.shape[1] == depth.shape[1]
x = random.randint(0, img.shape[1] - width)
y = random.randint(0, img.shape[0] - height)
img = img[y:y + height, x:x + width, :]
depth = depth[y:y + height, x:x + width, :]
return img, depth
def augment_image(self, image):
# gamma augmentation
gamma = random.uniform(0.9, 1.1)
image_aug = image ** gamma
# brightness augmentation
brightness = random.uniform(0.75, 1.25)
image_aug = image_aug * brightness
# color augmentation
colors = np.random.uniform(0.9, 1.1, size=3)
white = np.ones((image.shape[0], image.shape[1]))
color_image = np.stack([white * colors[i] for i in range(3)], axis=2)
image_aug *= color_image
image_aug = np.clip(image_aug, 0, 1)
return image_aug
================================================
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FILE: data_split/kitti_eigen_test.txt
================================================
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================================================
FILE: data_split/kitti_eigen_train.txt
================================================
2011_09_26 0057 train 116
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2011_09_29 0026 val 13
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gitextract_ko94q_er/
├── LICENSE
├── README.md
├── ckpts/
│ └── download.py
├── data/
│ ├── dataloader_7scenes.py
│ ├── dataloader_7scenes_D.py
│ ├── dataloader_kitti.py
│ ├── dataloader_kitti_D.py
│ ├── dataloader_scannet.py
│ ├── dataloader_scannet_D.py
│ └── scannet_raw_WH.json
├── data_split/
│ ├── kitti_eigen_test.txt
│ ├── kitti_eigen_train.txt
│ ├── kitti_eigen_val.txt
│ ├── kitti_official_test.txt
│ ├── kitti_official_train.txt
│ ├── scannet_long_test.txt
│ ├── scannet_rob_test.txt
│ ├── scannet_train.txt
│ └── sevenscenes_long_test.txt
├── models/
│ ├── DNET.py
│ ├── FNET.py
│ ├── MAGNET.py
│ └── submodules/
│ ├── D_dense_depth.py
│ ├── F_psmnet.py
│ └── homography.py
├── requirements.txt
├── test_DNet.py
├── test_MaGNet.py
├── test_scripts/
│ ├── dnet/
│ │ ├── 7scenes.txt
│ │ ├── kitti_eigen.txt
│ │ ├── kitti_official.txt
│ │ └── scannet.txt
│ └── magnet/
│ ├── 7scenes.txt
│ ├── kitti_eigen.txt
│ ├── kitti_official.txt
│ └── scannet.txt
├── train_DNet.py
├── train_FNet.py
├── train_MaGNet.py
├── train_scripts/
│ ├── dnet/
│ │ ├── kitti_eigen.txt
│ │ ├── kitti_official.txt
│ │ └── scannet.txt
│ ├── fnet/
│ │ ├── kitti_eigen.txt
│ │ ├── kitti_official.txt
│ │ └── scannet.txt
│ └── magnet/
│ ├── kitti_eigen.txt
│ ├── kitti_official.txt
│ └── scannet.txt
└── utils/
├── losses.py
└── utils.py
SYMBOL INDEX (146 symbols across 20 files)
FILE: ckpts/download.py
function download_file_from_google_drive (line 6) | def download_file_from_google_drive(id, destination):
FILE: data/dataloader_7scenes.py
function _read_ExtM_from_txt (line 16) | def _read_ExtM_from_txt(fpath_txt):
class SevenScenesLoader (line 30) | class SevenScenesLoader(object):
method __init__ (line 31) | def __init__(self, args, mode):
class SevenScenesLoadPreprocess (line 36) | class SevenScenesLoadPreprocess(Dataset):
method __init__ (line 37) | def __init__(self, args, mode):
method __len__ (line 68) | def __len__(self):
method get_ray_array (line 72) | def get_ray_array(self):
method get_cam_intrinsics (line 83) | def get_cam_intrinsics(self):
method __getitem__ (line 118) | def __getitem__(self, idx):
FILE: data/dataloader_7scenes_D.py
class SevenScenesLoader (line 16) | class SevenScenesLoader(object):
method __init__ (line 17) | def __init__(self, args, mode):
class SevenScenesLoadPreprocess (line 22) | class SevenScenesLoadPreprocess(Dataset):
method __init__ (line 23) | def __init__(self, args, mode):
method __len__ (line 38) | def __len__(self):
method __getitem__ (line 41) | def __getitem__(self, idx):
FILE: data/dataloader_kitti.py
class KittiLoader (line 17) | class KittiLoader(object):
method __init__ (line 18) | def __init__(self, args, mode):
class KittiLoadPreprocess (line 38) | class KittiLoadPreprocess(Dataset):
method __init__ (line 39) | def __init__(self, args, mode):
method __len__ (line 79) | def __len__(self):
method get_ray_array (line 83) | def get_ray_array(self):
method get_cam_intrinsics (line 94) | def get_cam_intrinsics(self, p_data):
method __getitem__ (line 129) | def __getitem__(self, idx):
method augment_image (line 201) | def augment_image(self, image, gamma, brightness, colors):
FILE: data/dataloader_kitti_D.py
class KittiLoader (line 16) | class KittiLoader(object):
method __init__ (line 17) | def __init__(self, args, mode):
class KittiLoadPreprocess (line 37) | class KittiLoadPreprocess(Dataset):
method __init__ (line 38) | def __init__(self, args, mode):
method __len__ (line 66) | def __len__(self):
method __getitem__ (line 69) | def __getitem__(self, idx):
method random_crop (line 134) | def random_crop(self, img, depth, height, width):
method augment_image (line 145) | def augment_image(self, image):
FILE: data/dataloader_scannet.py
function _read_ExtM_from_txt (line 16) | def _read_ExtM_from_txt(fpath_txt):
function _read_IntM_from_txt (line 30) | def _read_IntM_from_txt(fpath_txt):
class ScannetLoader (line 43) | class ScannetLoader(object):
method __init__ (line 44) | def __init__(self, args, mode):
class ScannetLoadPreprocess (line 64) | class ScannetLoadPreprocess(Dataset):
method __init__ (line 65) | def __init__(self, args, mode):
method __len__ (line 109) | def __len__(self):
method get_ray_array (line 113) | def get_ray_array(self):
method get_cam_intrinsics (line 124) | def get_cam_intrinsics(self, scene_dir, scene_name):
method __getitem__ (line 155) | def __getitem__(self, idx):
method augment_image (line 219) | def augment_image(self, image, gamma, brightness, colors):
FILE: data/dataloader_scannet_D.py
class ScannetLoader (line 16) | class ScannetLoader(object):
method __init__ (line 17) | def __init__(self, args, mode):
class ScannetLoadPreprocess (line 37) | class ScannetLoadPreprocess(Dataset):
method __init__ (line 38) | def __init__(self, args, mode):
method __len__ (line 70) | def __len__(self):
method __getitem__ (line 73) | def __getitem__(self, idx):
method random_crop (line 129) | def random_crop(self, img, depth, height, width):
method augment_image (line 140) | def augment_image(self, image):
FILE: models/DNET.py
class DNET (line 7) | class DNET(nn.Module):
method __init__ (line 8) | def __init__(self, args, dnet=True):
method forward (line 50) | def forward(self, img, **kwargs):
method activation_none (line 53) | def activation_none(self, out):
method activation_G (line 56) | def activation_G(self, out):
method activation_G_magnet (line 62) | def activation_G_magnet(self, outs):
FILE: models/FNET.py
class FNET (line 7) | class FNET(nn.Module):
method __init__ (line 8) | def __init__(self, args):
method forward (line 19) | def forward(self, img):
FILE: models/MAGNET.py
function upsample_depth_via_mask (line 15) | def upsample_depth_via_mask(depth, up_mask, k):
function load_checkpoint (line 31) | def load_checkpoint(fpath, model):
class GNET (line 47) | class GNET(nn.Module):
method __init__ (line 48) | def __init__(self, ch_in, ch_out=2):
method forward (line 58) | def forward(self, cost_volume, ref_gmm):
class MAGNET (line 73) | class MAGNET(nn.Module):
method __init__ (line 74) | def __init__(self, args):
method depth_sampling (line 120) | def depth_sampling(self):
method forward (line 130) | def forward(self, ref_img, nghbr_imgs, nghbr_poses, is_valid, cam_intr...
class MAGNET_F (line 179) | class MAGNET_F(nn.Module):
method __init__ (line 180) | def __init__(self, args):
method forward (line 184) | def forward(self, ref_img, nghbr_imgs, nghbr_poses, is_valid, cam_intr...
FILE: models/submodules/D_dense_depth.py
class Encoder (line 7) | class Encoder(nn.Module):
method __init__ (line 8) | def __init__(self):
method forward (line 17) | def forward(self, x):
class UpSampleBN (line 29) | class UpSampleBN(nn.Module):
method __init__ (line 30) | def __init__(self, skip_input, output_features):
method forward (line 39) | def forward(self, x, concat_with):
class UpSampleGN (line 46) | class UpSampleGN(nn.Module):
method __init__ (line 47) | def __init__(self, skip_input, output_features):
method forward (line 56) | def forward(self, x, concat_with):
class Conv2d (line 63) | class Conv2d(nn.Conv2d):
method __init__ (line 64) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
method forward (line 69) | def forward(self, x):
function upsample_depth_via_bilinear (line 81) | def upsample_depth_via_bilinear(depth, up_mask, downsample_ratio):
function upsample_depth_via_mask (line 86) | def upsample_depth_via_mask(depth, up_mask, downsample_ratio):
class Decoder (line 104) | class Decoder(nn.Module):
method __init__ (line 105) | def __init__(self, num_classes, downsample_ratio, learned_upsampling, ...
method forward (line 166) | def forward(self, features):
class DenseDepth (line 199) | class DenseDepth(nn.Module):
method __init__ (line 200) | def __init__(self, n_bins, downsample_ratio, learned_upsampling, BN=Tr...
method forward (line 205) | def forward(self, x):
method get_1x_lr_params (line 208) | def get_1x_lr_params(self): # lr/10 learning rate
method get_10x_lr_params (line 211) | def get_10x_lr_params(self): # lr learning rate
FILE: models/submodules/F_psmnet.py
function convbn (line 10) | def convbn(in_planes, out_planes, kernel_size, stride, pad, dilation):
class BasicBlock (line 18) | class BasicBlock(nn.Module):
method __init__ (line 20) | def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
method forward (line 28) | def forward(self, x):
class PSMNet (line 37) | class PSMNet(nn.Module):
method __init__ (line 38) | def __init__(self, feature_dim=32):
method _make_layer (line 87) | def _make_layer(self, block, planes, blocks, stride, pad, dilation):
method forward (line 103) | def forward(self, x):
FILE: models/submodules/homography.py
function est_costvolume_F (line 10) | def est_costvolume_F(d_center, ref_feat, nghbr_feat, R, t, is_valid, cam...
function _compute_cost_F (line 50) | def _compute_cost_F(ref_feat_, nghbr_feat_, d_center, term1_pix, term2_p...
function est_costvolume_CW (line 79) | def est_costvolume_CW(d_volume, ref_feat, nghbr_feat, ref_gmms, nghbr_gmms,
function _compute_cost_CW (line 124) | def _compute_cost_CW(ref_feat_, nghbr_feat_, nghbr_mu_, nghbr_sigma_, d_...
FILE: test_DNet.py
function validate (line 22) | def validate(model, args, test_loader, device='cpu', vis_dir=None):
FILE: test_MaGNet.py
function validate (line 27) | def validate(model, args, test_loader, device, vis_dir=None):
FILE: train_DNet.py
function train (line 19) | def train(model, args, device):
function validate (line 130) | def validate(model, args, test_loader, device='cpu'):
function main_worker (line 180) | def main_worker(gpu, ngpus_per_node, args):
FILE: train_FNet.py
function train (line 19) | def train(model, args, device):
function validate (line 148) | def validate(model, args, test_loader, device, d_center):
function main_worker (line 199) | def main_worker(gpu, ngpus_per_node, args):
FILE: train_MaGNet.py
function train (line 19) | def train(model, args, device):
function validate (line 132) | def validate(model, args, test_loader, device='cpu'):
function main_worker (line 186) | def main_worker(gpu, ngpus_per_node, args):
FILE: utils/losses.py
class DnetLoss (line 8) | class DnetLoss(nn.Module):
method __init__ (line 9) | def __init__(self, args):
method forward (line 13) | def forward(self, pred, gt_depth, gt_depth_mask):
class MagnetLoss (line 28) | class MagnetLoss(nn.Module):
method __init__ (line 29) | def __init__(self, args):
method forward (line 34) | def forward(self, pred_list, gt_depth, gt_depth_mask):
FILE: utils/utils.py
function convert_arg_line_to_args (line 18) | def convert_arg_line_to_args(arg_line):
function save_args (line 25) | def save_args(args, filename):
function write_to_log (line 31) | def write_to_log(txt_filename, msg):
function makedir (line 36) | def makedir(dirpath):
function make_dir_from_list (line 41) | def make_dir_from_list(dirpath_list):
function load_checkpoint (line 46) | def load_checkpoint(fpath, model):
function split_data_array (line 64) | def split_data_array(data_array):
function data_preprocess (line 72) | def data_preprocess(data_array, cur_batch_size):
function compute_depth_errors (line 106) | def compute_depth_errors(gt, pred, var=None):
class RunningAverage (line 147) | class RunningAverage:
method __init__ (line 148) | def __init__(self):
method append (line 152) | def append(self, value):
method get_value (line 156) | def get_value(self):
class RunningAverageDict (line 160) | class RunningAverageDict:
method __init__ (line 161) | def __init__(self):
method update (line 164) | def update(self, new_dict):
method get_value (line 173) | def get_value(self):
function log_metrics (line 177) | def log_metrics(txt_path, metrics, first_line):
function unnormalize (line 206) | def unnormalize(img_in):
function visualize_D (line 216) | def visualize_D(args, img, gt_dmap, gt_dmap_mask, out, total_iter):
function visualize_F (line 257) | def visualize_F(args, img, gt_dmap, gt_dmap_mask, pred_dmap, total_iter):
function visualize_MaG (line 294) | def visualize_MaG(args, img, gt_dmap, gt_dmap_mask, pred_list, total_iter):
Condensed preview — 50 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (9,300K chars).
[
{
"path": "LICENSE",
"chars": 1069,
"preview": "MIT License\n\nCopyright (c) 2021 Gwangbin Bae\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "README.md",
"chars": 6201,
"preview": "\n# Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry\n\nOfficial implementation"
},
{
"path": "ckpts/download.py",
"chars": 2210,
"preview": "# Download network weights\n# Source: https://stackoverflow.com/a/39225039\nimport requests\n\n\ndef download_file_from_googl"
},
{
"path": "data/dataloader_7scenes.py",
"chars": 6298,
"preview": "# dataloader for 7-Scenes / when testing F-Net and MaGNet\nimport os\nimport random\nimport glob\n\nimport numpy as np\nimport"
},
{
"path": "data/dataloader_7scenes_D.py",
"chars": 2482,
"preview": "# dataloader for 7-Scenes / when testing D-Net\nimport os\nimport random\nimport glob\n\nimport numpy as np\nimport torch\nimpo"
},
{
"path": "data/dataloader_kitti.py",
"chars": 8475,
"preview": "# dataloader for KITTI / when training & testing F-Net and MaGNet\nimport os\nimport random\nimport glob\n\nimport numpy as n"
},
{
"path": "data/dataloader_kitti_D.py",
"chars": 6429,
"preview": "# dataloader for KITTI / when training & testing D-Net\nimport os\nimport random\nimport glob\n\nimport numpy as np\nimport to"
},
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"path": "data/dataloader_scannet.py",
"chars": 8780,
"preview": "# dataloader for ScanNet / when training & testing F-Net MaGNet\nimport os\nimport random\nimport glob\n\nimport numpy as np\n"
},
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"path": "data/dataloader_scannet_D.py",
"chars": 5986,
"preview": "# dataloader for ScanNet / when training & testing DNet\nimport os\nimport random\nimport glob\n\nimport numpy as np\nimport t"
},
{
"path": "data/scannet_raw_WH.json",
"chars": 95127,
"preview": "{\n \"scene0000_00\": [\n 1296.0,\n 968.0\n ],\n \"scene0000_01\": [\n 1296.0,\n 968.0\n ],\n"
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"path": "data_split/kitti_eigen_train.txt",
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"chars": 22826,
"preview": "2011_09_30 0033 train 200\n2011_09_26 0039 train 149\n2011_10_03 0034 train 1212\n2011_09_26 0011 train 181\n2011_09_30 0020"
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"path": "data_split/kitti_official_test.txt",
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"chars": 16763,
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"path": "data_split/scannet_train.txt",
"chars": 6359892,
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"path": "models/FNET.py",
"chars": 491,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n# F-Net\nclass FNET(nn.Module):\n def __init__(sel"
},
{
"path": "models/MAGNET.py",
"chars": 7972,
"preview": "import os\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.data\nimport numpy as np\n"
},
{
"path": "models/submodules/D_dense_depth.py",
"chars": 8952,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n# EfficientNet B5\nclass Encoder(nn.Module):\n def"
},
{
"path": "models/submodules/F_psmnet.py",
"chars": 5687,
"preview": "# PSM-Net\nfrom __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nimport torch."
},
{
"path": "models/submodules/homography.py",
"chars": 8569,
"preview": "# Differentiable Homography\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport math\nfrom torch.distr"
},
{
"path": "requirements.txt",
"chars": 81,
"preview": "torch==1.6.0\ntorchvision==0.7.0\nPillow\nnumpy\nmatplotlib\nargparse\ntqdm\nglob2\nscipy"
},
{
"path": "test_DNet.py",
"chars": 7434,
"preview": "import argparse\nimport os\nimport sys\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nimport torch.distributed as "
},
{
"path": "test_MaGNet.py",
"chars": 8538,
"preview": "import argparse\nimport os\nimport sys\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nimport torch.distributed as "
},
{
"path": "test_scripts/dnet/7scenes.txt",
"chars": 349,
"preview": "--exp_name 7scenes\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n--DNET_"
},
{
"path": "test_scripts/dnet/kitti_eigen.txt",
"chars": 423,
"preview": "--exp_name kitti_eigen\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n--D"
},
{
"path": "test_scripts/dnet/kitti_official.txt",
"chars": 413,
"preview": "--exp_name kitti_official\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n"
},
{
"path": "test_scripts/dnet/scannet.txt",
"chars": 380,
"preview": "--exp_name scannet\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n--DNET_"
},
{
"path": "test_scripts/magnet/7scenes.txt",
"chars": 522,
"preview": "--exp_name 7scenes\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_scannet.pt\n--FNET_ckpt ./ckpts/FN"
},
{
"path": "test_scripts/magnet/kitti_eigen.txt",
"chars": 548,
"preview": "--exp_name kitti_eigen\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_kitti_eigen.pt\n--FNET_ckpt ./"
},
{
"path": "test_scripts/magnet/kitti_official.txt",
"chars": 550,
"preview": "--exp_name kitti_official\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_kitti_official.pt\n--FNET_c"
},
{
"path": "test_scripts/magnet/scannet.txt",
"chars": 518,
"preview": "--exp_name scannet\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_scannet.pt\n--FNET_ckpt ./ckpts/FN"
},
{
"path": "train_DNet.py",
"chars": 13755,
"preview": "import argparse\nimport os\nimport sys\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nimport torch.distributed as "
},
{
"path": "train_FNet.py",
"chars": 14752,
"preview": "import argparse\nimport os\nimport sys\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nimport torch.distributed as "
},
{
"path": "train_MaGNet.py",
"chars": 14918,
"preview": "import argparse\nimport os\nimport sys\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nimport torch.distributed as "
},
{
"path": "train_scripts/dnet/kitti_eigen.txt",
"chars": 458,
"preview": "--exp_name kitti_eigen\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n--D"
},
{
"path": "train_scripts/dnet/kitti_official.txt",
"chars": 445,
"preview": "--exp_name kitti_official\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n"
},
{
"path": "train_scripts/dnet/scannet.txt",
"chars": 420,
"preview": "--exp_name scannet\n--exp_dir ./exp/DNET/\n--visible_gpus 01\n\n--output_dim 2\n--output_type G\n--downsample_ratio 4\n\n--DNET_"
},
{
"path": "train_scripts/fnet/kitti_eigen.txt",
"chars": 380,
"preview": "--exp_name kitti_eigen\n--exp_dir ./exp/FNET/\n--visible_gpus 01\n\n--MAGNET_window_radius 2\n--MAGNET_num_source_views 2\n\n--"
},
{
"path": "train_scripts/fnet/kitti_official.txt",
"chars": 386,
"preview": "--exp_name kitti_official\n--exp_dir ./exp/FNET/\n--visible_gpus 01\n\n--MAGNET_window_radius 2\n--MAGNET_num_source_views 2\n"
},
{
"path": "train_scripts/fnet/scannet.txt",
"chars": 378,
"preview": "--exp_name scannet\n--exp_dir ./exp/FNET/\n--visible_gpus 01\n\n--MAGNET_window_radius 20\n--MAGNET_num_source_views 4\n\n--los"
},
{
"path": "train_scripts/magnet/kitti_eigen.txt",
"chars": 626,
"preview": "--exp_name kitti_eigen\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_kitti_eigen.pt\n--FNET_ckpt ./"
},
{
"path": "train_scripts/magnet/kitti_official.txt",
"chars": 638,
"preview": "--exp_name kitti_official\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_kitti_official.pt\n--FNET_c"
},
{
"path": "train_scripts/magnet/scannet.txt",
"chars": 603,
"preview": "--exp_name scannet\n--exp_dir ./exp/MAGNET/\n--visible_gpus 01\n\n--DNET_ckpt ./ckpts/DNET_scannet.pt\n--FNET_ckpt ./ckpts/FN"
},
{
"path": "utils/losses.py",
"chars": 1696,
"preview": "# loss functions\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n# Loss for training D-Net\nclass Dn"
},
{
"path": "utils/utils.py",
"chars": 10941,
"preview": "# utils\nimport os\nimport numpy as np\nfrom PIL import Image\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyp"
}
]
About this extraction
This page contains the full source code of the baegwangbin/MaGNet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 50 files (8.4 MB), approximately 2.2M tokens, and a symbol index with 146 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.