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metric_depth/README.md
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metric_depth/README.md
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# Depth Anything V2 for Metric Depth Estimation
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We here provide a simple codebase to fine-tune our Depth Anything V2 pre-trained encoder for metric depth estimation. Built on our powerful encoder, we use a simple DPT head to regress the depth. We fine-tune our pre-trained encoder on synthetic Hypersim / Virtual KITTI datasets for indoor / outdoor metric depth estimation, respectively.
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## Usage
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### Inference
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Please first download our pre-trained metric depth models and put them under the `checkpoints` directory:
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- [Indoor model from Hypersim](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Large/resolve/main/depth_anything_v2_metric_hypersim_vitl.pth?download=true)
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- [Outdoor model from Virtual KITTI 2](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Large/resolve/main/depth_anything_v2_metric_vkitti_vitl.pth?download=true)
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```bash
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# indoor scenes
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python run.py \
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--encoder vitl --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
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--max-depth 20 --img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
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# outdoor scenes
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python run.py \
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--encoder vitl --load-from checkpoints/depth_anything_v2_metric_vkitti_vitl.pth \
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--max-depth 80 --img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
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```
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You can also project 2D images to point clouds:
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```bash
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python depth_to_pointcloud.py \
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--encoder vitl --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
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--max-depth 20 --img-path <path> --outdir <outdir>
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```
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### Reproduce training
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Please first prepare the [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets. Then:
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```bash
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bash dist_train.sh
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```
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## Citation
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If you find this project useful, please consider citing:
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```bibtex
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@article{depth_anything_v2,
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title={Depth Anything V2},
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
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journal={arXiv:2406.09414},
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year={2024}
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}
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```
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metric_depth/assets/compare_zoedepth.png
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metric_depth/assets/compare_zoedepth.png
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metric_depth/dataset/hypersim.py
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metric_depth/dataset/hypersim.py
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import cv2
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import h5py
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from torchvision.transforms import Compose
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from dataset.transform import Resize, NormalizeImage, PrepareForNet, Crop
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def hypersim_distance_to_depth(npyDistance):
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intWidth, intHeight, fltFocal = 1024, 768, 886.81
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npyImageplaneX = np.linspace((-0.5 * intWidth) + 0.5, (0.5 * intWidth) - 0.5, intWidth).reshape(
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1, intWidth).repeat(intHeight, 0).astype(np.float32)[:, :, None]
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npyImageplaneY = np.linspace((-0.5 * intHeight) + 0.5, (0.5 * intHeight) - 0.5,
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intHeight).reshape(intHeight, 1).repeat(intWidth, 1).astype(np.float32)[:, :, None]
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npyImageplaneZ = np.full([intHeight, intWidth, 1], fltFocal, np.float32)
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npyImageplane = np.concatenate(
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[npyImageplaneX, npyImageplaneY, npyImageplaneZ], 2)
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npyDepth = npyDistance / np.linalg.norm(npyImageplane, 2, 2) * fltFocal
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return npyDepth
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class Hypersim(Dataset):
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def __init__(self, filelist_path, mode, size=(518, 518)):
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self.mode = mode
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self.size = size
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with open(filelist_path, 'r') as f:
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self.filelist = f.read().splitlines()
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net_w, net_h = size
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self.transform = Compose([
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Resize(
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width=net_w,
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height=net_h,
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resize_target=True if mode == 'train' else False,
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keep_aspect_ratio=True,
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ensure_multiple_of=14,
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resize_method='lower_bound',
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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PrepareForNet(),
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] + ([Crop(size[0])] if self.mode == 'train' else []))
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def __getitem__(self, item):
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img_path = self.filelist[item].split(' ')[0]
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depth_path = self.filelist[item].split(' ')[1]
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image = cv2.imread(img_path)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
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depth_fd = h5py.File(depth_path, "r")
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distance_meters = np.array(depth_fd['dataset'])
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depth = hypersim_distance_to_depth(distance_meters)
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sample = self.transform({'image': image, 'depth': depth})
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sample['image'] = torch.from_numpy(sample['image'])
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sample['depth'] = torch.from_numpy(sample['depth'])
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sample['valid_mask'] = (torch.isnan(sample['depth']) == 0)
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sample['depth'][sample['valid_mask'] == 0] = 0
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sample['image_path'] = self.filelist[item].split(' ')[0]
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return sample
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def __len__(self):
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return len(self.filelist)
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metric_depth/dataset/kitti.py
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metric_depth/dataset/kitti.py
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import cv2
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import torch
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from torch.utils.data import Dataset
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from torchvision.transforms import Compose
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from dataset.transform import Resize, NormalizeImage, PrepareForNet
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class KITTI(Dataset):
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def __init__(self, filelist_path, mode, size=(518, 518)):
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if mode != 'val':
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raise NotImplementedError
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self.mode = mode
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self.size = size
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with open(filelist_path, 'r') as f:
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self.filelist = f.read().splitlines()
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net_w, net_h = size
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self.transform = Compose([
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Resize(
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width=net_w,
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height=net_h,
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resize_target=True if mode == 'train' else False,
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keep_aspect_ratio=True,
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ensure_multiple_of=14,
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resize_method='lower_bound',
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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PrepareForNet(),
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])
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def __getitem__(self, item):
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img_path = self.filelist[item].split(' ')[0]
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depth_path = self.filelist[item].split(' ')[1]
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image = cv2.imread(img_path)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
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depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED).astype('float32')
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sample = self.transform({'image': image, 'depth': depth})
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sample['image'] = torch.from_numpy(sample['image'])
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sample['depth'] = torch.from_numpy(sample['depth'])
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sample['depth'] = sample['depth'] / 256.0 # convert in meters
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sample['valid_mask'] = sample['depth'] > 0
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sample['image_path'] = self.filelist[item].split(' ')[0]
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return sample
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def __len__(self):
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return len(self.filelist)
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metric_depth/dataset/splits/hypersim/train.txt
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metric_depth/dataset/splits/hypersim/val.txt
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metric_depth/dataset/splits/kitti/val.txt
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/mnt/bn/liheyang/Kitti/raw_data/2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000069.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_09_26_drive_0002_sync/proj_depth/groundtruth/image_02/0000000069.png
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/mnt/bn/liheyang/Kitti/raw_data/2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000054.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_09_26_drive_0002_sync/proj_depth/groundtruth/image_02/0000000054.png
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/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000794.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000794.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000328.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000328.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000615.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000615.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000917.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000917.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000369.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000369.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000287.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000287.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000123.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000123.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000876.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000876.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000410.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000410.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000492.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000492.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000958.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000958.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000656.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000656.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000753.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000753.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000574.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000574.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000001081.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000001081.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000041.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000041.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000246.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000246.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000002906.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000002906.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000002544.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000002544.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000000362.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000000362.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000004535.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000004535.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000000734.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000000734.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000001096.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000001096.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000004173.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000004173.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000000543.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000000543.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000001277.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000001277.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000004354.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000004354.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000001458.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000001458.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000001820.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000001820.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003449.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003449.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003268.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003268.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000000915.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000000915.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000002363.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000002363.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000002725.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000002725.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000000181.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000000181.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000001639.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000001639.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003992.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003992.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003087.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003087.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000002001.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000002001.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003811.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003811.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003630.png /mnt/bn/liheyang/Kitti/data_depth_annotated/train/2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003630.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000096.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000096.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000800.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000800.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000320.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000320.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000576.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000576.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000480.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000480.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000640.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000640.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000032.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000032.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000384.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000384.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000160.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000160.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000704.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000704.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000736.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000736.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000672.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000672.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000064.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000064.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000288.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000288.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000352.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000352.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000512.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000512.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000544.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000544.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000608.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000608.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000128.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000128.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000224.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000224.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000416.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000416.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000192.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000192.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000448.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000448.png
|
||||
/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000768.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000768.png
|
||||
19559
metric_depth/dataset/splits/vkitti2/train.txt
Normal file
19559
metric_depth/dataset/splits/vkitti2/train.txt
Normal file
File diff suppressed because it is too large
Load Diff
277
metric_depth/dataset/transform.py
Normal file
277
metric_depth/dataset/transform.py
Normal file
@@ -0,0 +1,277 @@
|
||||
import cv2
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
|
||||
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
|
||||
|
||||
Args:
|
||||
sample (dict): sample
|
||||
size (tuple): image size
|
||||
|
||||
Returns:
|
||||
tuple: new size
|
||||
"""
|
||||
shape = list(sample["disparity"].shape)
|
||||
|
||||
if shape[0] >= size[0] and shape[1] >= size[1]:
|
||||
return sample
|
||||
|
||||
scale = [0, 0]
|
||||
scale[0] = size[0] / shape[0]
|
||||
scale[1] = size[1] / shape[1]
|
||||
|
||||
scale = max(scale)
|
||||
|
||||
shape[0] = math.ceil(scale * shape[0])
|
||||
shape[1] = math.ceil(scale * shape[1])
|
||||
|
||||
# resize
|
||||
sample["image"] = cv2.resize(
|
||||
sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
|
||||
)
|
||||
|
||||
sample["disparity"] = cv2.resize(
|
||||
sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
|
||||
)
|
||||
sample["mask"] = cv2.resize(
|
||||
sample["mask"].astype(np.float32),
|
||||
tuple(shape[::-1]),
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
)
|
||||
sample["mask"] = sample["mask"].astype(bool)
|
||||
|
||||
return tuple(shape)
|
||||
|
||||
|
||||
class Resize(object):
|
||||
"""Resize sample to given size (width, height).
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
width,
|
||||
height,
|
||||
resize_target=True,
|
||||
keep_aspect_ratio=False,
|
||||
ensure_multiple_of=1,
|
||||
resize_method="lower_bound",
|
||||
image_interpolation_method=cv2.INTER_AREA,
|
||||
):
|
||||
"""Init.
|
||||
|
||||
Args:
|
||||
width (int): desired output width
|
||||
height (int): desired output height
|
||||
resize_target (bool, optional):
|
||||
True: Resize the full sample (image, mask, target).
|
||||
False: Resize image only.
|
||||
Defaults to True.
|
||||
keep_aspect_ratio (bool, optional):
|
||||
True: Keep the aspect ratio of the input sample.
|
||||
Output sample might not have the given width and height, and
|
||||
resize behaviour depends on the parameter 'resize_method'.
|
||||
Defaults to False.
|
||||
ensure_multiple_of (int, optional):
|
||||
Output width and height is constrained to be multiple of this parameter.
|
||||
Defaults to 1.
|
||||
resize_method (str, optional):
|
||||
"lower_bound": Output will be at least as large as the given size.
|
||||
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
|
||||
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
|
||||
Defaults to "lower_bound".
|
||||
"""
|
||||
self.__width = width
|
||||
self.__height = height
|
||||
|
||||
self.__resize_target = resize_target
|
||||
self.__keep_aspect_ratio = keep_aspect_ratio
|
||||
self.__multiple_of = ensure_multiple_of
|
||||
self.__resize_method = resize_method
|
||||
self.__image_interpolation_method = image_interpolation_method
|
||||
|
||||
def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
|
||||
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
|
||||
|
||||
if max_val is not None and y > max_val:
|
||||
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
|
||||
|
||||
if y < min_val:
|
||||
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
|
||||
|
||||
return y
|
||||
|
||||
def get_size(self, width, height):
|
||||
# determine new height and width
|
||||
scale_height = self.__height / height
|
||||
scale_width = self.__width / width
|
||||
|
||||
if self.__keep_aspect_ratio:
|
||||
if self.__resize_method == "lower_bound":
|
||||
# scale such that output size is lower bound
|
||||
if scale_width > scale_height:
|
||||
# fit width
|
||||
scale_height = scale_width
|
||||
else:
|
||||
# fit height
|
||||
scale_width = scale_height
|
||||
elif self.__resize_method == "upper_bound":
|
||||
# scale such that output size is upper bound
|
||||
if scale_width < scale_height:
|
||||
# fit width
|
||||
scale_height = scale_width
|
||||
else:
|
||||
# fit height
|
||||
scale_width = scale_height
|
||||
elif self.__resize_method == "minimal":
|
||||
# scale as least as possbile
|
||||
if abs(1 - scale_width) < abs(1 - scale_height):
|
||||
# fit width
|
||||
scale_height = scale_width
|
||||
else:
|
||||
# fit height
|
||||
scale_width = scale_height
|
||||
else:
|
||||
raise ValueError(
|
||||
f"resize_method {self.__resize_method} not implemented"
|
||||
)
|
||||
|
||||
if self.__resize_method == "lower_bound":
|
||||
new_height = self.constrain_to_multiple_of(
|
||||
scale_height * height, min_val=self.__height
|
||||
)
|
||||
new_width = self.constrain_to_multiple_of(
|
||||
scale_width * width, min_val=self.__width
|
||||
)
|
||||
elif self.__resize_method == "upper_bound":
|
||||
new_height = self.constrain_to_multiple_of(
|
||||
scale_height * height, max_val=self.__height
|
||||
)
|
||||
new_width = self.constrain_to_multiple_of(
|
||||
scale_width * width, max_val=self.__width
|
||||
)
|
||||
elif self.__resize_method == "minimal":
|
||||
new_height = self.constrain_to_multiple_of(scale_height * height)
|
||||
new_width = self.constrain_to_multiple_of(scale_width * width)
|
||||
else:
|
||||
raise ValueError(f"resize_method {self.__resize_method} not implemented")
|
||||
|
||||
return (new_width, new_height)
|
||||
|
||||
def __call__(self, sample):
|
||||
width, height = self.get_size(
|
||||
sample["image"].shape[1], sample["image"].shape[0]
|
||||
)
|
||||
|
||||
# resize sample
|
||||
sample["image"] = cv2.resize(
|
||||
sample["image"],
|
||||
(width, height),
|
||||
interpolation=self.__image_interpolation_method,
|
||||
)
|
||||
|
||||
if self.__resize_target:
|
||||
if "disparity" in sample:
|
||||
sample["disparity"] = cv2.resize(
|
||||
sample["disparity"],
|
||||
(width, height),
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
)
|
||||
|
||||
if "depth" in sample:
|
||||
sample["depth"] = cv2.resize(
|
||||
sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
|
||||
)
|
||||
|
||||
if "semseg_mask" in sample:
|
||||
# sample["semseg_mask"] = cv2.resize(
|
||||
# sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
|
||||
# )
|
||||
sample["semseg_mask"] = F.interpolate(torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode='nearest').numpy()[0, 0]
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = cv2.resize(
|
||||
sample["mask"].astype(np.float32),
|
||||
(width, height),
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
)
|
||||
# sample["mask"] = sample["mask"].astype(bool)
|
||||
|
||||
# print(sample['image'].shape, sample['depth'].shape)
|
||||
return sample
|
||||
|
||||
|
||||
class NormalizeImage(object):
|
||||
"""Normlize image by given mean and std.
|
||||
"""
|
||||
|
||||
def __init__(self, mean, std):
|
||||
self.__mean = mean
|
||||
self.__std = std
|
||||
|
||||
def __call__(self, sample):
|
||||
sample["image"] = (sample["image"] - self.__mean) / self.__std
|
||||
|
||||
return sample
|
||||
|
||||
|
||||
class PrepareForNet(object):
|
||||
"""Prepare sample for usage as network input.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, sample):
|
||||
image = np.transpose(sample["image"], (2, 0, 1))
|
||||
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = sample["mask"].astype(np.float32)
|
||||
sample["mask"] = np.ascontiguousarray(sample["mask"])
|
||||
|
||||
if "depth" in sample:
|
||||
depth = sample["depth"].astype(np.float32)
|
||||
sample["depth"] = np.ascontiguousarray(depth)
|
||||
|
||||
if "semseg_mask" in sample:
|
||||
sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
|
||||
sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
|
||||
|
||||
return sample
|
||||
|
||||
|
||||
class Crop(object):
|
||||
"""Crop sample for batch-wise training. Image is of shape CxHxW
|
||||
"""
|
||||
|
||||
def __init__(self, size):
|
||||
if isinstance(size, int):
|
||||
self.size = (size, size)
|
||||
else:
|
||||
self.size = size
|
||||
|
||||
def __call__(self, sample):
|
||||
h, w = sample['image'].shape[-2:]
|
||||
assert h >= self.size[0] and w >= self.size[1], 'Wrong size'
|
||||
|
||||
h_start = np.random.randint(0, h - self.size[0] + 1)
|
||||
w_start = np.random.randint(0, w - self.size[1] + 1)
|
||||
h_end = h_start + self.size[0]
|
||||
w_end = w_start + self.size[1]
|
||||
|
||||
sample['image'] = sample['image'][:, h_start: h_end, w_start: w_end]
|
||||
|
||||
if "depth" in sample:
|
||||
sample["depth"] = sample["depth"][h_start: h_end, w_start: w_end]
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = sample["mask"][h_start: h_end, w_start: w_end]
|
||||
|
||||
if "semseg_mask" in sample:
|
||||
sample["semseg_mask"] = sample["semseg_mask"][h_start: h_end, w_start: w_end]
|
||||
|
||||
return sample
|
||||
54
metric_depth/dataset/vkitti2.py
Normal file
54
metric_depth/dataset/vkitti2.py
Normal file
@@ -0,0 +1,54 @@
|
||||
import cv2
|
||||
import torch
|
||||
from torch.utils.data import Dataset
|
||||
from torchvision.transforms import Compose
|
||||
|
||||
from dataset.transform import Resize, NormalizeImage, PrepareForNet, Crop
|
||||
|
||||
|
||||
class VKITTI2(Dataset):
|
||||
def __init__(self, filelist_path, mode, size=(518, 518)):
|
||||
|
||||
self.mode = mode
|
||||
self.size = size
|
||||
|
||||
with open(filelist_path, 'r') as f:
|
||||
self.filelist = f.read().splitlines()
|
||||
|
||||
net_w, net_h = size
|
||||
self.transform = Compose([
|
||||
Resize(
|
||||
width=net_w,
|
||||
height=net_h,
|
||||
resize_target=True if mode == 'train' else False,
|
||||
keep_aspect_ratio=True,
|
||||
ensure_multiple_of=14,
|
||||
resize_method='lower_bound',
|
||||
image_interpolation_method=cv2.INTER_CUBIC,
|
||||
),
|
||||
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
||||
PrepareForNet(),
|
||||
] + ([Crop(size[0])] if self.mode == 'train' else []))
|
||||
|
||||
def __getitem__(self, item):
|
||||
img_path = self.filelist[item].split(' ')[0]
|
||||
depth_path = self.filelist[item].split(' ')[1]
|
||||
|
||||
image = cv2.imread(img_path)
|
||||
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
|
||||
|
||||
depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) / 100.0 # cm to m
|
||||
|
||||
sample = self.transform({'image': image, 'depth': depth})
|
||||
|
||||
sample['image'] = torch.from_numpy(sample['image'])
|
||||
sample['depth'] = torch.from_numpy(sample['depth'])
|
||||
|
||||
sample['valid_mask'] = (sample['depth'] <= 80)
|
||||
|
||||
sample['image_path'] = self.filelist[item].split(' ')[0]
|
||||
|
||||
return sample
|
||||
|
||||
def __len__(self):
|
||||
return len(self.filelist)
|
||||
415
metric_depth/depth_anything_v2/dinov2.py
Normal file
415
metric_depth/depth_anything_v2/dinov2.py
Normal file
@@ -0,0 +1,415 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
#
|
||||
# This source code is licensed under the Apache License, Version 2.0
|
||||
# found in the LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
|
||||
|
||||
from functools import partial
|
||||
import math
|
||||
import logging
|
||||
from typing import Sequence, Tuple, Union, Callable
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.checkpoint
|
||||
from torch.nn.init import trunc_normal_
|
||||
|
||||
from .dinov2_layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
|
||||
|
||||
|
||||
logger = logging.getLogger("dinov2")
|
||||
|
||||
|
||||
def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
|
||||
if not depth_first and include_root:
|
||||
fn(module=module, name=name)
|
||||
for child_name, child_module in module.named_children():
|
||||
child_name = ".".join((name, child_name)) if name else child_name
|
||||
named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
|
||||
if depth_first and include_root:
|
||||
fn(module=module, name=name)
|
||||
return module
|
||||
|
||||
|
||||
class BlockChunk(nn.ModuleList):
|
||||
def forward(self, x):
|
||||
for b in self:
|
||||
x = b(x)
|
||||
return x
|
||||
|
||||
|
||||
class DinoVisionTransformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
img_size=224,
|
||||
patch_size=16,
|
||||
in_chans=3,
|
||||
embed_dim=768,
|
||||
depth=12,
|
||||
num_heads=12,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
ffn_bias=True,
|
||||
proj_bias=True,
|
||||
drop_path_rate=0.0,
|
||||
drop_path_uniform=False,
|
||||
init_values=None, # for layerscale: None or 0 => no layerscale
|
||||
embed_layer=PatchEmbed,
|
||||
act_layer=nn.GELU,
|
||||
block_fn=Block,
|
||||
ffn_layer="mlp",
|
||||
block_chunks=1,
|
||||
num_register_tokens=0,
|
||||
interpolate_antialias=False,
|
||||
interpolate_offset=0.1,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
img_size (int, tuple): input image size
|
||||
patch_size (int, tuple): patch size
|
||||
in_chans (int): number of input channels
|
||||
embed_dim (int): embedding dimension
|
||||
depth (int): depth of transformer
|
||||
num_heads (int): number of attention heads
|
||||
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
||||
qkv_bias (bool): enable bias for qkv if True
|
||||
proj_bias (bool): enable bias for proj in attn if True
|
||||
ffn_bias (bool): enable bias for ffn if True
|
||||
drop_path_rate (float): stochastic depth rate
|
||||
drop_path_uniform (bool): apply uniform drop rate across blocks
|
||||
weight_init (str): weight init scheme
|
||||
init_values (float): layer-scale init values
|
||||
embed_layer (nn.Module): patch embedding layer
|
||||
act_layer (nn.Module): MLP activation layer
|
||||
block_fn (nn.Module): transformer block class
|
||||
ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
|
||||
block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
|
||||
num_register_tokens: (int) number of extra cls tokens (so-called "registers")
|
||||
interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
|
||||
interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
|
||||
"""
|
||||
super().__init__()
|
||||
norm_layer = partial(nn.LayerNorm, eps=1e-6)
|
||||
|
||||
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
||||
self.num_tokens = 1
|
||||
self.n_blocks = depth
|
||||
self.num_heads = num_heads
|
||||
self.patch_size = patch_size
|
||||
self.num_register_tokens = num_register_tokens
|
||||
self.interpolate_antialias = interpolate_antialias
|
||||
self.interpolate_offset = interpolate_offset
|
||||
|
||||
self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
||||
num_patches = self.patch_embed.num_patches
|
||||
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||||
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
|
||||
assert num_register_tokens >= 0
|
||||
self.register_tokens = (
|
||||
nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
|
||||
)
|
||||
|
||||
if drop_path_uniform is True:
|
||||
dpr = [drop_path_rate] * depth
|
||||
else:
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||||
|
||||
if ffn_layer == "mlp":
|
||||
logger.info("using MLP layer as FFN")
|
||||
ffn_layer = Mlp
|
||||
elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
|
||||
logger.info("using SwiGLU layer as FFN")
|
||||
ffn_layer = SwiGLUFFNFused
|
||||
elif ffn_layer == "identity":
|
||||
logger.info("using Identity layer as FFN")
|
||||
|
||||
def f(*args, **kwargs):
|
||||
return nn.Identity()
|
||||
|
||||
ffn_layer = f
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
blocks_list = [
|
||||
block_fn(
|
||||
dim=embed_dim,
|
||||
num_heads=num_heads,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
proj_bias=proj_bias,
|
||||
ffn_bias=ffn_bias,
|
||||
drop_path=dpr[i],
|
||||
norm_layer=norm_layer,
|
||||
act_layer=act_layer,
|
||||
ffn_layer=ffn_layer,
|
||||
init_values=init_values,
|
||||
)
|
||||
for i in range(depth)
|
||||
]
|
||||
if block_chunks > 0:
|
||||
self.chunked_blocks = True
|
||||
chunked_blocks = []
|
||||
chunksize = depth // block_chunks
|
||||
for i in range(0, depth, chunksize):
|
||||
# this is to keep the block index consistent if we chunk the block list
|
||||
chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
|
||||
self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
|
||||
else:
|
||||
self.chunked_blocks = False
|
||||
self.blocks = nn.ModuleList(blocks_list)
|
||||
|
||||
self.norm = norm_layer(embed_dim)
|
||||
self.head = nn.Identity()
|
||||
|
||||
self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def init_weights(self):
|
||||
trunc_normal_(self.pos_embed, std=0.02)
|
||||
nn.init.normal_(self.cls_token, std=1e-6)
|
||||
if self.register_tokens is not None:
|
||||
nn.init.normal_(self.register_tokens, std=1e-6)
|
||||
named_apply(init_weights_vit_timm, self)
|
||||
|
||||
def interpolate_pos_encoding(self, x, w, h):
|
||||
previous_dtype = x.dtype
|
||||
npatch = x.shape[1] - 1
|
||||
N = self.pos_embed.shape[1] - 1
|
||||
if npatch == N and w == h:
|
||||
return self.pos_embed
|
||||
pos_embed = self.pos_embed.float()
|
||||
class_pos_embed = pos_embed[:, 0]
|
||||
patch_pos_embed = pos_embed[:, 1:]
|
||||
dim = x.shape[-1]
|
||||
w0 = w // self.patch_size
|
||||
h0 = h // self.patch_size
|
||||
# we add a small number to avoid floating point error in the interpolation
|
||||
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
||||
# DINOv2 with register modify the interpolate_offset from 0.1 to 0.0
|
||||
w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
|
||||
# w0, h0 = w0 + 0.1, h0 + 0.1
|
||||
|
||||
sqrt_N = math.sqrt(N)
|
||||
sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N
|
||||
patch_pos_embed = nn.functional.interpolate(
|
||||
patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),
|
||||
scale_factor=(sx, sy),
|
||||
# (int(w0), int(h0)), # to solve the upsampling shape issue
|
||||
mode="bicubic",
|
||||
antialias=self.interpolate_antialias
|
||||
)
|
||||
|
||||
assert int(w0) == patch_pos_embed.shape[-2]
|
||||
assert int(h0) == patch_pos_embed.shape[-1]
|
||||
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
||||
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
|
||||
|
||||
def prepare_tokens_with_masks(self, x, masks=None):
|
||||
B, nc, w, h = x.shape
|
||||
x = self.patch_embed(x)
|
||||
if masks is not None:
|
||||
x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
|
||||
|
||||
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
|
||||
x = x + self.interpolate_pos_encoding(x, w, h)
|
||||
|
||||
if self.register_tokens is not None:
|
||||
x = torch.cat(
|
||||
(
|
||||
x[:, :1],
|
||||
self.register_tokens.expand(x.shape[0], -1, -1),
|
||||
x[:, 1:],
|
||||
),
|
||||
dim=1,
|
||||
)
|
||||
|
||||
return x
|
||||
|
||||
def forward_features_list(self, x_list, masks_list):
|
||||
x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
|
||||
for blk in self.blocks:
|
||||
x = blk(x)
|
||||
|
||||
all_x = x
|
||||
output = []
|
||||
for x, masks in zip(all_x, masks_list):
|
||||
x_norm = self.norm(x)
|
||||
output.append(
|
||||
{
|
||||
"x_norm_clstoken": x_norm[:, 0],
|
||||
"x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
|
||||
"x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
|
||||
"x_prenorm": x,
|
||||
"masks": masks,
|
||||
}
|
||||
)
|
||||
return output
|
||||
|
||||
def forward_features(self, x, masks=None):
|
||||
if isinstance(x, list):
|
||||
return self.forward_features_list(x, masks)
|
||||
|
||||
x = self.prepare_tokens_with_masks(x, masks)
|
||||
|
||||
for blk in self.blocks:
|
||||
x = blk(x)
|
||||
|
||||
x_norm = self.norm(x)
|
||||
return {
|
||||
"x_norm_clstoken": x_norm[:, 0],
|
||||
"x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
|
||||
"x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
|
||||
"x_prenorm": x,
|
||||
"masks": masks,
|
||||
}
|
||||
|
||||
def _get_intermediate_layers_not_chunked(self, x, n=1):
|
||||
x = self.prepare_tokens_with_masks(x)
|
||||
# If n is an int, take the n last blocks. If it's a list, take them
|
||||
output, total_block_len = [], len(self.blocks)
|
||||
blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
|
||||
for i, blk in enumerate(self.blocks):
|
||||
x = blk(x)
|
||||
if i in blocks_to_take:
|
||||
output.append(x)
|
||||
assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
|
||||
return output
|
||||
|
||||
def _get_intermediate_layers_chunked(self, x, n=1):
|
||||
x = self.prepare_tokens_with_masks(x)
|
||||
output, i, total_block_len = [], 0, len(self.blocks[-1])
|
||||
# If n is an int, take the n last blocks. If it's a list, take them
|
||||
blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
|
||||
for block_chunk in self.blocks:
|
||||
for blk in block_chunk[i:]: # Passing the nn.Identity()
|
||||
x = blk(x)
|
||||
if i in blocks_to_take:
|
||||
output.append(x)
|
||||
i += 1
|
||||
assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
|
||||
return output
|
||||
|
||||
def get_intermediate_layers(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
n: Union[int, Sequence] = 1, # Layers or n last layers to take
|
||||
reshape: bool = False,
|
||||
return_class_token: bool = False,
|
||||
norm=True
|
||||
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
|
||||
if self.chunked_blocks:
|
||||
outputs = self._get_intermediate_layers_chunked(x, n)
|
||||
else:
|
||||
outputs = self._get_intermediate_layers_not_chunked(x, n)
|
||||
if norm:
|
||||
outputs = [self.norm(out) for out in outputs]
|
||||
class_tokens = [out[:, 0] for out in outputs]
|
||||
outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs]
|
||||
if reshape:
|
||||
B, _, w, h = x.shape
|
||||
outputs = [
|
||||
out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
|
||||
for out in outputs
|
||||
]
|
||||
if return_class_token:
|
||||
return tuple(zip(outputs, class_tokens))
|
||||
return tuple(outputs)
|
||||
|
||||
def forward(self, *args, is_training=False, **kwargs):
|
||||
ret = self.forward_features(*args, **kwargs)
|
||||
if is_training:
|
||||
return ret
|
||||
else:
|
||||
return self.head(ret["x_norm_clstoken"])
|
||||
|
||||
|
||||
def init_weights_vit_timm(module: nn.Module, name: str = ""):
|
||||
"""ViT weight initialization, original timm impl (for reproducibility)"""
|
||||
if isinstance(module, nn.Linear):
|
||||
trunc_normal_(module.weight, std=0.02)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
|
||||
|
||||
def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=384,
|
||||
depth=12,
|
||||
num_heads=6,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=768,
|
||||
depth=12,
|
||||
num_heads=12,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=1024,
|
||||
depth=24,
|
||||
num_heads=16,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
"""
|
||||
Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
|
||||
"""
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=1536,
|
||||
depth=40,
|
||||
num_heads=24,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def DINOv2(model_name):
|
||||
model_zoo = {
|
||||
"vits": vit_small,
|
||||
"vitb": vit_base,
|
||||
"vitl": vit_large,
|
||||
"vitg": vit_giant2
|
||||
}
|
||||
|
||||
return model_zoo[model_name](
|
||||
img_size=518,
|
||||
patch_size=14,
|
||||
init_values=1.0,
|
||||
ffn_layer="mlp" if model_name != "vitg" else "swiglufused",
|
||||
block_chunks=0,
|
||||
num_register_tokens=0,
|
||||
interpolate_antialias=False,
|
||||
interpolate_offset=0.1
|
||||
)
|
||||
11
metric_depth/depth_anything_v2/dinov2_layers/__init__.py
Normal file
11
metric_depth/depth_anything_v2/dinov2_layers/__init__.py
Normal file
@@ -0,0 +1,11 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from .mlp import Mlp
|
||||
from .patch_embed import PatchEmbed
|
||||
from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
|
||||
from .block import NestedTensorBlock
|
||||
from .attention import MemEffAttention
|
||||
83
metric_depth/depth_anything_v2/dinov2_layers/attention.py
Normal file
83
metric_depth/depth_anything_v2/dinov2_layers/attention.py
Normal file
@@ -0,0 +1,83 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
|
||||
|
||||
import logging
|
||||
|
||||
from torch import Tensor
|
||||
from torch import nn
|
||||
|
||||
|
||||
logger = logging.getLogger("dinov2")
|
||||
|
||||
|
||||
try:
|
||||
from xformers.ops import memory_efficient_attention, unbind, fmha
|
||||
|
||||
XFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
logger.warning("xFormers not available")
|
||||
XFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_heads: int = 8,
|
||||
qkv_bias: bool = False,
|
||||
proj_bias: bool = True,
|
||||
attn_drop: float = 0.0,
|
||||
proj_drop: float = 0.0,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = head_dim**-0.5
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim, bias=proj_bias)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
|
||||
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
|
||||
attn = q @ k.transpose(-2, -1)
|
||||
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class MemEffAttention(Attention):
|
||||
def forward(self, x: Tensor, attn_bias=None) -> Tensor:
|
||||
if not XFORMERS_AVAILABLE:
|
||||
assert attn_bias is None, "xFormers is required for nested tensors usage"
|
||||
return super().forward(x)
|
||||
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
|
||||
|
||||
q, k, v = unbind(qkv, 2)
|
||||
|
||||
x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
|
||||
x = x.reshape([B, N, C])
|
||||
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
252
metric_depth/depth_anything_v2/dinov2_layers/block.py
Normal file
252
metric_depth/depth_anything_v2/dinov2_layers/block.py
Normal file
@@ -0,0 +1,252 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
|
||||
|
||||
import logging
|
||||
from typing import Callable, List, Any, Tuple, Dict
|
||||
|
||||
import torch
|
||||
from torch import nn, Tensor
|
||||
|
||||
from .attention import Attention, MemEffAttention
|
||||
from .drop_path import DropPath
|
||||
from .layer_scale import LayerScale
|
||||
from .mlp import Mlp
|
||||
|
||||
|
||||
logger = logging.getLogger("dinov2")
|
||||
|
||||
|
||||
try:
|
||||
from xformers.ops import fmha
|
||||
from xformers.ops import scaled_index_add, index_select_cat
|
||||
|
||||
XFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
logger.warning("xFormers not available")
|
||||
XFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_heads: int,
|
||||
mlp_ratio: float = 4.0,
|
||||
qkv_bias: bool = False,
|
||||
proj_bias: bool = True,
|
||||
ffn_bias: bool = True,
|
||||
drop: float = 0.0,
|
||||
attn_drop: float = 0.0,
|
||||
init_values=None,
|
||||
drop_path: float = 0.0,
|
||||
act_layer: Callable[..., nn.Module] = nn.GELU,
|
||||
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
|
||||
attn_class: Callable[..., nn.Module] = Attention,
|
||||
ffn_layer: Callable[..., nn.Module] = Mlp,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
# print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = attn_class(
|
||||
dim,
|
||||
num_heads=num_heads,
|
||||
qkv_bias=qkv_bias,
|
||||
proj_bias=proj_bias,
|
||||
attn_drop=attn_drop,
|
||||
proj_drop=drop,
|
||||
)
|
||||
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
||||
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = ffn_layer(
|
||||
in_features=dim,
|
||||
hidden_features=mlp_hidden_dim,
|
||||
act_layer=act_layer,
|
||||
drop=drop,
|
||||
bias=ffn_bias,
|
||||
)
|
||||
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
||||
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
|
||||
self.sample_drop_ratio = drop_path
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
def attn_residual_func(x: Tensor) -> Tensor:
|
||||
return self.ls1(self.attn(self.norm1(x)))
|
||||
|
||||
def ffn_residual_func(x: Tensor) -> Tensor:
|
||||
return self.ls2(self.mlp(self.norm2(x)))
|
||||
|
||||
if self.training and self.sample_drop_ratio > 0.1:
|
||||
# the overhead is compensated only for a drop path rate larger than 0.1
|
||||
x = drop_add_residual_stochastic_depth(
|
||||
x,
|
||||
residual_func=attn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
)
|
||||
x = drop_add_residual_stochastic_depth(
|
||||
x,
|
||||
residual_func=ffn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
)
|
||||
elif self.training and self.sample_drop_ratio > 0.0:
|
||||
x = x + self.drop_path1(attn_residual_func(x))
|
||||
x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2
|
||||
else:
|
||||
x = x + attn_residual_func(x)
|
||||
x = x + ffn_residual_func(x)
|
||||
return x
|
||||
|
||||
|
||||
def drop_add_residual_stochastic_depth(
|
||||
x: Tensor,
|
||||
residual_func: Callable[[Tensor], Tensor],
|
||||
sample_drop_ratio: float = 0.0,
|
||||
) -> Tensor:
|
||||
# 1) extract subset using permutation
|
||||
b, n, d = x.shape
|
||||
sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
|
||||
brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
|
||||
x_subset = x[brange]
|
||||
|
||||
# 2) apply residual_func to get residual
|
||||
residual = residual_func(x_subset)
|
||||
|
||||
x_flat = x.flatten(1)
|
||||
residual = residual.flatten(1)
|
||||
|
||||
residual_scale_factor = b / sample_subset_size
|
||||
|
||||
# 3) add the residual
|
||||
x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
|
||||
return x_plus_residual.view_as(x)
|
||||
|
||||
|
||||
def get_branges_scales(x, sample_drop_ratio=0.0):
|
||||
b, n, d = x.shape
|
||||
sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
|
||||
brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
|
||||
residual_scale_factor = b / sample_subset_size
|
||||
return brange, residual_scale_factor
|
||||
|
||||
|
||||
def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
|
||||
if scaling_vector is None:
|
||||
x_flat = x.flatten(1)
|
||||
residual = residual.flatten(1)
|
||||
x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
|
||||
else:
|
||||
x_plus_residual = scaled_index_add(
|
||||
x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
|
||||
)
|
||||
return x_plus_residual
|
||||
|
||||
|
||||
attn_bias_cache: Dict[Tuple, Any] = {}
|
||||
|
||||
|
||||
def get_attn_bias_and_cat(x_list, branges=None):
|
||||
"""
|
||||
this will perform the index select, cat the tensors, and provide the attn_bias from cache
|
||||
"""
|
||||
batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
|
||||
all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
|
||||
if all_shapes not in attn_bias_cache.keys():
|
||||
seqlens = []
|
||||
for b, x in zip(batch_sizes, x_list):
|
||||
for _ in range(b):
|
||||
seqlens.append(x.shape[1])
|
||||
attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
|
||||
attn_bias._batch_sizes = batch_sizes
|
||||
attn_bias_cache[all_shapes] = attn_bias
|
||||
|
||||
if branges is not None:
|
||||
cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
|
||||
else:
|
||||
tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
|
||||
cat_tensors = torch.cat(tensors_bs1, dim=1)
|
||||
|
||||
return attn_bias_cache[all_shapes], cat_tensors
|
||||
|
||||
|
||||
def drop_add_residual_stochastic_depth_list(
|
||||
x_list: List[Tensor],
|
||||
residual_func: Callable[[Tensor, Any], Tensor],
|
||||
sample_drop_ratio: float = 0.0,
|
||||
scaling_vector=None,
|
||||
) -> Tensor:
|
||||
# 1) generate random set of indices for dropping samples in the batch
|
||||
branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
|
||||
branges = [s[0] for s in branges_scales]
|
||||
residual_scale_factors = [s[1] for s in branges_scales]
|
||||
|
||||
# 2) get attention bias and index+concat the tensors
|
||||
attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
|
||||
|
||||
# 3) apply residual_func to get residual, and split the result
|
||||
residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore
|
||||
|
||||
outputs = []
|
||||
for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
|
||||
outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
|
||||
return outputs
|
||||
|
||||
|
||||
class NestedTensorBlock(Block):
|
||||
def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
|
||||
"""
|
||||
x_list contains a list of tensors to nest together and run
|
||||
"""
|
||||
assert isinstance(self.attn, MemEffAttention)
|
||||
|
||||
if self.training and self.sample_drop_ratio > 0.0:
|
||||
|
||||
def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.attn(self.norm1(x), attn_bias=attn_bias)
|
||||
|
||||
def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.mlp(self.norm2(x))
|
||||
|
||||
x_list = drop_add_residual_stochastic_depth_list(
|
||||
x_list,
|
||||
residual_func=attn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
|
||||
)
|
||||
x_list = drop_add_residual_stochastic_depth_list(
|
||||
x_list,
|
||||
residual_func=ffn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
|
||||
)
|
||||
return x_list
|
||||
else:
|
||||
|
||||
def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
|
||||
|
||||
def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.ls2(self.mlp(self.norm2(x)))
|
||||
|
||||
attn_bias, x = get_attn_bias_and_cat(x_list)
|
||||
x = x + attn_residual_func(x, attn_bias=attn_bias)
|
||||
x = x + ffn_residual_func(x)
|
||||
return attn_bias.split(x)
|
||||
|
||||
def forward(self, x_or_x_list):
|
||||
if isinstance(x_or_x_list, Tensor):
|
||||
return super().forward(x_or_x_list)
|
||||
elif isinstance(x_or_x_list, list):
|
||||
assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
|
||||
return self.forward_nested(x_or_x_list)
|
||||
else:
|
||||
raise AssertionError
|
||||
35
metric_depth/depth_anything_v2/dinov2_layers/drop_path.py
Normal file
35
metric_depth/depth_anything_v2/dinov2_layers/drop_path.py
Normal file
@@ -0,0 +1,35 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
|
||||
|
||||
|
||||
from torch import nn
|
||||
|
||||
|
||||
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
|
||||
if drop_prob == 0.0 or not training:
|
||||
return x
|
||||
keep_prob = 1 - drop_prob
|
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
||||
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
||||
if keep_prob > 0.0:
|
||||
random_tensor.div_(keep_prob)
|
||||
output = x * random_tensor
|
||||
return output
|
||||
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
|
||||
|
||||
def __init__(self, drop_prob=None):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training)
|
||||
28
metric_depth/depth_anything_v2/dinov2_layers/layer_scale.py
Normal file
28
metric_depth/depth_anything_v2/dinov2_layers/layer_scale.py
Normal file
@@ -0,0 +1,28 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
|
||||
|
||||
from typing import Union
|
||||
|
||||
import torch
|
||||
from torch import Tensor
|
||||
from torch import nn
|
||||
|
||||
|
||||
class LayerScale(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
init_values: Union[float, Tensor] = 1e-5,
|
||||
inplace: bool = False,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.inplace = inplace
|
||||
self.gamma = nn.Parameter(init_values * torch.ones(dim))
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return x.mul_(self.gamma) if self.inplace else x * self.gamma
|
||||
41
metric_depth/depth_anything_v2/dinov2_layers/mlp.py
Normal file
41
metric_depth/depth_anything_v2/dinov2_layers/mlp.py
Normal file
@@ -0,0 +1,41 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
|
||||
|
||||
|
||||
from typing import Callable, Optional
|
||||
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: Optional[int] = None,
|
||||
out_features: Optional[int] = None,
|
||||
act_layer: Callable[..., nn.Module] = nn.GELU,
|
||||
drop: float = 0.0,
|
||||
bias: bool = True,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
89
metric_depth/depth_anything_v2/dinov2_layers/patch_embed.py
Normal file
89
metric_depth/depth_anything_v2/dinov2_layers/patch_embed.py
Normal file
@@ -0,0 +1,89 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
|
||||
|
||||
from typing import Callable, Optional, Tuple, Union
|
||||
|
||||
from torch import Tensor
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def make_2tuple(x):
|
||||
if isinstance(x, tuple):
|
||||
assert len(x) == 2
|
||||
return x
|
||||
|
||||
assert isinstance(x, int)
|
||||
return (x, x)
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
"""
|
||||
2D image to patch embedding: (B,C,H,W) -> (B,N,D)
|
||||
|
||||
Args:
|
||||
img_size: Image size.
|
||||
patch_size: Patch token size.
|
||||
in_chans: Number of input image channels.
|
||||
embed_dim: Number of linear projection output channels.
|
||||
norm_layer: Normalization layer.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
img_size: Union[int, Tuple[int, int]] = 224,
|
||||
patch_size: Union[int, Tuple[int, int]] = 16,
|
||||
in_chans: int = 3,
|
||||
embed_dim: int = 768,
|
||||
norm_layer: Optional[Callable] = None,
|
||||
flatten_embedding: bool = True,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
image_HW = make_2tuple(img_size)
|
||||
patch_HW = make_2tuple(patch_size)
|
||||
patch_grid_size = (
|
||||
image_HW[0] // patch_HW[0],
|
||||
image_HW[1] // patch_HW[1],
|
||||
)
|
||||
|
||||
self.img_size = image_HW
|
||||
self.patch_size = patch_HW
|
||||
self.patches_resolution = patch_grid_size
|
||||
self.num_patches = patch_grid_size[0] * patch_grid_size[1]
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.flatten_embedding = flatten_embedding
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
|
||||
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
_, _, H, W = x.shape
|
||||
patch_H, patch_W = self.patch_size
|
||||
|
||||
assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
|
||||
assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
|
||||
|
||||
x = self.proj(x) # B C H W
|
||||
H, W = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2) # B HW C
|
||||
x = self.norm(x)
|
||||
if not self.flatten_embedding:
|
||||
x = x.reshape(-1, H, W, self.embed_dim) # B H W C
|
||||
return x
|
||||
|
||||
def flops(self) -> float:
|
||||
Ho, Wo = self.patches_resolution
|
||||
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
|
||||
if self.norm is not None:
|
||||
flops += Ho * Wo * self.embed_dim
|
||||
return flops
|
||||
63
metric_depth/depth_anything_v2/dinov2_layers/swiglu_ffn.py
Normal file
63
metric_depth/depth_anything_v2/dinov2_layers/swiglu_ffn.py
Normal file
@@ -0,0 +1,63 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from typing import Callable, Optional
|
||||
|
||||
from torch import Tensor, nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class SwiGLUFFN(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: Optional[int] = None,
|
||||
out_features: Optional[int] = None,
|
||||
act_layer: Callable[..., nn.Module] = None,
|
||||
drop: float = 0.0,
|
||||
bias: bool = True,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
|
||||
self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
x12 = self.w12(x)
|
||||
x1, x2 = x12.chunk(2, dim=-1)
|
||||
hidden = F.silu(x1) * x2
|
||||
return self.w3(hidden)
|
||||
|
||||
|
||||
try:
|
||||
from xformers.ops import SwiGLU
|
||||
|
||||
XFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
SwiGLU = SwiGLUFFN
|
||||
XFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class SwiGLUFFNFused(SwiGLU):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: Optional[int] = None,
|
||||
out_features: Optional[int] = None,
|
||||
act_layer: Callable[..., nn.Module] = None,
|
||||
drop: float = 0.0,
|
||||
bias: bool = True,
|
||||
) -> None:
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
|
||||
super().__init__(
|
||||
in_features=in_features,
|
||||
hidden_features=hidden_features,
|
||||
out_features=out_features,
|
||||
bias=bias,
|
||||
)
|
||||
222
metric_depth/depth_anything_v2/dpt.py
Normal file
222
metric_depth/depth_anything_v2/dpt.py
Normal file
@@ -0,0 +1,222 @@
|
||||
import cv2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchvision.transforms import Compose
|
||||
|
||||
from .dinov2 import DINOv2
|
||||
from .util.blocks import FeatureFusionBlock, _make_scratch
|
||||
from .util.transform import Resize, NormalizeImage, PrepareForNet
|
||||
|
||||
|
||||
def _make_fusion_block(features, use_bn, size=None):
|
||||
return FeatureFusionBlock(
|
||||
features,
|
||||
nn.ReLU(False),
|
||||
deconv=False,
|
||||
bn=use_bn,
|
||||
expand=False,
|
||||
align_corners=True,
|
||||
size=size,
|
||||
)
|
||||
|
||||
|
||||
class ConvBlock(nn.Module):
|
||||
def __init__(self, in_feature, out_feature):
|
||||
super().__init__()
|
||||
|
||||
self.conv_block = nn.Sequential(
|
||||
nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1),
|
||||
nn.BatchNorm2d(out_feature),
|
||||
nn.ReLU(True)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv_block(x)
|
||||
|
||||
|
||||
class DPTHead(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
features=256,
|
||||
use_bn=False,
|
||||
out_channels=[256, 512, 1024, 1024],
|
||||
use_clstoken=False
|
||||
):
|
||||
super(DPTHead, self).__init__()
|
||||
|
||||
self.use_clstoken = use_clstoken
|
||||
|
||||
self.projects = nn.ModuleList([
|
||||
nn.Conv2d(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channel,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
) for out_channel in out_channels
|
||||
])
|
||||
|
||||
self.resize_layers = nn.ModuleList([
|
||||
nn.ConvTranspose2d(
|
||||
in_channels=out_channels[0],
|
||||
out_channels=out_channels[0],
|
||||
kernel_size=4,
|
||||
stride=4,
|
||||
padding=0),
|
||||
nn.ConvTranspose2d(
|
||||
in_channels=out_channels[1],
|
||||
out_channels=out_channels[1],
|
||||
kernel_size=2,
|
||||
stride=2,
|
||||
padding=0),
|
||||
nn.Identity(),
|
||||
nn.Conv2d(
|
||||
in_channels=out_channels[3],
|
||||
out_channels=out_channels[3],
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1)
|
||||
])
|
||||
|
||||
if use_clstoken:
|
||||
self.readout_projects = nn.ModuleList()
|
||||
for _ in range(len(self.projects)):
|
||||
self.readout_projects.append(
|
||||
nn.Sequential(
|
||||
nn.Linear(2 * in_channels, in_channels),
|
||||
nn.GELU()))
|
||||
|
||||
self.scratch = _make_scratch(
|
||||
out_channels,
|
||||
features,
|
||||
groups=1,
|
||||
expand=False,
|
||||
)
|
||||
|
||||
self.scratch.stem_transpose = None
|
||||
|
||||
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
|
||||
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
|
||||
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
|
||||
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
|
||||
|
||||
head_features_1 = features
|
||||
head_features_2 = 32
|
||||
|
||||
self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
|
||||
self.scratch.output_conv2 = nn.Sequential(
|
||||
nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
|
||||
nn.ReLU(True),
|
||||
nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
|
||||
nn.Sigmoid()
|
||||
)
|
||||
|
||||
def forward(self, out_features, patch_h, patch_w):
|
||||
out = []
|
||||
for i, x in enumerate(out_features):
|
||||
if self.use_clstoken:
|
||||
x, cls_token = x[0], x[1]
|
||||
readout = cls_token.unsqueeze(1).expand_as(x)
|
||||
x = self.readout_projects[i](torch.cat((x, readout), -1))
|
||||
else:
|
||||
x = x[0]
|
||||
|
||||
x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
|
||||
|
||||
x = self.projects[i](x)
|
||||
x = self.resize_layers[i](x)
|
||||
|
||||
out.append(x)
|
||||
|
||||
layer_1, layer_2, layer_3, layer_4 = out
|
||||
|
||||
layer_1_rn = self.scratch.layer1_rn(layer_1)
|
||||
layer_2_rn = self.scratch.layer2_rn(layer_2)
|
||||
layer_3_rn = self.scratch.layer3_rn(layer_3)
|
||||
layer_4_rn = self.scratch.layer4_rn(layer_4)
|
||||
|
||||
path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
|
||||
path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
|
||||
path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
|
||||
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
|
||||
|
||||
out = self.scratch.output_conv1(path_1)
|
||||
out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
|
||||
out = self.scratch.output_conv2(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class DepthAnythingV2(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
encoder='vitl',
|
||||
features=256,
|
||||
out_channels=[256, 512, 1024, 1024],
|
||||
use_bn=False,
|
||||
use_clstoken=False,
|
||||
max_depth=20.0
|
||||
):
|
||||
super(DepthAnythingV2, self).__init__()
|
||||
|
||||
self.intermediate_layer_idx = {
|
||||
'vits': [2, 5, 8, 11],
|
||||
'vitb': [2, 5, 8, 11],
|
||||
'vitl': [4, 11, 17, 23],
|
||||
'vitg': [9, 19, 29, 39]
|
||||
}
|
||||
|
||||
self.max_depth = max_depth
|
||||
|
||||
self.encoder = encoder
|
||||
self.pretrained = DINOv2(model_name=encoder)
|
||||
|
||||
self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
|
||||
|
||||
def forward(self, x):
|
||||
patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14
|
||||
|
||||
features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True)
|
||||
|
||||
depth = self.depth_head(features, patch_h, patch_w) * self.max_depth
|
||||
|
||||
return depth.squeeze(1)
|
||||
|
||||
@torch.no_grad()
|
||||
def infer_image(self, raw_image, input_size=518):
|
||||
image, (h, w) = self.image2tensor(raw_image, input_size)
|
||||
|
||||
depth = self.forward(image)
|
||||
|
||||
depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0]
|
||||
|
||||
return depth.cpu().numpy()
|
||||
|
||||
def image2tensor(self, raw_image, input_size=518):
|
||||
transform = Compose([
|
||||
Resize(
|
||||
width=input_size,
|
||||
height=input_size,
|
||||
resize_target=False,
|
||||
keep_aspect_ratio=True,
|
||||
ensure_multiple_of=14,
|
||||
resize_method='lower_bound',
|
||||
image_interpolation_method=cv2.INTER_CUBIC,
|
||||
),
|
||||
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
||||
PrepareForNet(),
|
||||
])
|
||||
|
||||
h, w = raw_image.shape[:2]
|
||||
|
||||
image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0
|
||||
|
||||
image = transform({'image': image})['image']
|
||||
image = torch.from_numpy(image).unsqueeze(0)
|
||||
|
||||
DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
|
||||
image = image.to(DEVICE)
|
||||
|
||||
return image, (h, w)
|
||||
148
metric_depth/depth_anything_v2/util/blocks.py
Normal file
148
metric_depth/depth_anything_v2/util/blocks.py
Normal file
@@ -0,0 +1,148 @@
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
|
||||
scratch = nn.Module()
|
||||
|
||||
out_shape1 = out_shape
|
||||
out_shape2 = out_shape
|
||||
out_shape3 = out_shape
|
||||
if len(in_shape) >= 4:
|
||||
out_shape4 = out_shape
|
||||
|
||||
if expand:
|
||||
out_shape1 = out_shape
|
||||
out_shape2 = out_shape * 2
|
||||
out_shape3 = out_shape * 4
|
||||
if len(in_shape) >= 4:
|
||||
out_shape4 = out_shape * 8
|
||||
|
||||
scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
|
||||
scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
|
||||
scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
|
||||
if len(in_shape) >= 4:
|
||||
scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
|
||||
|
||||
return scratch
|
||||
|
||||
|
||||
class ResidualConvUnit(nn.Module):
|
||||
"""Residual convolution module.
|
||||
"""
|
||||
|
||||
def __init__(self, features, activation, bn):
|
||||
"""Init.
|
||||
|
||||
Args:
|
||||
features (int): number of features
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.bn = bn
|
||||
|
||||
self.groups=1
|
||||
|
||||
self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
|
||||
|
||||
self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
|
||||
|
||||
if self.bn == True:
|
||||
self.bn1 = nn.BatchNorm2d(features)
|
||||
self.bn2 = nn.BatchNorm2d(features)
|
||||
|
||||
self.activation = activation
|
||||
|
||||
self.skip_add = nn.quantized.FloatFunctional()
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward pass.
|
||||
|
||||
Args:
|
||||
x (tensor): input
|
||||
|
||||
Returns:
|
||||
tensor: output
|
||||
"""
|
||||
|
||||
out = self.activation(x)
|
||||
out = self.conv1(out)
|
||||
if self.bn == True:
|
||||
out = self.bn1(out)
|
||||
|
||||
out = self.activation(out)
|
||||
out = self.conv2(out)
|
||||
if self.bn == True:
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.groups > 1:
|
||||
out = self.conv_merge(out)
|
||||
|
||||
return self.skip_add.add(out, x)
|
||||
|
||||
|
||||
class FeatureFusionBlock(nn.Module):
|
||||
"""Feature fusion block.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
features,
|
||||
activation,
|
||||
deconv=False,
|
||||
bn=False,
|
||||
expand=False,
|
||||
align_corners=True,
|
||||
size=None
|
||||
):
|
||||
"""Init.
|
||||
|
||||
Args:
|
||||
features (int): number of features
|
||||
"""
|
||||
super(FeatureFusionBlock, self).__init__()
|
||||
|
||||
self.deconv = deconv
|
||||
self.align_corners = align_corners
|
||||
|
||||
self.groups=1
|
||||
|
||||
self.expand = expand
|
||||
out_features = features
|
||||
if self.expand == True:
|
||||
out_features = features // 2
|
||||
|
||||
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
|
||||
|
||||
self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
|
||||
self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
|
||||
|
||||
self.skip_add = nn.quantized.FloatFunctional()
|
||||
|
||||
self.size=size
|
||||
|
||||
def forward(self, *xs, size=None):
|
||||
"""Forward pass.
|
||||
|
||||
Returns:
|
||||
tensor: output
|
||||
"""
|
||||
output = xs[0]
|
||||
|
||||
if len(xs) == 2:
|
||||
res = self.resConfUnit1(xs[1])
|
||||
output = self.skip_add.add(output, res)
|
||||
|
||||
output = self.resConfUnit2(output)
|
||||
|
||||
if (size is None) and (self.size is None):
|
||||
modifier = {"scale_factor": 2}
|
||||
elif size is None:
|
||||
modifier = {"size": self.size}
|
||||
else:
|
||||
modifier = {"size": size}
|
||||
|
||||
output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
|
||||
|
||||
output = self.out_conv(output)
|
||||
|
||||
return output
|
||||
158
metric_depth/depth_anything_v2/util/transform.py
Normal file
158
metric_depth/depth_anything_v2/util/transform.py
Normal file
@@ -0,0 +1,158 @@
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
class Resize(object):
|
||||
"""Resize sample to given size (width, height).
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
width,
|
||||
height,
|
||||
resize_target=True,
|
||||
keep_aspect_ratio=False,
|
||||
ensure_multiple_of=1,
|
||||
resize_method="lower_bound",
|
||||
image_interpolation_method=cv2.INTER_AREA,
|
||||
):
|
||||
"""Init.
|
||||
|
||||
Args:
|
||||
width (int): desired output width
|
||||
height (int): desired output height
|
||||
resize_target (bool, optional):
|
||||
True: Resize the full sample (image, mask, target).
|
||||
False: Resize image only.
|
||||
Defaults to True.
|
||||
keep_aspect_ratio (bool, optional):
|
||||
True: Keep the aspect ratio of the input sample.
|
||||
Output sample might not have the given width and height, and
|
||||
resize behaviour depends on the parameter 'resize_method'.
|
||||
Defaults to False.
|
||||
ensure_multiple_of (int, optional):
|
||||
Output width and height is constrained to be multiple of this parameter.
|
||||
Defaults to 1.
|
||||
resize_method (str, optional):
|
||||
"lower_bound": Output will be at least as large as the given size.
|
||||
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
|
||||
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
|
||||
Defaults to "lower_bound".
|
||||
"""
|
||||
self.__width = width
|
||||
self.__height = height
|
||||
|
||||
self.__resize_target = resize_target
|
||||
self.__keep_aspect_ratio = keep_aspect_ratio
|
||||
self.__multiple_of = ensure_multiple_of
|
||||
self.__resize_method = resize_method
|
||||
self.__image_interpolation_method = image_interpolation_method
|
||||
|
||||
def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
|
||||
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
|
||||
|
||||
if max_val is not None and y > max_val:
|
||||
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
|
||||
|
||||
if y < min_val:
|
||||
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
|
||||
|
||||
return y
|
||||
|
||||
def get_size(self, width, height):
|
||||
# determine new height and width
|
||||
scale_height = self.__height / height
|
||||
scale_width = self.__width / width
|
||||
|
||||
if self.__keep_aspect_ratio:
|
||||
if self.__resize_method == "lower_bound":
|
||||
# scale such that output size is lower bound
|
||||
if scale_width > scale_height:
|
||||
# fit width
|
||||
scale_height = scale_width
|
||||
else:
|
||||
# fit height
|
||||
scale_width = scale_height
|
||||
elif self.__resize_method == "upper_bound":
|
||||
# scale such that output size is upper bound
|
||||
if scale_width < scale_height:
|
||||
# fit width
|
||||
scale_height = scale_width
|
||||
else:
|
||||
# fit height
|
||||
scale_width = scale_height
|
||||
elif self.__resize_method == "minimal":
|
||||
# scale as least as possbile
|
||||
if abs(1 - scale_width) < abs(1 - scale_height):
|
||||
# fit width
|
||||
scale_height = scale_width
|
||||
else:
|
||||
# fit height
|
||||
scale_width = scale_height
|
||||
else:
|
||||
raise ValueError(f"resize_method {self.__resize_method} not implemented")
|
||||
|
||||
if self.__resize_method == "lower_bound":
|
||||
new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
|
||||
new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
|
||||
elif self.__resize_method == "upper_bound":
|
||||
new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
|
||||
new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
|
||||
elif self.__resize_method == "minimal":
|
||||
new_height = self.constrain_to_multiple_of(scale_height * height)
|
||||
new_width = self.constrain_to_multiple_of(scale_width * width)
|
||||
else:
|
||||
raise ValueError(f"resize_method {self.__resize_method} not implemented")
|
||||
|
||||
return (new_width, new_height)
|
||||
|
||||
def __call__(self, sample):
|
||||
width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
|
||||
|
||||
# resize sample
|
||||
sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method)
|
||||
|
||||
if self.__resize_target:
|
||||
if "depth" in sample:
|
||||
sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST)
|
||||
|
||||
return sample
|
||||
|
||||
|
||||
class NormalizeImage(object):
|
||||
"""Normlize image by given mean and std.
|
||||
"""
|
||||
|
||||
def __init__(self, mean, std):
|
||||
self.__mean = mean
|
||||
self.__std = std
|
||||
|
||||
def __call__(self, sample):
|
||||
sample["image"] = (sample["image"] - self.__mean) / self.__std
|
||||
|
||||
return sample
|
||||
|
||||
|
||||
class PrepareForNet(object):
|
||||
"""Prepare sample for usage as network input.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, sample):
|
||||
image = np.transpose(sample["image"], (2, 0, 1))
|
||||
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
|
||||
|
||||
if "depth" in sample:
|
||||
depth = sample["depth"].astype(np.float32)
|
||||
sample["depth"] = np.ascontiguousarray(depth)
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = sample["mask"].astype(np.float32)
|
||||
sample["mask"] = np.ascontiguousarray(sample["mask"])
|
||||
|
||||
return sample
|
||||
83
metric_depth/depth_to_pointcloud.py
Normal file
83
metric_depth/depth_to_pointcloud.py
Normal file
@@ -0,0 +1,83 @@
|
||||
# Born out of Depth Anything V1 Issue 36
|
||||
# Make sure you have the necessary libraries
|
||||
# Code by @1ssb
|
||||
|
||||
import argparse
|
||||
import cv2
|
||||
import glob
|
||||
import numpy as np
|
||||
import open3d as o3d
|
||||
import os
|
||||
from PIL import Image
|
||||
import torch
|
||||
|
||||
from depth_anything_v2.dpt import DepthAnythingV2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--encoder', default='vitl', type=str, choices=['vits', 'vitb', 'vitl', 'vitg'])
|
||||
parser.add_argument('--load-from', default='', type=str)
|
||||
parser.add_argument('--max-depth', default=20, type=float)
|
||||
|
||||
parser.add_argument('--img-path', type=str)
|
||||
parser.add_argument('--outdir', type=str, default='./vis_pointcloud')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Global settings
|
||||
FL = 715.0873
|
||||
FY = 784 * 0.6
|
||||
FX = 784 * 0.6
|
||||
NYU_DATA = False
|
||||
FINAL_HEIGHT = 518
|
||||
FINAL_WIDTH = 518
|
||||
|
||||
DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
|
||||
|
||||
model_configs = {
|
||||
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
|
||||
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
|
||||
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
|
||||
'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
|
||||
}
|
||||
|
||||
depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth})
|
||||
depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu'))
|
||||
depth_anything = depth_anything.to(DEVICE).eval()
|
||||
|
||||
if os.path.isfile(args.img_path):
|
||||
if args.img_path.endswith('txt'):
|
||||
with open(args.img_path, 'r') as f:
|
||||
filenames = f.read().splitlines()
|
||||
else:
|
||||
filenames = [args.img_path]
|
||||
else:
|
||||
filenames = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True)
|
||||
|
||||
os.makedirs(args.outdir, exist_ok=True)
|
||||
|
||||
for k, filename in enumerate(filenames):
|
||||
print(f'Progress {k+1}/{len(filenames)}: {filename}')
|
||||
|
||||
color_image = Image.open(filename).convert('RGB')
|
||||
|
||||
image = cv2.imread(filename)
|
||||
pred = depth_anything.infer_image(image, FINAL_HEIGHT)
|
||||
|
||||
# Resize color image and depth to final size
|
||||
resized_color_image = color_image.resize((FINAL_WIDTH, FINAL_HEIGHT), Image.LANCZOS)
|
||||
resized_pred = Image.fromarray(pred).resize((FINAL_WIDTH, FINAL_HEIGHT), Image.NEAREST)
|
||||
|
||||
focal_length_x, focal_length_y = (FX, FY) if not NYU_DATA else (FL, FL)
|
||||
x, y = np.meshgrid(np.arange(FINAL_WIDTH), np.arange(FINAL_HEIGHT))
|
||||
x = (x - FINAL_WIDTH / 2) / focal_length_x
|
||||
y = (y - FINAL_HEIGHT / 2) / focal_length_y
|
||||
z = np.array(resized_pred)
|
||||
points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3)
|
||||
colors = np.array(resized_color_image).reshape(-1, 3) / 255.0
|
||||
|
||||
pcd = o3d.geometry.PointCloud()
|
||||
pcd.points = o3d.utility.Vector3dVector(points)
|
||||
pcd.colors = o3d.utility.Vector3dVector(colors)
|
||||
o3d.io.write_point_cloud(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + ".ply"), pcd)
|
||||
26
metric_depth/dist_train.sh
Normal file
26
metric_depth/dist_train.sh
Normal file
@@ -0,0 +1,26 @@
|
||||
#!/bin/bash
|
||||
now=$(date +"%Y%m%d_%H%M%S")
|
||||
|
||||
epoch=120
|
||||
bs=4
|
||||
gpus=8
|
||||
lr=0.000005
|
||||
encoder=vitl
|
||||
dataset=hypersim # vkitti
|
||||
img_size=518
|
||||
min_depth=0.001
|
||||
max_depth=20 # 80 for virtual kitti
|
||||
pretrained_from=../checkpoints/depth_anything_v2_${encoder}.pth
|
||||
save_path=exp/hypersim # exp/vkitti
|
||||
|
||||
mkdir -p $save_path
|
||||
|
||||
python3 -m torch.distributed.launch \
|
||||
--nproc_per_node=$gpus \
|
||||
--nnodes 1 \
|
||||
--node_rank=0 \
|
||||
--master_addr=localhost \
|
||||
--master_port=20596 \
|
||||
train.py --epoch $epoch --encoder $encoder --bs $bs --lr $lr --save-path $save_path --dataset $dataset \
|
||||
--img-size $img_size --min-depth $min_depth --max-depth $max_depth --pretrained-from $pretrained_from \
|
||||
--port 20596 2>&1 | tee -a $save_path/$now.log
|
||||
5
metric_depth/requirements.txt
Normal file
5
metric_depth/requirements.txt
Normal file
@@ -0,0 +1,5 @@
|
||||
matplotlib
|
||||
opencv-python
|
||||
open3d
|
||||
torch
|
||||
torchvision
|
||||
81
metric_depth/run.py
Normal file
81
metric_depth/run.py
Normal file
@@ -0,0 +1,81 @@
|
||||
import argparse
|
||||
import cv2
|
||||
import glob
|
||||
import matplotlib
|
||||
import numpy as np
|
||||
import os
|
||||
import torch
|
||||
|
||||
from depth_anything_v2.dpt import DepthAnythingV2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Depth Anything V2 Metric Depth Estimation')
|
||||
|
||||
parser.add_argument('--img-path', type=str)
|
||||
parser.add_argument('--input-size', type=int, default=518)
|
||||
parser.add_argument('--outdir', type=str, default='./vis_depth')
|
||||
|
||||
parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
|
||||
parser.add_argument('--load-from', type=str, default='checkpoints/depth_anything_v2_metric_hypersim_vitl.pth')
|
||||
parser.add_argument('--max-depth', type=float, default=20)
|
||||
|
||||
parser.add_argument('--save-numpy', dest='save_numpy', action='store_true', help='save the model raw output')
|
||||
parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction')
|
||||
parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
|
||||
|
||||
model_configs = {
|
||||
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
|
||||
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
|
||||
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
|
||||
'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
|
||||
}
|
||||
|
||||
depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth})
|
||||
depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu'))
|
||||
depth_anything = depth_anything.to(DEVICE).eval()
|
||||
|
||||
if os.path.isfile(args.img_path):
|
||||
if args.img_path.endswith('txt'):
|
||||
with open(args.img_path, 'r') as f:
|
||||
filenames = f.read().splitlines()
|
||||
else:
|
||||
filenames = [args.img_path]
|
||||
else:
|
||||
filenames = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True)
|
||||
|
||||
os.makedirs(args.outdir, exist_ok=True)
|
||||
|
||||
cmap = matplotlib.colormaps.get_cmap('Spectral')
|
||||
|
||||
for k, filename in enumerate(filenames):
|
||||
print(f'Progress {k+1}/{len(filenames)}: {filename}')
|
||||
|
||||
raw_image = cv2.imread(filename)
|
||||
|
||||
depth = depth_anything.infer_image(raw_image, args.input_size)
|
||||
|
||||
if args.save_numpy:
|
||||
output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '_raw_depth_meter.npy')
|
||||
np.save(output_path, depth)
|
||||
|
||||
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
|
||||
depth = depth.astype(np.uint8)
|
||||
|
||||
if args.grayscale:
|
||||
depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
|
||||
else:
|
||||
depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
|
||||
|
||||
output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png')
|
||||
if args.pred_only:
|
||||
cv2.imwrite(output_path, depth)
|
||||
else:
|
||||
split_region = np.ones((raw_image.shape[0], 50, 3), dtype=np.uint8) * 255
|
||||
combined_result = cv2.hconcat([raw_image, split_region, depth])
|
||||
|
||||
cv2.imwrite(output_path, combined_result)
|
||||
212
metric_depth/train.py
Normal file
212
metric_depth/train.py
Normal file
@@ -0,0 +1,212 @@
|
||||
import argparse
|
||||
import logging
|
||||
import os
|
||||
import pprint
|
||||
import random
|
||||
|
||||
import warnings
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
import torch.distributed as dist
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.optim import AdamW
|
||||
import torch.nn.functional as F
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from dataset.hypersim import Hypersim
|
||||
from dataset.kitti import KITTI
|
||||
from dataset.vkitti2 import VKITTI2
|
||||
from depth_anything_v2.dpt import DepthAnythingV2
|
||||
from util.dist_helper import setup_distributed
|
||||
from util.loss import SiLogLoss
|
||||
from util.metric import eval_depth
|
||||
from util.utils import init_log
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser(description='Depth Anything V2 for Metric Depth Estimation')
|
||||
|
||||
parser.add_argument('--encoder', default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
|
||||
parser.add_argument('--dataset', default='hypersim', choices=['hypersim', 'vkitti'])
|
||||
parser.add_argument('--img-size', default=518, type=int)
|
||||
parser.add_argument('--min-depth', default=0.001, type=float)
|
||||
parser.add_argument('--max-depth', default=20, type=float)
|
||||
parser.add_argument('--epochs', default=40, type=int)
|
||||
parser.add_argument('--bs', default=2, type=int)
|
||||
parser.add_argument('--lr', default=0.000005, type=float)
|
||||
parser.add_argument('--pretrained-from', type=str)
|
||||
parser.add_argument('--save-path', type=str, required=True)
|
||||
parser.add_argument('--local-rank', default=0, type=int)
|
||||
parser.add_argument('--port', default=None, type=int)
|
||||
|
||||
|
||||
def main():
|
||||
args = parser.parse_args()
|
||||
|
||||
warnings.simplefilter('ignore', np.RankWarning)
|
||||
|
||||
logger = init_log('global', logging.INFO)
|
||||
logger.propagate = 0
|
||||
|
||||
rank, world_size = setup_distributed(port=args.port)
|
||||
|
||||
if rank == 0:
|
||||
all_args = {**vars(args), 'ngpus': world_size}
|
||||
logger.info('{}\n'.format(pprint.pformat(all_args)))
|
||||
writer = SummaryWriter(args.save_path)
|
||||
|
||||
cudnn.enabled = True
|
||||
cudnn.benchmark = True
|
||||
|
||||
size = (args.img_size, args.img_size)
|
||||
if args.dataset == 'hypersim':
|
||||
trainset = Hypersim('dataset/splits/hypersim/train.txt', 'train', size=size)
|
||||
elif args.dataset == 'vkitti':
|
||||
trainset = VKITTI2('dataset/splits/vkitti2/train.txt', 'train', size=size)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
trainsampler = torch.utils.data.distributed.DistributedSampler(trainset)
|
||||
trainloader = DataLoader(trainset, batch_size=args.bs, pin_memory=True, num_workers=4, drop_last=True, sampler=trainsampler)
|
||||
|
||||
if args.dataset == 'hypersim':
|
||||
valset = Hypersim('dataset/splits/hypersim/val.txt', 'val', size=size)
|
||||
elif args.dataset == 'vkitti':
|
||||
valset = KITTI('dataset/splits/kitti/val.txt', 'val', size=size)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
valsampler = torch.utils.data.distributed.DistributedSampler(valset)
|
||||
valloader = DataLoader(valset, batch_size=1, pin_memory=True, num_workers=4, drop_last=True, sampler=valsampler)
|
||||
|
||||
local_rank = int(os.environ["LOCAL_RANK"])
|
||||
|
||||
model_configs = {
|
||||
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
|
||||
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
|
||||
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
|
||||
'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
|
||||
}
|
||||
model = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth})
|
||||
|
||||
if args.pretrained_from:
|
||||
model.load_state_dict({k: v for k, v in torch.load(args.pretrained_from, map_location='cpu').items() if 'pretrained' in k}, strict=False)
|
||||
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
|
||||
model.cuda(local_rank)
|
||||
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], broadcast_buffers=False,
|
||||
output_device=local_rank, find_unused_parameters=True)
|
||||
|
||||
criterion = SiLogLoss().cuda(local_rank)
|
||||
|
||||
optimizer = AdamW([{'params': [param for name, param in model.named_parameters() if 'pretrained' in name], 'lr': args.lr},
|
||||
{'params': [param for name, param in model.named_parameters() if 'pretrained' not in name], 'lr': args.lr * 10.0}],
|
||||
lr=args.lr, betas=(0.9, 0.999), weight_decay=0.01)
|
||||
|
||||
total_iters = args.epochs * len(trainloader)
|
||||
|
||||
previous_best = {'d1': 0, 'd2': 0, 'd3': 0, 'abs_rel': 100, 'sq_rel': 100, 'rmse': 100, 'rmse_log': 100, 'log10': 100, 'silog': 100}
|
||||
|
||||
for epoch in range(args.epochs):
|
||||
if rank == 0:
|
||||
logger.info('===========> Epoch: {:}/{:}, d1: {:.3f}, d2: {:.3f}, d3: {:.3f}'.format(epoch, args.epochs, previous_best['d1'], previous_best['d2'], previous_best['d3']))
|
||||
logger.info('===========> Epoch: {:}/{:}, abs_rel: {:.3f}, sq_rel: {:.3f}, rmse: {:.3f}, rmse_log: {:.3f}, '
|
||||
'log10: {:.3f}, silog: {:.3f}'.format(
|
||||
epoch, args.epochs, previous_best['abs_rel'], previous_best['sq_rel'], previous_best['rmse'],
|
||||
previous_best['rmse_log'], previous_best['log10'], previous_best['silog']))
|
||||
|
||||
trainloader.sampler.set_epoch(epoch + 1)
|
||||
|
||||
model.train()
|
||||
total_loss = 0
|
||||
|
||||
for i, sample in enumerate(trainloader):
|
||||
optimizer.zero_grad()
|
||||
|
||||
img, depth, valid_mask = sample['image'].cuda(), sample['depth'].cuda(), sample['valid_mask'].cuda()
|
||||
|
||||
if random.random() < 0.5:
|
||||
img = img.flip(-1)
|
||||
depth = depth.flip(-1)
|
||||
valid_mask = valid_mask.flip(-1)
|
||||
|
||||
pred = model(img)
|
||||
|
||||
loss = criterion(pred, depth, (valid_mask == 1) & (depth >= args.min_depth) & (depth <= args.max_depth))
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
total_loss += loss.item()
|
||||
|
||||
iters = epoch * len(trainloader) + i
|
||||
|
||||
lr = args.lr * (1 - iters / total_iters) ** 0.9
|
||||
|
||||
optimizer.param_groups[0]["lr"] = lr
|
||||
optimizer.param_groups[1]["lr"] = lr * 10.0
|
||||
|
||||
if rank == 0:
|
||||
writer.add_scalar('train/loss', loss.item(), iters)
|
||||
|
||||
if rank == 0 and i % 100 == 0:
|
||||
logger.info('Iter: {}/{}, LR: {:.7f}, Loss: {:.3f}'.format(i, len(trainloader), optimizer.param_groups[0]['lr'], loss.item()))
|
||||
|
||||
model.eval()
|
||||
|
||||
results = {'d1': torch.tensor([0.0]).cuda(), 'd2': torch.tensor([0.0]).cuda(), 'd3': torch.tensor([0.0]).cuda(),
|
||||
'abs_rel': torch.tensor([0.0]).cuda(), 'sq_rel': torch.tensor([0.0]).cuda(), 'rmse': torch.tensor([0.0]).cuda(),
|
||||
'rmse_log': torch.tensor([0.0]).cuda(), 'log10': torch.tensor([0.0]).cuda(), 'silog': torch.tensor([0.0]).cuda()}
|
||||
nsamples = torch.tensor([0.0]).cuda()
|
||||
|
||||
for i, sample in enumerate(valloader):
|
||||
|
||||
img, depth, valid_mask = sample['image'].cuda().float(), sample['depth'].cuda()[0], sample['valid_mask'].cuda()[0]
|
||||
|
||||
with torch.no_grad():
|
||||
pred = model(img)
|
||||
pred = F.interpolate(pred[:, None], depth.shape[-2:], mode='bilinear', align_corners=True)[0, 0]
|
||||
|
||||
valid_mask = (valid_mask == 1) & (depth >= args.min_depth) & (depth <= args.max_depth)
|
||||
|
||||
if valid_mask.sum() < 10:
|
||||
continue
|
||||
|
||||
cur_results = eval_depth(pred[valid_mask], depth[valid_mask])
|
||||
|
||||
for k in results.keys():
|
||||
results[k] += cur_results[k]
|
||||
nsamples += 1
|
||||
|
||||
torch.distributed.barrier()
|
||||
|
||||
for k in results.keys():
|
||||
dist.reduce(results[k], dst=0)
|
||||
dist.reduce(nsamples, dst=0)
|
||||
|
||||
if rank == 0:
|
||||
logger.info('==========================================================================================')
|
||||
logger.info('{:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}'.format(*tuple(results.keys())))
|
||||
logger.info('{:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}'.format(*tuple([(v / nsamples).item() for v in results.values()])))
|
||||
logger.info('==========================================================================================')
|
||||
print()
|
||||
|
||||
for name, metric in results.items():
|
||||
writer.add_scalar(f'eval/{name}', (metric / nsamples).item(), epoch)
|
||||
|
||||
for k in results.keys():
|
||||
if k in ['d1', 'd2', 'd3']:
|
||||
previous_best[k] = max(previous_best[k], (results[k] / nsamples).item())
|
||||
else:
|
||||
previous_best[k] = min(previous_best[k], (results[k] / nsamples).item())
|
||||
|
||||
if rank == 0:
|
||||
checkpoint = {
|
||||
'model': model.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'epoch': epoch,
|
||||
'previous_best': previous_best,
|
||||
}
|
||||
torch.save(checkpoint, os.path.join(args.save_path, 'latest.pth'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
41
metric_depth/util/dist_helper.py
Normal file
41
metric_depth/util/dist_helper.py
Normal file
@@ -0,0 +1,41 @@
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
|
||||
def setup_distributed(backend="nccl", port=None):
|
||||
"""AdaHessian Optimizer
|
||||
Lifted from https://github.com/BIGBALLON/distribuuuu/blob/master/distribuuuu/utils.py
|
||||
Originally licensed MIT, Copyright (c) 2020 Wei Li
|
||||
"""
|
||||
num_gpus = torch.cuda.device_count()
|
||||
|
||||
if "SLURM_JOB_ID" in os.environ:
|
||||
rank = int(os.environ["SLURM_PROCID"])
|
||||
world_size = int(os.environ["SLURM_NTASKS"])
|
||||
node_list = os.environ["SLURM_NODELIST"]
|
||||
addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
|
||||
# specify master port
|
||||
if port is not None:
|
||||
os.environ["MASTER_PORT"] = str(port)
|
||||
elif "MASTER_PORT" not in os.environ:
|
||||
os.environ["MASTER_PORT"] = "10685"
|
||||
if "MASTER_ADDR" not in os.environ:
|
||||
os.environ["MASTER_ADDR"] = addr
|
||||
os.environ["WORLD_SIZE"] = str(world_size)
|
||||
os.environ["LOCAL_RANK"] = str(rank % num_gpus)
|
||||
os.environ["RANK"] = str(rank)
|
||||
else:
|
||||
rank = int(os.environ["RANK"])
|
||||
world_size = int(os.environ["WORLD_SIZE"])
|
||||
|
||||
torch.cuda.set_device(rank % num_gpus)
|
||||
|
||||
dist.init_process_group(
|
||||
backend=backend,
|
||||
world_size=world_size,
|
||||
rank=rank,
|
||||
)
|
||||
return rank, world_size
|
||||
16
metric_depth/util/loss.py
Normal file
16
metric_depth/util/loss.py
Normal file
@@ -0,0 +1,16 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
class SiLogLoss(nn.Module):
|
||||
def __init__(self, lambd=0.5):
|
||||
super().__init__()
|
||||
self.lambd = lambd
|
||||
|
||||
def forward(self, pred, target, valid_mask):
|
||||
valid_mask = valid_mask.detach()
|
||||
diff_log = torch.log(target[valid_mask]) - torch.log(pred[valid_mask])
|
||||
loss = torch.sqrt(torch.pow(diff_log, 2).mean() -
|
||||
self.lambd * torch.pow(diff_log.mean(), 2))
|
||||
|
||||
return loss
|
||||
26
metric_depth/util/metric.py
Normal file
26
metric_depth/util/metric.py
Normal file
@@ -0,0 +1,26 @@
|
||||
import torch
|
||||
|
||||
|
||||
def eval_depth(pred, target):
|
||||
assert pred.shape == target.shape
|
||||
|
||||
thresh = torch.max((target / pred), (pred / target))
|
||||
|
||||
d1 = torch.sum(thresh < 1.25).float() / len(thresh)
|
||||
d2 = torch.sum(thresh < 1.25 ** 2).float() / len(thresh)
|
||||
d3 = torch.sum(thresh < 1.25 ** 3).float() / len(thresh)
|
||||
|
||||
diff = pred - target
|
||||
diff_log = torch.log(pred) - torch.log(target)
|
||||
|
||||
abs_rel = torch.mean(torch.abs(diff) / target)
|
||||
sq_rel = torch.mean(torch.pow(diff, 2) / target)
|
||||
|
||||
rmse = torch.sqrt(torch.mean(torch.pow(diff, 2)))
|
||||
rmse_log = torch.sqrt(torch.mean(torch.pow(diff_log , 2)))
|
||||
|
||||
log10 = torch.mean(torch.abs(torch.log10(pred) - torch.log10(target)))
|
||||
silog = torch.sqrt(torch.pow(diff_log, 2).mean() - 0.5 * torch.pow(diff_log.mean(), 2))
|
||||
|
||||
return {'d1': d1.item(), 'd2': d2.item(), 'd3': d3.item(), 'abs_rel': abs_rel.item(), 'sq_rel': sq_rel.item(),
|
||||
'rmse': rmse.item(), 'rmse_log': rmse_log.item(), 'log10':log10.item(), 'silog':silog.item()}
|
||||
26
metric_depth/util/utils.py
Normal file
26
metric_depth/util/utils.py
Normal file
@@ -0,0 +1,26 @@
|
||||
import os
|
||||
import re
|
||||
import numpy as np
|
||||
import logging
|
||||
|
||||
logs = set()
|
||||
|
||||
|
||||
def init_log(name, level=logging.INFO):
|
||||
if (name, level) in logs:
|
||||
return
|
||||
logs.add((name, level))
|
||||
logger = logging.getLogger(name)
|
||||
logger.setLevel(level)
|
||||
ch = logging.StreamHandler()
|
||||
ch.setLevel(level)
|
||||
if "SLURM_PROCID" in os.environ:
|
||||
rank = int(os.environ["SLURM_PROCID"])
|
||||
logger.addFilter(lambda record: rank == 0)
|
||||
else:
|
||||
rank = 0
|
||||
format_str = "[%(asctime)s][%(levelname)8s] %(message)s"
|
||||
formatter = logging.Formatter(format_str)
|
||||
ch.setFormatter(formatter)
|
||||
logger.addHandler(ch)
|
||||
return logger
|
||||
Reference in New Issue
Block a user