Delete metric_depth

metric_depth/README.md

@@ -1,55 +0,0 @@
-# Depth Anything V2 for Metric Depth Estimation
-
-![teaser](./assets/compare_zoedepth.png)
-
-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.
-
-
-## Usage
-
-### Inference
-
-Please first download our pre-trained metric depth models and put them under the `checkpoints` directory: 
-- [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)
-- [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)
-
-```bash
-# indoor scenes
-python run.py \
-  --encoder vitl --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
-  --max-depth 20 --img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
-
-# outdoor scenes
-python run.py \
-  --encoder vitl --load-from checkpoints/depth_anything_v2_metric_vkitti_vitl.pth \
-  --max-depth 80 --img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
-```
-
-You can also project 2D images to point clouds:
-```bash
-python depth_to_pointcloud.py \
-  --encoder vitl --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
-  --max-depth 20 --img-path <path> --outdir <outdir>
-```
-
-### Reproduce training
-
-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:
-
-```bash
-bash dist_train.sh
-```
-
-
-## Citation
-
-If you find this project useful, please consider citing:
-
-```bibtex
-@article{depth_anything_v2,
-  title={Depth Anything V2},
-  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-  journal={arXiv:2406.09414},
-  year={2024}
-}
-```

metric_depth/dataset/hypersim.py

@@ -1,74 +0,0 @@
-import cv2
-import h5py
-import numpy as np
-import torch
-from torch.utils.data import Dataset
-from torchvision.transforms import Compose
-
-from dataset.transform import Resize, NormalizeImage, PrepareForNet, Crop
-
-
-def hypersim_distance_to_depth(npyDistance):
-    intWidth, intHeight, fltFocal = 1024, 768, 886.81
-
-    npyImageplaneX = np.linspace((-0.5 * intWidth) + 0.5, (0.5 * intWidth) - 0.5, intWidth).reshape(
-        1, intWidth).repeat(intHeight, 0).astype(np.float32)[:, :, None]
-    npyImageplaneY = np.linspace((-0.5 * intHeight) + 0.5, (0.5 * intHeight) - 0.5,
-                                 intHeight).reshape(intHeight, 1).repeat(intWidth, 1).astype(np.float32)[:, :, None]
-    npyImageplaneZ = np.full([intHeight, intWidth, 1], fltFocal, np.float32)
-    npyImageplane = np.concatenate(
-        [npyImageplaneX, npyImageplaneY, npyImageplaneZ], 2)
-
-    npyDepth = npyDistance / np.linalg.norm(npyImageplane, 2, 2) * fltFocal
-    return npyDepth
-
-
-class Hypersim(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_fd = h5py.File(depth_path, "r")
-        distance_meters = np.array(depth_fd['dataset'])
-        depth = hypersim_distance_to_depth(distance_meters)
-        
-        sample = self.transform({'image': image, 'depth': depth})
-
-        sample['image'] = torch.from_numpy(sample['image'])
-        sample['depth'] = torch.from_numpy(sample['depth'])
-        
-        sample['valid_mask'] = (torch.isnan(sample['depth']) == 0)
-        sample['depth'][sample['valid_mask'] == 0] = 0
-        
-        sample['image_path'] = self.filelist[item].split(' ')[0]
-        
-        return sample
-
-    def __len__(self):
-        return len(self.filelist)

metric_depth/dataset/kitti.py

@@ -1,57 +0,0 @@
-import cv2
-import torch
-from torch.utils.data import Dataset
-from torchvision.transforms import Compose
-
-from dataset.transform import Resize, NormalizeImage, PrepareForNet
-
-
-class KITTI(Dataset):
-    def __init__(self, filelist_path, mode, size=(518, 518)):
-        if mode != 'val':
-            raise NotImplementedError
-        
-        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(),
-        ])
-    
-    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_UNCHANGED).astype('float32')
-        
-        sample = self.transform({'image': image, 'depth': depth})
-        
-        sample['image'] = torch.from_numpy(sample['image'])
-        sample['depth'] = torch.from_numpy(sample['depth'])
-        sample['depth'] = sample['depth'] / 256.0  # convert in meters
-        
-        sample['valid_mask'] = sample['depth'] > 0
-        
-        sample['image_path'] = self.filelist[item].split(' ')[0]
-        
-        return sample
-
-    def __len__(self):
-        return len(self.filelist)

metric_depth/dataset/splits/hypersim/train.txt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:47beb7c615a54d08dfa2f053787897455e845ad1b54d268194a6b431b01a04d0
-size 13694890

metric_depth/dataset/transform.py

@@ -1,277 +0,0 @@
-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

metric_depth/dataset/vkitti2.py

@@ -1,54 +0,0 @@
-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)

metric_depth/depth_anything_v2/dinov2.py

@@ -1,415 +0,0 @@
-# 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
-    )

metric_depth/depth_anything_v2/dinov2_layers/__init__.py

@@ -1,11 +0,0 @@
-# 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

metric_depth/depth_anything_v2/dinov2_layers/attention.py

@@ -1,83 +0,0 @@
-# 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
-
-        

metric_depth/depth_anything_v2/dinov2_layers/block.py

@@ -1,252 +0,0 @@
-# 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

metric_depth/depth_anything_v2/dinov2_layers/drop_path.py

@@ -1,35 +0,0 @@
-# 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)

metric_depth/depth_anything_v2/dinov2_layers/layer_scale.py

@@ -1,28 +0,0 @@
-# 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

metric_depth/depth_anything_v2/dinov2_layers/mlp.py

@@ -1,41 +0,0 @@
-# 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

metric_depth/depth_anything_v2/dinov2_layers/patch_embed.py

@@ -1,89 +0,0 @@
-# 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

metric_depth/depth_anything_v2/dinov2_layers/swiglu_ffn.py

@@ -1,63 +0,0 @@
-# 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,
-        )

metric_depth/depth_anything_v2/dpt.py

@@ -1,222 +0,0 @@
-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)

metric_depth/depth_anything_v2/util/blocks.py

@@ -1,148 +0,0 @@
-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

metric_depth/depth_anything_v2/util/transform.py

@@ -1,158 +0,0 @@
-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

metric_depth/depth_to_pointcloud.py

@@ -1,83 +0,0 @@
-# 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)

metric_depth/dist_train.sh

@@ -1,26 +0,0 @@
-#!/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

metric_depth/requirements.txt

@@ -1,5 +0,0 @@
-matplotlib
-opencv-python
-open3d
-torch
-torchvision

metric_depth/run.py

@@ -1,81 +0,0 @@
-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)

metric_depth/train.py

@@ -1,212 +0,0 @@
-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()

metric_depth/util/dist_helper.py

@@ -1,41 +0,0 @@
-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

metric_depth/util/loss.py

@@ -1,16 +0,0 @@
-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

metric_depth/util/metric.py

@@ -1,26 +0,0 @@
-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()}

metric_depth/util/utils.py

@@ -1,26 +0,0 @@
-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