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DA-2K.md

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+# DA-2K Evaluation Benchmark
+
+## Introduction
+
+![DA-2K](assets/DA-2K.png)
+
+DA-2K is proposed in [Depth Anything V2](https://depth-anything-v2.github.io) to evaluate the relative depth estimation capability. It encompasses eight representative scenarios of `indoor`, `outdoor`, `non_real`, `transparent_reflective`, `adverse_style`, `aerial`, `underwater`, and `object`. It consists of 1K diverse high-quality images and 2K precise pair-wise relative depth annotations.
+
+Please refer to our [paper](https://depth-anything-v2.github.io) for details in constructing this benchmark.
+
+
+## Usage
+
+Please first [download the benchmark]().
+
+All annotations are stored in [annotations.json](./annotations.json). The annotation file is a JSON object where each key is the path to an image file, and the value is a list of annotations associated with that image. Each annotation describes two points and identifies which point is closer to the camera. The structure is detailed below:
+
+```
+{
+  "image_path": [
+    {
+      "point1": [h1, w1], # (vertical position, horizontal position)
+      "point2": [h2, w2], # (vertical position, horizontal position)
+      "closer_point": "point1" # we always set "point1" as the closer one
+    },
+    ...
+  ],
+  ...
+}
+```
+
+To visualize the annotations:
+```bash
+python visualize.py [--scene-type <type>]
+```
+
+**Options**
+- `--scene-type <type>` (optional): Specify the scene type (`indoor`, `outdoor`, `non_real`, `transparent_reflective`, `adverse_style`, `aerial`, `underwater`, and `object`). Skip this argument or set <type> as `""` to include all scene types.
+
+## Citation
+
+If you find this benchmark 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 preprint arXiv:},
+  year={2024}
+}
+```

README.md

@@ -1,2 +1,125 @@
-# Depth-Anything-V2
-Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation
+<div align="center">
+<h1>Depth Anything V2</h1>
+
+[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://bingykang.github.io/)<sup>2&dagger;</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup>
+<br>
+[**Zhen Zhao**](http://zhaozhen.me/) · [**Xiaogang Xu**](https://xiaogang00.github.io/) · [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1*</sup>
+
+<sup>1</sup>HKU&emsp;&emsp;&emsp;<sup>2</sup>TikTok
+<br>
+&dagger;project lead&emsp;*corresponding author
+
+<a href=""><img src='https://img.shields.io/badge/arXiv-Depth Anything V2-red' alt='Paper PDF'></a>
+<a href='https://depth-anything-v2.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything V2-green' alt='Project Page'></a>
+<a href='https://huggingface.co/spaces/depth-anything/Depth-Anything-V2'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
+<a href='https://huggingface.co/datasets/depth-anything/DA-2K'><img src='https://img.shields.io/badge/Benchmark-DA--2K-green' alt='Benchmark'></a>
+</div>
+
+This work presents Depth Anything V2. Compared with V1, this version produces significantly more fine-grained and robust depth predictions. Compared with SD-based models, it is much more efficient and lightweight.
+
+![teaser](assets/teaser.png)
+
+## News
+
+- **2024-06-14:** Paper, project page, code, models, demo, and benchmark are all released.
+
+
+## Pre-trained Models
+
+We provide **four models** of varying scales for robust relative depth estimation:
+
+| Model | Params | Checkpoint |
+|:-|-:|:-:|
+| Depth-Anything-V2-Small | 24.8M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Small/resolve/main/depth_anything_v2_vits.pth?download=true) |
+| Depth-Anything-V2-Base | 97.5M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Base/resolve/main/depth_anything_v2_vitb.pth?download=true) |
+| Depth-Anything-V2-Large | 335.3M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true) |
+| Depth-Anything-V2-Giant | 1.3B | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Giant/resolve/main/depth_anything_v2_vitg.pth?download=true) |
+
+
+### Code snippet to use our models
+```python
+import cv2
+import torch
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+# take depth-anything-v2-giant as an example
+model = DepthAnythingV2(encoder='vitg', features=384, out_channels=[1536, 1536, 1536, 1536])
+model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vitg.pth', map_location='cpu'))
+model.eval()
+
+raw_img = cv2.imread('your/image/path')
+depth = model.infer_img(raw_img) # HxW raw depth map
+```
+
+## Usage
+
+### Installation
+
+```bash
+git clone https://github.com/DepthAnything/Depth-Anything-V2
+cd Depth-Anything-V2
+pip install -r requirements.txt
+```
+
+### Running
+
+```bash
+python run.py --encoder <vits | vitb | vitl | vitg> --img-path <path> --outdir <outdir> [--input-size <size>] [--pred-only] [--grayscale]
+```
+Options:
+- `--img-path`: You can either 1) point it to an image directory storing all interested images, 2) point it to a single image, or 3) point it to a text file storing all image paths.
+- `--input-size` (optional): By default, we use input size `518` for model inference. **You can increase the size for even more fine-grained results.**
+- `--pred-only` (optional): Only save the predicted depth map, without raw image.
+- `--grayscale` (optional): Save the grayscale depth map, without applying color palette.
+
+For example:
+```bash
+python run.py --encoder vitg --img-path assets/examples --outdir depth_vis
+```
+
+**If you want to use Depth Anything V2 on videos:**
+
+```bash
+python run_video.py --encoder vitg --video-path assets/examples_video --outdir video_depth_vis
+```
+
+*Please note that our larger model has better temporal consistency on videos.*
+
+
+### Gradio demo
+
+To use our gradio demo locally:
+
+```bash
+python app.py
+```
+
+You can also try our [online demo](https://huggingface.co/spaces/Depth-Anything/Depth-Anything-V2).
+
+**Note:** Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)). In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use intermediate features instead. Although this modification did not improve details or accuracy, we decided to follow this common practice.
+
+
+
+## Fine-tuned to Metric Depth Estimation
+
+Please refer to [metric depth estimation](./metric_depth).
+
+
+## DA-2K Evaluation Benchmark
+
+Please refer to [DA-2K benchmark](./DA-2K.md).
+
+
+## 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 preprint arXiv:},
+  year={2024}
+}
+```

app.py

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+import glob
+import gradio as gr
+import matplotlib
+import numpy as np
+from PIL import Image
+import torch
+import tempfile
+from gradio_imageslider import ImageSlider
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+css = """
+#img-display-container {
+    max-height: 100vh;
+}
+#img-display-input {
+    max-height: 80vh;
+}
+#img-display-output {
+    max-height: 80vh;
+}
+#download {
+    height: 62px;
+}
+"""
+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]}
+}
+encoder = 'vitl'
+model = DepthAnythingV2(**model_configs[encoder])
+state_dict = torch.load(f'checkpoints/depth_anything_v2_{encoder}.pth', map_location="cpu")
+model.load_state_dict(state_dict)
+model = model.to(DEVICE).eval()
+
+title = "# Depth Anything V2"
+description = """Official demo for **Depth Anything V2**.
+Please refer to our [paper](), [project page](https://depth-anything-v2.github.io), or [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
+
+def predict_depth(image):
+    return model.infer_image(image)
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    gr.Markdown("### Depth Prediction demo")
+
+    with gr.Row():
+        input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
+        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
+    submit = gr.Button(value="Compute Depth")
+    gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",)
+    raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",)
+
+    cmap = matplotlib.colormaps.get_cmap('Spectral_r')
+
+    def on_submit(image):
+        original_image = image.copy()
+
+        h, w = image.shape[:2]
+
+        depth = predict_depth(image[:, :, ::-1])
+
+        raw_depth = Image.fromarray(depth.astype('uint16'))
+        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        raw_depth.save(tmp_raw_depth.name)
+
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.astype(np.uint8)
+        colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8)
+
+        gray_depth = Image.fromarray(depth)
+        tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        gray_depth.save(tmp_gray_depth.name)
+
+        return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
+
+    submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file])
+
+    example_files = glob.glob('assets/examples/*')
+    examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
+
+
+if __name__ == '__main__':
+    demo.queue().launch()

depth_anything_v2/dinov2.py

@@ -0,0 +1,415 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from .dinov2_layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0
+        w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
+        # w0, h0 = w0 + 0.1, h0 + 0.1
+        
+        sqrt_N = math.sqrt(N)
+        sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),
+            scale_factor=(sx, sy),
+            # (int(w0), int(h0)), # to solve the upsampling shape issue
+            mode="bicubic",
+            antialias=self.interpolate_antialias
+        )
+        
+        assert int(w0) == patch_pos_embed.shape[-2]
+        assert int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list):
+        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+
+        x = self.prepare_tokens_with_masks(x, masks)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def DINOv2(model_name):
+    model_zoo = {
+        "vits": vit_small, 
+        "vitb": vit_base, 
+        "vitl": vit_large, 
+        "vitg": vit_giant2
+    }
+    
+    return model_zoo[model_name](
+        img_size=518,
+        patch_size=14,
+        init_values=1.0,
+        ffn_layer="mlp" if model_name != "vitg" else "swiglufused",
+        block_chunks=0,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1
+    )

depth_anything_v2/dinov2_layers/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .attention import MemEffAttention

depth_anything_v2/dinov2_layers/attention.py

@@ -0,0 +1,83 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+
+from torch import Tensor
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import memory_efficient_attention, unbind, fmha
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+        

depth_anything_v2/dinov2_layers/block.py

@@ -0,0 +1,252 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+from typing import Callable, List, Any, Tuple, Dict
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import fmha
+    from xformers.ops import scaled_index_add, index_select_cat
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+
+    def forward(self, x_or_x_list):
+        if isinstance(x_or_x_list, Tensor):
+            return super().forward(x_or_x_list)
+        elif isinstance(x_or_x_list, list):
+            assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError

depth_anything_v2/dinov2_layers/drop_path.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)

depth_anything_v2/dinov2_layers/layer_scale.py

@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma

depth_anything_v2/dinov2_layers/mlp.py

@@ -0,0 +1,41 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
+
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x

depth_anything_v2/dinov2_layers/patch_embed.py

@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops

depth_anything_v2/dinov2_layers/swiglu_ffn.py

@@ -0,0 +1,63 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+try:
+    from xformers.ops import SwiGLU
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    SwiGLU = SwiGLUFFN
+    XFORMERS_AVAILABLE = False
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )

depth_anything_v2/dpt.py

@@ -0,0 +1,221 @@
+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.ReLU(True),
+            nn.Identity(),
+        )
+    
+    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
+    ):
+        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.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)
+        depth = F.relu(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)

depth_anything_v2/util/blocks.py

@@ -0,0 +1,148 @@
+import torch.nn as nn
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    if len(in_shape) >= 4:
+        out_shape4 = out_shape
+
+    if expand:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        if len(in_shape) >= 4:
+            out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    if len(in_shape) >= 4:
+        scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+
+    return scratch
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+        
+        self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(
+        self, 
+        features, 
+        activation, 
+        deconv=False, 
+        bn=False, 
+        expand=False, 
+        align_corners=True,
+        size=None
+    ):
+        """Init.
+        
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+        self.size=size
+
+    def forward(self, *xs, size=None):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+
+        output = self.resConfUnit2(output)
+
+        if (size is None) and (self.size is None):
+            modifier = {"scale_factor": 2}
+        elif size is None:
+            modifier = {"size": self.size}
+        else:
+            modifier = {"size": size}
+
+        output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
+        
+        output = self.out_conv(output)
+
+        return output

depth_anything_v2/util/transform.py

@@ -0,0 +1,158 @@
+import numpy as np
+import cv2
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
+        
+        # resize sample
+        sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method)
+
+        if self.__resize_target:
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
+                
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST)
+        
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+        
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+        
+        return sample

metric_depth/README.md

@@ -0,0 +1,55 @@
+# 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.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.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 preprint arXiv:},
+  year={2024}
+}
+```

metric_depth/dataset/hypersim.py

@@ -0,0 +1,74 @@
+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

@@ -0,0 +1,57 @@
+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/kitti/val.txt

@@ -0,0 +1,652 @@
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+/mnt/bn/liheyang/Kitti/raw_data/2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000768.png /mnt/bn/liheyang/Kitti/data_depth_annotated/val/2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000768.png

metric_depth/dataset/transform.py

@@ -0,0 +1,277 @@
+import cv2
+import math
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            if "semseg_mask" in sample:
+                # sample["semseg_mask"] = cv2.resize(
+                #     sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
+                # )
+                sample["semseg_mask"] = F.interpolate(torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode='nearest').numpy()[0, 0]
+                
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(
+                    sample["mask"].astype(np.float32),
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+                # sample["mask"] = sample["mask"].astype(bool)
+
+        # print(sample['image'].shape, sample['depth'].shape)
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+        
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+            
+        if "semseg_mask" in sample:
+            sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
+            sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
+
+        return sample
+
+
+class Crop(object):
+    """Crop sample for batch-wise training. Image is of shape CxHxW
+    """
+
+    def __init__(self, size):
+        if isinstance(size, int):
+            self.size = (size, size)
+        else:
+            self.size = size
+
+    def __call__(self, sample):
+        h, w = sample['image'].shape[-2:]
+        assert h >= self.size[0] and w >= self.size[1], 'Wrong size'
+        
+        h_start = np.random.randint(0, h - self.size[0] + 1)
+        w_start = np.random.randint(0, w - self.size[1] + 1)
+        h_end = h_start + self.size[0]
+        w_end = w_start + self.size[1]
+        
+        sample['image'] = sample['image'][:, h_start: h_end, w_start: w_end]
+        
+        if "depth" in sample:
+            sample["depth"] = sample["depth"][h_start: h_end, w_start: w_end]
+        
+        if "mask" in sample:
+            sample["mask"] = sample["mask"][h_start: h_end, w_start: w_end]
+            
+        if "semseg_mask" in sample:
+            sample["semseg_mask"] = sample["semseg_mask"][h_start: h_end, w_start: w_end]
+            
+        return sample

metric_depth/dataset/vkitti2.py

@@ -0,0 +1,54 @@
+import cv2
+import torch
+from torch.utils.data import Dataset
+from torchvision.transforms import Compose
+
+from dataset.transform import Resize, NormalizeImage, PrepareForNet, Crop
+
+
+class VKITTI2(Dataset):
+    def __init__(self, filelist_path, mode, size=(518, 518)):
+        
+        self.mode = mode
+        self.size = size
+        
+        with open(filelist_path, 'r') as f:
+            self.filelist = f.read().splitlines()
+        
+        net_w, net_h = size
+        self.transform = Compose([
+            Resize(
+                width=net_w,
+                height=net_h,
+                resize_target=True if mode == 'train' else False,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=14,
+                resize_method='lower_bound',
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            PrepareForNet(),
+        ] + ([Crop(size[0])] if self.mode == 'train' else []))
+    
+    def __getitem__(self, item):
+        img_path = self.filelist[item].split(' ')[0]
+        depth_path = self.filelist[item].split(' ')[1]
+        
+        image = cv2.imread(img_path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+        
+        depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) / 100.0  # cm to m
+        
+        sample = self.transform({'image': image, 'depth': depth})
+
+        sample['image'] = torch.from_numpy(sample['image'])
+        sample['depth'] = torch.from_numpy(sample['depth'])
+        
+        sample['valid_mask'] = (sample['depth'] <= 80)
+        
+        sample['image_path'] = self.filelist[item].split(' ')[0]
+        
+        return sample
+
+    def __len__(self):
+        return len(self.filelist)

metric_depth/depth_anything_v2/dinov2.py

@@ -0,0 +1,415 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from .dinov2_layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0
+        w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
+        # w0, h0 = w0 + 0.1, h0 + 0.1
+        
+        sqrt_N = math.sqrt(N)
+        sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),
+            scale_factor=(sx, sy),
+            # (int(w0), int(h0)), # to solve the upsampling shape issue
+            mode="bicubic",
+            antialias=self.interpolate_antialias
+        )
+        
+        assert int(w0) == patch_pos_embed.shape[-2]
+        assert int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list):
+        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+
+        x = self.prepare_tokens_with_masks(x, masks)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def DINOv2(model_name):
+    model_zoo = {
+        "vits": vit_small, 
+        "vitb": vit_base, 
+        "vitl": vit_large, 
+        "vitg": vit_giant2
+    }
+    
+    return model_zoo[model_name](
+        img_size=518,
+        patch_size=14,
+        init_values=1.0,
+        ffn_layer="mlp" if model_name != "vitg" else "swiglufused",
+        block_chunks=0,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1
+    )

metric_depth/depth_anything_v2/dinov2_layers/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .attention import MemEffAttention

metric_depth/depth_anything_v2/dinov2_layers/attention.py

@@ -0,0 +1,83 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+
+from torch import Tensor
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import memory_efficient_attention, unbind, fmha
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+        

metric_depth/depth_anything_v2/dinov2_layers/block.py

@@ -0,0 +1,252 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+from typing import Callable, List, Any, Tuple, Dict
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import fmha
+    from xformers.ops import scaled_index_add, index_select_cat
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+
+    def forward(self, x_or_x_list):
+        if isinstance(x_or_x_list, Tensor):
+            return super().forward(x_or_x_list)
+        elif isinstance(x_or_x_list, list):
+            assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError

metric_depth/depth_anything_v2/dinov2_layers/drop_path.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)

metric_depth/depth_anything_v2/dinov2_layers/layer_scale.py

@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma

metric_depth/depth_anything_v2/dinov2_layers/mlp.py

@@ -0,0 +1,41 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
+
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x

metric_depth/depth_anything_v2/dinov2_layers/patch_embed.py

@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops

metric_depth/depth_anything_v2/dinov2_layers/swiglu_ffn.py

@@ -0,0 +1,63 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+try:
+    from xformers.ops import SwiGLU
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    SwiGLU = SwiGLUFFN
+    XFORMERS_AVAILABLE = False
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )

metric_depth/depth_anything_v2/dpt.py

@@ -0,0 +1,222 @@
+import cv2
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.transforms import Compose
+
+from .dinov2 import DINOv2
+from .util.blocks import FeatureFusionBlock, _make_scratch
+from .util.transform import Resize, NormalizeImage, PrepareForNet
+
+
+def _make_fusion_block(features, use_bn, size=None):
+    return FeatureFusionBlock(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+        size=size,
+    )
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_feature, out_feature):
+        super().__init__()
+        
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(out_feature),
+            nn.ReLU(True)
+        )
+    
+    def forward(self, x):
+        return self.conv_block(x)
+
+
+class DPTHead(nn.Module):
+    def __init__(
+        self, 
+        in_channels, 
+        features=256, 
+        use_bn=False, 
+        out_channels=[256, 512, 1024, 1024], 
+        use_clstoken=False
+    ):
+        super(DPTHead, self).__init__()
+        
+        self.use_clstoken = use_clstoken
+        
+        self.projects = nn.ModuleList([
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channel,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ) for out_channel in out_channels
+        ])
+        
+        self.resize_layers = nn.ModuleList([
+            nn.ConvTranspose2d(
+                in_channels=out_channels[0],
+                out_channels=out_channels[0],
+                kernel_size=4,
+                stride=4,
+                padding=0),
+            nn.ConvTranspose2d(
+                in_channels=out_channels[1],
+                out_channels=out_channels[1],
+                kernel_size=2,
+                stride=2,
+                padding=0),
+            nn.Identity(),
+            nn.Conv2d(
+                in_channels=out_channels[3],
+                out_channels=out_channels[3],
+                kernel_size=3,
+                stride=2,
+                padding=1)
+        ])
+        
+        if use_clstoken:
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(
+                    nn.Sequential(
+                        nn.Linear(2 * in_channels, in_channels),
+                        nn.GELU()))
+        
+        self.scratch = _make_scratch(
+            out_channels,
+            features,
+            groups=1,
+            expand=False,
+        )
+        
+        self.scratch.stem_transpose = None
+        
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+        
+        head_features_1 = features
+        head_features_2 = 32
+        
+        self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
+        self.scratch.output_conv2 = nn.Sequential(
+            nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
+            nn.Sigmoid()
+        )
+    
+    def forward(self, out_features, patch_h, patch_w):
+        out = []
+        for i, x in enumerate(out_features):
+            if self.use_clstoken:
+                x, cls_token = x[0], x[1]
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+            else:
+                x = x[0]
+            
+            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
+            
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            
+            out.append(x)
+        
+        layer_1, layer_2, layer_3, layer_4 = out
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+        
+        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])        
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv1(path_1)
+        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
+        out = self.scratch.output_conv2(out)
+        
+        return out
+
+
+class DepthAnythingV2(nn.Module):
+    def __init__(
+        self, 
+        encoder='vitl', 
+        features=256, 
+        out_channels=[256, 512, 1024, 1024], 
+        use_bn=False, 
+        use_clstoken=False,
+        max_depth=20.0
+    ):
+        super(DepthAnythingV2, self).__init__()
+        
+        self.intermediate_layer_idx = {
+            'vits': [2, 5, 8, 11],
+            'vitb': [2, 5, 8, 11], 
+            'vitl': [4, 11, 17, 23], 
+            'vitg': [9, 19, 29, 39]
+        }
+        
+        self.max_depth = max_depth
+        
+        self.encoder = encoder
+        self.pretrained = DINOv2(model_name=encoder)
+        
+        self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
+    
+    def forward(self, x):
+        patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14
+        
+        features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True)
+        
+        depth = self.depth_head(features, patch_h, patch_w) * self.max_depth
+        
+        return depth.squeeze(1)
+    
+    @torch.no_grad()
+    def infer_image(self, raw_image, input_size=518):
+        image, (h, w) = self.image2tensor(raw_image, input_size)
+        
+        depth = self.forward(image)
+        
+        depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0]
+        
+        return depth.cpu().numpy()
+    
+    def image2tensor(self, raw_image, input_size=518):        
+        transform = Compose([
+            Resize(
+                width=input_size,
+                height=input_size,
+                resize_target=False,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=14,
+                resize_method='lower_bound',
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            PrepareForNet(),
+        ])
+        
+        h, w = raw_image.shape[:2]
+        
+        image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0
+        
+        image = transform({'image': image})['image']
+        image = torch.from_numpy(image).unsqueeze(0)
+        
+        DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+        image = image.to(DEVICE)
+        
+        return image, (h, w)

metric_depth/depth_anything_v2/util/blocks.py

@@ -0,0 +1,148 @@
+import torch.nn as nn
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    if len(in_shape) >= 4:
+        out_shape4 = out_shape
+
+    if expand:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        if len(in_shape) >= 4:
+            out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    if len(in_shape) >= 4:
+        scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+
+    return scratch
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+        
+        self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(
+        self, 
+        features, 
+        activation, 
+        deconv=False, 
+        bn=False, 
+        expand=False, 
+        align_corners=True,
+        size=None
+    ):
+        """Init.
+        
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+        self.size=size
+
+    def forward(self, *xs, size=None):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+
+        output = self.resConfUnit2(output)
+
+        if (size is None) and (self.size is None):
+            modifier = {"scale_factor": 2}
+        elif size is None:
+            modifier = {"size": self.size}
+        else:
+            modifier = {"size": size}
+
+        output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
+        
+        output = self.out_conv(output)
+
+        return output

metric_depth/depth_anything_v2/util/transform.py

@@ -0,0 +1,158 @@
+import numpy as np
+import cv2
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
+        
+        # resize sample
+        sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method)
+
+        if self.__resize_target:
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
+                
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST)
+        
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+        
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+        
+        return sample

metric_depth/depth_to_pointcloud.py

@@ -0,0 +1,83 @@
+# Born out of Depth Anything V1 Issue 36
+# Make sure you have the necessary libraries
+# Code by @1ssb
+
+import argparse
+import cv2
+import glob
+import numpy as np
+import open3d as o3d
+import os
+from PIL import Image
+import torch
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--encoder', default='vitl', type=str, choices=['vits', 'vitb', 'vitl', 'vitg'])
+    parser.add_argument('--load-from', default='', type=str)
+    parser.add_argument('--max-depth', default=20, type=float)
+    
+    parser.add_argument('--img-path', type=str)
+    parser.add_argument('--outdir', type=str, default='./vis_pointcloud')
+    
+    args = parser.parse_args()
+    
+    # Global settings
+    FL = 715.0873
+    FY = 784 * 0.6
+    FX = 784 * 0.6
+    NYU_DATA = False
+    FINAL_HEIGHT = 518
+    FINAL_WIDTH = 518
+    
+    DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+    
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+    
+    depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth})
+    depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu'))
+    depth_anything = depth_anything.to(DEVICE).eval()
+    
+    if os.path.isfile(args.img_path):
+        if args.img_path.endswith('txt'):
+            with open(args.img_path, 'r') as f:
+                filenames = f.read().splitlines()
+        else:
+            filenames = [args.img_path]
+    else:
+        filenames = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True)
+    
+    os.makedirs(args.outdir, exist_ok=True)
+    
+    for k, filename in enumerate(filenames):
+        print(f'Progress {k+1}/{len(filenames)}: {filename}')
+        
+        color_image = Image.open(filename).convert('RGB')
+        
+        image = cv2.imread(filename)
+        pred = depth_anything.infer_image(image, FINAL_HEIGHT)
+        
+        # Resize color image and depth to final size
+        resized_color_image = color_image.resize((FINAL_WIDTH, FINAL_HEIGHT), Image.LANCZOS)
+        resized_pred = Image.fromarray(pred).resize((FINAL_WIDTH, FINAL_HEIGHT), Image.NEAREST)
+        
+        focal_length_x, focal_length_y = (FX, FY) if not NYU_DATA else (FL, FL)
+        x, y = np.meshgrid(np.arange(FINAL_WIDTH), np.arange(FINAL_HEIGHT))
+        x = (x - FINAL_WIDTH / 2) / focal_length_x
+        y = (y - FINAL_HEIGHT / 2) / focal_length_y
+        z = np.array(resized_pred)
+        points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3)
+        colors = np.array(resized_color_image).reshape(-1, 3) / 255.0
+        
+        pcd = o3d.geometry.PointCloud()
+        pcd.points = o3d.utility.Vector3dVector(points)
+        pcd.colors = o3d.utility.Vector3dVector(colors)
+        o3d.io.write_point_cloud(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + ".ply"), pcd)

metric_depth/dist_train.sh

@@ -0,0 +1,26 @@
+#!/bin/bash
+now=$(date +"%Y%m%d_%H%M%S")
+
+epoch=120
+bs=4
+gpus=8
+lr=0.000005
+encoder=vitl
+dataset=hypersim # vkitti
+img_size=518
+min_depth=0.001
+max_depth=20 # 80 for virtual kitti
+pretrained_from=../checkpoints/depth_anything_v2_${encoder}.pth
+save_path=exp/hypersim # exp/vkitti
+
+mkdir -p $save_path
+
+python3 -m torch.distributed.launch \
+    --nproc_per_node=$gpus \
+    --nnodes 1 \
+    --node_rank=0 \
+    --master_addr=localhost \
+    --master_port=20596 \
+    train.py --epoch $epoch --encoder $encoder --bs $bs --lr $lr --save-path $save_path --dataset $dataset \
+    --img-size $img_size --min-depth $min_depth --max-depth $max_depth --pretrained-from $pretrained_from \
+    --port 20596 2>&1 | tee -a $save_path/$now.log

metric_depth/requirements.txt

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

metric_depth/run.py

@@ -0,0 +1,81 @@
+import argparse
+import cv2
+import glob
+import matplotlib
+import numpy as np
+import os
+import torch
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Depth Anything V2 Metric Depth Estimation')
+    
+    parser.add_argument('--img-path', type=str)
+    parser.add_argument('--input-size', type=int, default=518)
+    parser.add_argument('--outdir', type=str, default='./vis_depth')
+    
+    parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
+    parser.add_argument('--load-from', type=str, default='checkpoints/depth_anything_v2_metric_hypersim_vitl.pth')
+    parser.add_argument('--max-depth', type=float, default=20)
+    
+    parser.add_argument('--save-numpy', dest='save_numpy', action='store_true', help='save the model raw output')
+    parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction')
+    parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette')
+    
+    args = parser.parse_args()
+    
+    DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+    
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+    
+    depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth})
+    depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu'))
+    depth_anything = depth_anything.to(DEVICE).eval()
+    
+    if os.path.isfile(args.img_path):
+        if args.img_path.endswith('txt'):
+            with open(args.img_path, 'r') as f:
+                filenames = f.read().splitlines()
+        else:
+            filenames = [args.img_path]
+    else:
+        filenames = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True)
+    
+    os.makedirs(args.outdir, exist_ok=True)
+    
+    cmap = matplotlib.colormaps.get_cmap('Spectral')
+    
+    for k, filename in enumerate(filenames):
+        print(f'Progress {k+1}/{len(filenames)}: {filename}')
+        
+        raw_image = cv2.imread(filename)
+        
+        depth = depth_anything.infer_image(raw_image, args.input_size)
+        
+        if args.save_numpy:
+            output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '_raw_depth_meter.npy')
+            np.save(output_path, depth)
+        
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.astype(np.uint8)
+        
+        if args.grayscale:
+            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
+        else:
+            depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
+        
+        output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png')
+        if args.pred_only:
+            cv2.imwrite(output_path, depth)
+        else:
+            split_region = np.ones((raw_image.shape[0], 50, 3), dtype=np.uint8) * 255
+            combined_result = cv2.hconcat([raw_image, split_region, depth])
+            
+            cv2.imwrite(output_path, combined_result)

metric_depth/train.py

@@ -0,0 +1,212 @@
+import argparse
+import logging
+import os
+import pprint
+import random
+
+import warnings
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+from torch.utils.data import DataLoader
+from torch.optim import AdamW
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+
+from dataset.hypersim import Hypersim
+from dataset.kitti import KITTI
+from dataset.vkitti2 import VKITTI2
+from depth_anything_v2.dpt import DepthAnythingV2
+from util.dist_helper import setup_distributed
+from util.loss import SiLogLoss
+from util.metric import eval_depth
+from util.utils import init_log
+
+
+parser = argparse.ArgumentParser(description='Depth Anything V2 for Metric Depth Estimation')
+
+parser.add_argument('--encoder', default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
+parser.add_argument('--dataset', default='hypersim', choices=['hypersim', 'vkitti'])
+parser.add_argument('--img-size', default=518, type=int)
+parser.add_argument('--min-depth', default=0.001, type=float)
+parser.add_argument('--max-depth', default=20, type=float)
+parser.add_argument('--epochs', default=40, type=int)
+parser.add_argument('--bs', default=2, type=int)
+parser.add_argument('--lr', default=0.000005, type=float)
+parser.add_argument('--pretrained-from', type=str)
+parser.add_argument('--save-path', type=str, required=True)
+parser.add_argument('--local-rank', default=0, type=int)
+parser.add_argument('--port', default=None, type=int)
+
+
+def main():
+    args = parser.parse_args()
+    
+    warnings.simplefilter('ignore', np.RankWarning)
+    
+    logger = init_log('global', logging.INFO)
+    logger.propagate = 0
+    
+    rank, world_size = setup_distributed(port=args.port)
+    
+    if rank == 0:
+        all_args = {**vars(args), 'ngpus': world_size}
+        logger.info('{}\n'.format(pprint.pformat(all_args)))
+        writer = SummaryWriter(args.save_path)
+    
+    cudnn.enabled = True
+    cudnn.benchmark = True
+    
+    size = (args.img_size, args.img_size)
+    if args.dataset == 'hypersim':
+        trainset = Hypersim('dataset/splits/hypersim/train.txt', 'train', size=size)
+    elif args.dataset == 'vkitti':
+        trainset = VKITTI2('dataset/splits/vkitti2/train.txt', 'train', size=size)
+    else:
+        raise NotImplementedError
+    trainsampler = torch.utils.data.distributed.DistributedSampler(trainset)
+    trainloader = DataLoader(trainset, batch_size=args.bs, pin_memory=True, num_workers=4, drop_last=True, sampler=trainsampler)
+    
+    if args.dataset == 'hypersim':
+        valset = Hypersim('dataset/splits/hypersim/val.txt', 'val', size=size)
+    elif args.dataset == 'vkitti':
+        valset = KITTI('dataset/splits/kitti/val.txt', 'val', size=size)
+    else:
+        raise NotImplementedError
+    valsampler = torch.utils.data.distributed.DistributedSampler(valset)
+    valloader = DataLoader(valset, batch_size=1, pin_memory=True, num_workers=4, drop_last=True, sampler=valsampler)
+    
+    local_rank = int(os.environ["LOCAL_RANK"])
+    
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+    model = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth})
+    
+    if args.pretrained_from:
+        model.load_state_dict({k: v for k, v in torch.load(args.pretrained_from, map_location='cpu').items() if 'pretrained' in k}, strict=False)
+    
+    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+    model.cuda(local_rank)
+    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], broadcast_buffers=False,
+                                                      output_device=local_rank, find_unused_parameters=True)
+    
+    criterion = SiLogLoss().cuda(local_rank)
+    
+    optimizer = AdamW([{'params': [param for name, param in model.named_parameters() if 'pretrained' in name], 'lr': args.lr},
+                       {'params': [param for name, param in model.named_parameters() if 'pretrained' not in name], 'lr': args.lr * 10.0}],
+                      lr=args.lr, betas=(0.9, 0.999), weight_decay=0.01)
+    
+    total_iters = args.epochs * len(trainloader)
+    
+    previous_best = {'d1': 0, 'd2': 0, 'd3': 0, 'abs_rel': 100, 'sq_rel': 100, 'rmse': 100, 'rmse_log': 100, 'log10': 100, 'silog': 100}
+    
+    for epoch in range(args.epochs):
+        if rank == 0:
+            logger.info('===========> Epoch: {:}/{:}, d1: {:.3f}, d2: {:.3f}, d3: {:.3f}'.format(epoch, args.epochs, previous_best['d1'], previous_best['d2'], previous_best['d3']))
+            logger.info('===========> Epoch: {:}/{:}, abs_rel: {:.3f}, sq_rel: {:.3f}, rmse: {:.3f}, rmse_log: {:.3f}, '
+                        'log10: {:.3f}, silog: {:.3f}'.format(
+                            epoch, args.epochs, previous_best['abs_rel'], previous_best['sq_rel'], previous_best['rmse'], 
+                            previous_best['rmse_log'], previous_best['log10'], previous_best['silog']))
+        
+        trainloader.sampler.set_epoch(epoch + 1)
+        
+        model.train()
+        total_loss = 0
+        
+        for i, sample in enumerate(trainloader):
+            optimizer.zero_grad()
+            
+            img, depth, valid_mask = sample['image'].cuda(), sample['depth'].cuda(), sample['valid_mask'].cuda()
+            
+            if random.random() < 0.5:
+                img = img.flip(-1)
+                depth = depth.flip(-1)
+                valid_mask = valid_mask.flip(-1)
+            
+            pred = model(img)
+            
+            loss = criterion(pred, depth, (valid_mask == 1) & (depth >= args.min_depth) & (depth <= args.max_depth))
+            
+            loss.backward()
+            optimizer.step()
+            
+            total_loss += loss.item()
+            
+            iters = epoch * len(trainloader) + i
+            
+            lr = args.lr * (1 - iters / total_iters) ** 0.9
+            
+            optimizer.param_groups[0]["lr"] = lr
+            optimizer.param_groups[1]["lr"] = lr * 10.0
+            
+            if rank == 0:
+                writer.add_scalar('train/loss', loss.item(), iters)
+            
+            if rank == 0 and i % 100 == 0:
+                logger.info('Iter: {}/{}, LR: {:.7f}, Loss: {:.3f}'.format(i, len(trainloader), optimizer.param_groups[0]['lr'], loss.item()))
+        
+        model.eval()
+        
+        results = {'d1': torch.tensor([0.0]).cuda(), 'd2': torch.tensor([0.0]).cuda(), 'd3': torch.tensor([0.0]).cuda(), 
+                   'abs_rel': torch.tensor([0.0]).cuda(), 'sq_rel': torch.tensor([0.0]).cuda(), 'rmse': torch.tensor([0.0]).cuda(), 
+                   'rmse_log': torch.tensor([0.0]).cuda(), 'log10': torch.tensor([0.0]).cuda(), 'silog': torch.tensor([0.0]).cuda()}
+        nsamples = torch.tensor([0.0]).cuda()
+        
+        for i, sample in enumerate(valloader):
+            
+            img, depth, valid_mask = sample['image'].cuda().float(), sample['depth'].cuda()[0], sample['valid_mask'].cuda()[0]
+            
+            with torch.no_grad():
+                pred = model(img)
+                pred = F.interpolate(pred[:, None], depth.shape[-2:], mode='bilinear', align_corners=True)[0, 0]
+            
+            valid_mask = (valid_mask == 1) & (depth >= args.min_depth) & (depth <= args.max_depth)
+            
+            if valid_mask.sum() < 10:
+                continue
+            
+            cur_results = eval_depth(pred[valid_mask], depth[valid_mask])
+            
+            for k in results.keys():
+                results[k] += cur_results[k]
+            nsamples += 1
+        
+        torch.distributed.barrier()
+        
+        for k in results.keys():
+            dist.reduce(results[k], dst=0)
+        dist.reduce(nsamples, dst=0)
+        
+        if rank == 0:
+            logger.info('==========================================================================================')
+            logger.info('{:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}, {:>8}'.format(*tuple(results.keys())))
+            logger.info('{:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}, {:8.3f}'.format(*tuple([(v / nsamples).item() for v in results.values()])))
+            logger.info('==========================================================================================')
+            print()
+            
+            for name, metric in results.items():
+                writer.add_scalar(f'eval/{name}', (metric / nsamples).item(), epoch)
+        
+        for k in results.keys():
+            if k in ['d1', 'd2', 'd3']:
+                previous_best[k] = max(previous_best[k], (results[k] / nsamples).item())
+            else:
+                previous_best[k] = min(previous_best[k], (results[k] / nsamples).item())
+        
+        if rank == 0:
+            checkpoint = {
+                'model': model.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'epoch': epoch,
+                'previous_best': previous_best,
+            }
+            torch.save(checkpoint, os.path.join(args.save_path, 'latest.pth'))
+
+
+if __name__ == '__main__':
+    main()

metric_depth/util/dist_helper.py

@@ -0,0 +1,41 @@
+import os
+import subprocess
+
+import torch
+import torch.distributed as dist
+
+
+def setup_distributed(backend="nccl", port=None):
+    """AdaHessian Optimizer
+    Lifted from https://github.com/BIGBALLON/distribuuuu/blob/master/distribuuuu/utils.py
+    Originally licensed MIT, Copyright (c) 2020 Wei Li
+    """
+    num_gpus = torch.cuda.device_count()
+
+    if "SLURM_JOB_ID" in os.environ:
+        rank = int(os.environ["SLURM_PROCID"])
+        world_size = int(os.environ["SLURM_NTASKS"])
+        node_list = os.environ["SLURM_NODELIST"]
+        addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
+        # specify master port
+        if port is not None:
+            os.environ["MASTER_PORT"] = str(port)
+        elif "MASTER_PORT" not in os.environ:
+            os.environ["MASTER_PORT"] = "10685"
+        if "MASTER_ADDR" not in os.environ:
+            os.environ["MASTER_ADDR"] = addr
+        os.environ["WORLD_SIZE"] = str(world_size)
+        os.environ["LOCAL_RANK"] = str(rank % num_gpus)
+        os.environ["RANK"] = str(rank)
+    else:
+        rank = int(os.environ["RANK"])
+        world_size = int(os.environ["WORLD_SIZE"])
+
+    torch.cuda.set_device(rank % num_gpus)
+
+    dist.init_process_group(
+        backend=backend,
+        world_size=world_size,
+        rank=rank,
+    )
+    return rank, world_size

metric_depth/util/loss.py

@@ -0,0 +1,16 @@
+import torch
+from torch import nn
+
+
+class SiLogLoss(nn.Module):
+    def __init__(self, lambd=0.5):
+        super().__init__()
+        self.lambd = lambd
+
+    def forward(self, pred, target, valid_mask):
+        valid_mask = valid_mask.detach()
+        diff_log = torch.log(target[valid_mask]) - torch.log(pred[valid_mask])
+        loss = torch.sqrt(torch.pow(diff_log, 2).mean() -
+                          self.lambd * torch.pow(diff_log.mean(), 2))
+
+        return loss

metric_depth/util/metric.py

@@ -0,0 +1,26 @@
+import torch
+
+
+def eval_depth(pred, target):
+    assert pred.shape == target.shape
+
+    thresh = torch.max((target / pred), (pred / target))
+
+    d1 = torch.sum(thresh < 1.25).float() / len(thresh)
+    d2 = torch.sum(thresh < 1.25 ** 2).float() / len(thresh)
+    d3 = torch.sum(thresh < 1.25 ** 3).float() / len(thresh)
+
+    diff = pred - target
+    diff_log = torch.log(pred) - torch.log(target)
+
+    abs_rel = torch.mean(torch.abs(diff) / target)
+    sq_rel = torch.mean(torch.pow(diff, 2) / target)
+
+    rmse = torch.sqrt(torch.mean(torch.pow(diff, 2)))
+    rmse_log = torch.sqrt(torch.mean(torch.pow(diff_log , 2)))
+
+    log10 = torch.mean(torch.abs(torch.log10(pred) - torch.log10(target)))
+    silog = torch.sqrt(torch.pow(diff_log, 2).mean() - 0.5 * torch.pow(diff_log.mean(), 2))
+
+    return {'d1': d1.item(), 'd2': d2.item(), 'd3': d3.item(), 'abs_rel': abs_rel.item(), 'sq_rel': sq_rel.item(), 
+            'rmse': rmse.item(), 'rmse_log': rmse_log.item(), 'log10':log10.item(), 'silog':silog.item()}

metric_depth/util/utils.py

@@ -0,0 +1,26 @@
+import os
+import re
+import numpy as np
+import logging
+
+logs = set()
+
+
+def init_log(name, level=logging.INFO):
+    if (name, level) in logs:
+        return
+    logs.add((name, level))
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    ch = logging.StreamHandler()
+    ch.setLevel(level)
+    if "SLURM_PROCID" in os.environ:
+        rank = int(os.environ["SLURM_PROCID"])
+        logger.addFilter(lambda record: rank == 0)
+    else:
+        rank = 0
+    format_str = "[%(asctime)s][%(levelname)8s] %(message)s"
+    formatter = logging.Formatter(format_str)
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
+    return logger

requirements.txt

@@ -0,0 +1,6 @@
+gradio_imageslider
+gradio==4.29.0
+matplotlib
+opencv-python
+torch
+torchvision

run.py

@@ -0,0 +1,73 @@
+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')
+    
+    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('--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])
+    depth_anything.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{args.encoder}.pth', 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_r')
+    
+    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)
+        
+        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)
+        
+        if args.pred_only:
+            cv2.imwrite(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), 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(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), combined_result)

run_video.py

@@ -0,0 +1,88 @@
+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')
+    
+    parser.add_argument('--video-path', type=str)
+    parser.add_argument('--input-size', type=int, default=518)
+    parser.add_argument('--outdir', type=str, default='./vis_video_depth')
+    
+    parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
+    
+    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])
+    depth_anything.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{args.encoder}.pth', map_location='cpu'))
+    depth_anything = depth_anything.to(DEVICE).eval()
+    
+    if os.path.isfile(args.video_path):
+        if args.video_path.endswith('txt'):
+            with open(args.video_path, 'r') as f:
+                lines = f.read().splitlines()
+        else:
+            filenames = [args.video_path]
+    else:
+        filenames = glob.glob(os.path.join(args.video_path, '**/*'), recursive=True)
+    
+    os.makedirs(args.outdir, exist_ok=True)
+    
+    margin_width = 50
+    cmap = matplotlib.colormaps.get_cmap('Spectral_r')
+    
+    for k, filename in enumerate(filenames):
+        print(f'Progress {k+1}/{len(filenames)}: {filename}')
+        
+        raw_video = cv2.VideoCapture(filename)
+        frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS))
+        output_width = frame_width * 2 + margin_width
+        
+        output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.mp4')
+        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (output_width, frame_height))
+        
+        while raw_video.isOpened():
+            ret, raw_frame = raw_video.read()
+            if not ret:
+                break
+            
+            depth = depth_anything.infer_image(raw_frame, args.input_size)
+            
+            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)
+            
+            if args.pred_only:
+                out.write(depth)
+            else:
+                split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255
+                combined_frame = cv2.hconcat([raw_frame, split_region, depth])
+                
+                out.write(combined_frame)
+        
+        raw_video.release()
+        out.release()