Initial release v1.0

Author: Shariq F. Bhat

.gitignore

@@ -1,11 +1,28 @@
+*.png
+**.gif
+.vscode/
+*.rdb
+**.xml
+wandb/
+slurm/
+tmp/
+.logs/
+checkpoints/
+external_jobs/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
-
+ptlflow_logs/
+output/
+log/
+.idea/
 # C extensions
 *.so
-
+results/
+**.DS_Store
+**.pt
+demo/
 # Distribution / packaging
 .Python
 build/
@@ -26,7 +43,9 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
-
+~shortcuts/
+**/wandb_logs/
+**.db
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.

README.md

@@ -1 +1,206 @@
-# ZoeDepth
+# **ZoeDepth: Combining relative and metric depth** (Official implementation)  <!-- omit in toc -->
+
+## **Table of Contents** <!-- omit in toc -->
+- [**Usage**](#usage)
+  - [Using torch hub](#using-torch-hub)
+  - [Using local copy](#using-local-copy)
+    - [Using local torch hub](#using-local-torch-hub)
+    - [or load the models manually](#or-load-the-models-manually)
+  - [Using ZoeD models to predict depth](#using-zoed-models-to-predict-depth)
+- [**Environment setup**](#environment-setup)
+- [**Sanity checks** (Recommended)](#sanity-checks-recommended)
+- [Model files](#model-files)
+- [**Evaluation**](#evaluation)
+  - [Evaluating offical models](#evaluating-offical-models)
+  - [Evaluating local checkpoint](#evaluating-local-checkpoint)
+- [**Training**](#training)
+- [**Citation**](#citation)
+
+
+## **Usage**
+It is recommended to fetch the latest [MiDaS repo](https://github.com/isl-org/MiDaS) via torch hub before proceeding:
+```python
+import torch
+
+torch.hub.help("intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True)  # Triggers fresh download of MiDaS repo
+```
+### **ZoeDepth models** <!-- omit in toc -->
+### Using torch hub
+```python
+import torch
+
+repo = "intel-isl/ZoeDepth"
+# Zoe_N
+model_zoe_n = torch.hub.load(repo, "ZoeD_N", pretrained=True)
+
+# Zoe_K
+model_zoe_k = torch.hub.load(repo, "ZoeD_K", pretrained=True)
+
+# Zoe_NK
+model_zoe_nk = torch.hub.load(repo, "ZoeD_NK", pretrained=True)
+```
+### Using local copy
+Clone this repo:
+```bash
+git clone https://github.com/isl-org/ZoeDepth.git && cd ZoeDepth
+```
+#### Using local torch hub
+You can use local source for torch hub to load the ZoeDepth models, for example: 
+```python
+import torch
+
+# Zoe_N
+model_zoe_n = torch.hub.load(".", "ZoeD_N", source="local" pretrained=True)
+```
+
+#### or load the models manually
+```python
+from zoedepth.models.builder import build_model
+from zoedepth.utils.config import get_config
+
+# ZoeD_N
+conf = get_config("zoedepth", "infer")
+model_zoe_n = build_model(conf)
+
+# ZoeD_K
+conf = get_config("zoedepth", "infer", config_version="kitti")
+model_zoe_k = build_model(conf)
+
+# ZoeD_NK
+conf = get_config("zoedepth_nk", "infer")
+model_zoe_nk = build_model(conf)
+```
+
+### Using ZoeD models to predict depth 
+```python
+##### sample prediction
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+zoe = model_zoe_n.to(DEVICE)
+
+
+# Local file
+from PIL import Image
+image = Image.open("/path/to/image.jpg").convert("RGB")  # load
+depth_numpy = zoe.infer_pil(image)  # as numpy
+
+depth_pil = zoe.infer_pil(image, output_type="pil")  # as 16-bit PIL Image
+
+depth_tensor = zoe.infer_pil(image, output_type="tensor")  # as torch tensor
+
+
+
+# Tensor 
+from zoedepth.utils.misc import pil_to_batched_tensor
+X = pil_to_batched_tensor(image).to(DEVICE)
+depth_tensor = zoe.infer(X)
+
+
+
+# From URL
+from zoedepth.utils.misc import get_image_from_url
+
+# Example URL
+URL = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS4W8H_Nxk_rs3Vje_zj6mglPOH7bnPhQitBH8WkqjlqQVotdtDEG37BsnGofME3_u6lDk&usqp=CAU"
+
+
+image = get_image_from_url(URL)  # fetch
+depth = zoe.infer_pil(image)
+
+# Save raw
+from zoedepth.utils.misc import save_raw_16bit
+fpath = "/path/to/output.png"
+save_raw_16bit(depth, fpath)
+
+# Colorize output
+from zoedepth.utils.misc import colorize
+
+colored = colorize(depth)
+
+# save colored output
+fpath_colored = "/path/to/output_colored.png"
+Image.fromarray(colored).save(fpath_colored)
+```
+
+## **Environment setup**
+The project depends on :
+- [pytorch](https://pytorch.org/) (Main framework)
+- [timm](https://timm.fast.ai/)  (Backbone helper for MiDaS)
+- pillow, matplotlib, scipy, h5py, opencv (utilities)
+
+Install environment using `environment.yml` : 
+
+Using [mamba](https://github.com/mamba-org/mamba) (fastest):
+```bash
+mamba env create -n zoe --file environment.yml
+mamba activate zoe
+```
+Using conda : 
+
+```bash
+conda env create -n zoe --file environment.yml
+conda activate zoe
+```
+
+## **Sanity checks** (Recommended)
+Check if models can be loaded: 
+```bash
+python sanity_hub.py
+```
+Try a demo prediction pipeline:
+```bash
+python sanity.py
+```
+This will save a file `pred.png` in the root folder, showing RGB and corresponding predicted depth side-by-side.
+## Model files
+Models are defined under `models/` folder, with `models/<model_name>_<version>.py` containing model definitions and  `models/config_<model_name>.json` containing configuration.
+
+Single metric head models (Zoe_N and Zoe_K from the paper) have the common definition and are defined under `models/zoedepth` while as the multi-headed model (Zoe_NK) is defined under `models/zoedepth_nk`.
+## **Evaluation**
+Download the required dataset and change the `DATASETS_CONFIG` dictionary in `utils/config.py` accordingly. 
+### Evaluating offical models
+On NYU-Depth-v2 for example:
+
+For ZoeD_N:
+```bash
+python evaluate.py -m zoedepth -d nyu
+```
+
+For ZoeD_NK:
+```bash
+python evaluate.py -m zoedepth_nk -d nyu
+```
+
+### Evaluating local checkpoint
+```bash
+python evaluate.py -m zoedepth --pretrained_resource="local::/path/to/local/ckpt.pt" -d nyu
+```
+Pretrained resources are prefixed with `url::` to indicate weights should be fetched from a url, or `local::` to indicate path is a local file. Refer to `models/model_io.py` for details. 
+
+The dataset name should match the corresponding key in `utils.config.DATASETS_CONFIG` .
+
+## **Training**
+Download training datasets as per instructions given [here](https://github.com/cleinc/bts/tree/master/pytorch#nyu-depvh-v2). Then for training a single head model on NYU-Depth-v2 :
+```bash
+python train_mono.py -m zoedepth --pretrained_resource=""
+```
+
+For training the Zoe-NK model:
+```bash
+python train_mix.py -m zoedepth_nk --pretrained_resource=""
+```
+
+## **Citation**
+TODO: Add reference here after release
+
+
+
+
+
+
+
+
+
+
+
+
+

environment.yml

@@ -0,0 +1,26 @@
+name: zoe
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+dependencies:
+  - cuda=11.7.1
+  - h5py=3.7.0
+  - hdf5=1.12.2
+  - matplotlib=3.6.2
+  - matplotlib-base=3.6.2
+  - numpy=1.24.1
+  - opencv=4.6.0
+  - pip=22.3.1
+  - python=3.9.7
+  - pytorch=1.13.1
+  - pytorch-cuda=11.7
+  - pytorch-mutex=1.0
+  - scipy=1.10.0
+  - torchaudio=0.13.1
+  - torchvision=0.14.1
+  - pip:
+    - huggingface-hub==0.11.1
+    - timm==0.6.12
+    - tqdm==4.64.1
+    - wandb==0.13.9