add comments for configs

cake-lab · Jul 24, 2024 · f883e32 · f883e32
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diff --git a/README.md b/README.md
@@ -10,15 +10,15 @@
 <a href=''><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-yellow'></a>
 </div>
 
-
 This work presents HybridDepth. HybridDepth is a practical depth estimation solution based on focal stack images captured from a camera. This approach outperforms state-of-the-art models across several well-known datasets, including NYU V2, DDFF12, and ARKitScenes.
 ![teaser](assets/teaser.png)
 
-
 ## News
+
 - **2024-07-23:** Model and Github repository is online.
 
 ## TODOs
+
 - [ ] Add pre-trained models.
 - [ ] Add Hugging Face model.
 - [ ] Release Android Mobile Client for HybridDepth.
@@ -47,6 +47,7 @@ conda activate hybriddepth
 Download the checkpoints listed [here](#pre-trained-models) and put them under the `checkpoints` directory.
 
 ### Using HybridDepth model for prediction
+
 For inference you can run the provided notebook `test.ipynb` or use the following command:
 
 ```python
@@ -62,7 +63,6 @@ model = model.to('cuda')
 
 After loading the model, you can use the following code to process the input images and get the depth map:
 
-
 ```python
 
 from utils.io import prepare_input_image
@@ -85,13 +85,12 @@ First setup the configuration file `config.yaml` in the `configs` directory. We
 
 ```yaml
 data:
-  class_path: dataloader.dataset.NYUDataModule
+  class_path: dataloader.dataset.NYUDataModule # Path to your dataloader Module in dataset.py
   init_args:
-    nyuv2_data_root: 'path to the NYUv2 dataset'
-    img_size: [480, 640]  # Adjust if your NYUDataModule expects a tuple for img_size
+    nyuv2_data_root: 'path to the NYUv2 dataset' # path to the specific dataset
+    img_size: [480, 640]  # Adjust if your DataModule expects a tuple for img_size
     remove_white_border: True
-    batch_size: 1
-    num_workers: 0  # due to the synthetic data, we don't need multiple workers
+    num_workers: 0  # if you are using synthetic data, you don't need multiple workers
     use_labels: True
     num_cluster: 5
 
@@ -100,7 +99,14 @@ model:
 
 ckpt_path: checkpoints/hybrid_depth_nyu.pth
 ```
-Then specify the configuration file in the `evaluate.sh` script. Finally, run the following command:
+
+Then specify the configuration file in the `test.sh` script.
+
+```bash
+python cli_run.py test  --config configs/config_file_name.yaml
+```
+
+Finally, run the following command:
 
 ```bash
 cd scripts
@@ -117,25 +123,30 @@ For example, you can use the following configuration file for training on the NY
 model:
   invert_depth: True
   # learning rate
-  lr: 3e-4
+  lr: 3e-4 # you can adjust this value
   # weight decay
-  wd: 0.001
+  wd: 0.001 # you can adjust this value
 
 data:
-  class_path: dataloader.dataset.NYUDataModule
+  class_path: dataloader.dataset.NYUDataModule # Path to your dataloader Module in dataset.py
   init_args:
-    nyuv2_data_root: 'root to the NYUv2 dataset'
+    nyuv2_data_root: 'root to the NYUv2 dataset' # path to the specific dataset
     img_size: [480, 640]  # Adjust if your NYUDataModule expects a tuple for img_size
     remove_white_border: True
-    batch_size: 24
-    num_workers: 0  # based on the synthetic data, we don't need multiple workers
+    batch_size: 24 # Adjust the batch size
+    num_workers: 0  # if you are using synthetic data, you don't need multiple workers
     use_labels: True
     num_cluster: 5
 ckpt_path: null
 ```
 
+Then specify the configuration file in the `train.sh` script.
+
+```bash
+python cli_run.py train  --config configs/config_file_name.yaml
+```
 
-Then specify the configuration file in the `train.sh` script. Finally, run the following command:
+Finally, run the following command:
 
 ```bash
 cd scripts

diff --git a/configs/config_fit_ARKitScenes.yaml b/configs/config_fit_ARKitScenes.yaml
@@ -58,12 +58,12 @@ model:
   # weight decay
   wd: 0.001
 data:
-  class_path: dataloader.dataset.ARKitScenesDataModule
+  class_path: dataloader.dataset.ARKitScenesDataModule # Path to your dataloader Module in dataset.py
   init_args:
-    arkitScenes_data_root: '/mnt/IRONWOLF1/ashkan/data/ARKitScenes/upsampling'
+    arkitScenes_data_root: '/mnt/IRONWOLF1/ashkan/data/ARKitScenes/upsampling' # Path to your dataset
     img_size: [480, 640] 
-    batch_size: 12 
-    num_workers: 0  # Adjust based on your requirements
+    batch_size: 12 # Adjust batch size
+    num_workers: 0  # leave it as zero since we are using synthesizing method
     num_cluster: 5
 
-ckpt_path: /home/ashkanganj/workspace/PrecisionDepthFocus/checkpoints/NYUBestScaleInv5Full.ckpt
+ckpt_path: null # checkpoints to continue the training.
diff --git a/configs/config_fit_DDFF12.yaml b/configs/config_fit_DDFF12.yaml
@@ -58,12 +58,11 @@ model:
   # weight decay
   wd: 0.001
 data:
-  class_path: dataloader.dataset.DDFF12DataModule
+  class_path: dataloader.dataset.DDFF12DataModule # Path to your dataloader Module in dataset.py
   init_args:
-    ddff12_data_root: '/home/ashka/data/my_ddff_trainVal.h5'
+    ddff12_data_root: '/home/ashka/data/my_ddff_trainVal.h5' # Path to your dataset
     img_size: [224,224] 
-    batch_size: 8
+    batch_size: 8 # Adjust batch size
     num_workers: 0  # Adjust based on your requirements
     num_cluster: 5
-# ckpt_path: /home/ashkanganj/workspace/PrecisionDepthFocus/logs/lightning_logs/version_0/checkpoints/epoch=599-step=60000.ckpt
-ckpt_path: null
+ckpt_path: null # checkpoints to continue the training.
diff --git a/configs/config_fit_NYU.yaml b/configs/config_fit_NYU.yaml
@@ -60,13 +60,11 @@ model:
 data:
   class_path: dataloader.dataset.NYUDataModule
   init_args:
-    # nyuv2_data_root: '/home/ashka/data/nyu_depth_v2_labeled.mat'
     nyuv2_data_root: '/home/ashka/data/nyu_depth_v2/official_splits/'
     img_size: [480, 640]  # Adjust if your NYUDataModule expects a tuple for img_size
     remove_white_border: True
-    batch_size: 24
-    num_workers: 0  # Adjust based on your requirements
+    batch_size: 24 # Adjust batch size
+    num_workers: 0   # leave it as zero since we are using synthesizing method
     use_labels: True
-    num_cluster: 5
-# ckpt_path: /home/ashkanganj/workspace/PrecisionDepthFocus/logs/lightning_logs/version_0/checkpoints/epoch=599-step=60000.ckpt
-ckpt_path: null
+    num_cluster: 5  
+ckpt_path: null # checkpoints to continue the training.
diff --git a/configs/config_test_ARKitScenes.yaml b/configs/config_test_ARKitScenes.yaml
@@ -1,14 +1,14 @@
 # pytorch_lightning==2.1.0
 data:
-  class_path: dataloader.dataset.ARKitScenesDataModule
+  class_path: dataloader.dataset.ARKitScenesDataModule # Path to your dataloader Module in dataset.py
   init_args:
-    arkitScenes_data_root: '/mnt/IRONWOLF1/ashkan/data/ARKitScenes/upsampling'
+    arkitScenes_data_root: '/mnt/IRONWOLF1/ashkan/data/ARKitScenes/upsampling' # path to the dataset
     img_size: [480, 640] 
     batch_size: 1
     num_workers: 0  # Adjust based on your requirements
     num_cluster: 5
 
 model:
-  invert_depth: True
+  invert_depth: True 
 
-ckpt_path: /home/ashkanganj/workspace/2023-HybridDepth-DepthProject/checkpoints/scaleInvARKitScenes5.ckpt
+ckpt_path: ./checkpoints/checkpoint.pth #path to the checkpoint
diff --git a/configs/config_test_DDFF12.yaml b/configs/config_test_DDFF12.yaml
@@ -1,9 +1,9 @@
 # pytorch_lightning==2.1.0
 data:
   # For DDFF12DataModule
-  class_path: dataloader.dataset.DDFF12DataModule
+  class_path: dataloader.dataset.DDFF12DataModule # Path to your dataloader Module in dataset.py
   init_args:
-    ddff12_data_root: '/home/ashkanganj/workspace/dataset/dff_trainVal.h5'
+    ddff12_data_root: '/home/ashkanganj/workspace/dataset/dff_trainVal.h5'  # path to the specific dataset
     img_size: [383, 552]  # Adjust if your DDFF12DataModule expects a tuple for img_size
     remove_white_border: True
     batch_size: 1
@@ -13,4 +13,4 @@ data:
 model:
   invert_depth: False
 
-ckpt_path: /home/ashkanganj/workspace/2023-HybridDepth-DepthProject/checkpoints/DDFF12BestScaleInv.ckpt
+ckpt_path: ./checkpoints/checkpoint.pth #path to the checkpoint
diff --git a/configs/config_test_NYU.yaml b/configs/config_test_NYU.yaml
@@ -1,9 +1,8 @@
 # pytorch_lightning==2.1.0
 data:
-  class_path: dataloader.dataset.NYUDataModule
+  class_path: dataloader.dataset.NYUDataModule # Path to your dataloader Module in dataset.py
   init_args:
-    nyuv2_data_root: '/home/ashkanganj/workspace/dataset/nyu_depth_v2/official_splits'
-    # nyuv2_data_root: '/home/ashka/data/nyu_depth_v2_labeled.mat'
+    nyuv2_data_root: '/home/ashkanganj/workspace/dataset/nyu_depth_v2/official_splits' # path to the specific dataset
     img_size: [480, 640]  # Adjust if your NYUDataModule expects a tuple for img_size
     remove_white_border: True
     batch_size: 1
@@ -14,4 +13,4 @@ data:
 model:
   invert_depth: True
 
-ckpt_path: /home/ashkanganj/workspace/PrecisionDepthFocus/checkpoints/NYUBestScaleInv10Full.ckpt
+ckpt_path: ./checkpoints/checkpoint.pth #path to checkpoint