update readme.md

LeReS/README.md

@@ -74,7 +74,7 @@ pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git@v1.2.0
    sh train_demo.sh
    ```
 
-2. Download the training data. For taskonomy and DiverseDepth, please run 'download_data.sh'; For HWRSI and Holopix50k, please download from [link]. All data are organized under the `Train/datasets`. The structure of all data are as follows.
+2. Download the training data. For taskonomy and DiverseDepth, please run 'download_data.sh'; For HWRSI and Holopix50k, please download from link(to be uploaded). All data are organized under the `Train/datasets`. The structure of all data are as follows.
 
     ```
     |--Train

LeReS/Train/scripts/test.sh

@@ -9,5 +9,5 @@ LOG="./$TIME.txt"
 python ../tools/test_multiauxiv2_nyu.py \
 --dataroot ./datasets \
 --batchsize 1 \
---load_ckpt /home/gk-ai/桌面/AdelaiDepth/LeReS/Train/scripts/output11111/May29-21-21-39_gk-ai-ustc/ckpt/epoch0_step10.pth \
+--load_ckpt path_to_ckpt.pth \
 $1 2>&1 | tee $LOG

LeReS/Train/scripts/train.sh

@@ -1,19 +1,19 @@
 
 export PYTHONPATH=../../Train:${PYTHONPATH}
-# export CUDA_VISIBLE_DEVICES=0,1,2,3
+export CUDA_VISIBLE_DEVICES=0,1,2,3
 
-TIME=`date +%Y-%m-%d_%H-%M-%S`
-
-LOG="./$TIME.txt"
 
 python ../tools/train.py \
 --dataroot datasets \
---dataset_list taskonomy DIML_GANet DiverseDepth \
+--backbone resnet50 \
+--dataset_list taskonomy DiverseDepth HRWSI Holopix50k \
 --batchsize 16 \
 --base_lr 0.001 \
 --use_tfboard \
 --thread 4 \
---loss_mode _ssil_vnl_ranking_ \
+--loss_mode _ranking-edge_pairwise-normal-regress-edge_msgil-normal_meanstd-tanh_pairwise-normal-regress-plane_ranking-edge-auxi_meanstd-tanh-auxi_ \
 --epoch 50 \
 --lr_scheduler_multiepochs 10 25 40 \
-$1 2>&1 | tee $LOG
+--val_step 5000 \
+--snapshot_iters 5000 \
+--log_interval 10

LeReS/Train/scripts/train_demo.sh

@@ -1,22 +1,16 @@
-
-# export PYTHONPATH=../../Train:${PYTHONPATH}
-export PYTHONPATH=../
-# export CUDA_VISIBLE_DEVICES=0,1,2,3
+export PYTHONPATH=../../Train:${PYTHONPATH}
 
 python ../tools/train.py \
 --dataroot datasets \
 --backbone resnet50 \
 --dataset_list demo \
 --batchsize 2 \
---base_lr 0.1 \
+--base_lr 0.001 \
 --use_tfboard \
 --thread 4 \
 --loss_mode _ranking-edge_pairwise-normal-regress-edge_msgil-normal_meanstd-tanh_pairwise-normal-regress-plane_ranking-edge-auxi_meanstd-tanh-auxi_ \
---epoch 100 \
---lr_scheduler_multiepochs 10 25 40 \
---output_dir output11111 \
---val_step 10 \
---snapshot_iters 10 \
+--epoch 2 \
+--lr_scheduler_multiepochs 1 \
+--val_step 50 \
+--snapshot_iters 50 \
 --log_interval 5 
-
-#  DiverseDepth

LeReS/Train/tools/train.py

@@ -253,6 +253,9 @@ def main():
     val_args.batchsize = 1
     val_args.thread = 0
 
+    if 'HRWSI' in val_args.dataset_list:
+        val_args.dataset_list.remove('HRWSI')
+
     print('Using PyTorch version: ', torch.__version__, torch.version.cuda)
     ngpus_per_node = torch.cuda.device_count()
     train_args.world_size = ngpus_per_node * train_args.nnodes

README.md

@@ -12,8 +12,8 @@ Monocular Video Depth (Boosting Monocular Depth Estimation with Sparse Guided Po
 * Depth Estimation Using Deep Convolutional Neural Fields: [Code](https://bitbucket.org/fayao/dcnf-fcsp/src/master/), [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields, TPAMI'16, CVPR'15](https://arxiv.org/abs/1502.07411)
 
 ## News:
-* [May. 30, 2022] Code for local recovery strategy of BoostingDepth is released.
-* [May. 30, 2022] Training code and data of LeReS project have been released.
+* [May. 31, 2022] Code for local recovery strategy of BoostingDepth is released.
+* [May. 31, 2022] Training code and data of LeReS project have been released.
 * [Feb. 13, 2022] Training code and data of DiverseDepth project have been released.
 * [Jun. 13, 2021] Our "Learning to Recover 3D Scene Shape from a Single Image" work is one of the CVPR'21 Best Paper Finalists.
 * [Jun. 6, 2021] We have made the training data of DiverseDepth available.