Merge pull request #5 from zhmeishi/train

Train

README.md

@@ -1,4 +1,5 @@
 
+
 
 # Deep Online Fused Video Stabilization
 
@@ -18,7 +19,6 @@ pip install -r requirements.txt --ignore-installed
 ## Data Preparation
 Download sample video [here](https://drive.google.com/file/d/1nju9H8ohYZh6dGsdrQjQXFgfgkrFtkRi/view?usp=sharing). 
 Uncompress the *video* folder under the *dvs* folder.
-
 ```
 python load_frame_sensor_data.py 
 ```
@@ -52,7 +52,21 @@ In *s_114_outdoor_running_trail_daytime.jpg*, the blue curve is the output of ou
 *s_114_outdoor_running_trail_daytime_stab_crop.mp4* is cropped stabilized video. Note, the cropped video is generated after running the metrics code.   
 
 ## Training
-TBA
+Download dataset for training and test [here](https://storage.googleapis.com/dataset_release/all.zip). 
+Uncompress *all.zip* and move *dataset_release* folder under the *dvs* folder.
+
+Follow FlowNet2 Preparation Section.
+```
+python warp/read_write.py --dir_path ./dataset_release # video2frames
+cd flownet2
+bash run_release.sh # generate optical flow file for dataset
+``` 
+
+Run training code.
+```
+python train.py
+``` 
+The model is saved in *checkpoint/stabilzation_train*.
 
 ## Citation 
 If you use this code or dataset for your research, please cite our paper.
@@ -63,4 +77,4 @@ If you use this code or dataset for your research, please cite our paper.
   journal={arXiv preprint arXiv:2102.01279},
   year={2021}
 }
-``` 
+```

dvs/conf/stabilzation_train.yaml

@@ -0,0 +1,57 @@
+data:
+  exp: 'stabilzation_train'
+  checkpoints_dir: './checkpoint'
+  log: './log'
+  data_dir: './dataset_release'           
+  use_cuda: true
+  batch_size: 16
+  resize_ratio: 0.25
+  number_real: 10
+  number_virtual: 2
+  time_train: 2000  # ms
+  sample_freq: 40   # ms
+  channel_size: 1
+  num_workers: 16                    # num_workers for data_loader
+model:
+  load_model:  null
+  cnn:
+    activate_function: relu         # sigmoid, relu, tanh, quadratic
+    batch_norm: true
+    gap: false
+    layers:
+  rnn:
+    layers:  
+    - - 512                        
+      - true  
+    - - 512                        
+      - true    
+  fc:
+    activate_function: relu
+    batch_norm: false               # (batch_norm and drop_out) is False
+    layers:  
+    - - 256                        
+      - true  
+    - - 4                         # last layer should be equal to nr_class
+      - true
+    drop_out: 0
+train:
+  optimizer: "adam"                  # adam or sgd
+  momentum: 0.9                     # for sgd
+  decay_epoch: null
+  epoch: 400
+  snapshot: 2
+  init_lr: 0.0001
+  lr_decay: 0.5
+  lr_step: 200                       # if > 0 decay_epoch should be null
+  seed: 1
+  weight_decay: 0.0001
+  clip_norm: False
+  init: "xavier_uniform"            # xavier_uniform or xavier_normal
+loss:
+  follow: 10
+  angle: 1
+  smooth: 10 #10
+  c2_smooth: 200 #20
+  undefine: 2.0
+  opt: 0.1
+  stay: 0

dvs/dataset.py

@@ -34,7 +34,7 @@ def get_data_loader(cf, no_flo = False):
 def get_dataset(cf, no_flo = False):
     resize_ratio = cf["data"]["resize_ratio"]
     train_transform, test_transform = _data_transforms()
-    train_path = os.path.join(cf["data"]["data_dir"], "train")
+    train_path = os.path.join(cf["data"]["data_dir"], "training")
     test_path = os.path.join(cf["data"]["data_dir"], "test")
     if not os.path.exists(train_path):
         train_path = cf["data"]["data_dir"]

dvs/flownet2/run_release.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+python main.py --inference --model FlowNet2 --save_flow --inference_dataset Google \
+	--inference_dataset_root ./../dataset_release/test \
+	--resume ./FlowNet2_checkpoint.pth.tar \
+	--inference_visualize
+
+python main.py --inference --model FlowNet2 --save_flow --inference_dataset Google \
+	--inference_dataset_root ./../dataset_release/training \
+	--resume ./FlowNet2_checkpoint.pth.tar \
+	--inference_visualize

dvs/load_frame_sensor_data.py

@@ -91,21 +91,7 @@ def inference(cf, data_path, USE_CUDA):
     rotations_real, lens_offsets_real = get_rotations(data.frame[:data.length], data.gyro, data.ois, data.length)
     fig_path = os.path.join(data_path, video_name+"_real.jpg")
     visual_rotation(rotations_real, lens_offsets_real, None, None, None, None, fig_path)
-
-    # print("------Start Warping Video--------")
-    # grid = get_grid(test_loader.dataset.static_options, \
-    #     data.frame[:data.length], data.gyro, data.ois, virtual_queue[:data.length,1:], no_shutter = False) 
-
-    # grid_rm_shutter = get_grid(test_loader.dataset.static_options, \
-    #     data.frame[:data.length], data.gyro, np.zeros(data.ois.shape), virtual_queue[:data.length,1:], no_shutter = False) 
-
-    # video_path = os.path.join(data_path, video_name+".mp4")
-    # data_name = data_path.split("/")[-1]
-    # save_path = os.path.join(data_path, video_name+"_no_ois.mp4")
-    # warp_video(grid, video_path, save_path, losses = None)
-
-    # save_path = os.path.join(data_path, video_name+"_no_shutter.mp4")
-    # warp_video(grid_rm_shutter, video_path, save_path, losses = None)
+
     return
 
 def main(args = None):