Use relative paths in scripts arguments (#16)

* Add os.abspath

* Fix readme files

* Remove CI status

README.md

 
 # TTCF: Training Toolbox for Caffe
 
-[![Build Status](http://134.191.240.124/buildStatus/icon?job=caffe-toolbox/develop/trigger)](http://134.191.240.124/job/caffe-toolbox/job/develop/job/trigger/)
-
 This is a [BVLC Caffe](https://github.com/BVLC/caffe) fork that is intended for deployment multiple SSD-based detection models. It includes
 - action detection and action recognition models for smart classroom use-case, see [README_AD.md](README_AD.md),
 - person detection for smart classroom use-case, see [README_PD.md](README_PD.md),

README_AD.md

@@ -37,7 +37,7 @@ cd ./models/templates/person_detection_action_recognition_N_classes
 
 
 ### (optional) Prepare init weights from PD model
-1. Run docker in interactive sesion with mounted directory with WIDER dataset
+1. Run docker in interactive session with mounted directory with WIDER dataset
 ```Shell
 nvidia-docker --rm -it -v <path_to_folder_with_weights>:/workspace tccf bash
 ```
@@ -59,10 +59,10 @@ On next stage we should train the Action Recognition (AR) model which reuses det
 
 ```Shell
 cd ./models
-python train.py --model person_detection_action_recognition \ # name of model
-                --weights action_detection_0005.caffemodel \  # initialize weights from 'init_weights' directory
-                --data_dir <PATH_TO_DATA> \                   # path to directory with dataset
-                --work_dir <WORK_DIR> \                       # directory to collect file from training process
+python3 train.py --model person_detection_action_recognition \ # name of model
+                --weights action_detection_0005.caffemodel \   # initialize weights from 'init_weights' directory
+                --data_dir <PATH_TO_DATA> \                    # path to directory with dataset
+                --work_dir <WORK_DIR> \                        # directory to collect file from training process
                 --gpu <GPU_ID>
 ```
 
@@ -72,7 +72,7 @@ To evaluate the quality of trained Action Recognition model on your test data yo
 
 1. Frame independent evaluation:
 ```Shell
-python evaluate.py --type ad \
+python3 evaluate.py --type ad \
     --dir <EXPERIMENT_DIR> \
     --data_dir <DATA_DIR> \
     --annotaion test_tasks.txt \
@@ -81,7 +81,7 @@ python evaluate.py --type ad \
 
 2. Event-based evaluation:
 ```Shell
-python evaluate.py --type ad_event \
+python3 evaluate.py --type ad_event \
     --dir <EXPERIMENT_DIR> \
     --data_dir <DATA_DIR> \
     --annotaion test_tasks.txt \
@@ -91,7 +91,7 @@ python evaluate.py --type ad_event \
 ### Export to IR format
 
 ```Shell
-python mo_convert.py --name action_recognition --type ad \
+python3 mo_convert.py --name action_recognition --type ad \
     --dir <EXPERIMENT_DIR> \
     --iter <ITERATION_NUM> \
     --data_type FP32

README_AG.md

@@ -27,7 +27,7 @@ python3 $CAFFE_ROOT/python/gen_hdf5_data.py /data/<DATA_VAL_FILE> images_db_val
 python3 $CAFFE_ROOT/python/gen_hdf5_data.py /data/<DATA_TEST_FILE> images_db_test
 ```
 
-3. Close docker session by 'alt+D' and check that you have `images_db_<subset>.hd5` and `images_db_<subset>_list.txt` files in <DATA_DIR>.
+3. Close docker session by `ctrl+D` and check that you have `images_db_<subset>.hd5` and `images_db_<subset>_list.txt` files in <DATA_DIR>.
 
 
 ## Model training and evaluation

README_CR.md

@@ -9,7 +9,7 @@ As an example of usage please download a small dataset from [here](https://downl
 
 To create LMDB files go to the '$CAFFE_ROOT/python/lmdb_utils/' directory and run the following scripts:
 
-1. Run docker in interactive sesion with mounted directory with WIDER dataset
+1. Run docker in interactive session with mounted directory with WIDER dataset
 ```Shell
 nvidia-docker run --rm -it --user=$(id -u) -v <DATA_DIR>:/data ttcf bash
 ```
@@ -22,7 +22,7 @@ python3 $CAFFE_ROOT/python/lmdb_utils/convert_to_voc_format.py /data/annotation_
 ```Shell
 bash $CAFFE_ROOT/python/lmdb_utils/create_cr_lmdb.sh
 ```
-4. Close docker session by 'alt+D' and check that you have lmdb files in <DATA_DIR>/lmdb.
+4. Close docker session by `ctrl+D` and check that you have lmdb files in <DATA_DIR>/lmdb.
 
 
 ###
@@ -32,7 +32,7 @@ On next stage we should train the Person-vehicle-bike crossroad (four class) det
 
 ```Shell
 cd ./models
-python train.py --model crossroad \
+python3 train.py --model crossroad \
                 --weights person-vehicle-bike-detection-crossroad-0078.caffemodel \
                 --data_dir <DATA_DIR> \
                 --work_dir<WORK_DIR> \
@@ -44,7 +44,7 @@ python train.py --model crossroad \
 To evaluate the quality of trained Person-vehicle-bike crossroad detection model on your test data you can use provided scripts.
 
 ```Shell
-python evaluate.py --type cr \
+python3 evaluate.py --type cr \
     --dir <WORK_DIR>/crossroad/<EXPERIMENT_NUM> \
     --data_dir <DATA_DIR> \
     --annotation annotation_val_cvt.json \
@@ -54,7 +54,7 @@ python evaluate.py --type cr \
 ### Export to IR format
 
 ```Shell
-python mo_convert.py --type cr \
+python3 mo_convert.py --type cr \
     --name crossroad \
     --dir <WORK_DIR>/crossroad/<EXPERIMENT_NUM> \
     --iter <ITERATION_NUM> \

README_FD.md

@@ -8,7 +8,7 @@ The training procedure can be done using data in LMDB format. To launch training
 
 To create LMDB files go to the '$CAFFE_ROOT/python/lmdb_utils/' directory and run the following scripts:
 
-1. Run docker in interactive sesion with mounted directory with WIDER dataset
+1. Run docker in interactive session with mounted directory with WIDER dataset
 ```Shell
 nvidia-docker run --rm -it --user=$(id -u) -v <DATA_DIR>:/data ttcf bash
 ```
@@ -29,7 +29,7 @@ python3 $CAFFE_ROOT/python/lmdb_utils/xml_to_ssd.py --ssd_path /data --xml_path_
 bash $CAFFE_ROOT/python/lmdb_utils/create_wider_lmdb.sh
 ```
 
-5. Close docker session by 'alt+D' and check that you have lmdb files in <DATA_DIR>.
+5. Close docker session by `ctrl+D` and check that you have lmdb files in <DATA_DIR>.
 
 
 ###
@@ -39,7 +39,7 @@ On next stage we should train the Face Detection model. To do this follow next s
 
 ```Shell
 cd ./models
-python train.py --model face_detection \                           # name of model
+python3 train.py --model face_detection \                          # name of model
                 --weights face-detection-retail-0044.caffemodel \  # initialize weights from 'init_weights' directory
                 --data_dir <DATA_DIR> \                            # path to directory with dataset
                 --work_dir <WORK_DIR> \                            # directory to collect file from training process
@@ -51,7 +51,7 @@ python train.py --model face_detection \                           # name of mod
 To evaluate the quality of trained Face Detection model on your test data you can use provided scripts.
 
 ```Shell
-python evaluate.py --type fd \
+python3 evaluate.py --type fd \
     --dir <WORK_DIR>/face_detection/<EXPERIMENT_NUM> \
     --data_dir <DATA_DIR> \
     --annotation wider_val.xml \
@@ -61,7 +61,7 @@ python evaluate.py --type fd \
 ### Export to IR format
 
 ```Shell
-python mo_convert.py --name face_detection \
+python3 mo_convert.py --name face_detection \
     --dir <WORK_DIR>/face_detection/<EXPERIMENT_NUM> \
     --iter <ITERATION_NUM> \
     --data_type FP32

README_PD.md

@@ -13,7 +13,7 @@ On first stage you should train the SSD-based person (two class) detector. To do
 
 ```Shell
 cd ./models
-python train.py --model person_detection \                    # name of model
+python3 train.py --model person_detection \                   # name of model
                 --weights person_detection_0022.caffemodel \  # initialize weights from 'init_weights' directory
                 --data_dir <PATH_TO_DATA> \                   # path to directory with dataset
                 --work_dir <WORK_DIR>                         # directory to collect file from training process
@@ -28,7 +28,7 @@ Note: to get more accurate model it's recommended to use pre-training of backbon
 
 ```Shell
 cd ./models
-python mo_convert.py --name face_detection \
+python3 mo_convert.py --name face_detection \
     --dir <WORK_DIR>/person_detection/<EXPERIMENT_NUM> \
     --iter <INTERATION> \
     --data_type FP32

models/evaluate.py

@@ -102,8 +102,8 @@ def main():
         exec_bin, 'run', '--rm',
         '--name', container_name,
         '--user=%s:%s' % (os.getuid(), os.getgid()),
-        '-v', '%s:/workspace' % args.dir,
-        '-v', '%s:/data:ro' % args.data_dir,  # Mount directory with dataset
+        '-v', '%s:/workspace' % os.path.abspath(args.dir),
+        '-v', '%s:/data:ro' % os.path.abspath(args.data_dir),  # Mount directory with dataset
         args.image
     ]
 

models/mo_convert.py

@@ -75,7 +75,7 @@ def main():
     docker_command = [
         exec_bin, 'run', '--rm',
         '--user=%s:%s' % (os.getuid(), os.getgid()),
-        '-v', '%s:/workspace' % args.dir,
+        '-v', '%s:/workspace' % os.path.abspath(args.dir),
         args.image
     ]
 

models/train.py

@@ -73,8 +73,8 @@ def main():
         exec_bin, 'run', '--rm',
         '--user=%s:%s' % (os.getuid(), os.getgid()),
         '--name', container_name,  # Name of container
-        '-v', '%s:/workspace' % experiment_dir,  # Mout work directory
-        '-v', '%s:/data:ro' % args.data_dir,  # Mount directory with dataset
+        '-v', '%s:/workspace' % os.path.abspath(experiment_dir),  # Mout work directory
+        '-v', '%s:/data:ro' % os.path.abspath(args.data_dir),  # Mount directory with dataset
         '-v', '%s:/init_weights:ro' % os.path.abspath('../init_weights'),  # Mount directory with init weights
         args.image
     ]