Update

data/widok_01_21/widok_01_21.data

@@ -1,6 +1,6 @@
 classes=21
-train=./data/widok_01_21/widok_01_21_train_labels.txt
+train=/data/widok_01_21/widok_01_21_train_labels.txt
-valid=./data/widok_01_21/widok_01_21_test_labels.txt
+valid=/data/widok_01_21/widok_01_21_test_labels.txt
-names=./data/widok_01_21/widok_01_21.names
+names=/data/widok_01_21/widok_01_21.names
 backup=backup/
 eval=coco

our_scripts/config.yml

@@ -43,7 +43,7 @@ train:
 experiments:
   dir: ./experiments
 detect:
-  source: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
+  source: ./data/widok_01_19/widok_01_19_test_labels.txt
   test-img-size: 1024
   conf-thres: 0.3
   iou-thres: 0.6
@@ -51,7 +51,7 @@ detect:
   agnostic-nms:
   augment:
 confussion-matrix:
-  labels-dir: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_labels
+  labels-dir: ./data/widok_01_19/widok_01_19_labels
 bayes:
   todo: todo
 

our_scripts/config_bayes.py

@@ -34,7 +34,7 @@ class Configuration:
             for key, value in config_file['bayes'].items():
                 self.__dict__[key] = value
 
-    def __init__(self, config_path='./config_bayes.yml') -> None:
+    def __init__(self, config_path) -> None:
         self.config_path = config_path
         file = yaml.load(open(config_path, 'r'), Loader=yaml.Loader)
         self.train = self.Train(file)

our_scripts/config_bayes.yml

@@ -18,12 +18,12 @@ train:
   img-size-start:
     type: discrete
     min: 512
-    max: 1088
+    max: 576
     step: 64
   img-size-end:
     type: discrete
     min: 512
-    max: 1088
+    max: 576
     step: 64
   rect:
     type: discrete
@@ -122,7 +122,7 @@ detect:
   test-img-size:
     type: discrete
     min: 512
-    max: 1088
+    max: 576
     step: 64
   conf-thres:
     type: continuous

our_scripts/run_yolov3_process_bayes.py

@@ -6,19 +6,22 @@ import shutil
 import traceback
 
 import GPyOpt
-import numpy as np
-
 from config_bayes import Configuration
+
 from utils import call_subprocess, get_values_from_conff_matrix
 
-config = Configuration()
+dir_path = os.path.dirname(os.path.realpath(__file__))
+PROJECT_ROOT = os.path.join(dir_path, '..')
+
+bayes_config_yaml = os.path.join(dir_path, 'config_bayes.yml')
+config = Configuration(bayes_config_yaml)
 
 date_string = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
-bayes_params_file = open(os.path.join(config.experiments.dir, f"{date_string}_bayes_params.txt"), 'a+')
+bayes_params_file = open(os.path.join(PROJECT_ROOT, config.experiments.dir, f"{date_string}_bayes_params.txt"), 'a+')
 
 
 def call_training_script(gaussian_hyps):
-    cmd = 'python-u /home/tomekb/yolov3/train.py'
+    cmd = 'python -u train.py'
     cmd += ' --epochs ' + gaussian_hyps['epochs'].__str__()
     cmd += ' --batch-size ' + gaussian_hyps['batch-size'].__str__()
     cmd += ' --cfg ' + config.train.cfg.__str__()
@@ -40,11 +43,7 @@ def call_training_script(gaussian_hyps):
     print("_______ CALLING TRAINING SCRIPT _______")
     print(cmd)
 
-    dir_path = os.path.dirname(os.path.realpath(__file__))
-    os.chdir(os.path.join(dir_path, '..'))  # change to project root directory
-
     call_subprocess(cmd)
-
     return cmd
 
 
@@ -58,7 +57,7 @@ def move_training_results_to_experiments_dir():
     os.mkdir(training_results_dir_path)
 
     weights_path = os.path.join(training_results_dir_path, 'best.pt')
-    shutil.move('/home/tomekb/yolov3/weights/best.pt', weights_path)  # move best weights
+    shutil.move('./weights/best.pt', weights_path)  # move best weights
 
     names_path = open(config.train.data).readlines()[3].split('=')[-1].rstrip()  # read names path from file
     names_file_name = ntpath.basename(names_path)
@@ -66,7 +65,7 @@ def move_training_results_to_experiments_dir():
     shutil.copy(names_path,
                 experiment_names_path)  # copy names file from *.data file to created experiment dir with training results
 
-    tensorboard_dir = '/home/tomekb/yolov3/runs'
+    tensorboard_dir = './runs'
     tensorboard_events_files = glob.glob(os.path.join(tensorboard_dir, '*'))
     last_modified_events_file = max(tensorboard_events_files, key=os.path.getmtime)
     shutil.move(last_modified_events_file,
@@ -74,17 +73,12 @@ def move_training_results_to_experiments_dir():
 
     shutil.copy2(config.config_path, training_results_dir_path)  # copying configuration yaml
 
-    # for test purposes only
-    # TODO CHANGE ME AFTER TESTS
-    shutil.copy2('/home/tomekb/yolov3/experiments/yolov3-spp-100-epochs-freeze-layers/best.pt',
-                 training_results_dir_path)
-
     return weights_path, experiment_names_path, training_results_dir_path
 
 
 def call_detection_script(gaussian_hyps, weights_path, names_path, dir):
     detect_output_dir = os.path.join(dir, 'output')
-    cmd = f"""/home/tomekb/miniconda3/envs/conda3.7/bin/python -u /home/tomekb/yolov3/detect.py
+    cmd = f"""python -u ./detect.py
           --cfg {config.train.cfg}
           --source {config.detect.source}
           --output {detect_output_dir}
@@ -107,7 +101,7 @@ def call_detection_script(gaussian_hyps, weights_path, names_path, dir):
 def call_generate_confussion_matrix(detect_output_dir, names_path, train_results_dir):
     labels_dir = getattr(config.confussion_matrix, 'labels-dir')
     conff_matrix_path = os.path.join(train_results_dir, 'confussion-matrix.tsv')
-    cmd = f"node /home/tomekb/yolov3/our_scripts/generate-confusion-matrix.js {detect_output_dir} {labels_dir} {names_path} > {conff_matrix_path}"
+    cmd = f"node ./our_scripts/generate-confusion-matrix.js {detect_output_dir} {labels_dir} {names_path} > {conff_matrix_path}"
     print("_______ CALLING CONFUSSION MATRIX SCRIPT _______")
     print(cmd)
     call_subprocess(cmd)
@@ -171,10 +165,7 @@ def yolov3(x):
         bayes_params_file.writelines([line, '\n'])
 
 
-
-# uruchamiać z
 if __name__ == '__main__':
-
     bounds = config.get_bayes_bounds()
 
     # for b in bounds:
@@ -184,6 +175,8 @@ if __name__ == '__main__':
     X = None
     Y = None
 
+    os.chdir(PROJECT_ROOT)  # change to project root directory
+
     bayes_optimizer = GPyOpt.methods.BayesianOptimization(f=yolov3, domain=bounds, X=X, Y=Y, verbosity=True,
                                                           initial_design_numdata=2)
     bayes_optimizer.run_optimization(config.bayes.iterations, verbosity=True)