Merge pull request #275 from JdeRobot/issue-274

General app refactor
JdeRobot · Jan 5, 2022 · 4a92e28 · 4a92e28
2 parents 8c3b4b0 + dd320e6
commit 4a92e28
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Showing 20 changed files with 171 additions and 178 deletions.
diff --git a/behavior_metrics/analyze_bag.py b/behavior_metrics/analyze_bag.py
@@ -60,17 +60,17 @@
                     data = json.loads(h)
                     metadata = json.loads(data['data'])
 
-                for topic, point, t in bag.read_messages(topics=['/lap_stats']):
+                for topic, point, t in bag.read_messages(topics=['/lap_metrics']):
                     y = yaml.load(str(point), Loader=yaml.FullLoader)
                     h = json.dumps(y, indent=4)
                     data = json.loads(h)
                     lapdata = json.loads(data['data'])
 
-                for topic, point, t in bag.read_messages(topics=['/time_stats']):
+                for topic, point, t in bag.read_messages(topics=['/time_metrics']):
                     y = yaml.load(str(point), Loader=yaml.FullLoader)
                     h = json.dumps(y, indent=4)
                     data = json.loads(h)
-                    time_stats_metadata = json.loads(data['data'])
+                    time_metrics_metadata = json.loads(data['data'])
 
                 for topic, point, t in bag.read_messages(topics=['/first_image']):
                     first_image = bridge.imgmsg_to_cv2(point, desired_encoding='passthrough')

diff --git a/behavior_metrics/brains/f1/brain_f1_keras-lstm.py b/behavior_metrics/brains/f1/brain_f1_keras-lstm.py
@@ -44,7 +44,7 @@ def __init__(self, sensors, actuators, model=None, handler=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
         self.previous_images = np.zeros((1, 50, 100, 3))
         print(self.previous_images.shape)
         print(self.previous_images.shape)

diff --git a/behavior_metrics/brains/f1/brain_f1_keras.py b/behavior_metrics/brains/f1/brain_f1_keras.py
@@ -51,7 +51,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
             os.environ["CUDA_VISIBLE_DEVICES"]="-1"
         import tensorflow as tf
 
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
 
         if model:
             if not path.exists(PRETRAINED_MODELS + model):

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_classification-bw.py b/behavior_metrics/brains/f1/brain_f1_keras_classification-bw.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
 
         if model:
             if not path.exists(PRETRAINED_MODELS + model):

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_classification.py b/behavior_metrics/brains/f1/brain_f1_keras_classification.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
 
         if model:
             if not path.exists(PRETRAINED_MODELS + model):

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_preprocessed.py b/behavior_metrics/brains/f1/brain_f1_keras_preprocessed.py
@@ -36,7 +36,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
 
         if model:
             if not path.exists(PRETRAINED_MODELS + model):

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm.py b/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
         self.previous_images = np.zeros((2, 13))
         if model:
             print(model)

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm_3_elements-60pts.py b/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm_3_elements-60pts.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
         self.previous_images = np.zeros((3, 60))
 
         print(model)

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm_3_elements.py b/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm_3_elements.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
         #self.previous_images = np.zeros((25, 13))
         self.previous_images = np.zeros((3, 31))
         #self.previous_images_1 = np.zeros((5, 13))

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm_5_elements.py b/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_lstm_5_elements.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
         self.previous_images = np.zeros((25, 13))
 
         if model:

diff --git a/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_v_w.py b/behavior_metrics/brains/f1/brain_f1_keras_preprocessed_v_w.py
@@ -39,7 +39,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.handler = handler
         self.cont = 0
         self.inference_times = []
-        self.gpu_inferencing = True if tf.test.gpu_device_name() else False
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
 
         if model:
             if not path.exists(PRETRAINED_MODELS + model[0]) or not path.exists(PRETRAINED_MODELS + model[1]):

diff --git a/behavior_metrics/brains/f1/brain_f1_torch.py b/behavior_metrics/brains/f1/brain_f1_torch.py
@@ -44,7 +44,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.cont = 0
         self.inference_times = []
         self.device = torch.device("cpu")
-        self.gpu_inferencing = torch.cuda.is_available()
+        self.gpu_inference = torch.cuda.is_available()
         self.first_image = None
         self.transformations = transforms.Compose([
                                         transforms.ToTensor()

diff --git a/behavior_metrics/brains/f1/brain_f1_torchstacked.py b/behavior_metrics/brains/f1/brain_f1_torchstacked.py
@@ -46,7 +46,7 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
         self.inference_times = []
         self.device = torch.device("cpu")
 
-        self.gpu_inferencing = torch.cuda.is_available()
+        self.gpu_inference = torch.cuda.is_available()
         self.first_image = None
         if config:
             if 'ImageCrop' in config.keys():

diff --git a/behavior_metrics/pilot.py b/behavior_metrics/pilot.py
@@ -120,14 +120,14 @@ def run(self):
         "TODO: cleanup measure of ips"
         it = 0
         ss = time.time()
-        stopped_brain_stats = False
+        stopped_brain_metrics = False
         successful_iteration = False
         brain_iterations_time = []
         while not self.kill_event.is_set():
             start_time = datetime.now()
             if not self.stop_event.is_set():
                 self.execution_completed = False
-                stopped_brain_stats = True
+                stopped_brain_metrics = True
                 try:
                     self.brains.active_brain.execute()
                     successful_iteration = True
@@ -136,40 +136,36 @@ def run(self):
                     logger.error(e)
                     successful_iteration = False
             else:
-                if stopped_brain_stats:
+                if stopped_brain_metrics:
                     self.execution_completed = False
-                    stopped_brain_stats = False
+                    stopped_brain_metrics = False
                     successful_iteration = False
                     try:
-                        logger.info('----- MEAN INFERENCE TIME -----')
                         self.brains.active_brain.inference_times = self.brains.active_brain.inference_times[10:-10]
                         mean_inference_time = sum(self.brains.active_brain.inference_times) / len(
                             self.brains.active_brain.inference_times)
                         frame_rate = len(self.brains.active_brain.inference_times) / sum(
                             self.brains.active_brain.inference_times)
-                        gpu_inferencing = self.brains.active_brain.gpu_inferencing
+                        gpu_inference = self.brains.active_brain.gpu_inference
                         first_image = self.brains.active_brain.first_image
-                        logger.info(mean_inference_time)
-                        logger.info(frame_rate)
-                        logger.info('-------------------')
+                        logger.info('* Mean network inference time ---> ' + str(mean_inference_time) + ' s')
+                        logger.info('* Frame rate ---> ' + str(frame_rate) + ' fps')
                     except Exception as e:
                         logger.error(e)
                         mean_inference_time = 0
                         frame_rate = 0
-                        gpu_inferencing = False
+                        gpu_inference = False
                         first_image = None
                         logger.info('No inference brain')
 
-                    logger.info('----- MEAN ITERATION TIME -----')
                     mean_iteration_time = sum(brain_iterations_time) / len(brain_iterations_time)
-                    logger.info(mean_iteration_time)
-                    logger.info('-------------------')
-                    logger.info(hasattr(self.controller, 'stats_filename'))
-                    if hasattr(self.controller, 'stats_filename'):
+                    logger.info('* Mean brain iteration time ---> ' + str(mean_iteration_time) + ' s')
+                    logger.info(hasattr(self.controller, 'experiment_metrics_filename'))
+                    if hasattr(self.controller, 'experiment_metrics_filename'):
                         try:
-                            logger.info('Entering Stats into Bag')
-                            self.controller.save_time_stats(mean_iteration_time, mean_inference_time, frame_rate,
-                                                            gpu_inferencing, first_image)
+                            logger.info('Saving metrics to ROS bag')
+                            self.controller.save_metrics(mean_iteration_time, mean_inference_time, frame_rate,
+                                                         gpu_inference, first_image)
                         except Exception as e:
                             logger.info('Empty ROS bag')
                             logger.error(e)