fixed merge conflict

angel_system/global_step_prediction/run_expirement.py

@@ -35,7 +35,10 @@ def run_inference_all_vids(
     preds, gt = [], []
     avg_smd, avg_smd_normd = np.array([]), np.array([])
     mean_F1s = np.array([])
+<<<<<<< HEAD
     tasks_completed = np.array([])
+=======
+>>>>>>> 7c524b3c2c8e0d3b3952bf4f2cc97ba28aa00560
     for vid_id in all_vid_ids:
         print(f"vid_id {vid_id}===========================")
 
@@ -144,12 +147,19 @@ def get_unique(activity_ids):
         mean_F1s = np.append(mean_F1s, mean_F1)
 
         pred_history = step_predictor.get_single_tracker_pred_history()
+<<<<<<< HEAD
         smds, smds_normd, task_completed = get_start_moment_distances(pred_history, activity_gt_maxes)
         tasks_completed = np.append(tasks_completed, task_completed)
 
         if not math.isnan(compute_mean_smd(smds)):
             avg_smd = np.append(avg_smd, compute_mean_smd(smds))
             avg_smd_normd = np.append(avg_smd_normd, compute_mean_smd(smds_normd))
+=======
+        smds, smds_normd = get_start_moment_distances(pred_history, activity_gt_maxes)
+
+        avg_smd = np.append(avg_smd, np.mean(smds))
+        avg_smd_normd = np.append(avg_smd_normd, np.mean(smds_normd))
+>>>>>>> 7c524b3c2c8e0d3b3952bf4f2cc97ba28aa00560
 
         print(f"smds (# frames): {smds}, normalized:{smds_normd}")
         try:
@@ -165,7 +175,10 @@ def get_unique(activity_ids):
         )
     print("########## OVERALL")
     print(f"Overall average smd: {np.mean(avg_smd)}. Normalized: {np.mean(avg_smd_normd)}")
+<<<<<<< HEAD
     print(f"tasks completed: {np.sum(tasks_completed)} / {len(tasks_completed)}")
+=======
+>>>>>>> 7c524b3c2c8e0d3b3952bf4f2cc97ba28aa00560
     print(f"overall mean F1: {np.mean(mean_F1s)}")
 
 def get_start_moment_distances(pred_history, activity_gt_maxes):
@@ -180,12 +193,16 @@ def get_start_moment_distances(pred_history, activity_gt_maxes):
     vid_length = len(activity_gt_maxes)
     smds = np.zeros(num_classes)
     smds_normd = np.zeros(num_classes)
+<<<<<<< HEAD
     task_completed = 1
+=======
+>>>>>>> 7c524b3c2c8e0d3b3952bf4f2cc97ba28aa00560
     for i in range(num_classes):
         if i+1 in pred_history:
             smds[i] = abs(np.where(pred_history == i+1)[0][0] - activity_gt_maxes.index(i+1))
             smds_normd[i] = smds[i] / vid_length
         else:
+<<<<<<< HEAD
             smds[i] = -1
             smds_normd[i] = -1
             if i+1 == num_classes:
@@ -200,6 +217,11 @@ def compute_mean_smd(smd_array):
 
     mask = smd_array >= 0
     return np.mean(smd_array[mask])
+=======
+            smds[i] = vid_length
+            smds_normd[i] = 1
+    return smds, smds_normd
+>>>>>>> 7c524b3c2c8e0d3b3952bf4f2cc97ba28aa00560
 
 
 def compute_class_f1s_and_mean_f1(TP,FP,FN):