updates

utils/utils.py

@@ -11,7 +11,6 @@ from PIL import Image
 from tqdm import tqdm
 
 from . import torch_utils
-from . import parse_config
 
 matplotlib.rc('font', **{'size': 12})
 
@@ -295,11 +294,9 @@ def compute_loss(p, targets, model, giou_loss=False):  # predictions, targets, m
             tconf[b, a, gj, gi] = 1  # conf
             # pi[..., 2:4] = torch.sigmoid(pi[..., 2:4])  # wh power loss (uncomment)
 
-            # Build GIoU boxes
-            pbox = torch.cat((torch.sigmoid(pi[..., 0:2]), torch.exp(pi[..., 2:4]) * anchor_vec[i]), 1)  # predicted box
-            giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)
-
             if giou_loss:
+                pbox = torch.cat((torch.sigmoid(pi[..., 0:2]), torch.exp(pi[..., 2:4]) * anchor_vec[i]), 1)  # predicted
+                giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)  # giou computation
                 lxy += (k * h['giou']) * (1.0 - giou).mean()  # giou loss
             else:
                 lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
@@ -490,14 +487,6 @@ def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
     torch.save(a, filename.replace('.pt', '_lite.pt'))
 
 
-def extract_bounding_boxes(data_cfg='data/coco_64img.data'):  # from utils.utils import *; extract_bounding_boxes()
-    # Extract bounding boxes into a new classification dataset
-    data_dict = parse_config.parse_data_cfg(data_cfg)
-    train_path = data_dict['train']
-    nc = int(data_dict['classes'])  # number of classes
-
-
-
 def coco_class_count(path='../coco/labels/train2014/'):
     # Histogram of occurrences per class
     nc = 80  # number classes