nGT to nT

models.py

@@ -2,8 +2,8 @@ from collections import defaultdict
 
 import torch.nn as nn
 
-from utils.utils import *
 from utils.parse_config import *
+from utils.utils import *
 
 
 def create_modules(module_defs):
@@ -151,7 +151,7 @@ class YOLOLayer(nn.Module):
 
             # Mask outputs to ignore non-existing objects (but keep confidence predictions)
             nM = mask.sum().float()
-            nGT = sum([len(x) for x in targets])
+            nT = sum([len(x) for x in targets])
             if nM > 0:
                 lx = 5 * MSELoss(x[mask], tx[mask])
                 ly = 5 * MSELoss(y[mask], ty[mask])
@@ -177,7 +177,7 @@ class YOLOLayer(nn.Module):
                     FPe[c] += 1
 
             return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
-                   nGT, TP, FP, FPe, FN, TC
+                   nT, TP, FP, FPe, FN, TC
 
         else:
             pred_boxes[..., 0] = x.data + self.grid_x
@@ -200,7 +200,7 @@ class Darknet(nn.Module):
         self.module_defs[0]['height'] = img_size
         self.hyperparams, self.module_list = create_modules(self.module_defs)
         self.img_size = img_size
-        self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nGT', 'TP', 'FP', 'FPe', 'FN', 'TC']
+        self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC']
 
     def forward(self, x, targets=None, requestPrecision=False, epoch=None):
         is_training = targets is not None
@@ -230,7 +230,7 @@ class Darknet(nn.Module):
             layer_outputs.append(x)
 
         if is_training:
-            self.losses['nGT'] /= 3
+            self.losses['nT'] /= 3
             self.losses['TC'] /= 3
             metrics = torch.zeros(4, len(self.losses['FPe']))  # TP, FP, FN, target_count
 

train.py

@@ -145,7 +145,7 @@ def main(opt):
                 mean_recall = 0
 
             s = ('%10s%10s' + '%10.3g' * 14) % (
-                 '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
+                '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
                 rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
                 rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
                 model.losses['FP'], model.losses['FN'], time.time() - t1)

utils/utils.py

@@ -190,7 +190,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True):
 
 def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG, requestPrecision):
     """
-    returns nGT, nCorrect, tx, ty, tw, th, tconf, tcls
+    returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
     """
     nB = len(target)  # target.shape[0]
     nT = [len(x) for x in target]  # torch.argmin(target[:, :, 4], 1)  # targets per image