updates

test.py

@@ -44,10 +44,8 @@ def test(
     outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], []
     AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
     for batch_i, (imgs, targets) in enumerate(dataloader):
-
-        with torch.no_grad():
-            output = model(imgs.to(device))
-            output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
+        output = model(imgs.to(device))
+        output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
 
         # Compute average precision for each sample
         for sample_i, (labels, detections) in enumerate(zip(targets, output)):
@@ -134,13 +132,14 @@ if __name__ == '__main__':
     opt = parser.parse_args()
     print(opt, end='\n\n')
 
-    mAP = test(
-        opt.cfg,
-        opt.data_cfg,
-        opt.weights,
-        opt.batch_size,
-        opt.img_size,
-        opt.iou_thres,
-        opt.conf_thres,
-        opt.nms_thres
-    )
+    with torch.no_grad():
+        mAP = test(
+            opt.cfg,
+            opt.data_cfg,
+            opt.weights,
+            opt.batch_size,
+            opt.img_size,
+            opt.iou_thres,
+            opt.conf_thres,
+            opt.nms_thres
+        )

train.py

@@ -195,7 +195,8 @@ def train(
             os.system('cp ' + latest + ' ' + os.path.join(weights, 'backup{}.pt'.format(epoch)))
 
         # Calculate mAP
-        mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
+        with torch.no_grad():
+            mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
 
         # Write epoch results
         with open('results.txt', 'a') as file: