Cat apriori to autolabels (ultralytics#1484)

detect.py

@@ -137,7 +137,8 @@ def detect(save_img=False):
                     vid_writer.write(im0)
 
     if save_txt or save_img:
-        print('Results saved to %s' % save_dir)
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        print(f"Results saved to {save_dir}{s}")
 
     print('Done. (%.3fs)' % (time.time() - t0))
 

test.py

@@ -101,22 +101,22 @@ def test(data,
         img /= 255.0  # 0 - 255 to 0.0 - 1.0
         targets = targets.to(device)
         nb, _, height, width = img.shape  # batch size, channels, height, width
-        whwh = torch.Tensor([width, height, width, height]).to(device)
+        targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)
 
-        # Disable gradients
         with torch.no_grad():
             # Run model
             t = time_synchronized()
             inf_out, train_out = model(img, augment=augment)  # inference and training outputs
             t0 += time_synchronized() - t
 
             # Compute loss
-            if training:  # if model has loss hyperparameters
+            if training:
                 loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls
 
             # Run NMS
             t = time_synchronized()
-            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)
+            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_txt else []  # for autolabelling
+            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb)
             t1 += time_synchronized() - t
 
         # Statistics per image
@@ -174,7 +174,7 @@ def test(data,
                 tcls_tensor = labels[:, 0]
 
                 # target boxes
-                tbox = xywh2xyxy(labels[:, 1:5]) * whwh
+                tbox = xywh2xyxy(labels[:, 1:5])
                 scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])  # native-space labels
 
                 # Per target class
@@ -264,7 +264,8 @@ def test(data,
 
     # Return results
     if not training:
-        print('Results saved to %s' % save_dir)
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        print(f"Results saved to {save_dir}{s}")
     model.float()  # for training
     maps = np.zeros(nc) + map
     for i, c in enumerate(ap_class):

utils/general.py

@@ -263,7 +263,7 @@ def wh_iou(wh1, wh2):
     return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
 
 
-def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False):
+def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, classes=None, agnostic=False, labels=()):
     """Performs Non-Maximum Suppression (NMS) on inference results
 
     Returns:
@@ -279,6 +279,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False,
     time_limit = 10.0  # seconds to quit after
     redundant = True  # require redundant detections
     multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
 
     t = time.time()
     output = [torch.zeros(0, 6)] * prediction.shape[0]
@@ -287,6 +288,15 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False,
         # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
         x = x[xc[xi]]  # confidence
 
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            l = labels[xi]
+            v = torch.zeros((len(l), nc + 5), device=x.device)
+            v[:, :4] = l[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
         # If none remain process next image
         if not x.shape[0]:
             continue