Merge pull request #27 from Laughing-q/instance_seg

update F.interpolate&&clean up

segment/predict.py

@@ -208,7 +208,7 @@ def run(
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
-        # LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
 
     # Print results
     t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image

segment/train.py

@@ -350,8 +350,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                 #    return
 
                 # Mosaic plots
-                if mask_ratio != 1:
-                    masks = F.interpolate(masks[None].float(), (imgsz, imgsz), mode="bilinear", align_corners=False)[0]
                 if plots:
                     if ni < 3:
                         plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg")

utils/segment/loss.py

@@ -83,7 +83,7 @@ def __call__(self, preds, targets, masks):  # predictions, targets, model
 
                 # Mask regression
                 if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
-                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode="bilinear", align_corners=False)[0]
+                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
                 marea = xywhn[i][:, 2:].prod(1)  # mask width, height normalized
                 mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
                 for bi in b.unique():