Merge pull request ultralytics#20 from Laughing-q/instance_seg

speed up evaluation

segment/val.py

@@ -122,6 +122,7 @@ def process_batch_masks(predn, pred_masks, gt_masks, labels, iouv, overlap):
             mode="bilinear",
             align_corners=False,
         ).squeeze(0)
+        gt_masks = gt_masks.gt_(0.5)
 
     iou = mask_iou(
         gt_masks.view(gt_masks.shape[0], -1),
@@ -171,7 +172,7 @@ def run(
         mask_downsample_ratio=1,
         compute_loss=None,
 ):
-    process = process_mask_upsample if plots else process_mask
+    process = process_mask_upsample if save_json else process_mask
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py
@@ -304,9 +305,6 @@ def run(
             proto_out = train_out[1][si]
             pred_masks = process(proto_out, pred[:, 6:], pred[:, :4],
                                  shape=im[si].shape[1:]).permute(2, 0, 1).contiguous().float()
-            if plots and batch_i < 3:
-                # filter top 15 to plot
-                plot_masks.append(torch.as_tensor(pred_masks[:15], dtype=torch.uint8).cpu())
 
             # Predictions
             if single_cls:
@@ -326,6 +324,12 @@ def run(
             stats.append(
                 (correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0]))  # (correct, conf, pcls, tcls)
 
+            # convert pred_masks to uint8
+            pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
+            if plots and batch_i < 3:
+                # filter top 15 to plot
+                plot_masks.append(pred_masks[:15].cpu())
+
             # Save/log
             if save_txt:
                 save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
@@ -336,13 +340,6 @@ def run(
 
         # Plot images
         if plots and batch_i < 3:
-            if masks.shape[1:] != im.shape[2:]:
-                masks = F.interpolate(
-                    masks.unsqueeze(0).float(),
-                    im.shape[2:],
-                    mode="bilinear",
-                    align_corners=False,
-                ).squeeze(0)
             plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)
             if len(plot_masks):
                 plot_masks = torch.cat(plot_masks, dim=0)

utils/segment/general.py

@@ -44,7 +44,7 @@ def process_mask_upsample(proto_out, out_masks, bboxes, shape):
     """
 
     c, mh, mw = proto_out.shape  # CHW
-    masks = (out_masks.tanh() @ proto_out.view(c, -1)).sigmoid().view(-1, mh, mw)
+    masks = (out_masks.tanh() @ proto_out.float().view(c, -1)).sigmoid().view(-1, mh, mw)
     masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
     masks = crop(masks.permute(1, 2, 0).contiguous(), bboxes)  # HWC
     return masks.gt_(0.5)
@@ -63,7 +63,7 @@ def process_mask(proto_out, out_masks, bboxes, shape, upsample=False):
 
     c, mh, mw = proto_out.shape  # CHW
     ih, iw = shape
-    masks = (out_masks.tanh() @ proto_out.view(c, -1)).sigmoid().view(-1, mh, mw)  # CHW
+    masks = (out_masks.tanh() @ proto_out.float().view(c, -1)).sigmoid().view(-1, mh, mw)  # CHW
 
     downsampled_bboxes = bboxes.clone()
     downsampled_bboxes[:, 0] *= mw / iw

utils/segment/plots.py

@@ -93,8 +93,8 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
         if paths:
             annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
-            j = targets[:, 0] == i
-            ti = targets[j]  # image targets
+            idx = targets[:, 0] == i
+            ti = targets[idx]  # image targets
 
             boxes = xywh2xyxy(ti[:, 2:6]).T
             classes = ti[:, 1].astype('int')
@@ -126,13 +126,14 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
                     image_masks = np.repeat(image_masks, nl, axis=0)
                     image_masks = np.where(image_masks == index, 1.0, 0.0)
                 else:
-                    image_masks = masks[j]
+                    image_masks = masks[idx]
 
                 im = np.asarray(annotator.im).copy()
                 for j, box in enumerate(boxes.T.tolist()):
                     if labels or conf[j] > 0.25:  # 0.25 conf thresh
                         color = colors(classes[j])
-                        if scale < 1:
+                        mh, mw = image_masks[j].shape
+                        if mh != h or mw != w:
                             mask = image_masks[j].astype(np.uint8)
                             mask = cv2.resize(mask, (w, h))
                             mask = mask.astype(np.bool)