Fix & speed up segment plot (#10350)

segment/predict.py

@@ -46,7 +46,7 @@ from utils.general import (LOGGER, Profile, check_file, check_img_size, check_im
                            increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
                            strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
-from utils.segment.general import masks2segments, process_mask
+from utils.segment.general import masks2segments, process_mask, process_mask_native
 from utils.torch_utils import select_device, smart_inference_mode
 
 
@@ -151,13 +151,20 @@ def run(
             imc = im0.copy() if save_crop else im0  # for save_crop
             annotator = Annotator(im0, line_width=line_thickness, example=str(names))
             if len(det):
-                masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)  # HWC
-                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()  # rescale boxes to im0 size
+                if retina_masks:
+                    # scale bbox first the crop masks
+                    det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()  # rescale boxes to im0 size
+                    masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2])  # HWC
+                else:
+                    masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)  # HWC
+                    det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()  # rescale boxes to im0 size
 
                 # Segments
                 if save_txt:
                     segments = reversed(masks2segments(masks))
-                    segments = [scale_segments(im.shape[2:], x, im0.shape, normalize=True) for x in segments]
+                    segments = [
+                        scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
+                        for x in segments]
 
                 # Print results
                 for c in det[:, 5].unique():
@@ -165,9 +172,9 @@ def run(
                     s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
 
                 # Mask plotting
-                annotator.masks(masks,
-                                colors=[colors(x, True) for x in det[:, 5]],
-                                im_gpu=None if retina_masks else im[i])
+                plot_img = torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / 255. \
+                        if retina_masks else im[i]
+                annotator.masks(masks, colors=[colors(x, True) for x in det[:, 5]], im_gpu=plot_img)
 
                 # Write results
                 for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):

utils/plots.py

@@ -114,7 +114,7 @@ class Annotator:
                             thickness=tf,
                             lineType=cv2.LINE_AA)
 
-    def masks(self, masks, colors, im_gpu=None, alpha=0.5):
+    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
         """Plot masks at once.
         Args:
             masks (tensor): predicted masks on cuda, shape: [n, h, w]
@@ -125,37 +125,21 @@ class Annotator:
         if self.pil:
             # convert to numpy first
             self.im = np.asarray(self.im).copy()
-        if im_gpu is None:
-            # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...)
-            if len(masks) == 0:
-                return
-            if isinstance(masks, torch.Tensor):
-                masks = torch.as_tensor(masks, dtype=torch.uint8)
-                masks = masks.permute(1, 2, 0).contiguous()
-                masks = masks.cpu().numpy()
-            # masks = np.ascontiguousarray(masks.transpose(1, 2, 0))
-            masks = scale_image(masks.shape[:2], masks, self.im.shape)
-            masks = np.asarray(masks, dtype=np.float32)
-            colors = np.asarray(colors, dtype=np.float32)  # shape(n,3)
-            s = masks.sum(2, keepdims=True).clip(0, 1)  # add all masks together
-            masks = (masks @ colors).clip(0, 255)  # (h,w,n) @ (n,3) = (h,w,3)
-            self.im[:] = masks * alpha + self.im * (1 - s * alpha)
-        else:
-            if len(masks) == 0:
-                self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
-            colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
-            colors = colors[:, None, None]  # shape(n,1,1,3)
-            masks = masks.unsqueeze(3)  # shape(n,h,w,1)
-            masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
-
-            inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
-            mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)
-
-            im_gpu = im_gpu.flip(dims=[0])  # flip channel
-            im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
-            im_gpu = im_gpu * inv_alph_masks[-1] + mcs
-            im_mask = (im_gpu * 255).byte().cpu().numpy()
-            self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape)
+        if len(masks) == 0:
+            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
+        colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
+        colors = colors[:, None, None]  # shape(n,1,1,3)
+        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
+        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
+
+        inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
+        mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)
+
+        im_gpu = im_gpu.flip(dims=[0])  # flip channel
+        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
+        im_gpu = im_gpu * inv_alph_masks[-1] + mcs
+        im_mask = (im_gpu * 255).byte().cpu().numpy()
+        self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
         if self.pil:
             # convert im back to PIL and update draw
             self.fromarray(self.im)

utils/segment/general.py

@@ -67,6 +67,29 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
     return masks.gt_(0.5)
 
 
+def process_mask_native(protos, masks_in, bboxes, dst_shape):
+    """
+    Crop after upsample.
+    proto_out: [mask_dim, mask_h, mask_w]
+    out_masks: [n, mask_dim], n is number of masks after nms
+    bboxes: [n, 4], n is number of masks after nms
+    shape:input_image_size, (h, w)
+
+    return: h, w, n
+    """
+    c, mh, mw = protos.shape  # CHW
+    masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
+    gain = min(mh / dst_shape[0], mw / dst_shape[1])  # gain  = old / new
+    pad = (mw - dst_shape[1] * gain) / 2, (mh - dst_shape[0] * gain) / 2  # wh padding
+    top, left = int(pad[1]), int(pad[0])  # y, x
+    bottom, right = int(mh - pad[1]), int(mw - pad[0])
+    masks = masks[:, top:bottom, left:right]
+
+    masks = F.interpolate(masks[None], dst_shape, mode='bilinear', align_corners=False)[0]  # CHW
+    masks = crop_mask(masks, bboxes)  # CHW
+    return masks.gt_(0.5)
+
+
 def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
     """
     img1_shape: model input shape, [h, w]