process_mask_native() cleanup (#10366)

* process_mask_native() cleanup

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix arg name

* cleanup anno_json

* Remove scale_image

* Remove scale_image

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update to native

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

segment/predict.py

@@ -44,7 +44,7 @@ from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
-                           strip_optimizer, xyxy2xywh)
+                           strip_optimizer)
 from utils.plots import Annotator, colors, save_one_box
 from utils.segment.general import masks2segments, process_mask, process_mask_native
 from utils.torch_utils import select_device, smart_inference_mode
@@ -161,10 +161,9 @@ def run(
 
                 # Segments
                 if save_txt:
-                    segments = reversed(masks2segments(masks))
                     segments = [
                         scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
-                        for x in segments]
+                        for x in reversed(masks2segments(masks))]
 
                 # Print results
                 for c in det[:, 5].unique():
@@ -172,15 +171,17 @@ def run(
                     s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
 
                 # Mask plotting
-                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)
+                annotator.masks(
+                    masks,
+                    colors=[colors(x, True) for x in det[:, 5]],
+                    im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() /
+                    255 if retina_masks else im[i])
 
                 # Write results
                 for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
                     if save_txt:  # Write to file
-                        segj = segments[j].reshape(-1)  # (n,2) to (n*2)
-                        line = (cls, *segj, conf) if save_conf else (cls, *segj)  # label format
+                        seg = segments[j].reshape(-1)  # (n,2) to (n*2)
+                        line = (cls, *seg, conf) if save_conf else (cls, *seg)  # label format
                         with open(f'{txt_path}.txt', 'a') as f:
                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 

segment/val.py

@@ -48,7 +48,7 @@ from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_
 from utils.metrics import ConfusionMatrix, box_iou
 from utils.plots import output_to_target, plot_val_study
 from utils.segment.dataloaders import create_dataloader
-from utils.segment.general import mask_iou, process_mask, process_mask_upsample, scale_image
+from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
 from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
 from utils.segment.plots import plot_images_and_masks
 from utils.torch_utils import de_parallel, select_device, smart_inference_mode
@@ -160,7 +160,7 @@ def run(
 ):
     if save_json:
         check_requirements(['pycocotools'])
-        process = process_mask_upsample  # more accurate
+        process = process_mask_native  # more accurate
     else:
         process = process_mask  # faster
 
@@ -312,7 +312,7 @@ def run(
 
             pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
             if plots and batch_i < 3:
-                plot_masks.append(pred_masks[:15].cpu())  # filter top 15 to plot
+                plot_masks.append(pred_masks[:15])  # filter top 15 to plot
 
             # Save/log
             if save_txt:
@@ -367,8 +367,8 @@ def run(
     # Save JSON
     if save_json and len(jdict):
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
-        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
-        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
+        anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json'))  # annotations
+        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions
         LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
         with open(pred_json, 'w') as f:
             json.dump(jdict, f)