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test updates, revert Results to CPU

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This commit is contained in:
Glenn Jocher 2023-02-24 03:22:33 -08:00
parent 3ea659411b
commit bfc078b32f
3 changed files with 9 additions and 7 deletions
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6
tests/test_cli.py
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@ -48,7 +48,7 @@ def test_val_classify():
# Predict checks ------------------------------------------------------------------------------------------------------- # Predict checks -------------------------------------------------------------------------------------------------------
def test_predict_detect(): def test_predict_detect():
run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32") run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save")
if checks.check_online(): if checks.check_online():
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32') run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32') run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
@ -56,11 +56,11 @@ def test_predict_detect():
def test_predict_segment(): def test_predict_segment():
run(f"yolo predict model={MODEL}-seg.pt source={ROOT / 'assets'} imgsz=32") run(f"yolo predict model={MODEL}-seg.pt source={ROOT / 'assets'} imgsz=32 save")
def test_predict_classify(): def test_predict_classify():
run(f"yolo predict model={MODEL}-cls.pt source={ROOT / 'assets'} imgsz=32") run(f"yolo predict model={MODEL}-cls.pt source={ROOT / 'assets'} imgsz=32 save")
# Export checks -------------------------------------------------------------------------------------------------------- # Export checks --------------------------------------------------------------------------------------------------------

6
ultralytics/yolo/engine/results.py
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@ -42,9 +42,9 @@ class Results:
def __init__(self, orig_img, path, names, boxes=None, masks=None, probs=None) -> None: def __init__(self, orig_img, path, names, boxes=None, masks=None, probs=None) -> None:
self.orig_img = orig_img self.orig_img = orig_img
self.orig_shape = orig_img.shape[:2] self.orig_shape = orig_img.shape[:2]
self.boxes = Boxes(boxes.cpu(), self.orig_shape) if boxes is not None else None # native size boxes self.boxes = Boxes(boxes, self.orig_shape) if boxes is not None else None # native size boxes
self.masks = Masks(masks.cpu(), self.orig_shape) if masks is not None else None # native size or imgsz masks self.masks = Masks(masks, self.orig_shape) if masks is not None else None # native size or imgsz masks
self.probs = probs.cpu() if probs is not None else None self.probs = probs if probs is not None else None
self.names = names self.names = names
self.path = path self.path = path
self._keys = (k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None) self._keys = (k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None)

4
ultralytics/yolo/utils/plotting.py
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@ -114,7 +114,9 @@ class Annotator:
self.im = np.asarray(self.im).copy() self.im = np.asarray(self.im).copy()
if len(masks) == 0: if len(masks) == 0:
self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 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 if im_gpu.device != masks.device:
im_gpu = im_gpu.to(masks.device)
colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0
colors = colors[:, None, None] # shape(n,1,1,3) colors = colors[:, None, None] # shape(n,1,1,3)
masks = masks.unsqueeze(3) # shape(n,h,w,1) masks = masks.unsqueeze(3) # shape(n,h,w,1)
masks_color = masks * (colors * alpha) # shape(n,h,w,3) masks_color = masks * (colors * alpha) # shape(n,h,w,3)