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ultralytics 8.0.221 fix Apple MPS inference bug (#6694)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Johnny <johnnync13@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-11-30 13:46:17 +01:00 committed by GitHub
parent e504520448
commit 2e71f7f50e
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 22 additions and 25 deletions
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3
ultralytics/engine/results.py
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@ -117,8 +117,7 @@ class Results(SimpleClass):
def update(self, boxes=None, masks=None, probs=None): def update(self, boxes=None, masks=None, probs=None):
"""Update the boxes, masks, and probs attributes of the Results object.""" """Update the boxes, masks, and probs attributes of the Results object."""
if boxes is not None: if boxes is not None:
ops.clip_boxes(boxes, self.orig_shape) # clip boxes self.boxes = Boxes(ops.clip_boxes(boxes, self.orig_shape), self.orig_shape)
self.boxes = Boxes(boxes, self.orig_shape)
if masks is not None: if masks is not None:
self.masks = Masks(masks, self.orig_shape) self.masks = Masks(masks, self.orig_shape)
if probs is not None: if probs is not None:

2
ultralytics/nn/modules/head.py
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@ -141,7 +141,7 @@ class Pose(Detect):
else: else:
y = kpts.clone() y = kpts.clone()
if ndim == 3: if ndim == 3:
y[:, 2::3].sigmoid_() # inplace sigmoid y[:, 2::3] = y[:, 2::3].sigmoid() # sigmoid (WARNING: inplace .sigmoid_() Apple MPS bug)
y[:, 0::ndim] = (y[:, 0::ndim] * 2.0 + (self.anchors[0] - 0.5)) * self.strides y[:, 0::ndim] = (y[:, 0::ndim] * 2.0 + (self.anchors[0] - 0.5)) * self.strides
y[:, 1::ndim] = (y[:, 1::ndim] * 2.0 + (self.anchors[1] - 0.5)) * self.strides y[:, 1::ndim] = (y[:, 1::ndim] * 2.0 + (self.anchors[1] - 0.5)) * self.strides
return y return y

36
ultralytics/utils/ops.py
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@ -109,8 +109,7 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None, padding=True):
boxes[..., [0, 2]] -= pad[0] # x padding boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape) return clip_boxes(boxes, img0_shape)
return boxes
def make_divisible(x, divisor): def make_divisible(x, divisor):
@ -179,10 +178,6 @@ def non_max_suppression(
if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out) if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out)
prediction = prediction[0] # select only inference output prediction = prediction[0] # select only inference output
device = prediction.device
mps = 'mps' in device.type # Apple MPS
if mps: # MPS not fully supported yet, convert tensors to CPU before NMS
prediction = prediction.cpu()
bs = prediction.shape[0] # batch size bs = prediction.shape[0] # batch size
nc = nc or (prediction.shape[1] - 4) # number of classes nc = nc or (prediction.shape[1] - 4) # number of classes
nm = prediction.shape[1] - nc - 4 nm = prediction.shape[1] - nc - 4
@ -256,8 +251,6 @@ def non_max_suppression(
# i = i[iou.sum(1) > 1] # require redundancy # i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i] output[xi] = x[i]
if mps:
output[xi] = output[xi].to(device)
if (time.time() - t) > time_limit: if (time.time() - t) > time_limit:
LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')
break # time limit exceeded break # time limit exceeded
@ -272,15 +265,19 @@ def clip_boxes(boxes, shape):
Args: Args:
boxes (torch.Tensor): the bounding boxes to clip boxes (torch.Tensor): the bounding boxes to clip
shape (tuple): the shape of the image shape (tuple): the shape of the image
Returns:
(torch.Tensor | numpy.ndarray): Clipped boxes
""" """
if isinstance(boxes, torch.Tensor): # faster individually if isinstance(boxes, torch.Tensor): # faster individually (WARNING: inplace .clamp_() Apple MPS bug)
boxes[..., 0].clamp_(0, shape[1]) # x1 boxes[..., 0] = boxes[..., 0].clamp(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1 boxes[..., 1] = boxes[..., 1].clamp(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2 boxes[..., 2] = boxes[..., 2].clamp(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2 boxes[..., 3] = boxes[..., 3].clamp(0, shape[0]) # y2
else: # np.array (faster grouped) else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
return boxes
def clip_coords(coords, shape): def clip_coords(coords, shape):
@ -292,14 +289,15 @@ def clip_coords(coords, shape):
shape (tuple): A tuple of integers representing the size of the image in the format (height, width). shape (tuple): A tuple of integers representing the size of the image in the format (height, width).
Returns: Returns:
(None): The function modifies the input `coordinates` in place, by clipping each coordinate to the image boundaries. (torch.Tensor | numpy.ndarray): Clipped coordinates
""" """
if isinstance(coords, torch.Tensor): # faster individually if isinstance(coords, torch.Tensor): # faster individually (WARNING: inplace .clamp_() Apple MPS bug)
coords[..., 0].clamp_(0, shape[1]) # x coords[..., 0] = coords[..., 0].clamp(0, shape[1]) # x
coords[..., 1].clamp_(0, shape[0]) # y coords[..., 1] = coords[..., 1].clamp(0, shape[0]) # y
else: # np.array (faster grouped) else: # np.array (faster grouped)
coords[..., 0] = coords[..., 0].clip(0, shape[1]) # x coords[..., 0] = coords[..., 0].clip(0, shape[1]) # x
coords[..., 1] = coords[..., 1].clip(0, shape[0]) # y coords[..., 1] = coords[..., 1].clip(0, shape[0]) # y
return coords
def scale_image(masks, im0_shape, ratio_pad=None): def scale_image(masks, im0_shape, ratio_pad=None):
@ -418,7 +416,7 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height, normalized) format y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height, normalized) format
""" """
if clip: if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip x = clip_boxes(x, (h - eps, w - eps))
assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}' assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
@ -740,7 +738,7 @@ def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False
coords[..., 1] -= pad[1] # y padding coords[..., 1] -= pad[1] # y padding
coords[..., 0] /= gain coords[..., 0] /= gain
coords[..., 1] /= gain coords[..., 1] /= gain
clip_coords(coords, img0_shape) coords = clip_coords(coords, img0_shape)
if normalize: if normalize:
coords[..., 0] /= img0_shape[1] # width coords[..., 0] /= img0_shape[1] # width
coords[..., 1] /= img0_shape[0] # height coords[..., 1] /= img0_shape[0] # height

2
ultralytics/utils/plotting.py
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@ -353,7 +353,7 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
xyxy = ops.xywh2xyxy(b).long() xyxy = ops.xywh2xyxy(b).long()
ops.clip_boxes(xyxy, im.shape) xyxy = ops.clip_boxes(xyxy, im.shape)
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
if save: if save:
file.parent.mkdir(parents=True, exist_ok=True) # make directory file.parent.mkdir(parents=True, exist_ok=True) # make directory