ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -52,7 +52,7 @@ class Profile(contextlib.ContextDecorator):
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def __str__(self):
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"""Returns a human-readable string representing the accumulated elapsed time in the profiler."""
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return f'Elapsed time is {self.t} s'
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return f"Elapsed time is {self.t} s"
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def time(self):
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"""Get current time."""
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@ -76,9 +76,13 @@ def segment2box(segment, width=640, height=640):
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# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
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x, y = segment.T # segment xy
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inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
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x, y, = x[inside], y[inside]
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return np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype) if any(x) else np.zeros(
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4, dtype=segment.dtype) # xyxy
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x = x[inside]
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y = y[inside]
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return (
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np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype)
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if any(x)
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else np.zeros(4, dtype=segment.dtype)
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) # xyxy
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def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None, padding=True, xywh=False):
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@ -101,8 +105,10 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None, padding=True, xyw
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"""
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if ratio_pad is None: # calculate from img0_shape
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gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
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pad = round((img1_shape[1] - img0_shape[1] * gain) / 2 - 0.1), round(
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(img1_shape[0] - img0_shape[0] * gain) / 2 - 0.1) # wh padding
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pad = (
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round((img1_shape[1] - img0_shape[1] * gain) / 2 - 0.1),
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round((img1_shape[0] - img0_shape[0] * gain) / 2 - 0.1),
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) # wh padding
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else:
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gain = ratio_pad[0][0]
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pad = ratio_pad[1]
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@ -145,7 +151,7 @@ def nms_rotated(boxes, scores, threshold=0.45):
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Returns:
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"""
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if len(boxes) == 0:
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return np.empty((0, ), dtype=np.int8)
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return np.empty((0,), dtype=np.int8)
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sorted_idx = torch.argsort(scores, descending=True)
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boxes = boxes[sorted_idx]
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ious = batch_probiou(boxes, boxes).triu_(diagonal=1)
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@ -199,8 +205,8 @@ def non_max_suppression(
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"""
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# Checks
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assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
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assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
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assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
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assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
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if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out)
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prediction = prediction[0] # select only inference output
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@ -284,7 +290,7 @@ def non_max_suppression(
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output[xi] = x[i]
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if (time.time() - t) > time_limit:
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LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')
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LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded")
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break # time limit exceeded
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return output
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@ -378,7 +384,7 @@ def xyxy2xywh(x):
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Returns:
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y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
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"""
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assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
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assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
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y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
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y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
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@ -398,7 +404,7 @@ def xywh2xyxy(x):
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Returns:
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y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
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"""
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assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
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assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
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dw = x[..., 2] / 2 # half-width
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dh = x[..., 3] / 2 # half-height
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@ -423,7 +429,7 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
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y (np.ndarray | torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where
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x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
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"""
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assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
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assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
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y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
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y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
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@ -449,7 +455,7 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
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"""
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if clip:
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x = clip_boxes(x, (h - eps, w - eps))
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assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
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assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
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y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
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y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
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y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
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@ -526,8 +532,11 @@ def xyxyxyxy2xywhr(corners):
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# especially some objects are cut off by augmentations in dataloader.
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(x, y), (w, h), angle = cv2.minAreaRect(pts)
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rboxes.append([x, y, w, h, angle / 180 * np.pi])
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rboxes = torch.tensor(rboxes, device=corners.device, dtype=corners.dtype) if is_torch else np.asarray(
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rboxes, dtype=points.dtype)
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rboxes = (
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torch.tensor(rboxes, device=corners.device, dtype=corners.dtype)
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if is_torch
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else np.asarray(rboxes, dtype=points.dtype)
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)
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return rboxes
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@ -546,7 +555,7 @@ def xywhr2xyxyxyxy(center):
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cos, sin = (np.cos, np.sin) if is_numpy else (torch.cos, torch.sin)
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ctr = center[..., :2]
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w, h, angle = (center[..., i:i + 1] for i in range(2, 5))
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w, h, angle = (center[..., i : i + 1] for i in range(2, 5))
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cos_value, sin_value = cos(angle), sin(angle)
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vec1 = [w / 2 * cos_value, w / 2 * sin_value]
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vec2 = [-h / 2 * sin_value, h / 2 * cos_value]
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@ -607,8 +616,9 @@ def resample_segments(segments, n=1000):
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s = np.concatenate((s, s[0:1, :]), axis=0)
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x = np.linspace(0, len(s) - 1, n)
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xp = np.arange(len(s))
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segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)],
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dtype=np.float32).reshape(2, -1).T # segment xy
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segments[i] = (
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np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)], dtype=np.float32).reshape(2, -1).T
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) # segment xy
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return segments
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@ -647,7 +657,7 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
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"""
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c, mh, mw = protos.shape # CHW
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masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
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masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
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masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
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masks = crop_mask(masks, bboxes) # CHW
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return masks.gt_(0.5)
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@ -680,7 +690,7 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
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masks = crop_mask(masks, downsampled_bboxes) # CHW
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if upsample:
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masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
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masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
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return masks.gt_(0.5)
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@ -724,7 +734,7 @@ def scale_masks(masks, shape, padding=True):
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bottom, right = (int(round(mh - pad[1] + 0.1)), int(round(mw - pad[0] + 0.1)))
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masks = masks[..., top:bottom, left:right]
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masks = F.interpolate(masks, shape, mode='bilinear', align_corners=False) # NCHW
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masks = F.interpolate(masks, shape, mode="bilinear", align_corners=False) # NCHW
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return masks
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@ -763,7 +773,7 @@ def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False
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return coords
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def masks2segments(masks, strategy='largest'):
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def masks2segments(masks, strategy="largest"):
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"""
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It takes a list of masks(n,h,w) and returns a list of segments(n,xy)
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@ -775,16 +785,16 @@ def masks2segments(masks, strategy='largest'):
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segments (List): list of segment masks
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"""
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segments = []
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for x in masks.int().cpu().numpy().astype('uint8'):
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for x in masks.int().cpu().numpy().astype("uint8"):
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c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
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if c:
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if strategy == 'concat': # concatenate all segments
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if strategy == "concat": # concatenate all segments
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c = np.concatenate([x.reshape(-1, 2) for x in c])
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elif strategy == 'largest': # select largest segment
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elif strategy == "largest": # select largest segment
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c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
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else:
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c = np.zeros((0, 2)) # no segments found
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segments.append(c.astype('float32'))
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segments.append(c.astype("float32"))
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return segments
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@ -811,4 +821,4 @@ def clean_str(s):
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Returns:
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(str): a string with special characters replaced by an underscore _
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"""
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return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s)
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return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
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