YOLOv5 Segmentation Dataloader Updates (#2188)
* Update C3 module * Update C3 module * Update C3 module * Update C3 module * update * update * update * update * update * update * update * update * update * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * updates * updates * updates * updates * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update datasets * update * update * update * update attempt_downlaod() * merge * merge * update * update * update * update * update * update * update * update * update * update * parameterize eps * comments * gs-multiple * update * max_nms implemented * Create one_cycle() function * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * GitHub API rate limit fix * update * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * astuple * epochs * update * update * ComputeLoss() * update * update * update * update * update * update * update * update * update * update * update * merge * merge * merge * merge * update * update * update * update * commit=tag == tags[-1] * Update cudnn.benchmark * update * update * update * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * update * mosaic9 * update * update * update * update * update * update * institute cache versioning * only display on existing cache * reverse cache exists booleans
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@@ -225,7 +225,7 @@ def xywh2xyxy(x):
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return y
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def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
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def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
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# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
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@@ -235,6 +235,40 @@ def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
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return y
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def xyn2xy(x, w=640, h=640, padw=0, padh=0):
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# Convert normalized segments into pixel segments, shape (n,2)
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = w * x[:, 0] + padw # top left x
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y[:, 1] = h * x[:, 1] + padh # top left y
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return y
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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()]) if any(x) else np.zeros((1, 4)) # cls, xyxy
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def segments2boxes(segments):
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# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
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boxes = []
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for s in segments:
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x, y = s.T # segment xy
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boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
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return xyxy2xywh(np.array(boxes)) # cls, xywh
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def resample_segments(segments, n=1000):
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# Up-sample an (n,2) segment
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for i, s in enumerate(segments):
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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)]).reshape(2, -1).T # segment xy
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return segments
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def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
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# Rescale coords (xyxy) from img1_shape to img0_shape
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if ratio_pad is None: # calculate from img0_shape
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