ultralytics 8.0.80 single-line docstring fixes (#2060)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
GitHub
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31db8ed163
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5bce1c3021
48 changed files with 418 additions and 420 deletions
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@ -66,7 +66,7 @@ class Compose:
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class BaseMixTransform:
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"""This implementation is from mmyolo"""
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"""This implementation is from mmyolo."""
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def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
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self.dataset = dataset
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@ -77,12 +77,12 @@ class BaseMixTransform:
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if random.uniform(0, 1) > self.p:
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return labels
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# get index of one or three other images
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# Get index of one or three other images
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indexes = self.get_indexes()
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if isinstance(indexes, int):
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indexes = [indexes]
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# get images information will be used for Mosaic or MixUp
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# Get images information will be used for Mosaic or MixUp
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mix_labels = [self.dataset.get_label_info(i) for i in indexes]
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if self.pre_transform is not None:
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@ -132,7 +132,7 @@ class Mosaic(BaseMixTransform):
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img = labels_patch['img']
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h, w = labels_patch.pop('resized_shape')
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# place img in img4
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# Place img in img4
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if i == 0: # top left
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img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
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@ -158,7 +158,7 @@ class Mosaic(BaseMixTransform):
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return final_labels
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def _update_labels(self, labels, padw, padh):
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"""Update labels"""
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"""Update labels."""
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nh, nw = labels['img'].shape[:2]
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labels['instances'].convert_bbox(format='xyxy')
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labels['instances'].denormalize(nw, nh)
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@ -193,7 +193,7 @@ class MixUp(BaseMixTransform):
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return random.randint(0, len(self.dataset) - 1)
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def _mix_transform(self, labels):
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# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
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"""Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf."""
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r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
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labels2 = labels['mix_labels'][0]
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labels['img'] = (labels['img'] * r + labels2['img'] * (1 - r)).astype(np.uint8)
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@ -217,12 +217,12 @@ class RandomPerspective:
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self.scale = scale
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self.shear = shear
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self.perspective = perspective
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# mosaic border
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# Mosaic border
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self.border = border
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self.pre_transform = pre_transform
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def affine_transform(self, img, border):
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# Center
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"""Center."""
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C = np.eye(3, dtype=np.float32)
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C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
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@ -253,7 +253,7 @@ class RandomPerspective:
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# Combined rotation matrix
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M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
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# affine image
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# Affine image
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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
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if self.perspective:
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img = cv2.warpPerspective(img, M, dsize=self.size, borderValue=(114, 114, 114))
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@ -281,7 +281,7 @@ class RandomPerspective:
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
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# create new boxes
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# Create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T
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@ -348,7 +348,7 @@ class RandomPerspective:
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img = labels['img']
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cls = labels['cls']
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instances = labels.pop('instances')
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# make sure the coord formats are right
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# Make sure the coord formats are right
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instances.convert_bbox(format='xyxy')
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instances.denormalize(*img.shape[:2][::-1])
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@ -362,19 +362,19 @@ class RandomPerspective:
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segments = instances.segments
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keypoints = instances.keypoints
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# update bboxes if there are segments.
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# Update bboxes if there are segments.
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if len(segments):
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bboxes, segments = self.apply_segments(segments, M)
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if keypoints is not None:
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keypoints = self.apply_keypoints(keypoints, M)
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new_instances = Instances(bboxes, segments, keypoints, bbox_format='xyxy', normalized=False)
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# clip
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# Clip
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new_instances.clip(*self.size)
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# filter instances
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# Filter instances
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instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
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# make the bboxes have the same scale with new_bboxes
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# Make the bboxes have the same scale with new_bboxes
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i = self.box_candidates(box1=instances.bboxes.T,
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box2=new_instances.bboxes.T,
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area_thr=0.01 if len(segments) else 0.10)
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@ -441,7 +441,7 @@ class RandomFlip:
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if self.direction == 'horizontal' and random.random() < self.p:
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img = np.fliplr(img)
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instances.fliplr(w)
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# for keypoints
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# For keypoints
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if self.flip_idx is not None and instances.keypoints is not None:
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instances.keypoints = np.ascontiguousarray(instances.keypoints[:, self.flip_idx, :])
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labels['img'] = np.ascontiguousarray(img)
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@ -450,7 +450,7 @@ class RandomFlip:
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class LetterBox:
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"""Resize image and padding for detection, instance segmentation, pose"""
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"""Resize image and padding for detection, instance segmentation, pose."""
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def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, stride=32):
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self.new_shape = new_shape
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@ -505,7 +505,7 @@ class LetterBox:
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return img
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def _update_labels(self, labels, ratio, padw, padh):
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"""Update labels"""
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"""Update labels."""
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labels['instances'].convert_bbox(format='xyxy')
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labels['instances'].denormalize(*labels['img'].shape[:2][::-1])
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labels['instances'].scale(*ratio)
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@ -519,7 +519,7 @@ class CopyPaste:
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self.p = p
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def __call__(self, labels):
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# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
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"""Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)."""
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im = labels['img']
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cls = labels['cls']
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h, w = im.shape[:2]
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@ -531,7 +531,7 @@ class CopyPaste:
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_, w, _ = im.shape # height, width, channels
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im_new = np.zeros(im.shape, np.uint8)
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# calculate ioa first then select indexes randomly
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# Calculate ioa first then select indexes randomly
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ins_flip = deepcopy(instances)
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ins_flip.fliplr(w)
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@ -641,7 +641,7 @@ class Format:
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labels['bboxes'] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
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if self.return_keypoint:
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labels['keypoints'] = torch.from_numpy(instances.keypoints)
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# then we can use collate_fn
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# Then we can use collate_fn
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if self.batch_idx:
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labels['batch_idx'] = torch.zeros(nl)
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return labels
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@ -654,7 +654,7 @@ class Format:
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return img
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def _format_segments(self, instances, cls, w, h):
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"""convert polygon points to bitmap"""
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"""convert polygon points to bitmap."""
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segments = instances.segments
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if self.mask_overlap:
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masks, sorted_idx = polygons2masks_overlap((h, w), segments, downsample_ratio=self.mask_ratio)
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