Update .pre-commit-config.yaml (#1026)
This commit is contained in:
Glenn Jocher
GitHub
parent
9047d737f4
commit
edd3ff1669
76 changed files with 928 additions and 935 deletions
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@ -55,11 +55,11 @@ class Compose:
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return self.transforms
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def __repr__(self):
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format_string = f"{self.__class__.__name__}("
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format_string = f'{self.__class__.__name__}('
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for t in self.transforms:
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format_string += "\n"
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format_string += f" {t}"
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format_string += "\n)"
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format_string += '\n'
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format_string += f' {t}'
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format_string += '\n)'
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return format_string
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@ -86,11 +86,11 @@ class BaseMixTransform:
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if self.pre_transform is not None:
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for i, data in enumerate(mix_labels):
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mix_labels[i] = self.pre_transform(data)
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labels["mix_labels"] = mix_labels
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labels['mix_labels'] = mix_labels
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# Mosaic or MixUp
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labels = self._mix_transform(labels)
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labels.pop("mix_labels", None)
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labels.pop('mix_labels', None)
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return labels
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def _mix_transform(self, labels):
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@ -109,7 +109,7 @@ class Mosaic(BaseMixTransform):
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"""
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def __init__(self, dataset, imgsz=640, p=1.0, border=(0, 0)):
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assert 0 <= p <= 1.0, "The probability should be in range [0, 1]. " f"got {p}."
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assert 0 <= p <= 1.0, 'The probability should be in range [0, 1]. ' f'got {p}.'
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super().__init__(dataset=dataset, p=p)
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self.dataset = dataset
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self.imgsz = imgsz
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@ -120,15 +120,15 @@ class Mosaic(BaseMixTransform):
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def _mix_transform(self, labels):
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mosaic_labels = []
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assert labels.get("rect_shape", None) is None, "rect and mosaic is exclusive."
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assert len(labels.get("mix_labels", [])) > 0, "There are no other images for mosaic augment."
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assert labels.get('rect_shape', None) is None, 'rect and mosaic is exclusive.'
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assert len(labels.get('mix_labels', [])) > 0, 'There are no other images for mosaic augment.'
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s = self.imgsz
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yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
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for i in range(4):
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labels_patch = (labels if i == 0 else labels["mix_labels"][i - 1]).copy()
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labels_patch = (labels if i == 0 else labels['mix_labels'][i - 1]).copy()
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# Load image
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img = labels_patch["img"]
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h, w = labels_patch.pop("resized_shape")
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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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if i == 0: # top left
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@ -152,15 +152,15 @@ class Mosaic(BaseMixTransform):
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labels_patch = self._update_labels(labels_patch, padw, padh)
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mosaic_labels.append(labels_patch)
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final_labels = self._cat_labels(mosaic_labels)
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final_labels["img"] = img4
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final_labels['img'] = img4
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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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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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labels["instances"].add_padding(padw, padh)
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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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labels['instances'].add_padding(padw, padh)
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return labels
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def _cat_labels(self, mosaic_labels):
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@ -169,16 +169,16 @@ class Mosaic(BaseMixTransform):
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cls = []
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instances = []
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for labels in mosaic_labels:
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cls.append(labels["cls"])
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instances.append(labels["instances"])
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cls.append(labels['cls'])
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instances.append(labels['instances'])
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final_labels = {
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"im_file": mosaic_labels[0]["im_file"],
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"ori_shape": mosaic_labels[0]["ori_shape"],
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"resized_shape": (self.imgsz * 2, self.imgsz * 2),
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"cls": np.concatenate(cls, 0),
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"instances": Instances.concatenate(instances, axis=0),
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"mosaic_border": self.border}
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final_labels["instances"].clip(self.imgsz * 2, self.imgsz * 2)
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'im_file': mosaic_labels[0]['im_file'],
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'ori_shape': mosaic_labels[0]['ori_shape'],
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'resized_shape': (self.imgsz * 2, self.imgsz * 2),
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'cls': np.concatenate(cls, 0),
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'instances': Instances.concatenate(instances, axis=0),
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'mosaic_border': self.border}
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final_labels['instances'].clip(self.imgsz * 2, self.imgsz * 2)
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return final_labels
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@ -193,10 +193,10 @@ class MixUp(BaseMixTransform):
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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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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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labels["instances"] = Instances.concatenate([labels["instances"], labels2["instances"]], axis=0)
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labels["cls"] = np.concatenate([labels["cls"], labels2["cls"]], 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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labels['instances'] = Instances.concatenate([labels['instances'], labels2['instances']], axis=0)
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labels['cls'] = np.concatenate([labels['cls'], labels2['cls']], 0)
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return labels
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@ -338,18 +338,18 @@ class RandomPerspective:
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Args:
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labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
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"""
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if self.pre_transform and "mosaic_border" not in labels:
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if self.pre_transform and 'mosaic_border' not in labels:
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labels = self.pre_transform(labels)
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labels.pop("ratio_pad") # do not need ratio pad
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labels.pop('ratio_pad') # do not need ratio pad
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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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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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instances.convert_bbox(format="xyxy")
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instances.convert_bbox(format='xyxy')
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instances.denormalize(*img.shape[:2][::-1])
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border = labels.pop("mosaic_border", self.border)
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border = labels.pop('mosaic_border', self.border)
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self.size = img.shape[1] + border[1] * 2, img.shape[0] + border[0] * 2 # w, h
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# M is affine matrix
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# scale for func:`box_candidates`
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@ -365,7 +365,7 @@ class RandomPerspective:
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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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new_instances = Instances(bboxes, segments, keypoints, bbox_format='xyxy', normalized=False)
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# clip
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new_instances.clip(*self.size)
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@ -375,10 +375,10 @@ class RandomPerspective:
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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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labels["instances"] = new_instances[i]
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labels["cls"] = cls[i]
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labels["img"] = img
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labels["resized_shape"] = img.shape[:2]
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labels['instances'] = new_instances[i]
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labels['cls'] = cls[i]
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labels['img'] = img
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labels['resized_shape'] = img.shape[:2]
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return labels
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def box_candidates(self, box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
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@ -397,7 +397,7 @@ class RandomHSV:
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self.vgain = vgain
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def __call__(self, labels):
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img = labels["img"]
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img = labels['img']
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if self.hgain or self.sgain or self.vgain:
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r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain] + 1 # random gains
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hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
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@ -415,30 +415,30 @@ class RandomHSV:
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class RandomFlip:
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def __init__(self, p=0.5, direction="horizontal") -> None:
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assert direction in ["horizontal", "vertical"], f"Support direction `horizontal` or `vertical`, got {direction}"
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def __init__(self, p=0.5, direction='horizontal') -> None:
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assert direction in ['horizontal', 'vertical'], f'Support direction `horizontal` or `vertical`, got {direction}'
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assert 0 <= p <= 1.0
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self.p = p
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self.direction = direction
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def __call__(self, labels):
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img = labels["img"]
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instances = labels.pop("instances")
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instances.convert_bbox(format="xywh")
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img = labels['img']
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instances = labels.pop('instances')
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instances.convert_bbox(format='xywh')
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h, w = img.shape[:2]
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h = 1 if instances.normalized else h
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w = 1 if instances.normalized else w
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# Flip up-down
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if self.direction == "vertical" and random.random() < self.p:
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if self.direction == 'vertical' and random.random() < self.p:
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img = np.flipud(img)
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instances.flipud(h)
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if self.direction == "horizontal" and random.random() < self.p:
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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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labels["img"] = np.ascontiguousarray(img)
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labels["instances"] = instances
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labels['img'] = np.ascontiguousarray(img)
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labels['instances'] = instances
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return labels
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@ -455,9 +455,9 @@ class LetterBox:
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def __call__(self, labels=None, image=None):
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if labels is None:
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labels = {}
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img = labels.get("img") if image is None else image
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img = labels.get('img') if image is None else image
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shape = img.shape[:2] # current shape [height, width]
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new_shape = labels.pop("rect_shape", self.new_shape)
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new_shape = labels.pop('rect_shape', self.new_shape)
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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@ -479,8 +479,8 @@ class LetterBox:
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if labels.get("ratio_pad"):
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labels["ratio_pad"] = (labels["ratio_pad"], (dw, dh)) # for evaluation
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if labels.get('ratio_pad'):
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labels['ratio_pad'] = (labels['ratio_pad'], (dw, dh)) # for evaluation
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if shape[::-1] != new_unpad: # resize
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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@ -491,18 +491,18 @@ class LetterBox:
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if len(labels):
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labels = self._update_labels(labels, ratio, dw, dh)
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labels["img"] = img
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labels["resized_shape"] = new_shape
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labels['img'] = img
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labels['resized_shape'] = new_shape
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return labels
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else:
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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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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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labels["instances"].add_padding(padw, padh)
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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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labels['instances'].add_padding(padw, padh)
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return labels
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@ -513,11 +513,11 @@ class CopyPaste:
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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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im = labels["img"]
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cls = labels["cls"]
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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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instances = labels.pop("instances")
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instances.convert_bbox(format="xyxy")
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instances = labels.pop('instances')
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instances.convert_bbox(format='xyxy')
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instances.denormalize(w, h)
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if self.p and len(instances.segments):
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n = len(instances)
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@ -540,9 +540,9 @@ class CopyPaste:
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i = cv2.flip(im_new, 1).astype(bool)
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im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
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labels["img"] = im
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labels["cls"] = cls
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labels["instances"] = instances
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labels['img'] = im
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labels['cls'] = cls
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labels['instances'] = instances
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return labels
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@ -551,11 +551,11 @@ class Albumentations:
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def __init__(self, p=1.0):
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self.p = p
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self.transform = None
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prefix = colorstr("albumentations: ")
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prefix = colorstr('albumentations: ')
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try:
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import albumentations as A
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check_version(A.__version__, "1.0.3", hard=True) # version requirement
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check_version(A.__version__, '1.0.3', hard=True) # version requirement
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T = [
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A.Blur(p=0.01),
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@ -565,28 +565,28 @@ class Albumentations:
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A.RandomBrightnessContrast(p=0.0),
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A.RandomGamma(p=0.0),
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A.ImageCompression(quality_lower=75, p=0.0),] # transforms
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self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
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self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
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LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
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except ImportError: # package not installed, skip
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pass
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except Exception as e:
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LOGGER.info(f"{prefix}{e}")
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LOGGER.info(f'{prefix}{e}')
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def __call__(self, labels):
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im = labels["img"]
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cls = labels["cls"]
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im = labels['img']
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cls = labels['cls']
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if len(cls):
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labels["instances"].convert_bbox("xywh")
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labels["instances"].normalize(*im.shape[:2][::-1])
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bboxes = labels["instances"].bboxes
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labels['instances'].convert_bbox('xywh')
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labels['instances'].normalize(*im.shape[:2][::-1])
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bboxes = labels['instances'].bboxes
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# TODO: add supports of segments and keypoints
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if self.transform and random.random() < self.p:
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new = self.transform(image=im, bboxes=bboxes, class_labels=cls) # transformed
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labels["img"] = new["image"]
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labels["cls"] = np.array(new["class_labels"])
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bboxes = np.array(new["bboxes"])
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labels["instances"].update(bboxes=bboxes)
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labels['img'] = new['image']
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labels['cls'] = np.array(new['class_labels'])
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bboxes = np.array(new['bboxes'])
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labels['instances'].update(bboxes=bboxes)
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return labels
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@ -594,7 +594,7 @@ class Albumentations:
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class Format:
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def __init__(self,
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bbox_format="xywh",
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bbox_format='xywh',
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normalize=True,
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return_mask=False,
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return_keypoint=False,
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@ -610,10 +610,10 @@ class Format:
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self.batch_idx = batch_idx # keep the batch indexes
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def __call__(self, labels):
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img = labels.pop("img")
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img = labels.pop('img')
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h, w = img.shape[:2]
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cls = labels.pop("cls")
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instances = labels.pop("instances")
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cls = labels.pop('cls')
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instances = labels.pop('instances')
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instances.convert_bbox(format=self.bbox_format)
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instances.denormalize(w, h)
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nl = len(instances)
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@ -625,17 +625,17 @@ class Format:
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else:
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masks = torch.zeros(1 if self.mask_overlap else nl, img.shape[0] // self.mask_ratio,
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img.shape[1] // self.mask_ratio)
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labels["masks"] = masks
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labels['masks'] = masks
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if self.normalize:
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instances.normalize(w, h)
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labels["img"] = self._format_img(img)
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labels["cls"] = torch.from_numpy(cls) if nl else torch.zeros(nl)
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labels["bboxes"] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
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labels['img'] = self._format_img(img)
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labels['cls'] = torch.from_numpy(cls) if nl else torch.zeros(nl)
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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) if nl else torch.zeros((nl, 17, 2))
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labels['keypoints'] = torch.from_numpy(instances.keypoints) if nl else torch.zeros((nl, 17, 2))
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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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labels['batch_idx'] = torch.zeros(nl)
|
||||
return labels
|
||||
|
||||
def _format_img(self, img):
|
||||
|
|
@ -676,15 +676,15 @@ def v8_transforms(dataset, imgsz, hyp):
|
|||
MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
|
||||
Albumentations(p=1.0),
|
||||
RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
|
||||
RandomFlip(direction="vertical", p=hyp.flipud),
|
||||
RandomFlip(direction="horizontal", p=hyp.fliplr),]) # transforms
|
||||
RandomFlip(direction='vertical', p=hyp.flipud),
|
||||
RandomFlip(direction='horizontal', p=hyp.fliplr),]) # transforms
|
||||
|
||||
|
||||
# Classification augmentations -----------------------------------------------------------------------------------------
|
||||
def classify_transforms(size=224):
|
||||
# Transforms to apply if albumentations not installed
|
||||
if not isinstance(size, int):
|
||||
raise TypeError(f"classify_transforms() size {size} must be integer, not (list, tuple)")
|
||||
raise TypeError(f'classify_transforms() size {size} must be integer, not (list, tuple)')
|
||||
# T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
|
||||
|
|
@ -701,17 +701,17 @@ def classify_albumentations(
|
|||
auto_aug=False,
|
||||
):
|
||||
# YOLOv8 classification Albumentations (optional, only used if package is installed)
|
||||
prefix = colorstr("albumentations: ")
|
||||
prefix = colorstr('albumentations: ')
|
||||
try:
|
||||
import albumentations as A
|
||||
from albumentations.pytorch import ToTensorV2
|
||||
|
||||
check_version(A.__version__, "1.0.3", hard=True) # version requirement
|
||||
check_version(A.__version__, '1.0.3', hard=True) # version requirement
|
||||
if augment: # Resize and crop
|
||||
T = [A.RandomResizedCrop(height=size, width=size, scale=scale)]
|
||||
if auto_aug:
|
||||
# TODO: implement AugMix, AutoAug & RandAug in albumentation
|
||||
LOGGER.info(f"{prefix}auto augmentations are currently not supported")
|
||||
LOGGER.info(f'{prefix}auto augmentations are currently not supported')
|
||||
else:
|
||||
if hflip > 0:
|
||||
T += [A.HorizontalFlip(p=hflip)]
|
||||
|
|
@ -723,13 +723,13 @@ def classify_albumentations(
|
|||
else: # Use fixed crop for eval set (reproducibility)
|
||||
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
|
||||
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
|
||||
LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
|
||||
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
|
||||
return A.Compose(T)
|
||||
|
||||
except ImportError: # package not installed, skip
|
||||
pass
|
||||
except Exception as e:
|
||||
LOGGER.info(f"{prefix}{e}")
|
||||
LOGGER.info(f'{prefix}{e}')
|
||||
|
||||
|
||||
class ClassifyLetterBox:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue