ultralytics 8.0.24 mosaic, DDP, download fixes (#703)
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -44,20 +44,8 @@ class Compose:
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self.transforms = transforms
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def __call__(self, data):
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mosaic_p = None
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mosaic_imgsz = None
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for t in self.transforms:
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if isinstance(t, Mosaic):
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temp = t(data)
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mosaic_p = False if temp == data else True
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mosaic_imgsz = t.imgsz
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data = temp
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else:
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if isinstance(t, RandomPerspective):
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t.border = [-mosaic_imgsz // 2, -mosaic_imgsz // 2] if mosaic_p else [0, 0]
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data = t(data)
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data = t(data)
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return data
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def append(self, transform):
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@ -140,7 +128,7 @@ class Mosaic(BaseMixTransform):
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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["resized_shape"]
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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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@ -184,11 +172,12 @@ class Mosaic(BaseMixTransform):
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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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"im_file": mosaic_labels[0]["im_file"],
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"cls": np.concatenate(cls, 0),
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"instances": Instances.concatenate(instances, axis=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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@ -213,7 +202,14 @@ class MixUp(BaseMixTransform):
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class RandomPerspective:
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def __init__(self, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, border=(0, 0)):
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def __init__(self,
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degrees=0.0,
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translate=0.1,
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scale=0.5,
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shear=0.0,
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perspective=0.0,
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border=(0, 0),
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pre_transform=None):
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self.degrees = degrees
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self.translate = translate
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self.scale = scale
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@ -221,8 +217,9 @@ class RandomPerspective:
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self.perspective = perspective
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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):
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def affine_transform(self, img, border):
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# Center
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C = np.eye(3)
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@ -255,7 +252,7 @@ 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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if (self.border[0] != 0) or (self.border[1] != 0) or (M != np.eye(3)).any(): # image changed
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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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else: # affine
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@ -341,6 +338,10 @@ 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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labels = self.pre_transform(labels)
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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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@ -348,10 +349,11 @@ class RandomPerspective:
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instances.convert_bbox(format="xyxy")
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instances.denormalize(*img.shape[:2][::-1])
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self.size = img.shape[1] + self.border[1] * 2, img.shape[0] + self.border[0] * 2 # w, h
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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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img, M, scale = self.affine_transform(img)
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img, M, scale = self.affine_transform(img, border)
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bboxes = self.apply_bboxes(instances.bboxes, M)
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@ -513,8 +515,10 @@ class CopyPaste:
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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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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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_, w, _ = im.shape # height, width, channels
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@ -605,7 +609,7 @@ 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["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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@ -654,7 +658,7 @@ class Format:
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return masks, instances, cls
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def mosaic_transforms(dataset, imgsz, hyp):
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def v8_transforms(dataset, imgsz, hyp):
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pre_transform = Compose([
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Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic, border=[-imgsz // 2, -imgsz // 2]),
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CopyPaste(p=hyp.copy_paste),
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@ -664,7 +668,7 @@ def mosaic_transforms(dataset, imgsz, hyp):
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scale=hyp.scale,
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shear=hyp.shear,
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perspective=hyp.perspective,
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border=[-imgsz // 2, -imgsz // 2],
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pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
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),])
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return Compose([
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pre_transform,
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@ -675,23 +679,6 @@ def mosaic_transforms(dataset, imgsz, hyp):
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RandomFlip(direction="horizontal", p=hyp.fliplr),]) # transforms
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def affine_transforms(imgsz, hyp):
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return Compose([
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LetterBox(new_shape=(imgsz, imgsz)),
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RandomPerspective(
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degrees=hyp.degrees,
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translate=hyp.translate,
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scale=hyp.scale,
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shear=hyp.shear,
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perspective=hyp.perspective,
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border=[0, 0],
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),
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Albumentations(p=1.0),
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RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
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RandomFlip(direction="vertical", p=hyp.flipud),
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RandomFlip(direction="horizontal", p=hyp.fliplr),]) # transforms
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# Classification augmentations -----------------------------------------------------------------------------------------
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def classify_transforms(size=224):
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# Transforms to apply if albumentations not installed
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@ -182,6 +182,7 @@ class BaseDataset(Dataset):
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def get_label_info(self, index):
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label = self.labels[index].copy()
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label.pop("shape", None) # shape is for rect, remove it
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label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
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label["ratio_pad"] = (
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label["resized_shape"][0] / label["ori_shape"][0],
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@ -136,8 +136,9 @@ class YOLODataset(BaseDataset):
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# TODO: use hyp config to set all these augmentations
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def build_transforms(self, hyp=None):
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if self.augment:
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mosaic = self.augment and not self.rect
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transforms = mosaic_transforms(self, self.imgsz, hyp) if mosaic else affine_transforms(self.imgsz, hyp)
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hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
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hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
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transforms = v8_transforms(self, self.imgsz, hyp)
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else:
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transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)])
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transforms.append(
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@ -151,15 +152,10 @@ class YOLODataset(BaseDataset):
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return transforms
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def close_mosaic(self, hyp):
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self.transforms = affine_transforms(self.imgsz, hyp)
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self.transforms.append(
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Format(bbox_format="xywh",
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normalize=True,
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return_mask=self.use_segments,
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return_keypoint=self.use_keypoints,
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batch_idx=True,
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mask_ratio=hyp.mask_ratio,
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mask_overlap=hyp.overlap_mask))
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hyp.mosaic = 0.0 # set mosaic ratio=0.0
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hyp.copy_paste = 0.0 # keep the same behavior as previous v8 close-mosaic
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hyp.mixup = 0.0 # keep the same behavior as previous v8 close-mosaic
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self.transforms = self.build_transforms(hyp)
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def update_labels_info(self, label):
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"""custom your label format here"""
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@ -175,8 +171,6 @@ class YOLODataset(BaseDataset):
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@staticmethod
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def collate_fn(batch):
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# TODO: returning a dict can make thing easier and cleaner when using dataset in training
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# but I don't know if this will slow down a little bit.
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new_batch = {}
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keys = batch[0].keys()
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values = list(zip(*[list(b.values()) for b in batch]))
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@ -246,7 +246,7 @@ def check_det_dataset(dataset, autodownload=True):
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r = exec(s, {'yaml': data}) # return None
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dt = f'({round(time.time() - t, 1)}s)'
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s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
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LOGGER.info(f"Dataset download {s}")
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LOGGER.info(f"Dataset download {s}\n")
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check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf') # download fonts
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return data # dictionary
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