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YOLOv5 updates (#90)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2022-12-25 14:33:18 +01:00 • committed by GitHub
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27 changed files with 281 additions and 161 deletions
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19
ultralytics/yolo/data/augment.py
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@ -82,7 +82,7 @@ class BaseMixTransform:
indexes = [indexes]
# get images information will be used for Mosaic or MixUp
mix_labels = [deepcopy(dataset.get_label_info(index)) for index in indexes]
mix_labels = [dataset.get_label_info(index) for index in indexes]
if self.pre_transform is not None:
for i, data in enumerate(mix_labels):
@ -134,9 +134,8 @@ class Mosaic(BaseMixTransform):
assert len(labels.get("mix_labels", [])) > 0, "There are no other images for mosaic augment."
s = self.imgsz
yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
mix_labels = labels["mix_labels"]
for i in range(4):
labels_patch = deepcopy(labels) if i == 0 else deepcopy(mix_labels[i - 1])
labels_patch = (labels if i == 0 else labels["mix_labels"][i - 1]).copy()
# Load image
img = labels_patch["img"]
h, w = labels_patch["resized_shape"]
@ -186,9 +185,8 @@ class Mosaic(BaseMixTransform):
"ori_shape": mosaic_labels[0]["ori_shape"],
"resized_shape": (self.imgsz * 2, self.imgsz * 2),
"im_file": mosaic_labels[0]["im_file"],
"cls": np.concatenate(cls, 0)}
final_labels["instances"] = Instances.concatenate(instances, axis=0)
"cls": np.concatenate(cls, 0),
"instances": Instances.concatenate(instances, axis=0)}
final_labels["instances"].clip(self.imgsz * 2, self.imgsz * 2)
return final_labels
@ -345,7 +343,6 @@ class RandomPerspective:
Affine images and targets.
Args:
img(ndarray): image.
labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
"""
img = labels["img"]
@ -387,7 +384,7 @@ class RandomPerspective:
return labels
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)
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
# Compute box candidates: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
@ -609,6 +606,7 @@ class Format:
self.batch_idx = batch_idx # keep the batch indexes
def __call__(self, labels):
labels.pop("dataset", None)
img = labels["img"]
h, w = img.shape[:2]
cls = labels.pop("cls")
@ -672,10 +670,7 @@ def mosaic_transforms(imgsz, hyp):
),])
return Compose([
pre_transform,
MixUp(
pre_transform=pre_transform,
p=hyp.mixup,
),
MixUp(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),