HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.0.235 YOLOv8 OBB train, val, predict and export (#4499)

Browse source 
Co-authored-by: Yash Khurana <ykhurana6@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Swamita Gupta <swamita2001@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1182102784@qq.com>
This commit is contained in:
Glenn Jocher 2024-01-05 03:00:26 +01:00 • committed by GitHub
parent f702b34a50
commit 072291bc78
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
52 changed files with 2090 additions and 524 deletions
Download patch file Download diff file Expand all files Collapse all files

25
ultralytics/data/dataset.py
View file

@ -11,6 +11,7 @@ import torchvision
from PIL import Image
from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
from ultralytics.utils.ops import resample_segments
from .augment import Compose, Format, Instances, LetterBox, classify_augmentations, classify_transforms, v8_transforms
from .base import BaseDataset
@ -26,17 +27,17 @@ class YOLODataset(BaseDataset):
Args:
data (dict, optional): A dataset YAML dictionary. Defaults to None.
use_segments (bool, optional): If True, segmentation masks are used as labels. Defaults to False.
use_keypoints (bool, optional): If True, keypoints are used as labels. Defaults to False.
task (str): An explicit arg to point current task, Defaults to 'detect'.
Returns:
(torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
"""
def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs):
def __init__(self, *args, data=None, task='detect', **kwargs):
"""Initializes the YOLODataset with optional configurations for segments and keypoints."""
self.use_segments = use_segments
self.use_keypoints = use_keypoints
self.use_segments = task == 'segment'
self.use_keypoints = task == 'pose'
self.use_obb = task == 'obb'
self.data = data
assert not (self.use_segments and self.use_keypoints), 'Can not use both segments and keypoints.'
super().__init__(*args, **kwargs)
@ -148,6 +149,7 @@ class YOLODataset(BaseDataset):
normalize=True,
return_mask=self.use_segments,
return_keypoint=self.use_keypoints,
return_obb=self.use_obb,
batch_idx=True,
mask_ratio=hyp.mask_ratio,
mask_overlap=hyp.overlap_mask))
@ -165,10 +167,19 @@ class YOLODataset(BaseDataset):
# NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label
# We can make it also support classification and semantic segmentation by add or remove some dict keys there.
bboxes = label.pop('bboxes')
segments = label.pop('segments')
segments = label.pop('segments', [])
keypoints = label.pop('keypoints', None)
bbox_format = label.pop('bbox_format')
normalized = label.pop('normalized')
# NOTE: do NOT resample oriented boxes
segment_resamples = 100 if self.use_obb else 1000
if len(segments) > 0:
# list[np.array(1000, 2)] * num_samples
# (N, 1000, 2)
segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0)
else:
segments = np.zeros((0, segment_resamples, 2), dtype=np.float32)
label['instances'] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized)
return label
@ -182,7 +193,7 @@ class YOLODataset(BaseDataset):
value = values[i]
if k == 'img':
value = torch.stack(value, 0)
if k in ['masks', 'keypoints', 'bboxes', 'cls']:
if k in ['masks', 'keypoints', 'bboxes', 'cls', 'segments', 'obb']:
value = torch.cat(value, 0)
new_batch[k] = value
new_batch['batch_idx'] = list(new_batch['batch_idx'])