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ultralytics 8.0.166 expand HUBDatasetStats to Classify tasks (#4635)

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Glenn Jocher 2023-08-29 16:17:59 +02:00 committed by GitHub
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commit 52afadcb97
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5 changed files with 67 additions and 37 deletions
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2
.github/workflows/ci.yaml vendored
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@ -133,7 +133,7 @@ jobs:
run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
- name: Benchmark ClassificationModel
shell: bash
run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.36
run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.16
- name: Benchmark PoseModel
shell: bash
run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17

2
tests/test_python.py
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@ -277,7 +277,7 @@ def test_data_utils():
# from ultralytics.utils.files import WorkingDirectory
# with WorkingDirectory(ROOT.parent / 'tests'):
for task in 'detect', 'segment', 'pose':
for task in 'detect', 'segment', 'pose', 'classify':
file = Path(TASK2DATA[task]).with_suffix('.zip') # i.e. coco8.zip
download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False, dir=TMP)
stats = HUBDatasetStats(TMP / file, task=task)

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.165'
__version__ = '8.0.166'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM

2
ultralytics/cfg/__init__.py
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@ -15,7 +15,7 @@ from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CF
# Define valid tasks and modes
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
TASKS = 'detect', 'segment', 'classify', 'pose'
TASK2DATA = {'detect': 'coco8.yaml', 'segment': 'coco8-seg.yaml', 'classify': 'imagenet100', 'pose': 'coco8-pose.yaml'}
TASK2DATA = {'detect': 'coco8.yaml', 'segment': 'coco8-seg.yaml', 'classify': 'imagenet10', 'pose': 'coco8-pose.yaml'}
TASK2MODEL = {
'detect': 'yolov8n.pt',
'segment': 'yolov8n-seg.pt',

96
ultralytics/data/utils.py
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@ -296,7 +296,7 @@ def check_det_dataset(dataset, autodownload=True):
return data # dictionary
def check_cls_dataset(dataset: str, split=''):
def check_cls_dataset(dataset, split=''):
"""
Checks a classification dataset such as Imagenet.
@ -304,7 +304,7 @@ def check_cls_dataset(dataset: str, split=''):
If the dataset is not found locally, it attempts to download the dataset from the internet and save it locally.
Args:
dataset (str): The name of the dataset.
dataset (str | Path): The name of the dataset.
split (str, optional): The split of the dataset. Either 'val', 'test', or ''. Defaults to ''.
Returns:
@ -360,7 +360,7 @@ def check_cls_dataset(dataset: str, split=''):
else:
LOGGER.info(f'{prefix} found {nf} images in {nd} classes ✅ ')
return {'train': train_set, 'val': val_set or test_set, 'test': test_set or val_set, 'nc': nc, 'names': names}
return {'train': train_set, 'val': val_set, 'test': test_set, 'nc': nc, 'names': names}
class HUBDatasetStats:
@ -373,14 +373,17 @@ class HUBDatasetStats:
autodownload (bool): Attempt to download dataset if not found locally. Default is False.
Example:
Download *.zip files from i.e. https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip.
Download *.zip files from https://github.com/ultralytics/hub/tree/main/example_datasets
i.e. https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip for coco8.zip.
```python
from ultralytics.data.utils import HUBDatasetStats
stats = HUBDatasetStats('path/to/coco8.zip', task='detect') # detect dataset
stats = HUBDatasetStats('path/to/coco8-seg.zip', task='segment') # segment dataset
stats = HUBDatasetStats('path/to/coco8-pose.zip', task='pose') # pose dataset
stats.get_json(save=False)
stats = HUBDatasetStats('path/to/imagenet10.zip', task='classify') # classification dataset
stats.get_json(save=True)
stats.process_images()
```
"""
@ -389,21 +392,27 @@ class HUBDatasetStats:
"""Initialize class."""
path = Path(path).resolve()
LOGGER.info(f'Starting HUB dataset checks for {path}....')
zipped, data_dir, yaml_path = self._unzip(path)
try:
# data = yaml_load(check_yaml(yaml_path)) # data dict
data = check_det_dataset(yaml_path, autodownload) # data dict
if zipped:
data['path'] = data_dir
except Exception as e:
raise Exception('error/HUB/dataset_stats/yaml_load') from e
self.hub_dir = Path(str(data['path']) + '-hub')
self.task = task # detect, segment, pose, classify
if self.task == 'classify':
unzip_dir = unzip_file(path)
data = check_cls_dataset(unzip_dir)
data['path'] = unzip_dir
else: # detect, segment, pose
zipped, data_dir, yaml_path = self._unzip(Path(path))
try:
# data = yaml_load(check_yaml(yaml_path)) # data dict
data = check_det_dataset(yaml_path, autodownload) # data dict
if zipped:
data['path'] = data_dir
except Exception as e:
raise Exception('error/HUB/dataset_stats/init') from e
self.hub_dir = Path(f'{data["path"]}-hub')
self.im_dir = self.hub_dir / 'images'
self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images
self.stats = {'nc': len(data['names']), 'names': list(data['names'].values())} # statistics dictionary
self.data = data
self.task = task # detect, segment, pose, classify
@staticmethod
def _find_yaml(dir):
@ -430,7 +439,6 @@ class HUBDatasetStats:
def get_json(self, save=False, verbose=False):
"""Return dataset JSON for Ultralytics HUB."""
from ultralytics.data import YOLODataset # ClassificationDataset
def _round(labels):
"""Update labels to integer class and 4 decimal place floats."""
@ -458,23 +466,45 @@ class HUBDatasetStats:
continue
# Get dataset statistics
dataset = YOLODataset(img_path=self.data[split],
data=self.data,
use_segments=self.task == 'segment',
use_keypoints=self.task == 'pose')
x = np.array([
np.bincount(label['cls'].astype(int).flatten(), minlength=self.data['nc'])
for label in tqdm(dataset.labels, total=len(dataset), desc='Statistics')]) # shape(128x80)
self.stats[split] = {
'instance_stats': {
'total': int(x.sum()),
'per_class': x.sum(0).tolist()},
'image_stats': {
'total': len(dataset),
'unlabelled': int(np.all(x == 0, 1).sum()),
'per_class': (x > 0).sum(0).tolist()},
'labels': [{
Path(k).name: _round(v)} for k, v in zip(dataset.im_files, dataset.labels)]}
if self.task == 'classify':
from torchvision.datasets import ImageFolder
dataset = ImageFolder(self.data[split])
x = np.zeros(len(dataset.classes)).astype(int)
for im in dataset.imgs:
x[im[1]] += 1
self.stats[split] = {
'instance_stats': {
'total': len(dataset),
'per_class': x.tolist()},
'image_stats': {
'total': len(dataset),
'unlabelled': 0,
'per_class': x.tolist()},
'labels': [{
Path(k).name: v} for k, v in dataset.imgs]}
else:
from ultralytics.data import YOLODataset
dataset = YOLODataset(img_path=self.data[split],
data=self.data,
use_segments=self.task == 'segment',
use_keypoints=self.task == 'pose')
x = np.array([
np.bincount(label['cls'].astype(int).flatten(), minlength=self.data['nc'])
for label in tqdm(dataset.labels, total=len(dataset), desc='Statistics')]) # shape(128x80)
self.stats[split] = {
'instance_stats': {
'total': int(x.sum()),
'per_class': x.sum(0).tolist()},
'image_stats': {
'total': len(dataset),
'unlabelled': int(np.all(x == 0, 1).sum()),
'per_class': (x > 0).sum(0).tolist()},
'labels': [{
Path(k).name: _round(v)} for k, v in zip(dataset.im_files, dataset.labels)]}
# Save, print and return
if save: