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ultralytics 8.0.43 optimized Results class and fixes (#1069)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher 2023-02-21 20:37:59 +01:00 committed by GitHub
parent f2a7a29e53
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22 changed files with 180 additions and 102 deletions
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15
.github/workflows/ci.yaml vendored
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@ -18,7 +18,7 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ['3.10'] # requires python<=3.9
python-version: ['3.10']
model: [yolov8n]
steps:
- uses: actions/checkout@v3
@ -51,17 +51,17 @@ jobs:
shell: python
run: |
from ultralytics.yolo.utils.benchmarks import run_benchmarks
run_benchmarks(model='yolov8n.pt', imgsz=160, half=False, hard_fail=False)
run_benchmarks(model='${{ matrix.model }}.pt', imgsz=160, half=False, hard_fail=False)
- name: Benchmark SegmentationModel
shell: python
run: |
from ultralytics.yolo.utils.benchmarks import run_benchmarks
run_benchmarks(model='yolov8n-seg.pt', imgsz=160, half=False, hard_fail=False)
run_benchmarks(model='${{ matrix.model }}-seg.pt', imgsz=160, half=False, hard_fail=False)
- name: Benchmark ClassificationModel
shell: python
run: |
from ultralytics.yolo.utils.benchmarks import run_benchmarks
run_benchmarks(model='yolov8n-cls.pt', imgsz=160, half=False, hard_fail=False)
run_benchmarks(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=False)
Tests:
timeout-minutes: 60
@ -70,13 +70,10 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ['3.10']
python-version: ['3.7', '3.8', '3.9', '3.10']
model: [yolov8n]
torch: [latest]
include:
- os: ubuntu-latest
python-version: '3.7' # '3.6.8' min
model: yolov8n
- os: ubuntu-latest
python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8
model: yolov8n
@ -123,9 +120,7 @@ jobs:
run: |
import os
import ultralytics
from ultralytics import hub, yolo
key = os.environ['APIKEY']
print(ultralytics.__version__)
ultralytics.checks()
# ultralytics.reset_model(key) # reset trained model
# ultralytics.start(key) # train model

1
docs/cfg.md
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@ -130,6 +130,7 @@ given task.
| half | False | use half precision (FP16) |
| device | null | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| show | False | show results if possible |
| save | False | save images with results |
| save_txt | False | save results as .txt file |
| save_conf | False | save results with confidence scores |
| save_crop | False | save cropped images with results |

2
setup.cfg
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@ -3,7 +3,7 @@
# Local usage: pip install pre-commit, pre-commit run --all-files
[metadata]
license_file = LICENSE
license_files = LICENSE
description_file = README.md
[tool:pytest]

4
tests/test_engine.py
View File

@ -59,7 +59,7 @@ def test_segment():
# Predictor
pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
assert len(result) == 2, 'predictor test failed'
assert len(result), 'predictor test failed'
# Test resume
overrides['resume'] = trainer.last
@ -97,4 +97,4 @@ def test_classify():
# Predictor
pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=trainer.best)
assert len(result) == 2, 'predictor test failed'
assert len(result), 'predictor test failed'

18
tests/test_python.py
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@ -14,6 +14,13 @@ from ultralytics.yolo.utils import LINUX, ROOT, SETTINGS
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n.pt'
CFG = 'yolov8n.yaml'
SOURCE = ROOT / 'assets/bus.jpg'
SOURCE_GREYSCALE = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
SOURCE_RGBA = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
# Convert SOURCE to greyscale and 4-ch
im = Image.open(SOURCE)
im.convert('L').save(SOURCE_GREYSCALE) # greyscale
im.convert('RGBA').save(SOURCE_RGBA) # 4-ch PNG with alpha
def test_model_forward():
@ -42,8 +49,7 @@ def test_predict_dir():
def test_predict_img():
model = YOLO(MODEL)
img = Image.open(str(SOURCE))
output = model(source=img, save=True, verbose=True) # PIL
output = model(source=Image.open(SOURCE), save=True, verbose=True) # PIL
assert len(output) == 1, 'predict test failed'
img = cv2.imread(str(SOURCE))
output = model(source=img, save=True, save_txt=True) # ndarray
@ -67,6 +73,13 @@ def test_predict_img():
assert len(output) == 6, 'predict test failed!'
def test_predict_grey_and_4ch():
model = YOLO(MODEL)
for f in SOURCE_RGBA, SOURCE_GREYSCALE:
for source in Image.open(f), cv2.imread(str(f)), f:
model(source, save=True, verbose=True)
def test_val():
model = YOLO(MODEL)
model.val(data='coco8.yaml', imgsz=32)
@ -151,6 +164,7 @@ def test_predict_callback_and_setup():
# results -> List[batch_size]
path, _, im0s, _, _ = predictor.batch
# print('on_predict_batch_end', im0s[0].shape)
im0s = im0s if isinstance(im0s, list) else [im0s]
bs = [predictor.dataset.bs for _ in range(len(path))]
predictor.results = zip(predictor.results, im0s, bs)

2
ultralytics/__init__.py
View File

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = '8.0.42'
__version__ = '8.0.43'
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils.checks import check_yolo as checks

1
ultralytics/tracker/__init__.py
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@ -1,3 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
from .track import register_tracker
from .trackers import BOTSORT, BYTETracker

9
ultralytics/tracker/track.py
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@ -1,13 +1,16 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
check_requirements('lap') # for linear_assignment
import torch
from ultralytics.tracker import BOTSORT, BYTETracker
from ultralytics.yolo.utils import IterableSimpleNamespace, yaml_load
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
from .trackers import BOTSORT, BYTETracker
TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
check_requirements('lap') # for linear_assignment
def on_predict_start(predictor):

4
ultralytics/yolo/cfg/__init__.py
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@ -18,7 +18,7 @@ CLI_HELP_MSG = \
yolo TASK MODE ARGS
Where TASK (optional) is one of [detect, segment, classify]
MODE (required) is one of [train, val, predict, export]
MODE (required) is one of [train, val, predict, export, track]
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
See all ARGS at https://docs.ultralytics.com/cfg or with 'yolo cfg'
@ -197,7 +197,7 @@ def entrypoint(debug=''):
# Define tasks and modes
tasks = 'detect', 'segment', 'classify'
modes = 'train', 'val', 'predict', 'export', 'track'
modes = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
special = {
'help': lambda: LOGGER.info(CLI_HELP_MSG),
'checks': checks.check_yolo,

4
ultralytics/yolo/data/dataloaders/stream_loaders.py
View File

@ -290,13 +290,15 @@ class LoadPilAndNumpy:
self.transforms = transforms
self.mode = 'image'
# generate fake paths
self.paths = [f'image{i}.jpg' for i in range(len(self.im0))]
self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(self.im0)]
self.bs = len(self.im0)
@staticmethod
def _single_check(im):
assert isinstance(im, (Image.Image, np.ndarray)), f'Expected PIL/np.ndarray image type, but got {type(im)}'
if isinstance(im, Image.Image):
if im.mode != 'RGB':
im = im.convert('RGB')
im = np.asarray(im)[:, :, ::-1]
im = np.ascontiguousarray(im) # contiguous
return im

18
ultralytics/yolo/data/dataloaders/v5loader.py
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@ -1045,7 +1045,7 @@ class HUBDatasetStats():
autodownload: Attempt to download dataset if not found locally
Usage
from utils.dataloaders import HUBDatasetStats
from ultralytics.yolo.data.dataloaders.v5loader import HUBDatasetStats
stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1
stats = HUBDatasetStats('path/to/coco128.zip') # usage 2
stats.get_json(save=False)
@ -1055,15 +1055,15 @@ class HUBDatasetStats():
def __init__(self, path='coco128.yaml', autodownload=False):
# Initialize class
zipped, data_dir, yaml_path = self._unzip(Path(path))
try:
data = yaml_load(check_yaml(yaml_path)) # data dict
if zipped:
data['path'] = data_dir
except Exception as e:
raise Exception('error/HUB/dataset_stats/yaml_load') from e
# try:
# data = yaml_load(check_yaml(yaml_path)) # data dict
# if zipped:
# data['path'] = data_dir
# except Exception as e:
# raise Exception('error/HUB/dataset_stats/yaml_load') from e
check_det_dataset(data, autodownload) # download dataset if missing
self.hub_dir = Path(data['path'] + '-hub')
data = check_det_dataset(yaml_path, autodownload) # download dataset if missing
self.hub_dir = Path(str(data['path']) + '-hub')
self.im_dir = self.hub_dir / 'images'
self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images
self.stats = {'nc': data['nc'], 'names': list(data['names'].values())} # statistics dictionary

18
ultralytics/yolo/engine/model.py
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@ -9,7 +9,7 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
guess_model_task, nn)
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
@ -203,7 +203,7 @@ class YOLO:
@smart_inference_mode()
def track(self, source=None, stream=False, **kwargs):
from ultralytics.tracker.track import register_tracker
from ultralytics.tracker import register_tracker
register_tracker(self)
# ByteTrack-based method needs low confidence predictions as input
conf = kwargs.get('conf') or 0.1
@ -237,6 +237,20 @@ class YOLO:
return validator.metrics
@smart_inference_mode()
def benchmark(self, **kwargs):
"""
Benchmark a model on all export formats.
Args:
**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
"""
from ultralytics.yolo.utils.benchmarks import run_benchmarks
overrides = self.model.args.copy()
overrides.update(kwargs)
overrides = {**DEFAULT_CFG_DICT, **overrides} # fill in missing overrides keys with defaults
return run_benchmarks(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
def export(self, **kwargs):
"""
Export model.

2
ultralytics/yolo/engine/predictor.py
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@ -194,7 +194,7 @@ class BasePredictor:
# Print time (inference-only)
if self.args.verbose:
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
LOGGER.info(f'{s}{self.dt[1].dt * 1E3:.1f}ms')
# Release assets
if isinstance(self.vid_writer[-1], cv2.VideoWriter):

89
ultralytics/yolo/engine/results.py
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@ -1,3 +1,10 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
"""
Ultralytics Results, Boxes and Masks classes for handling inference results
Usage: See https://docs.ultralytics.com/predict/
"""
from copy import deepcopy
from functools import lru_cache
@ -36,7 +43,7 @@ class Results:
self.probs = probs if probs is not None else None
self.names = names
self.path = path
self.comp = ['boxes', 'masks', 'probs']
self._keys = (k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None)
def pandas(self):
pass
@ -44,10 +51,8 @@ class Results:
def __getitem__(self, idx):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item)[idx])
for k in self._keys:
setattr(r, k, getattr(self, k)[idx])
return r
def update(self, boxes=None, masks=None, probs=None):
@ -60,57 +65,37 @@ class Results:
def cpu(self):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).cpu())
for k in self._keys:
setattr(r, k, getattr(self, k).cpu())
return r
def numpy(self):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).numpy())
for k in self._keys:
setattr(r, k, getattr(self, k).numpy())
return r
def cuda(self):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).cuda())
for k in self._keys:
setattr(r, k, getattr(self, k).cuda())
return r
def to(self, *args, **kwargs):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).to(*args, **kwargs))
for k in self._keys:
setattr(r, k, getattr(self, k).to(*args, **kwargs))
return r
def __len__(self):
for item in self.comp:
if getattr(self, item) is None:
continue
return len(getattr(self, item))
for k in self._keys:
return len(getattr(self, k))
def __str__(self):
str_out = ''
for item in self.comp:
if getattr(self, item) is None:
continue
str_out = str_out + getattr(self, item).__str__()
return str_out
return ''.join(getattr(self, k).__str__() for k in self._keys)
def __repr__(self):
str_out = ''
for item in self.comp:
if getattr(self, item) is None:
continue
str_out = str_out + getattr(self, item).__repr__()
return str_out
return ''.join(getattr(self, k).__repr__() for k in self._keys)
def __getattr__(self, attr):
name = self.__class__.__name__
@ -226,20 +211,16 @@ class Boxes:
return self.xywh / self.orig_shape[[1, 0, 1, 0]]
def cpu(self):
boxes = self.boxes.cpu()
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.cpu(), self.orig_shape)
def numpy(self):
boxes = self.boxes.numpy()
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.numpy(), self.orig_shape)
def cuda(self):
boxes = self.boxes.cuda()
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.cuda(), self.orig_shape)
def to(self, *args, **kwargs):
boxes = self.boxes.to(*args, **kwargs)
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.to(*args, **kwargs), self.orig_shape)
def pandas(self):
LOGGER.info('results.pandas() method not yet implemented')
@ -272,8 +253,7 @@ class Boxes:
f'shape: {self.boxes.shape}\n' + f'dtype: {self.boxes.dtype}\n + {self.boxes.__repr__()}')
def __getitem__(self, idx):
boxes = self.boxes[idx]
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes[idx], self.orig_shape)
def __getattr__(self, attr):
name = self.__class__.__name__
@ -331,20 +311,16 @@ class Masks:
return self.masks
def cpu(self):
masks = self.masks.cpu()
return Masks(masks, self.orig_shape)
return Masks(self.masks.cpu(), self.orig_shape)
def numpy(self):
masks = self.masks.numpy()
return Masks(masks, self.orig_shape)
return Masks(self.masks.numpy(), self.orig_shape)
def cuda(self):
masks = self.masks.cuda()
return Masks(masks, self.orig_shape)
return Masks(self.masks.cuda(), self.orig_shape)
def to(self, *args, **kwargs):
masks = self.masks.to(*args, **kwargs)
return Masks(masks, self.orig_shape)
return Masks(self.masks.to(*args, **kwargs), self.orig_shape)
def __len__(self): # override len(results)
return len(self.masks)
@ -357,8 +333,7 @@ class Masks:
f'shape: {self.masks.shape}\n' + f'dtype: {self.masks.dtype}\n + {self.masks.__repr__()}')
def __getitem__(self, idx):
masks = self.masks[idx]
return Masks(masks, self.orig_shape)
return Masks(self.masks[idx], self.orig_shape)
def __getattr__(self, attr):
name = self.__class__.__name__

5
ultralytics/yolo/engine/trainer.py
View File

@ -243,6 +243,8 @@ class BaseTrainer:
metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
self.metrics = dict(zip(metric_keys, [0] * len(metric_keys))) # TODO: init metrics for plot_results()?
self.ema = ModelEMA(self.model)
if self.args.plots:
self.plot_training_labels()
self.resume_training(ckpt)
self.scheduler.last_epoch = self.start_epoch - 1 # do not move
self.run_callbacks('on_pretrain_routine_end')
@ -501,6 +503,9 @@ class BaseTrainer:
def plot_training_samples(self, batch, ni):
pass
def plot_training_labels(self):
pass
def save_metrics(self, metrics):
keys, vals = list(metrics.keys()), list(metrics.values())
n = len(metrics) + 1 # number of cols

4
ultralytics/yolo/engine/validator.py
View File

@ -28,7 +28,7 @@ from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, emojis
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.ops import Profile
@ -194,6 +194,8 @@ class BaseValidator:
self.logger.info(f'Saving {f.name}...')
json.dump(self.jdict, f) # flatten and save
stats = self.eval_json(stats) # update stats
if self.args.plots or self.args.save_json:
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
return stats
def run_callbacks(self, event: str):

17
ultralytics/yolo/utils/benchmarks.py
View File

@ -27,13 +27,14 @@ import time
from pathlib import Path
import pandas as pd
import torch
from ultralytics import YOLO
from ultralytics.yolo.engine.exporter import export_formats
from ultralytics.yolo.utils import LOGGER, SETTINGS
from ultralytics.yolo.utils import LOGGER, ROOT, SETTINGS
from ultralytics.yolo.utils.checks import check_yolo
from ultralytics.yolo.utils.downloads import download
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.torch_utils import select_device
def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
@ -41,8 +42,9 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
half=False,
device='cpu',
hard_fail=False):
device = torch.device(int(device) if device.isnumeric() else device)
model = YOLO(model)
device = select_device(device, verbose=False)
if isinstance(model, (str, Path)):
model = YOLO(model)
y = []
t0 = time.time()
@ -65,6 +67,11 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
export = YOLO(filename)
assert suffix in str(filename), 'export failed'
# Predict
if not (ROOT / 'assets/bus.jpg').exists():
download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half) # test
# Validate
if model.task == 'detect':
data, key = 'coco128.yaml', 'metrics/mAP50-95(B)'
@ -96,6 +103,8 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: metric < floor {floor}'
return df
if __name__ == '__main__':
run_benchmarks()

53
ultralytics/yolo/utils/plotting.py
View File

@ -5,19 +5,24 @@ import math
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
import torch
from PIL import Image, ImageDraw, ImageFont
from PIL import __version__ as pil_version
from ultralytics.yolo.utils import LOGGER, threaded
from ultralytics.yolo.utils import LOGGER, TryExcept, threaded
from .checks import check_font, check_version, is_ascii
from .files import increment_path
from .ops import clip_coords, scale_image, xywh2xyxy, xyxy2xywh
matplotlib.rc('font', **{'size': 11})
matplotlib.use('Agg') # for writing to files only
class Colors:
# Ultralytics color palette https://ultralytics.com/
@ -152,6 +157,52 @@ class Annotator:
return np.asarray(self.im)
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
def plot_labels(boxes, cls, names=(), save_dir=Path('')):
# plot dataset labels
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
b = boxes.transpose() # classes, boxes
nc = int(cls.max() + 1) # number of classes
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
# seaborn correlogram
sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
plt.close()
# matplotlib labels
matplotlib.use('svg') # faster
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
with contextlib.suppress(Exception): # color histogram bars by class
[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
ax[0].set_ylabel('instances')
if 0 < len(names) < 30:
ax[0].set_xticks(range(len(names)))
ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
else:
ax[0].set_xlabel('classes')
sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
# rectangles
boxes[:, 0:2] = 0.5 # center
boxes = xywh2xyxy(boxes) * 2000
img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
for cls, box in zip(cls[:1000], boxes[:1000]):
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
ax[1].imshow(img)
ax[1].axis('off')
for a in [0, 1, 2, 3]:
for s in ['top', 'right', 'left', 'bottom']:
ax[a].spines[s].set_visible(False)
plt.savefig(save_dir / 'labels.jpg', dpi=200)
matplotlib.use('Agg')
plt.close()
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
xyxy = torch.Tensor(xyxy).view(-1, 4)

4
ultralytics/yolo/utils/torch_utils.py
View File

@ -59,7 +59,7 @@ def DDP_model(model):
return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
def select_device(device='', batch=0, newline=False):
def select_device(device='', batch=0, newline=False, verbose=True):
# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
device = str(device).lower()
@ -102,7 +102,7 @@ def select_device(device='', batch=0, newline=False):
s += 'CPU\n'
arg = 'cpu'
if RANK == -1:
if verbose and RANK == -1:
LOGGER.info(s if newline else s.rstrip())
return torch.device(arg)

2
ultralytics/yolo/v8/detect/predict.py
View File

@ -56,7 +56,7 @@ class DetectionPredictor(BasePredictor):
det = results[idx].boxes # TODO: make boxes inherit from tensors
if len(det) == 0:
return log_string
return f'{log_string}(no detections), '
for c in det.cls.unique():
n = (det.cls == c).sum() # detections per class
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "

8
ultralytics/yolo/v8/detect/train.py
View File

@ -1,6 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
from copy import copy
import numpy as np
import torch
import torch.nn as nn
@ -12,7 +13,7 @@ from ultralytics.yolo.engine.trainer import BaseTrainer
from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr
from ultralytics.yolo.utils.loss import BboxLoss
from ultralytics.yolo.utils.ops import xywh2xyxy
from ultralytics.yolo.utils.plotting import plot_images, plot_results
from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results
from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
from ultralytics.yolo.utils.torch_utils import de_parallel
@ -102,6 +103,11 @@ class DetectionTrainer(BaseTrainer):
def plot_metrics(self):
plot_results(file=self.csv) # save results.png
def plot_training_labels(self):
boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir)
# Criterion class for computing training losses
class Loss:

2
ultralytics/yolo/v8/segment/predict.py
View File

@ -59,7 +59,7 @@ class SegmentationPredictor(DetectionPredictor):
result = results[idx]
if len(result) == 0:
return log_string
return f'{log_string}(no detections), '
det, mask = result.boxes, result.masks # getting tensors TODO: mask mask,box inherit for tensor
# Print results