diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index 5d897da3..05408fd6 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -1,6 +1,11 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -# YOLO Continuous Integration (CI) GitHub Actions tests broken link checker -# Accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter) +# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee +# Ignores the following status codes to reduce false positives: +# - 403(OpenVINO, 'forbidden') +# - 429(Instagram, 'too many requests') +# - 500(Zenodo, 'cached') +# - 502(Zenodo, 'bad gateway') +# - 999(LinkedIn, 'unknown status code') name: Check Broken links @@ -28,7 +33,7 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' + command: lychee --accept 403,429,500,502,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' @@ -37,4 +42,4 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|url\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' diff --git a/docs/help/privacy.md b/docs/help/privacy.md index 28a87d6f..dfd8137e 100644 --- a/docs/help/privacy.md +++ b/docs/help/privacy.md @@ -7,7 +7,7 @@ keywords: Ultralytics, Data Collection, User Privacy, Google Analytics, Sentry, ## Overview -Ultralytics is dedicated to the continuous enhancement of the user experience and the capabilities of our Python package, including the advanced YOLO models we develop. Our approach involves the gathering of anonymized usage statistics and crash reports, helping us identify opportunities for improvement and ensuring the reliability of our software. This transparency document outlines what data we collect, its purpose, and the choice you have regarding this data collection. +[Ultralytics](https://ultralytics.com) is dedicated to the continuous enhancement of the user experience and the capabilities of our Python package, including the advanced YOLO models we develop. Our approach involves the gathering of anonymized usage statistics and crash reports, helping us identify opportunities for improvement and ensuring the reliability of our software. This transparency document outlines what data we collect, its purpose, and the choice you have regarding this data collection. ## Anonymized Google Analytics @@ -50,7 +50,7 @@ If the `sentry-sdk` Python package is pre-installed on your system a crash event - **Crash Logs**: Detailed reports on the application's condition at the time of a crash, which are vital for our debugging efforts. - **Error Messages**: We record error messages generated during the operation of our package to understand and resolve potential issues quickly. -To learn more about how Sentry handles data, please visit [Sentry Privacy Policy](https://sentry.io/privacy/). +To learn more about how Sentry handles data, please visit [Sentry's Privacy Policy](https://sentry.io/privacy/). ### How We Use This Data diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 45a338a4..552a8a1a 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -__version__ = '8.0.205' +__version__ = '8.0.206' from ultralytics.models import RTDETR, SAM, YOLO from ultralytics.models.fastsam import FastSAM diff --git a/ultralytics/engine/model.py b/ultralytics/engine/model.py index 806b0346..fa8dc3a1 100644 --- a/ultralytics/engine/model.py +++ b/ultralytics/engine/model.py @@ -9,7 +9,6 @@ from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir from ultralytics.hub.utils import HUB_WEB_ROOT from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load from ultralytics.utils import ASSETS, DEFAULT_CFG_DICT, LOGGER, RANK, callbacks, checks, emojis, yaml_load -from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS class Model(nn.Module): @@ -88,10 +87,8 @@ class Model(nn.Module): return # Load or create new YOLO model - suffix = Path(model).suffix - if not suffix and Path(model).stem in GITHUB_ASSETS_STEMS: - model, suffix = Path(model).with_suffix('.pt'), '.pt' # add suffix, i.e. yolov8n -> yolov8n.pt - if suffix in ('.yaml', '.yml'): + model = checks.check_model_file_from_stem(model) # add suffix, i.e. yolov8n -> yolov8n.pt + if Path(model).suffix in ('.yaml', '.yml'): self._new(model, task) else: self._load(model, task) diff --git a/ultralytics/engine/trainer.py b/ultralytics/engine/trainer.py index 4c08e019..f5126f16 100644 --- a/ultralytics/engine/trainer.py +++ b/ultralytics/engine/trainer.py @@ -19,8 +19,6 @@ import numpy as np import torch from torch import distributed as dist from torch import nn, optim -from torch.cuda import amp -from torch.nn.parallel import DistributedDataParallel as DDP from ultralytics.cfg import get_cfg, get_save_dir from ultralytics.data.utils import check_cls_dataset, check_det_dataset @@ -28,7 +26,7 @@ from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, TQDM, __version__, callbacks, clean_url, colorstr, emojis, yaml_save) from ultralytics.utils.autobatch import check_train_batch_size -from ultralytics.utils.checks import check_amp, check_file, check_imgsz, print_args +from ultralytics.utils.checks import check_amp, check_file, check_imgsz, check_model_file_from_stem, print_args from ultralytics.utils.dist import ddp_cleanup, generate_ddp_command from ultralytics.utils.files import get_latest_run from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle, select_device, @@ -43,7 +41,6 @@ class BaseTrainer: Attributes: args (SimpleNamespace): Configuration for the trainer. - check_resume (method): Method to check if training should be resumed from a saved checkpoint. validator (BaseValidator): Validator instance. model (nn.Module): Model instance. callbacks (defaultdict): Dictionary of callbacks. @@ -62,6 +59,7 @@ class BaseTrainer: trainset (torch.utils.data.Dataset): Training dataset. testset (torch.utils.data.Dataset): Testing dataset. ema (nn.Module): EMA (Exponential Moving Average) of the model. + resume (bool): Resume training from a checkpoint. lf (nn.Module): Loss function. scheduler (torch.optim.lr_scheduler._LRScheduler): Learning rate scheduler. best_fitness (float): The best fitness value achieved. @@ -84,7 +82,6 @@ class BaseTrainer: self.check_resume(overrides) self.device = select_device(self.args.device, self.args.batch) self.validator = None - self.model = None self.metrics = None self.plots = {} init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic) @@ -111,7 +108,7 @@ class BaseTrainer: self.args.workers = 0 # faster CPU training as time dominated by inference, not dataloading # Model and Dataset - self.model = self.args.model + self.model = check_model_file_from_stem(self.args.model) # add suffix, i.e. yolov8n -> yolov8n.pt try: if self.args.task == 'classify': self.data = check_cls_dataset(self.args.data) @@ -124,6 +121,7 @@ class BaseTrainer: self.trainset, self.testset = self.get_dataset(self.data) self.ema = None + self.resume = False # Optimization utils init self.lf = None @@ -236,9 +234,9 @@ class BaseTrainer: if RANK > -1 and world_size > 1: # DDP dist.broadcast(self.amp, src=0) # broadcast the tensor from rank 0 to all other ranks (returns None) self.amp = bool(self.amp) # as boolean - self.scaler = amp.GradScaler(enabled=self.amp) + self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) if world_size > 1: - self.model = DDP(self.model, device_ids=[RANK]) + self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[RANK]) # Check imgsz gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32) # grid size (max stride) @@ -311,11 +309,7 @@ class BaseTrainer: pbar = enumerate(self.train_loader) # Update dataloader attributes (optional) if epoch == (self.epochs - self.args.close_mosaic): - LOGGER.info('Closing dataloader mosaic') - if hasattr(self.train_loader.dataset, 'mosaic'): - self.train_loader.dataset.mosaic = False - if hasattr(self.train_loader.dataset, 'close_mosaic'): - self.train_loader.dataset.close_mosaic(hyp=self.args) + self._close_dataloader_mosaic() self.train_loader.reset() if RANK in (-1, 0): @@ -395,7 +389,7 @@ class BaseTrainer: self.epoch_time = tnow - self.epoch_time_start self.epoch_time_start = tnow self.run_callbacks('on_fit_epoch_end') - torch.cuda.empty_cache() # clears GPU vRAM at end of epoch, can help with out of memory errors + torch.cuda.empty_cache() # clear GPU memory at end of epoch, may help reduce CUDA out of memory errors # Early Stopping if RANK != -1: # if DDP training @@ -613,11 +607,15 @@ class BaseTrainer: self.best_fitness = best_fitness self.start_epoch = start_epoch if start_epoch > (self.epochs - self.args.close_mosaic): + self._close_dataloader_mosaic() + + def _close_dataloader_mosaic(self): + """Update dataloaders to stop using mosaic augmentation.""" + if hasattr(self.train_loader.dataset, 'mosaic'): + self.train_loader.dataset.mosaic = False + if hasattr(self.train_loader.dataset, 'close_mosaic'): LOGGER.info('Closing dataloader mosaic') - if hasattr(self.train_loader.dataset, 'mosaic'): - self.train_loader.dataset.mosaic = False - if hasattr(self.train_loader.dataset, 'close_mosaic'): - self.train_loader.dataset.close_mosaic(hyp=self.args) + self.train_loader.dataset.close_mosaic(hyp=self.args) def build_optimizer(self, model, name='auto', lr=0.001, momentum=0.9, decay=1e-5, iterations=1e5): """ diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py index a8c1608a..04c9f49e 100644 --- a/ultralytics/utils/checks.py +++ b/ultralytics/utils/checks.py @@ -426,6 +426,14 @@ def check_yolov5u_filename(file: str, verbose: bool = True): return file +def check_model_file_from_stem(model='yolov8n'): + """Return a model filename from a valid model stem.""" + if model and not Path(model).suffix and Path(model).stem in downloads.GITHUB_ASSETS_STEMS: + return Path(model).with_suffix('.pt') # add suffix, i.e. yolov8n -> yolov8n.pt + else: + return model + + def check_file(file, suffix='', download=True, hard=True): """Search/download file (if necessary) and return path.""" check_suffix(file, suffix) # optional diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py index 5d37c591..3be3df1e 100644 --- a/ultralytics/utils/ops.py +++ b/ultralytics/utils/ops.py @@ -324,8 +324,8 @@ def scale_image(masks, im0_shape, ratio_pad=None): else: gain = ratio_pad[0][0] pad = ratio_pad[1] - top, left = int(pad[1]), int(pad[0]) # y, x - bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + top, left = (int(round(pad[1] - 0.1)), int(round(pad[0] - 0.1))) # y, x + bottom, right = (int(round(im1_shape[0] - pad[1] + 0.1)), int(round(im1_shape[1] - pad[0] + 0.1))) if len(masks.shape) < 2: raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') @@ -704,8 +704,8 @@ def scale_masks(masks, shape, padding=True): if padding: pad[0] /= 2 pad[1] /= 2 - top, left = (int(pad[1]), int(pad[0])) if padding else (0, 0) # y, x - bottom, right = (int(mh - pad[1]), int(mw - pad[0])) + top, left = (int(round(pad[1] - 0.1)), int(round(pad[0] - 0.1))) if padding else (0, 0) # y, x + bottom, right = (int(round(mh - pad[1] + 0.1)), int(round(mw - pad[0] + 0.1))) masks = masks[..., top:bottom, left:right] masks = F.interpolate(masks, shape, mode='bilinear', align_corners=False) # NCHW