diff --git a/docs/en/guides/instance-segmentation-and-tracking.md b/docs/en/guides/instance-segmentation-and-tracking.md index f200cdbe..c08b75ce 100644 --- a/docs/en/guides/instance-segmentation-and-tracking.md +++ b/docs/en/guides/instance-segmentation-and-tracking.md @@ -8,8 +8,9 @@ keywords: Ultralytics, YOLOv8, Instance Segmentation, Object Detection, Object T ## What is Instance Segmentation? -[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) Instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging. -Two Types of instance segmentation by Ultralytics YOLOv8. +[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging. + +There are two types of instance segmentation tracking available in the Ultralytics package: - **Instance Segmentation with Class Objects:** Each class object is assigned a unique color for clear visual separation. @@ -22,7 +23,6 @@ Two Types of instance segmentation by Ultralytics YOLOv8. | ![Ultralytics Instance Segmentation](https://github.com/RizwanMunawar/ultralytics/assets/62513924/d4ad3499-1f33-4871-8fbc-1be0b2643aa2) | ![Ultralytics Instance Segmentation with Object Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/2e5c38cc-fd5c-4145-9682-fa94ae2010a0) | | Ultralytics Instance Segmentation 😍 | Ultralytics Instance Segmentation with Object Tracking 🔥 | - !!! Example "Instance Segmentation and Tracking" === "Instance Segmentation" diff --git a/docs/en/guides/vision-eye.md b/docs/en/guides/vision-eye.md index f8767440..2d49b4e4 100644 --- a/docs/en/guides/vision-eye.md +++ b/docs/en/guides/vision-eye.md @@ -22,12 +22,12 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi

## Samples + | VisionEye View | VisionEye View With Object Tracking | |:------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | ![VisionEye View Object Mapping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d593acc-2e37-41b0-ad0e-92b4ffae6647) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/fcd85952-390f-451e-8fb0-b82e943af89c) | | VisionEye View Object Mapping using Ultralytics YOLOv8 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8 | - !!! Example "VisionEye Object Mapping using YOLOv8" === "VisionEye Object Mapping" diff --git a/docs/en/modes/train.md b/docs/en/modes/train.md index 7d859df7..331d7cdd 100644 --- a/docs/en/modes/train.md +++ b/docs/en/modes/train.md @@ -180,6 +180,7 @@ Training settings for YOLO models refer to the various hyperparameters and confi | `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml | | `data` | `None` | path to data file, i.e. coco128.yaml | | `epochs` | `100` | number of epochs to train for | +| `time` | `None` | number of hours to train for, overrides epochs if supplied | | `patience` | `50` | epochs to wait for no observable improvement for early stopping of training | | `batch` | `16` | number of images per batch (-1 for AutoBatch) | | `imgsz` | `640` | size of input images as integer | diff --git a/docs/en/usage/cfg.md b/docs/en/usage/cfg.md index 45e22a39..ce0b1a23 100644 --- a/docs/en/usage/cfg.md +++ b/docs/en/usage/cfg.md @@ -88,6 +88,7 @@ The training settings for YOLO models encompass various hyperparameters and conf | `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml | | `data` | `None` | path to data file, i.e. coco128.yaml | | `epochs` | `100` | number of epochs to train for | +| `time` | `None` | number of hours to train for, overrides epochs if supplied | | `patience` | `50` | epochs to wait for no observable improvement for early stopping of training | | `batch` | `16` | number of images per batch (-1 for AutoBatch) | | `imgsz` | `640` | size of input images as integer | diff --git a/tests/test_cuda.py b/tests/test_cuda.py index 1ef1104f..eb49a08f 100644 --- a/tests/test_cuda.py +++ b/tests/test_cuda.py @@ -61,6 +61,7 @@ def test_autobatch(): check_train_batch_size(YOLO(MODEL).model.cuda(), imgsz=128, amp=True) +@pytest.mark.slow @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available') def test_utils_benchmarks(): """Profile YOLO models for performance benchmarks.""" diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 9f42aa82..5862abb8 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -__version__ = '8.0.227' +__version__ = '8.0.228' from ultralytics.models import RTDETR, SAM, YOLO from ultralytics.models.fastsam import FastSAM diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py index 88ef91db..d892b519 100644 --- a/ultralytics/cfg/__init__.py +++ b/ultralytics/cfg/__init__.py @@ -63,7 +63,7 @@ CLI_HELP_MSG = \ """ # Define keys for arg type checks -CFG_FLOAT_KEYS = 'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear' +CFG_FLOAT_KEYS = 'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear', 'time' CFG_FRACTION_KEYS = ('dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr', 'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'translate', 'scale', 'perspective', 'flipud', 'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou', 'fraction') # fraction floats 0.0 - 1.0 diff --git a/ultralytics/cfg/default.yaml b/ultralytics/cfg/default.yaml index c9df7ea1..b3499853 100644 --- a/ultralytics/cfg/default.yaml +++ b/ultralytics/cfg/default.yaml @@ -8,6 +8,7 @@ mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchma model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml data: # (str, optional) path to data file, i.e. coco128.yaml epochs: 100 # (int) number of epochs to train for +time: # (float, optional) number of hours to train for, overrides epochs if supplied patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training batch: 16 # (int) number of images per batch (-1 for AutoBatch) imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes diff --git a/ultralytics/data/build.py b/ultralytics/data/build.py index 07de91c8..1d961aee 100644 --- a/ultralytics/data/build.py +++ b/ultralytics/data/build.py @@ -100,7 +100,7 @@ def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1): """Return an InfiniteDataLoader or DataLoader for training or validation set.""" batch = min(batch, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices - nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers]) # number of workers + nw = min([os.cpu_count() // max(nd, 1), batch, workers]) # number of workers sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) generator = torch.Generator() generator.manual_seed(6148914691236517205 + RANK) diff --git a/ultralytics/engine/trainer.py b/ultralytics/engine/trainer.py index f5126f16..7a458453 100644 --- a/ultralytics/engine/trainer.py +++ b/ultralytics/engine/trainer.py @@ -189,6 +189,14 @@ class BaseTrainer: else: self._do_train(world_size) + def _setup_scheduler(self): + """Initialize training learning rate scheduler.""" + if self.args.cos_lr: + self.lf = one_cycle(1, self.args.lrf, self.epochs) # cosine 1->hyp['lrf'] + else: + self.lf = lambda x: max(1 - x / self.epochs, 0) * (1.0 - self.args.lrf) + self.args.lrf # linear + self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf) + def _setup_ddp(self, world_size): """Initializes and sets the DistributedDataParallel parameters for training.""" torch.cuda.set_device(RANK) @@ -269,11 +277,7 @@ class BaseTrainer: decay=weight_decay, iterations=iterations) # Scheduler - if self.args.cos_lr: - self.lf = one_cycle(1, self.args.lrf, self.epochs) # cosine 1->hyp['lrf'] - else: - self.lf = lambda x: (1 - x / self.epochs) * (1.0 - self.args.lrf) + self.args.lrf # linear - self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf) + self._setup_scheduler() self.stopper, self.stop = EarlyStopping(patience=self.args.patience), False self.resume_training(ckpt) self.scheduler.last_epoch = self.start_epoch - 1 # do not move @@ -285,17 +289,18 @@ class BaseTrainer: self._setup_ddp(world_size) self._setup_train(world_size) - self.epoch_time = None - self.epoch_time_start = time.time() - self.train_time_start = time.time() nb = len(self.train_loader) # number of batches nw = max(round(self.args.warmup_epochs * nb), 100) if self.args.warmup_epochs > 0 else -1 # warmup iterations last_opt_step = -1 + self.epoch_time = None + self.epoch_time_start = time.time() + self.train_time_start = time.time() self.run_callbacks('on_train_start') LOGGER.info(f'Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n' f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n' f"Logging results to {colorstr('bold', self.save_dir)}\n" - f'Starting training for {self.epochs} epochs...') + f'Starting training for ' + f'{self.args.time} hours...' if self.args.time else f'{self.epochs} epochs...') if self.args.close_mosaic: base_idx = (self.epochs - self.args.close_mosaic) * nb self.plot_idx.extend([base_idx, base_idx + 1, base_idx + 2]) @@ -323,7 +328,7 @@ class BaseTrainer: ni = i + nb * epoch if ni <= nw: xi = [0, nw] # x interp - self.accumulate = max(1, np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round()) + self.accumulate = max(1, int(np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round())) for j, x in enumerate(self.optimizer.param_groups): # Bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp( @@ -348,6 +353,16 @@ class BaseTrainer: self.optimizer_step() last_opt_step = ni + # Timed stopping + if self.args.time: + self.stop = (time.time() - self.train_time_start) > (self.args.time * 3600) + if RANK != -1: # if DDP training + broadcast_list = [self.stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + self.stop = broadcast_list[0] + if self.stop: # training time exceeded + break + # Log mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1 @@ -363,31 +378,37 @@ class BaseTrainer: self.run_callbacks('on_train_batch_end') self.lr = {f'lr/pg{ir}': x['lr'] for ir, x in enumerate(self.optimizer.param_groups)} # for loggers - - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress 'Detected lr_scheduler.step() before optimizer.step()' - self.scheduler.step() self.run_callbacks('on_train_epoch_end') - if RANK in (-1, 0): + final_epoch = epoch + 1 == self.epochs + self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights']) # Validation - self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights']) - final_epoch = (epoch + 1 == self.epochs) or self.stopper.possible_stop - - if self.args.val or final_epoch: + if self.args.val or final_epoch or self.stopper.possible_stop or self.stop: self.metrics, self.fitness = self.validate() self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **self.lr}) - self.stop = self.stopper(epoch + 1, self.fitness) + self.stop |= self.stopper(epoch + 1, self.fitness) + if self.args.time: + self.stop |= (time.time() - self.train_time_start) > (self.args.time * 3600) # Save model - if self.args.save or (epoch + 1 == self.epochs): + if self.args.save or final_epoch: self.save_model() self.run_callbacks('on_model_save') - tnow = time.time() - self.epoch_time = tnow - self.epoch_time_start - self.epoch_time_start = tnow + # Scheduler + t = time.time() + self.epoch_time = t - self.epoch_time_start + self.epoch_time_start = t + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress 'Detected lr_scheduler.step() before optimizer.step()' + if self.args.time: + mean_epoch_time = (t - self.train_time_start) / (epoch - self.start_epoch + 1) + self.epochs = self.args.epochs = math.ceil(self.args.time * 3600 / mean_epoch_time) + self._setup_scheduler() + self.scheduler.last_epoch = self.epoch # do not move + self.stop |= epoch >= self.epochs # stop if exceeded epochs + self.scheduler.step() self.run_callbacks('on_fit_epoch_end') torch.cuda.empty_cache() # clear GPU memory at end of epoch, may help reduce CUDA out of memory errors @@ -395,8 +416,7 @@ class BaseTrainer: if RANK != -1: # if DDP training broadcast_list = [self.stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks - if RANK != 0: - self.stop = broadcast_list[0] + self.stop = broadcast_list[0] if self.stop: break # must break all DDP ranks diff --git a/ultralytics/utils/torch_utils.py b/ultralytics/utils/torch_utils.py index be8aa3b2..3a17a9ac 100644 --- a/ultralytics/utils/torch_utils.py +++ b/ultralytics/utils/torch_utils.py @@ -363,7 +363,7 @@ def de_parallel(model): def one_cycle(y1=0.0, y2=1.0, steps=100): """Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf.""" - return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + return lambda x: max((1 - math.cos(x * math.pi / steps)) / 2, 0) * (y2 - y1) + y1 def init_seeds(seed=0, deterministic=False):