update

ultralytics/data/build.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import os
+import random
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import dataloader, distributed
+
+from ultralytics.data.loaders import (
+    LOADERS,
+    LoadImagesAndVideos,
+    LoadPilAndNumpy,
+    LoadScreenshots,
+    LoadStreams,
+    LoadTensor,
+    SourceTypes,
+    autocast_list,
+)
+from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
+from ultralytics.utils import RANK, colorstr
+from ultralytics.utils.checks import check_file
+from .dataset import YOLODataset
+from .utils import PIN_MEMORY
+
+
+class InfiniteDataLoader(dataloader.DataLoader):
+    """
+    Dataloader that reuses workers.
+
+    Uses same syntax as vanilla DataLoader.
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Dataloader that infinitely recycles workers, inherits from DataLoader."""
+        super().__init__(*args, **kwargs)
+        object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
+        self.iterator = super().__iter__()
+
+    def __len__(self):
+        """Returns the length of the batch sampler's sampler."""
+        return len(self.batch_sampler.sampler)
+
+    def __iter__(self):
+        """Creates a sampler that repeats indefinitely."""
+        for _ in range(len(self)):
+            yield next(self.iterator)
+
+    def reset(self):
+        """
+        Reset iterator.
+
+        This is useful when we want to modify settings of dataset while training.
+        """
+        self.iterator = self._get_iterator()
+
+
+class _RepeatSampler:
+    """
+    Sampler that repeats forever.
+
+    Args:
+        sampler (Dataset.sampler): The sampler to repeat.
+    """
+
+    def __init__(self, sampler):
+        """Initializes an object that repeats a given sampler indefinitely."""
+        self.sampler = sampler
+
+    def __iter__(self):
+        """Iterates over the 'sampler' and yields its contents."""
+        while True:
+            yield from iter(self.sampler)
+
+
+def seed_worker(worker_id):  # noqa
+    """Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
+    worker_seed = torch.initial_seed() % 2**32
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+
+
+def build_yolo_dataset(cfg, img_path, batch, data, mode="train", rect=False, stride=32):
+    """Build YOLO Dataset."""
+    return YOLODataset(
+        img_path=img_path,
+        imgsz=cfg.imgsz,
+        batch_size=batch,
+        augment=mode == "train",  # augmentation
+        hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
+        rect=cfg.rect or rect,  # rectangular batches
+        cache=cfg.cache or None,
+        single_cls=cfg.single_cls or False,
+        stride=int(stride),
+        pad=0.0 if mode == "train" else 0.5,
+        prefix=colorstr(f"{mode}: "),
+        task=cfg.task,
+        classes=cfg.classes,
+        data=data,
+        fraction=cfg.fraction if mode == "train" else 1.0,
+    )
+
+
+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), workers])  # number of workers
+    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+    generator = torch.Generator()
+    generator.manual_seed(6148914691236517205 + RANK)
+    return InfiniteDataLoader(
+        dataset=dataset,
+        batch_size=batch,
+        shuffle=shuffle and sampler is None,
+        num_workers=nw,
+        sampler=sampler,
+        pin_memory=PIN_MEMORY,
+        collate_fn=getattr(dataset, "collate_fn", None),
+        worker_init_fn=seed_worker,
+        generator=generator,
+    )
+
+
+def check_source(source):
+    """Check source type and return corresponding flag values."""
+    webcam, screenshot, from_img, in_memory, tensor = False, False, False, False, False
+    if isinstance(source, (str, int, Path)):  # int for local usb camera
+        source = str(source)
+        is_file = Path(source).suffix[1:] in (IMG_FORMATS | VID_FORMATS)
+        is_url = source.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://"))
+        webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+        screenshot = source.lower() == "screen"
+        if is_url and is_file:
+            source = check_file(source)  # download
+    elif isinstance(source, LOADERS):
+        in_memory = True
+    elif isinstance(source, (list, tuple)):
+        source = autocast_list(source)  # convert all list elements to PIL or np arrays
+        from_img = True
+    elif isinstance(source, (Image.Image, np.ndarray)):
+        from_img = True
+    elif isinstance(source, torch.Tensor):
+        tensor = True
+    else:
+        raise TypeError("Unsupported image type. For supported types see https://docs.ultralytics.com/modes/predict")
+
+    return source, webcam, screenshot, from_img, in_memory, tensor
+
+
+def load_inference_source(source=None, batch=1, vid_stride=1, buffer=False):
+    """
+    Loads an inference source for object detection and applies necessary transformations.
+
+    Args:
+        source (str, Path, Tensor, PIL.Image, np.ndarray): The input source for inference.
+        batch (int, optional): Batch size for dataloaders. Default is 1.
+        vid_stride (int, optional): The frame interval for video sources. Default is 1.
+        buffer (bool, optional): Determined whether stream frames will be buffered. Default is False.
+
+    Returns:
+        dataset (Dataset): A dataset object for the specified input source.
+    """
+    source, stream, screenshot, from_img, in_memory, tensor = check_source(source)
+    source_type = source.source_type if in_memory else SourceTypes(stream, screenshot, from_img, tensor)
+
+    # Dataloader
+    if tensor:
+        dataset = LoadTensor(source)
+    elif in_memory:
+        dataset = source
+    elif stream:
+        dataset = LoadStreams(source, vid_stride=vid_stride, buffer=buffer)
+    elif screenshot:
+        dataset = LoadScreenshots(source)
+    elif from_img:
+        dataset = LoadPilAndNumpy(source)
+    else:
+        dataset = LoadImagesAndVideos(source, batch=batch, vid_stride=vid_stride)
+
+    # Attach source types to the dataset
+    setattr(dataset, "source_type", source_type)
+
+    return dataset