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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.yolo.data.dataloaders.stream_loaders import (LOADERS, LoadImages, LoadPilAndNumpy, LoadScreenshots,
LoadStreams, LoadTensor, SourceTypes, autocast_list)
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils.checks import check_file
from ..utils import RANK, colorstr
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}: '),
use_segments=cfg.task == 'segment',
use_keypoints=cfg.task == 'pose',
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), batch if batch > 1 else 0, 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://'))
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, tuple(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, imgsz=640, vid_stride=1):
"""
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.
imgsz (int, optional): The size of the image for inference. Default is 640.
vid_stride (int, optional): The frame interval for video sources. Default is 1.
Returns:
dataset (Dataset): A dataset object for the specified input source.
"""
source, webcam, screenshot, from_img, in_memory, tensor = check_source(source)
source_type = source.source_type if in_memory else SourceTypes(webcam, screenshot, from_img, tensor)
# Dataloader
if tensor:
dataset = LoadTensor(source)
elif in_memory:
dataset = source
elif webcam:
dataset = LoadStreams(source, imgsz=imgsz, vid_stride=vid_stride)
elif screenshot:
dataset = LoadScreenshots(source, imgsz=imgsz)
elif from_img:
dataset = LoadPilAndNumpy(source, imgsz=imgsz)
else:
dataset = LoadImages(source, imgsz=imgsz, vid_stride=vid_stride)
# Attach source types to the dataset
setattr(dataset, 'source_type', source_type)
return dataset