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yolo-world-with-efficientvit-sam/LICENSE

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yolo-world-with-efficientvit-sam/Makefile

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+EFFICIENTVIT_SAM_URL := "https://huggingface.co/han-cai/efficientvit-sam/resolve/main"
+EFFICIENTVIT_SAM_MODEL := "xl1.pt"
+
+
+define download
+	@if [ ! -f $(2) ]; then \
+		echo "Download $(2)..."; \
+		wget "$(1)/$(2)"; \
+	fi
+endef
+
+
+setup:
+	pip install -r requirements.txt
+
+
+model:
+	$(call download,$(EFFICIENTVIT_SAM_URL),$(EFFICIENTVIT_SAM_MODEL))

yolo-world-with-efficientvit-sam/README.md

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+# YOLO-World + EfficientViT SAM
+
+🤗 [HuggingFace Space](https://huggingface.co/spaces/curt-park/yolo-world-with-efficientvit-sam)
+
+![example_0](https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/326bde19-d535-4be5-829e-782fce0c1d00)
+
+## Prerequisites
+This project is developed and tested on Python3.10.
+
+```bash
+# Create and activate a python 3.10 environment.
+conda create -n yolo-world-with-efficientvit-sam python=3.10 -y
+conda activate yolo-world-with-efficientvit-sam
+# Setup packages.
+make setup
+```
+
+## How to Run
+```bash
+python app.py
+```
+
+Open http://127.0.0.1:7860/ on your web browser.
+
+![example_1](https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/9388e4ee-6f71-4428-b17c-d218fd059949)
+
+## Core Components
+
+### YOLO-World
+[YOLO-World](https://github.com/AILab-CVC/YOLO-World) is an open-vocabulary object detection model with high efficiency.
+On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100,
+which outperforms many state-of-the-art methods in terms of both accuracy and speed.
+![image](https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/8a4a17bd-918d-478a-8451-f58e4a2dce79)
+<img width="1024" src="https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/fce57405-e18d-45f3-bea8-fc3971faf975">
+
+### EfficientViT SAM
+[EfficientViT SAM](https://github.com/mit-han-lab/efficientvit) is a new family of accelerated segment anything models.
+Thanks to the lightweight and hardware-efficient core building block,
+it delivers 48.9× measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance.
+
+<img width="1024" src="https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/9eec003f-47c9-43a5-86b0-82d6689e1bf9">
+<img width="1024" src="https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/d79973bb-0d80-4b64-a175-252de56d0d09">
+
+## Powered By
+```
+@misc{zhang2024efficientvitsam,
+  title={EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss},
+  author={Zhuoyang Zhang and Han Cai and Song Han},
+  year={2024},
+  eprint={2402.05008},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+
+@article{cheng2024yolow,
+  title={YOLO-World: Real-Time Open-Vocabulary Object Detection},
+  author={Cheng, Tianheng and Song, Lin and Ge, Yixiao and Liu, Wenyu and Wang, Xinggang and Shan, Ying},
+  journal={arXiv preprint arXiv:2401.17270},
+  year={2024}
+}
+
+@article{cai2022efficientvit,
+  title={Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition},
+  author={Cai, Han and Gan, Chuang and Han, Song},
+  journal={arXiv preprint arXiv:2205.14756},
+  year={2022}
+}
+```

yolo-world-with-efficientvit-sam/app.py

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+"""Fast text to segmentation with yolo-world and efficient-vit sam."""
+import os
+
+import cv2
+import gradio as gr
+import numpy as np
+import supervision as sv
+import torch
+from inference.models import YOLOWorld
+
+from efficientvit.models.efficientvit.sam import EfficientViTSamPredictor
+from efficientvit.sam_model_zoo import create_sam_model
+
+
+# Download model weights.
+os.system("make model")
+
+# Load models.
+yolo_world = YOLOWorld(model_id="yolo_world/l")
+#yolo_world = YOLOWorld("/Users/tounsi/Desktop/DOCTORIA/Doctoria\ Full\ Software/Doctoria\ CXR/Doctoria\ CXR\ Thoracic\ Abnormalities/YOLOv8/CXR\ YOLOv8l.pt")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+sam = EfficientViTSamPredictor(
+    create_sam_model(name="xl1", weight_url="xl1.pt").to(device).eval()
+)
+
+
+# Load annotators.
+BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
+MASK_ANNOTATOR = sv.MaskAnnotator()
+LABEL_ANNOTATOR = sv.LabelAnnotator()
+
+
+def detect(
+    image: np.ndarray,
+    query: str,
+    confidence_threshold: float,
+    nms_threshold: float,
+) -> np.ndarray:
+    # Preparation.
+    categories = [category.strip() for category in query.split(",")]
+    yolo_world.set_classes(categories)
+    print("categories:", categories)
+
+    # Object detection.
+    results = yolo_world.infer(image, confidence=confidence_threshold)
+    detections = sv.Detections.from_inference(results).with_nms(
+        class_agnostic=True, threshold=nms_threshold
+    )
+    print("detected:", detections)
+
+    # Segmentation.
+    sam.set_image(image, image_format="RGB")
+    masks = []
+    for xyxy in detections.xyxy:
+        mask, _, _ = sam.predict(box=xyxy, multimask_output=False)
+        masks.append(mask.squeeze())
+    detections.mask = np.array(masks)
+    print("masks shaped as", detections.mask.shape)
+
+    # Annotation.
+    output_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    labels = [
+        f"{categories[class_id]}: {confidence:.2f}"
+        for class_id, confidence in zip(detections.class_id, detections.confidence)
+    ]
+    output_image = MASK_ANNOTATOR.annotate(output_image, detections)
+    output_image = BOUNDING_BOX_ANNOTATOR.annotate(output_image, detections)
+    output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
+    return cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
+
+
+app = gr.Interface(
+    fn=detect,
+    inputs=[
+        gr.Image(type="numpy", label="input image"),
+        gr.Text(info="you can input multiple words with comma (,)"),
+        gr.Slider(
+            minimum=0,
+            maximum=1,
+            value=0.3,
+            step=0.01,
+            interactive=True,
+            label="Confidence Threshold",
+        ),
+        gr.Slider(
+            minimum=0,
+            maximum=1,
+            value=0.5,
+            step=0.01,
+            interactive=True,
+            label="NMS Threshold",
+        ),
+    ],
+    outputs=gr.Image(type="numpy", label="output image"),
+    allow_flagging="never",
+    title="Fast Text to Segmentation with YOLO-World + EfficientViT SAM",
+    description="""
+    ## Core components
+    ### YOLO-World
+    [YOLO-World](https://github.com/AILab-CVC/YOLO-World) is an open-vocabulary object detection model with high efficiency.
+    On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100,
+    which outperforms many state-of-the-art methods in terms of both accuracy and speed.
+
+    ### EfficientViT SAM
+    [EfficientViT SAM](https://github.com/mit-han-lab/efficientvit) is a new family of accelerated segment anything models.
+    Thanks to the lightweight and hardware-efficient core building block,
+    it delivers 48.9× measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance.
+
+    ## Demo especially powered by
+    Roboflow's [inference](https://github.com/roboflow/inference) and [supervision](https://github.com/roboflow/supervision).
+
+    ## Example images came from
+    [Segment Anything Demo](https://segment-anything.com/demo) and [Unsplash](https://unsplash.com/).
+    """,
+    examples=[
+        [
+            os.path.join(os.path.dirname(__file__), "examples/livingroom.jpg"),
+            "table, lamp, dog, sofa, plant, clock, carpet, frame on the wall",
+            0.05,
+            0.5
+        ],
+        [
+            os.path.join(os.path.dirname(__file__), "examples/cat_and_dogs.jpg"),
+            "cat, dog",
+            0.2,
+            0.5
+        ],
+    ],
+)
+
+
+app.launch(server_name="0.0.0.0")

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/__init__.py

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+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+from .augment import *
+from .base import *
+from .random_resolution import *

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/augment/__init__.py

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+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+from .bbox import *
+from .color_aug import *

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/augment/bbox.py

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+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import numpy as np
+
+__all__ = ["rand_bbox"]
+
+
+def rand_bbox(
+    h: int,
+    w: int,
+    lam: float,
+    rand_func: callable = np.random.uniform,
+) -> tuple[int, int, int, int]:
+    """randomly sample bbox, used in cutmix"""
+    cut_rat = np.sqrt(1.0 - lam)
+    cut_w = w * cut_rat
+    cut_h = h * cut_rat
+
+    # uniform
+    cx = rand_func(0, w)
+    cy = rand_func(0, h)
+
+    bbx1 = int(np.clip(cx - cut_w / 2, 0, w))
+    bby1 = int(np.clip(cy - cut_h / 2, 0, h))
+    bbx2 = int(np.clip(cx + cut_w / 2, 0, w))
+    bby2 = int(np.clip(cy + cut_h / 2, 0, h))
+
+    return bbx1, bby1, bbx2, bby2

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/augment/color_aug.py

@@ -0,0 +1,84 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import numpy as np
+import torchvision.transforms as transforms
+from PIL import Image
+from timm.data.auto_augment import rand_augment_transform
+
+__all__ = ["ColorAug", "RandAug"]
+
+
+class ImageAug:
+    def aug_image(self, image: Image.Image) -> Image.Image:
+        raise NotImplementedError
+
+    def __call__(
+        self, feed_dict: dict or np.ndarray or Image.Image
+    ) -> dict or np.ndarray or Image.Image:
+        if isinstance(feed_dict, dict):
+            output_dict = feed_dict
+            image = feed_dict[self.key]
+        else:
+            output_dict = None
+            image = feed_dict
+        is_ndarray = isinstance(image, np.ndarray)
+        if is_ndarray:
+            image = Image.fromarray(image)
+
+        image = self.aug_image(image)
+
+        if is_ndarray:
+            image = np.array(image)
+
+        if output_dict is None:
+            return image
+        else:
+            output_dict[self.key] = image
+            return output_dict
+
+
+class ColorAug(transforms.ColorJitter, ImageAug):
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, key="data"):
+        super().__init__(
+            brightness=brightness,
+            contrast=contrast,
+            saturation=saturation,
+            hue=hue,
+        )
+        self.key = key
+
+    def aug_image(self, image: Image.Image) -> Image.Image:
+        return transforms.ColorJitter.forward(self, image)
+
+    def forward(
+        self, feed_dict: dict or np.ndarray or Image.Image
+    ) -> dict or np.ndarray or Image.Image:
+        return ImageAug.__call__(self, feed_dict)
+
+
+class RandAug(ImageAug):
+    def __init__(
+        self, config: dict[str, any], mean: tuple[float, float, float], key="data"
+    ):
+        n = config.get("n", 2)
+        m = config.get("m", 9)
+        mstd = config.get("mstd", 1.0)
+        inc = config.get("inc", 1)
+        tpct = config.get("tpct", 0.45)
+        config_str = f"rand-n{n}-m{m}-mstd{mstd}-inc{inc}"
+
+        aa_params = dict(
+            translate_pct=tpct,
+            img_mean=tuple([min(255, round(255 * x)) for x in mean]),
+            interpolation=Image.BICUBIC,
+        )
+        self.aug_op = rand_augment_transform(config_str, aa_params)
+        self.key = key
+
+    def aug_image(self, image: Image.Image) -> Image.Image:
+        return self.aug_op(image)
+
+    def __repr__(self):
+        return self.aug_op.__repr__()

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/base.py

@@ -0,0 +1,223 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import copy
+import warnings
+
+import torch.utils.data
+from torch.utils.data.distributed import DistributedSampler
+
+from efficientvit.apps.data_provider.random_resolution import RRSController
+from efficientvit.models.utils import val2tuple
+
+__all__ = ["parse_image_size", "random_drop_data", "DataProvider"]
+
+
+def parse_image_size(size: int or str) -> tuple[int, int]:
+    if isinstance(size, str):
+        size = [int(val) for val in size.split("-")]
+        return size[0], size[1]
+    else:
+        return val2tuple(size, 2)
+
+
+def random_drop_data(dataset, drop_size: int, seed: int, keys=("samples",)):
+    g = torch.Generator()
+    g.manual_seed(seed)  # set random seed before sampling validation set
+    rand_indexes = torch.randperm(len(dataset), generator=g).tolist()
+
+    dropped_indexes = rand_indexes[:drop_size]
+    remaining_indexes = rand_indexes[drop_size:]
+
+    dropped_dataset = copy.deepcopy(dataset)
+    for key in keys:
+        setattr(
+            dropped_dataset,
+            key,
+            [getattr(dropped_dataset, key)[idx] for idx in dropped_indexes],
+        )
+        setattr(dataset, key, [getattr(dataset, key)[idx] for idx in remaining_indexes])
+    return dataset, dropped_dataset
+
+
+class DataProvider:
+    data_keys = ("samples",)
+    mean_std = {"mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225]}
+    SUB_SEED = 937162211  # random seed for sampling subset
+    VALID_SEED = 2147483647  # random seed for the validation set
+
+    name: str
+
+    def __init__(
+        self,
+        train_batch_size: int,
+        test_batch_size: int or None,
+        valid_size: int or float or None,
+        n_worker: int,
+        image_size: int or list[int] or str or list[str],
+        num_replicas: int or None = None,
+        rank: int or None = None,
+        train_ratio: float or None = None,
+        drop_last: bool = False,
+    ):
+        warnings.filterwarnings("ignore")
+        super().__init__()
+
+        # batch_size & valid_size
+        self.train_batch_size = train_batch_size
+        self.test_batch_size = test_batch_size or self.train_batch_size
+        self.valid_size = valid_size
+
+        # image size
+        if isinstance(image_size, list):
+            self.image_size = [parse_image_size(size) for size in image_size]
+            self.image_size.sort()  # e.g., 160 -> 224
+            RRSController.IMAGE_SIZE_LIST = copy.deepcopy(self.image_size)
+            self.active_image_size = RRSController.ACTIVE_SIZE = self.image_size[-1]
+        else:
+            self.image_size = parse_image_size(image_size)
+            RRSController.IMAGE_SIZE_LIST = [self.image_size]
+            self.active_image_size = RRSController.ACTIVE_SIZE = self.image_size
+
+        # distributed configs
+        self.num_replicas = num_replicas
+        self.rank = rank
+
+        # build datasets
+        train_dataset, val_dataset, test_dataset = self.build_datasets()
+
+        if train_ratio is not None and train_ratio < 1.0:
+            assert 0 < train_ratio < 1
+            _, train_dataset = random_drop_data(
+                train_dataset,
+                int(train_ratio * len(train_dataset)),
+                self.SUB_SEED,
+                self.data_keys,
+            )
+
+        # build data loader
+        self.train = self.build_dataloader(
+            train_dataset, train_batch_size, n_worker, drop_last=drop_last, train=True
+        )
+        self.valid = self.build_dataloader(
+            val_dataset, test_batch_size, n_worker, drop_last=False, train=False
+        )
+        self.test = self.build_dataloader(
+            test_dataset, test_batch_size, n_worker, drop_last=False, train=False
+        )
+        if self.valid is None:
+            self.valid = self.test
+        self.sub_train = None
+
+    @property
+    def data_shape(self) -> tuple[int, ...]:
+        return 3, self.active_image_size[0], self.active_image_size[1]
+
+    def build_valid_transform(self, image_size: tuple[int, int] or None = None) -> any:
+        raise NotImplementedError
+
+    def build_train_transform(self, image_size: tuple[int, int] or None = None) -> any:
+        raise NotImplementedError
+
+    def build_datasets(self) -> tuple[any, any, any]:
+        raise NotImplementedError
+
+    def build_dataloader(
+        self,
+        dataset: any or None,
+        batch_size: int,
+        n_worker: int,
+        drop_last: bool,
+        train: bool,
+    ):
+        if dataset is None:
+            return None
+        if isinstance(self.image_size, list) and train:
+            from efficientvit.apps.data_provider.random_resolution._data_loader import \
+                RRSDataLoader
+
+            dataloader_class = RRSDataLoader
+        else:
+            dataloader_class = torch.utils.data.DataLoader
+        if self.num_replicas is None:
+            return dataloader_class(
+                dataset=dataset,
+                batch_size=batch_size,
+                shuffle=True,
+                num_workers=n_worker,
+                pin_memory=True,
+                drop_last=drop_last,
+            )
+        else:
+            sampler = DistributedSampler(dataset, self.num_replicas, self.rank)
+            return dataloader_class(
+                dataset=dataset,
+                batch_size=batch_size,
+                sampler=sampler,
+                num_workers=n_worker,
+                pin_memory=True,
+                drop_last=drop_last,
+            )
+
+    def set_epoch(self, epoch: int) -> None:
+        RRSController.set_epoch(epoch, len(self.train))
+        if isinstance(self.train.sampler, DistributedSampler):
+            self.train.sampler.set_epoch(epoch)
+
+    def assign_active_image_size(self, new_size: int or tuple[int, int]) -> None:
+        self.active_image_size = val2tuple(new_size, 2)
+        new_transform = self.build_valid_transform(self.active_image_size)
+        # change the transform of the valid and test set
+        self.valid.dataset.transform = self.test.dataset.transform = new_transform
+
+    def sample_val_dataset(self, train_dataset, valid_transform) -> tuple[any, any]:
+        if self.valid_size is not None:
+            if 0 < self.valid_size < 1:
+                valid_size = int(self.valid_size * len(train_dataset))
+            else:
+                assert self.valid_size >= 1
+                valid_size = int(self.valid_size)
+            train_dataset, val_dataset = random_drop_data(
+                train_dataset,
+                valid_size,
+                self.VALID_SEED,
+                self.data_keys,
+            )
+            val_dataset.transform = valid_transform
+        else:
+            val_dataset = None
+        return train_dataset, val_dataset
+
+    def build_sub_train_loader(self, n_samples: int, batch_size: int) -> any:
+        # used for resetting BN running statistics
+        if self.sub_train is None:
+            self.sub_train = {}
+        if self.active_image_size in self.sub_train:
+            return self.sub_train[self.active_image_size]
+
+        # construct dataset and dataloader
+        train_dataset = copy.deepcopy(self.train.dataset)
+        if n_samples < len(train_dataset):
+            _, train_dataset = random_drop_data(
+                train_dataset,
+                n_samples,
+                self.SUB_SEED,
+                self.data_keys,
+            )
+        RRSController.ACTIVE_SIZE = self.active_image_size
+        train_dataset.transform = self.build_train_transform(
+            image_size=self.active_image_size
+        )
+        data_loader = self.build_dataloader(
+            train_dataset, batch_size, self.train.num_workers, True, False
+        )
+
+        # pre-fetch data
+        self.sub_train[self.active_image_size] = [
+            data
+            for data in data_loader
+            for _ in range(max(1, n_samples // len(train_dataset)))
+        ]
+
+        return self.sub_train[self.active_image_size]

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/random_resolution/__init__.py

@@ -0,0 +1,7 @@
+"""Random resolution data loader compatible with multi-processing and distributed training.
+
+Replace Pytorch's DataLoader with RRSDataLoader to support random resolution
+at the training time, resolution sampling is controlled by RRSController
+"""
+
+from .controller import *

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/random_resolution/_data_loader.py

@@ -0,0 +1,1603 @@
+r"""This file is based on torch/utils/data/data_loader.py
+
+Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter
+
+To support these two classes, in `./_utils` we define many utility methods and
+functions to be run in multiprocessing. E.g., the data loading worker loop is
+in `./_utils/worker.py`.
+"""
+
+import functools
+import itertools
+import logging
+import multiprocessing as python_multiprocessing
+import os
+import queue
+import threading
+import warnings
+from typing import (Any, Callable, Generic, Iterable, List, Optional, Sequence,
+                    TypeVar, Union)
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as multiprocessing
+import torch.utils.data.graph_settings
+from torch._utils import ExceptionWrapper
+from torch.utils.data import (BatchSampler, Dataset, IterableDataset,
+                              IterDataPipe, MapDataPipe, RandomSampler,
+                              Sampler, SequentialSampler, _utils)
+from torch.utils.data.datapipes.datapipe import (
+    _IterDataPipeSerializationWrapper, _MapDataPipeSerializationWrapper)
+
+from ._data_worker import _worker_loop
+
+__all__ = ["RRSDataLoader"]
+
+T_co = TypeVar("T_co", covariant=True)
+T = TypeVar("T")
+_worker_init_fn_t = Callable[[int], None]
+
+# Ideally we would parameterize `DataLoader` by the return type of `collate_fn`, but there is currently no way to have that
+# type parameter set to a default value if the user doesn't pass in a custom 'collate_fn'.
+# See https://github.com/python/mypy/issues/3737.
+_collate_fn_t = Callable[[List[T]], Any]
+
+
+# These functions used to be defined in this file. However, it was moved to
+# _utils/collate.py. Although it is rather hard to access this from user land
+# (one has to explicitly directly `import torch.utils.data.dataloader`), there
+# probably is user code out there using it. This aliasing maintains BC in this
+# aspect.
+default_collate: _collate_fn_t = _utils.collate.default_collate
+default_convert = _utils.collate.default_convert
+
+get_worker_info = _utils.worker.get_worker_info
+
+logger = logging.getLogger(__name__)
+
+
+class _DatasetKind:
+    Map = 0
+    Iterable = 1
+
+    @staticmethod
+    def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):
+        if kind == _DatasetKind.Map:
+            return _utils.fetch._MapDatasetFetcher(
+                dataset, auto_collation, collate_fn, drop_last
+            )
+        else:
+            return _utils.fetch._IterableDatasetFetcher(
+                dataset, auto_collation, collate_fn, drop_last
+            )
+
+
+class _InfiniteConstantSampler(Sampler):
+    r"""Analogous to ``itertools.repeat(None, None)``.
+    Used as sampler for :class:`~torch.utils.data.IterableDataset`.
+
+    Args:
+        data_source (Dataset): dataset to sample from
+    """
+
+    def __init__(self):
+        super().__init__(None)
+
+    def __iter__(self):
+        while True:
+            yield None
+
+
+def _get_distributed_settings():
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_world_size(), dist.get_rank()
+    else:
+        return 1, 0
+
+
+def _sharding_worker_init_fn(worker_init_fn, world_size, rank_id, worker_id):
+    global_worker_id = worker_id
+    info = torch.utils.data.get_worker_info()
+    assert info is not None
+    total_workers = info.num_workers
+    datapipe = info.dataset
+    assert isinstance(datapipe, (IterDataPipe, MapDataPipe))
+    # To distribute elements across distributed process evenly, we should shard data on distributed
+    # processes first then shard on worker processes
+    total_workers *= world_size
+    global_worker_id = global_worker_id * world_size + rank_id
+    # For BC, use default SHARDING_PRIORITIES
+    torch.utils.data.graph_settings.apply_sharding(
+        datapipe, total_workers, global_worker_id
+    )
+    if worker_init_fn is not None:
+        worker_init_fn(worker_id)
+
+
+def _share_dist_seed(generator, pg):
+    _shared_seed = torch.empty((), dtype=torch.int64).random_(generator=generator)
+    if isinstance(pg, dist.ProcessGroup):
+        dist.broadcast(_shared_seed, src=0, group=pg)
+    return _shared_seed.item()
+
+
+class RRSDataLoader(Generic[T_co]):
+    r"""
+    Data loader. Combines a dataset and a sampler, and provides an iterable over
+    the given dataset.
+
+    The :class:`~torch.utils.data.DataLoader` supports both map-style and
+    iterable-style datasets with single- or multi-process loading, customizing
+    loading order and optional automatic batching (collation) and memory pinning.
+
+    See :py:mod:`torch.utils.data` documentation page for more details.
+
+    Args:
+        dataset (Dataset): dataset from which to load the data.
+        batch_size (int, optional): how many samples per batch to load
+            (default: ``1``).
+        shuffle (bool, optional): set to ``True`` to have the data reshuffled
+            at every epoch (default: ``False``).
+        sampler (Sampler or Iterable, optional): defines the strategy to draw
+            samples from the dataset. Can be any ``Iterable`` with ``__len__``
+            implemented. If specified, :attr:`shuffle` must not be specified.
+        batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but
+            returns a batch of indices at a time. Mutually exclusive with
+            :attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,
+            and :attr:`drop_last`.
+        num_workers (int, optional): how many subprocesses to use for data
+            loading. ``0`` means that the data will be loaded in the main process.
+            (default: ``0``)
+        collate_fn (Callable, optional): merges a list of samples to form a
+            mini-batch of Tensor(s).  Used when using batched loading from a
+            map-style dataset.
+        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
+            into device/CUDA pinned memory before returning them.  If your data elements
+            are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
+            see the example below.
+        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+            if the dataset size is not divisible by the batch size. If ``False`` and
+            the size of dataset is not divisible by the batch size, then the last batch
+            will be smaller. (default: ``False``)
+        timeout (numeric, optional): if positive, the timeout value for collecting a batch
+            from workers. Should always be non-negative. (default: ``0``)
+        worker_init_fn (Callable, optional): If not ``None``, this will be called on each
+            worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
+            input, after seeding and before data loading. (default: ``None``)
+        generator (torch.Generator, optional): If not ``None``, this RNG will be used
+            by RandomSampler to generate random indexes and multiprocessing to generate
+            `base_seed` for workers. (default: ``None``)
+        prefetch_factor (int, optional, keyword-only arg): Number of batches loaded
+            in advance by each worker. ``2`` means there will be a total of
+            2 * num_workers batches prefetched across all workers. (default value depends
+            on the set value for num_workers. If value of num_workers=0 default is ``None``.
+            Otherwise if value of num_workers>0 default is ``2``).
+        persistent_workers (bool, optional): If ``True``, the data loader will not shutdown
+            the worker processes after a dataset has been consumed once. This allows to
+            maintain the workers `Dataset` instances alive. (default: ``False``)
+        pin_memory_device (str, optional): the data loader will copy Tensors
+            into device pinned memory before returning them if pin_memory is set to true.
+
+
+    .. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`
+                 cannot be an unpicklable object, e.g., a lambda function. See
+                 :ref:`multiprocessing-best-practices` on more details related
+                 to multiprocessing in PyTorch.
+
+    .. warning:: ``len(dataloader)`` heuristic is based on the length of the sampler used.
+                 When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
+                 it instead returns an estimate based on ``len(dataset) / batch_size``, with proper
+                 rounding depending on :attr:`drop_last`, regardless of multi-process loading
+                 configurations. This represents the best guess PyTorch can make because PyTorch
+                 trusts user :attr:`dataset` code in correctly handling multi-process
+                 loading to avoid duplicate data.
+
+                 However, if sharding results in multiple workers having incomplete last batches,
+                 this estimate can still be inaccurate, because (1) an otherwise complete batch can
+                 be broken into multiple ones and (2) more than one batch worth of samples can be
+                 dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such
+                 cases in general.
+
+                 See `Dataset Types`_ for more details on these two types of datasets and how
+                 :class:`~torch.utils.data.IterableDataset` interacts with
+                 `Multi-process data loading`_.
+
+    .. warning:: See :ref:`reproducibility`, and :ref:`dataloader-workers-random-seed`, and
+                 :ref:`data-loading-randomness` notes for random seed related questions.
+    """
+
+    dataset: Dataset[T_co]
+    batch_size: Optional[int]
+    num_workers: int
+    pin_memory: bool
+    drop_last: bool
+    timeout: float
+    sampler: Union[Sampler, Iterable]
+    pin_memory_device: str
+    prefetch_factor: Optional[int]
+    _iterator: Optional["_BaseDataLoaderIter"]
+    __initialized = False
+
+    def __init__(
+        self,
+        dataset: Dataset[T_co],
+        batch_size: Optional[int] = 1,
+        shuffle: Optional[bool] = None,
+        sampler: Union[Sampler, Iterable, None] = None,
+        batch_sampler: Union[Sampler[Sequence], Iterable[Sequence], None] = None,
+        num_workers: int = 0,
+        collate_fn: Optional[_collate_fn_t] = None,
+        pin_memory: bool = False,
+        drop_last: bool = False,
+        timeout: float = 0,
+        worker_init_fn: Optional[_worker_init_fn_t] = None,
+        multiprocessing_context=None,
+        generator=None,
+        *,
+        prefetch_factor: Optional[int] = None,
+        persistent_workers: bool = False,
+        pin_memory_device: str = ""
+    ):
+        torch._C._log_api_usage_once("python.data_loader")
+
+        if num_workers < 0:
+            raise ValueError(
+                "num_workers option should be non-negative; "
+                "use num_workers=0 to disable multiprocessing."
+            )
+
+        if timeout < 0:
+            raise ValueError("timeout option should be non-negative")
+
+        if num_workers == 0 and prefetch_factor is not None:
+            raise ValueError(
+                "prefetch_factor option could only be specified in multiprocessing."
+                "let num_workers > 0 to enable multiprocessing, otherwise set prefetch_factor to None."
+            )
+        elif num_workers > 0 and prefetch_factor is None:
+            prefetch_factor = 2
+        elif prefetch_factor is not None and prefetch_factor < 0:
+            raise ValueError("prefetch_factor option should be non-negative")
+
+        if persistent_workers and num_workers == 0:
+            raise ValueError("persistent_workers option needs num_workers > 0")
+
+        self.dataset = dataset
+        self.num_workers = num_workers
+        self.prefetch_factor = prefetch_factor
+        self.pin_memory = pin_memory
+        self.pin_memory_device = pin_memory_device
+        self.timeout = timeout
+        self.worker_init_fn = worker_init_fn
+        self.multiprocessing_context = multiprocessing_context
+
+        # Adds forward compatibilities so classic DataLoader can work with DataPipes:
+        #   _DataPipeSerializationWrapper container makes it easier to serialize without redefining pickler
+        if isinstance(self.dataset, IterDataPipe):
+            self.dataset = _IterDataPipeSerializationWrapper(self.dataset)
+        elif isinstance(self.dataset, MapDataPipe):
+            self.dataset = _MapDataPipeSerializationWrapper(self.dataset)
+
+        # Arg-check dataset related before checking samplers because we want to
+        # tell users that iterable-style datasets are incompatible with custom
+        # samplers first, so that they don't learn that this combo doesn't work
+        # after spending time fixing the custom sampler errors.
+        if isinstance(dataset, IterableDataset):
+            self._dataset_kind = _DatasetKind.Iterable
+            # NOTE [ Custom Samplers and IterableDataset ]
+            #
+            # `IterableDataset` does not support custom `batch_sampler` or
+            # `sampler` since the key is irrelevant (unless we support
+            # generator-style dataset one day...).
+            #
+            # For `sampler`, we always create a dummy sampler. This is an
+            # infinite sampler even when the dataset may have an implemented
+            # finite `__len__` because in multi-process data loading, naive
+            # settings will return duplicated data (which may be desired), and
+            # thus using a sampler with length matching that of dataset will
+            # cause data lost (you may have duplicates of the first couple
+            # batches, but never see anything afterwards). Therefore,
+            # `Iterabledataset` always uses an infinite sampler, an instance of
+            # `_InfiniteConstantSampler` defined above.
+            #
+            # A custom `batch_sampler` essentially only controls the batch size.
+            # However, it is unclear how useful it would be since an iterable-style
+            # dataset can handle that within itself. Moreover, it is pointless
+            # in multi-process data loading as the assignment order of batches
+            # to workers is an implementation detail so users can not control
+            # how to batchify each worker's iterable. Thus, we disable this
+            # option. If this turns out to be useful in future, we can re-enable
+            # this, and support custom samplers that specify the assignments to
+            # specific workers.
+            if isinstance(dataset, IterDataPipe):
+                if shuffle is not None:
+                    dataset = torch.utils.data.graph_settings.apply_shuffle_settings(
+                        dataset, shuffle=shuffle
+                    )
+            # We cannot check `shuffle is not None` here, since previously `shuffle=False` was the default.
+            elif shuffle not in {False, None}:
+                raise ValueError(
+                    "DataLoader with IterableDataset: expected unspecified "
+                    "shuffle option, but got shuffle={}".format(shuffle)
+                )
+
+            if sampler is not None:
+                # See NOTE [ Custom Samplers and IterableDataset ]
+                raise ValueError(
+                    "DataLoader with IterableDataset: expected unspecified "
+                    "sampler option, but got sampler={}".format(sampler)
+                )
+            elif batch_sampler is not None:
+                # See NOTE [ Custom Samplers and IterableDataset ]
+                raise ValueError(
+                    "DataLoader with IterableDataset: expected unspecified "
+                    "batch_sampler option, but got batch_sampler={}".format(
+                        batch_sampler
+                    )
+                )
+        else:
+            shuffle = bool(shuffle)
+            self._dataset_kind = _DatasetKind.Map
+
+        if sampler is not None and shuffle:
+            raise ValueError("sampler option is mutually exclusive with " "shuffle")
+
+        if batch_sampler is not None:
+            # auto_collation with custom batch_sampler
+            if batch_size != 1 or shuffle or sampler is not None or drop_last:
+                raise ValueError(
+                    "batch_sampler option is mutually exclusive "
+                    "with batch_size, shuffle, sampler, and "
+                    "drop_last"
+                )
+            batch_size = None
+            drop_last = False
+        elif batch_size is None:
+            # no auto_collation
+            if drop_last:
+                raise ValueError(
+                    "batch_size=None option disables auto-batching "
+                    "and is mutually exclusive with drop_last"
+                )
+
+        if sampler is None:  # give default samplers
+            if self._dataset_kind == _DatasetKind.Iterable:
+                # See NOTE [ Custom Samplers and IterableDataset ]
+                sampler = _InfiniteConstantSampler()
+            else:  # map-style
+                if shuffle:
+                    sampler = RandomSampler(dataset, generator=generator)  # type: ignore[arg-type]
+                else:
+                    sampler = SequentialSampler(dataset)  # type: ignore[arg-type]
+
+        if batch_size is not None and batch_sampler is None:
+            # auto_collation without custom batch_sampler
+            batch_sampler = BatchSampler(sampler, batch_size, drop_last)
+
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+        self.sampler = sampler
+        self.batch_sampler = batch_sampler
+        self.generator = generator
+
+        if collate_fn is None:
+            if self._auto_collation:
+                collate_fn = _utils.collate.default_collate
+            else:
+                collate_fn = _utils.collate.default_convert
+
+        self.collate_fn = collate_fn
+        self.persistent_workers = persistent_workers
+
+        self.__initialized = True
+        self._IterableDataset_len_called = (
+            None  # See NOTE [ IterableDataset and __len__ ]
+        )
+
+        self._iterator = None
+
+        self.check_worker_number_rationality()
+
+        torch.set_vital("Dataloader", "enabled", "True")  # type: ignore[attr-defined]
+
+    def _get_iterator(self) -> "_BaseDataLoaderIter":
+        if self.num_workers == 0:
+            return _SingleProcessDataLoaderIter(self)
+        else:
+            self.check_worker_number_rationality()
+            return _MultiProcessingDataLoaderIter(self)
+
+    @property
+    def multiprocessing_context(self):
+        return self.__multiprocessing_context
+
+    @multiprocessing_context.setter
+    def multiprocessing_context(self, multiprocessing_context):
+        if multiprocessing_context is not None:
+            if self.num_workers > 0:
+                if isinstance(multiprocessing_context, str):
+                    valid_start_methods = multiprocessing.get_all_start_methods()
+                    if multiprocessing_context not in valid_start_methods:
+                        raise ValueError(
+                            (
+                                "multiprocessing_context option "
+                                "should specify a valid start method in {!r}, but got "
+                                "multiprocessing_context={!r}"
+                            ).format(valid_start_methods, multiprocessing_context)
+                        )
+                    multiprocessing_context = multiprocessing.get_context(
+                        multiprocessing_context
+                    )
+
+                if not isinstance(
+                    multiprocessing_context, python_multiprocessing.context.BaseContext
+                ):
+                    raise TypeError(
+                        (
+                            "multiprocessing_context option should be a valid context "
+                            "object or a string specifying the start method, but got "
+                            "multiprocessing_context={}"
+                        ).format(multiprocessing_context)
+                    )
+            else:
+                raise ValueError(
+                    (
+                        "multiprocessing_context can only be used with "
+                        "multi-process loading (num_workers > 0), but got "
+                        "num_workers={}"
+                    ).format(self.num_workers)
+                )
+
+        self.__multiprocessing_context = multiprocessing_context
+
+    def __setattr__(self, attr, val):
+        if self.__initialized and attr in (
+            "batch_size",
+            "batch_sampler",
+            "sampler",
+            "drop_last",
+            "dataset",
+            "persistent_workers",
+        ):
+            raise ValueError(
+                "{} attribute should not be set after {} is "
+                "initialized".format(attr, self.__class__.__name__)
+            )
+
+        super().__setattr__(attr, val)
+
+    # We quote '_BaseDataLoaderIter' since it isn't defined yet and the definition can't be moved up
+    # since '_BaseDataLoaderIter' references 'DataLoader'.
+    def __iter__(self) -> "_BaseDataLoaderIter":
+        # When using a single worker the returned iterator should be
+        # created everytime to avoid reseting its state
+        # However, in the case of a multiple workers iterator
+        # the iterator is only created once in the lifetime of the
+        # DataLoader object so that workers can be reused
+        if self.persistent_workers and self.num_workers > 0:
+            if self._iterator is None:
+                self._iterator = self._get_iterator()
+            else:
+                self._iterator._reset(self)
+            return self._iterator
+        else:
+            return self._get_iterator()
+
+    @property
+    def _auto_collation(self):
+        return self.batch_sampler is not None
+
+    @property
+    def _index_sampler(self):
+        # The actual sampler used for generating indices for `_DatasetFetcher`
+        # (see _utils/fetch.py) to read data at each time. This would be
+        # `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.
+        # We can't change `.sampler` and `.batch_sampler` attributes for BC
+        # reasons.
+        if self._auto_collation:
+            return self.batch_sampler
+        else:
+            return self.sampler
+
+    def __len__(self) -> int:
+        if self._dataset_kind == _DatasetKind.Iterable:
+            # NOTE [ IterableDataset and __len__ ]
+            #
+            # For `IterableDataset`, `__len__` could be inaccurate when one naively
+            # does multi-processing data loading, since the samples will be duplicated.
+            # However, no real use case should be actually using that behavior, so
+            # it should count as a user error. We should generally trust user
+            # code to do the proper thing (e.g., configure each replica differently
+            # in `__iter__`), and give us the correct `__len__` if they choose to
+            # implement it (this will still throw if the dataset does not implement
+            # a `__len__`).
+            #
+            # To provide a further warning, we track if `__len__` was called on the
+            # `DataLoader`, save the returned value in `self._len_called`, and warn
+            # if the iterator ends up yielding more than this number of samples.
+
+            # Cannot statically verify that dataset is Sized
+            length = self._IterableDataset_len_called = len(self.dataset)  # type: ignore[assignment, arg-type]
+            if (
+                self.batch_size is not None
+            ):  # IterableDataset doesn't allow custom sampler or batch_sampler
+                from math import ceil
+
+                if self.drop_last:
+                    length = length // self.batch_size
+                else:
+                    length = ceil(length / self.batch_size)
+            return length
+        else:
+            return len(self._index_sampler)
+
+    def check_worker_number_rationality(self):
+        # This function check whether the dataloader's worker number is rational based on
+        # current system's resource. Current rule is that if the number of workers this
+        # Dataloader will create is bigger than the number of logical cpus that is allowed to
+        # use, than we will pop up a warning to let user pay attention.
+        #
+        # eg. If current system has 2 physical CPUs with 16 cores each. And each core support 2
+        #     threads, then the total logical cpus here is 2 * 16 * 2 = 64. Let's say current
+        #     DataLoader process can use half of them which is 32, then the rational max number of
+        #     worker that initiated from this process is 32.
+        #     Now, let's say the created DataLoader has num_works = 40, which is bigger than 32.
+        #     So the warning message is triggered to notify the user to lower the worker number if
+        #     necessary.
+        #
+        #
+        # [Note] Please note that this function repects `cpuset` only when os.sched_getaffinity is
+        #        available (available in most of Linux system, but not OSX and Windows).
+        #        When os.sched_getaffinity is not available, os.cpu_count() is called instead, but
+        #        it doesn't repect cpuset.
+        #        We don't take threading into account since each worker process is single threaded
+        #        at this time.
+        #
+        #        We don't set any threading flags (eg. OMP_NUM_THREADS, MKL_NUM_THREADS, etc)
+        #        other than `torch.set_num_threads` to 1 in the worker process, if the passing
+        #        in functions use 3rd party modules that rely on those threading flags to determine
+        #        how many thread to create (eg. numpy, etc), then it is caller's responsibility to
+        #        set those flags correctly.
+        def _create_warning_msg(num_worker_suggest, num_worker_created, cpuset_checked):
+
+            suggested_max_worker_msg = (
+                (
+                    (
+                        "Our suggested max number of worker in current system is {}{}, which is smaller "
+                        "than what this DataLoader is going to create."
+                    ).format(
+                        num_worker_suggest,
+                        (
+                            ""
+                            if cpuset_checked
+                            else " (`cpuset` is not taken into account)"
+                        ),
+                    )
+                )
+                if num_worker_suggest is not None
+                else (
+                    "DataLoader is not able to compute a suggested max number of worker in current system."
+                )
+            )
+
+            warn_msg = (
+                "This DataLoader will create {} worker processes in total. {} "
+                "Please be aware that excessive worker creation might get DataLoader running slow or even freeze, "
+                "lower the worker number to avoid potential slowness/freeze if necessary."
+            ).format(num_worker_created, suggested_max_worker_msg)
+            return warn_msg
+
+        if not self.num_workers or self.num_workers == 0:
+            return
+
+        # try to compute a suggested max number of worker based on system's resource
+        max_num_worker_suggest = None
+        cpuset_checked = False
+        if hasattr(os, "sched_getaffinity"):
+            try:
+                max_num_worker_suggest = len(os.sched_getaffinity(0))
+                cpuset_checked = True
+            except Exception:
+                pass
+        if max_num_worker_suggest is None:
+            # os.cpu_count() could return Optional[int]
+            # get cpu count first and check None in order to satify mypy check
+            cpu_count = os.cpu_count()
+            if cpu_count is not None:
+                max_num_worker_suggest = cpu_count
+
+        if max_num_worker_suggest is None:
+            warnings.warn(
+                _create_warning_msg(
+                    max_num_worker_suggest, self.num_workers, cpuset_checked
+                )
+            )
+            return
+
+        if self.num_workers > max_num_worker_suggest:
+            warnings.warn(
+                _create_warning_msg(
+                    max_num_worker_suggest, self.num_workers, cpuset_checked
+                )
+            )
+
+
+class _BaseDataLoaderIter:
+    def __init__(self, loader: RRSDataLoader) -> None:
+        self._dataset = loader.dataset
+        self._shared_seed = None
+        self._pg = None
+        if isinstance(self._dataset, IterDataPipe):
+            if dist.is_available() and dist.is_initialized():
+                self._pg = dist.new_group(backend="gloo")
+            self._shared_seed = _share_dist_seed(loader.generator, self._pg)
+            shared_rng = torch.Generator()
+            shared_rng.manual_seed(self._shared_seed)
+            self._dataset = torch.utils.data.graph_settings.apply_random_seed(
+                self._dataset, shared_rng
+            )
+        self._dataset_kind = loader._dataset_kind
+        self._IterableDataset_len_called = loader._IterableDataset_len_called
+        self._auto_collation = loader._auto_collation
+        self._drop_last = loader.drop_last
+        self._index_sampler = loader._index_sampler
+        self._num_workers = loader.num_workers
+        ws, rank = _get_distributed_settings()
+        self._world_size = ws
+        self._rank = rank
+        # for other backends, pin_memory_device need to set. if not set
+        # default behaviour is CUDA device. if pin_memory_device is selected
+        # and pin_memory is not set, the default behaviour false.
+        if len(loader.pin_memory_device) == 0:
+            self._pin_memory = loader.pin_memory and torch.cuda.is_available()
+            self._pin_memory_device = None
+        else:
+            if not loader.pin_memory:
+                warn_msg = (
+                    "pin memory device is set and pin_memory flag is not used then device pinned memory won't be used"
+                    "please set pin_memory to true, if you need to use the device pin memory"
+                )
+                warnings.warn(warn_msg)
+
+            self._pin_memory = loader.pin_memory
+            self._pin_memory_device = loader.pin_memory_device
+        self._timeout = loader.timeout
+        self._collate_fn = loader.collate_fn
+        self._sampler_iter = iter(self._index_sampler)
+        self._base_seed = (
+            torch.empty((), dtype=torch.int64)
+            .random_(generator=loader.generator)
+            .item()
+        )
+        self._persistent_workers = loader.persistent_workers
+        self._num_yielded = 0
+        self._profile_name = "enumerate(DataLoader)#{}.__next__".format(
+            self.__class__.__name__
+        )
+
+    def __iter__(self) -> "_BaseDataLoaderIter":
+        return self
+
+    def _reset(self, loader, first_iter=False):
+        self._sampler_iter = iter(self._index_sampler)
+        self._num_yielded = 0
+        self._IterableDataset_len_called = loader._IterableDataset_len_called
+        if isinstance(self._dataset, IterDataPipe):
+            self._shared_seed = _share_dist_seed(loader.generator, self._pg)
+            shared_rng = torch.Generator()
+            shared_rng.manual_seed(self._shared_seed)
+            self._dataset = torch.utils.data.graph_settings.apply_random_seed(
+                self._dataset, shared_rng
+            )
+
+    def _next_index(self):
+        return next(self._sampler_iter)  # may raise StopIteration
+
+    def _next_data(self):
+        raise NotImplementedError
+
+    def __next__(self) -> Any:
+        with torch.autograd.profiler.record_function(self._profile_name):
+            if self._sampler_iter is None:
+                # TODO(https://github.com/pytorch/pytorch/issues/76750)
+                self._reset()  # type: ignore[call-arg]
+            data = self._next_data()
+            self._num_yielded += 1
+            if (
+                self._dataset_kind == _DatasetKind.Iterable
+                and self._IterableDataset_len_called is not None
+                and self._num_yielded > self._IterableDataset_len_called
+            ):
+                warn_msg = (
+                    "Length of IterableDataset {} was reported to be {} (when accessing len(dataloader)), but {} "
+                    "samples have been fetched. "
+                ).format(
+                    self._dataset, self._IterableDataset_len_called, self._num_yielded
+                )
+                if self._num_workers > 0:
+                    warn_msg += (
+                        "For multiprocessing data-loading, this could be caused by not properly configuring the "
+                        "IterableDataset replica at each worker. Please see "
+                        "https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset for examples."
+                    )
+                warnings.warn(warn_msg)
+            return data
+
+    def __len__(self) -> int:
+        return len(self._index_sampler)
+
+    def __getstate__(self):
+        # TODO: add limited pickling support for sharing an iterator
+        # across multiple threads for HOGWILD.
+        # Probably the best way to do this is by moving the sample pushing
+        # to a separate thread and then just sharing the data queue
+        # but signalling the end is tricky without a non-blocking API
+        raise NotImplementedError("{} cannot be pickled", self.__class__.__name__)
+
+
+class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
+    def __init__(self, loader):
+        super().__init__(loader)
+        assert self._timeout == 0
+        assert self._num_workers == 0
+
+        # Adds forward compatibilities so classic DataLoader can work with DataPipes:
+        #   Taking care of distributed sharding
+        if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):
+            # For BC, use default SHARDING_PRIORITIES
+            torch.utils.data.graph_settings.apply_sharding(
+                self._dataset, self._world_size, self._rank
+            )
+
+        self._dataset_fetcher = _DatasetKind.create_fetcher(
+            self._dataset_kind,
+            self._dataset,
+            self._auto_collation,
+            self._collate_fn,
+            self._drop_last,
+        )
+
+    def _next_data(self):
+        index = self._next_index()  # may raise StopIteration
+        data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
+        if self._pin_memory:
+            data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
+        return data
+
+
+class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):
+    r"""Iterates once over the DataLoader's dataset, as specified by the sampler"""
+
+    # NOTE [ Data Loader Multiprocessing Shutdown Logic ]
+    #
+    # Preliminary:
+    #
+    # Our data model looks like this (queues are indicated with curly brackets):
+    #
+    #                main process                              ||
+    #                     |                                    ||
+    #               {index_queue}                              ||
+    #                     |                                    ||
+    #              worker processes                            ||     DATA
+    #                     |                                    ||
+    #            {worker_result_queue}                         ||     FLOW
+    #                     |                                    ||
+    #      pin_memory_thread of main process                   ||   DIRECTION
+    #                     |                                    ||
+    #               {data_queue}                               ||
+    #                     |                                    ||
+    #                data output                               \/
+    #
+    # P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if
+    #      `pin_memory=False`.
+    #
+    #
+    # Terminating multiprocessing logic requires very careful design. In
+    # particular, we need to make sure that
+    #
+    #   1. The iterator gracefully exits the workers when its last reference is
+    #      gone or it is depleted.
+    #
+    #      In this case, the workers should be gracefully exited because the
+    #      main process may still need to continue to run, and we want cleaning
+    #      up code in the workers to be executed (e.g., releasing GPU memory).
+    #      Naturally, we implement the shutdown logic in `__del__` of
+    #      DataLoaderIterator.
+    #
+    #      We delay the discussion on the logic in this case until later.
+    #
+    #   2. The iterator exits the workers when the loader process and/or worker
+    #      processes exits normally or with error.
+    #
+    #      We set all workers and `pin_memory_thread` to have `daemon=True`.
+    #
+    #      You may ask, why can't we make the workers non-daemonic, and
+    #      gracefully exit using the same logic as we have in `__del__` when the
+    #      iterator gets deleted (see 1 above)?
+    #
+    #      First of all, `__del__` is **not** guaranteed to be called when
+    #      interpreter exits. Even if it is called, by the time it executes,
+    #      many Python core library resources may alreay be freed, and even
+    #      simple things like acquiring an internal lock of a queue may hang.
+    #      Therefore, in this case, we actually need to prevent `__del__` from
+    #      being executed, and rely on the automatic termination of daemonic
+    #      children.
+    #
+    #      Thus, we register an `atexit` hook that sets a global flag
+    #      `_utils.python_exit_status`. Since `atexit` hooks are executed in the
+    #      reverse order of registration, we are guaranteed that this flag is
+    #      set before library resources we use are freed (which, at least in
+    #      CPython, is done via an `atexit` handler defined in
+    #      `multiprocessing/util.py`
+    #      https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/util.py#L320-L362
+    #      registered when an object requiring this mechanism is first
+    #      created, e.g., `mp.Queue`
+    #      https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/context.py#L100-L103
+    #      https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/queues.py#L29
+    #      )
+    #
+    #      So in `__del__`, we check if `_utils.python_exit_status` is set or
+    #      `None` (freed), and perform no-op if so.
+    #
+    #      However, simply letting library clean-up codes run can also be bad,
+    #      because such codes (i.e., `multiprocessing.util._exit_function()`)
+    #      include join putting threads for `mp.Queue`, which can be blocking.
+    #      Hence, the main process putting threads are called with
+    #      `cancel_join_thread` at creation.  See later section
+    #      [ 3b. A process won't hang when putting into a queue; ]
+    #      for more details.
+    #
+    #      Here are two example cases where library clean-up codes can run
+    #      before `__del__` is called:
+    #
+    #        1. If we hold onto a reference to the iterator, it more often
+    #           than not tries to do `multiprocessing` library cleaning before
+    #           clearing the alive referenced objects (https://github.com/pytorch/pytorch/issues/48666)
+    #           and thus prevents our cleaning-up code to run first.
+    #
+    #        2. A similar issue araises when a `DataLoader` is used in a subprocess.
+    #           When a process ends, it shuts the all its daemonic children
+    #           down with a SIGTERM (instead of joining them without a timeout).
+    #           Simiarly for threads, but by a different mechanism. This fact,
+    #           together with a few implementation details of multiprocessing, forces
+    #           us to make workers daemonic. All of our problems arise when a
+    #           DataLoader is used in a subprocess, and are caused by multiprocessing
+    #           code which looks more or less like this:
+    #
+    #               try:
+    #                   your_function_using_a_dataloader()
+    #               finally:
+    #                   multiprocessing.util._exit_function()
+    #
+    #           The joining/termination mentioned above happens inside
+    #           `_exit_function()`. Now, if `your_function_using_a_dataloader()`
+    #           throws, the stack trace stored in the exception will prevent the
+    #           frame which uses `DataLoaderIter` to be freed. If the frame has any
+    #           reference to the `DataLoaderIter` (e.g., in a method of the iter),
+    #           its  `__del__`, which starts the shutdown procedure, will not be
+    #           called. That, in turn, means that workers aren't notified. Attempting
+    #           to join in `_exit_function` will then result in a hang.
+    #
+    #           For context, `_exit_function` is also registered as an `atexit` call.
+    #           So it is unclear to me (@ssnl) why this is needed in a finally block.
+    #           The code dates back to 2008 and there is no comment on the original
+    #           PEP 371 or patch https://bugs.python.org/issue3050 (containing both
+    #           the finally block and the `atexit` registration) that explains this.
+    #
+    #
+    #      Finally, another choice is to just shutdown workers with logic in 1
+    #      above whenever we see an error in `next`. This isn't ideal because
+    #        a. It prevents users from using try-catch to resume data loading.
+    #        b. It doesn't prevent hanging if users have references to the
+    #           iterator.
+    #
+    #   3. All processes exit if any of them die unexpectedly by fatal signals.
+    #
+    #      As shown above, the workers are set as daemonic children of the main
+    #      process. However, automatic cleaning-up of such child processes only
+    #      happens if the parent process exits gracefully (e.g., not via fatal
+    #      signals like SIGKILL). So we must ensure that each process will exit
+    #      even the process that should send/receive data to/from it were
+    #      killed, i.e.,
+    #
+    #        a. A process won't hang when getting from a queue.
+    #
+    #           Even with carefully designed data dependencies (i.e., a `put()`
+    #           always corresponding to a `get()`), hanging on `get()` can still
+    #           happen when data in queue is corrupted (e.g., due to
+    #           `cancel_join_thread` or unexpected exit).
+    #
+    #           For child exit, we set a timeout whenever we try to get data
+    #           from `data_queue`, and check the workers' status on each timeout
+    #           and error.
+    #           See `_DataLoaderiter._get_batch()` and
+    #           `_DataLoaderiter._try_get_data()` for details.
+    #
+    #           Additionally, for child exit on non-Windows platforms, we also
+    #           register a SIGCHLD handler (which is supported on Windows) on
+    #           the main process, which checks if any of the workers fail in the
+    #           (Python) handler. This is more efficient and faster in detecting
+    #           worker failures, compared to only using the above mechanism.
+    #           See `DataLoader.cpp` and `_utils/signal_handling.py` for details.
+    #
+    #           For `.get()` calls where the sender(s) is not the workers, we
+    #           guard them with timeouts, and check the status of the sender
+    #           when timeout happens:
+    #             + in the workers, the `_utils.worker.ManagerWatchdog` class
+    #               checks the status of the main process.
+    #             + if `pin_memory=True`, when getting from `pin_memory_thread`,
+    #               check `pin_memory_thread` status periodically until `.get()`
+    #               returns or see that `pin_memory_thread` died.
+    #
+    #        b. A process won't hang when putting into a queue;
+    #
+    #           We use `mp.Queue` which has a separate background thread to put
+    #           objects from an unbounded buffer array. The background thread is
+    #           daemonic and usually automatically joined when the process
+    #           *exits*.
+    #
+    #           In case that the receiver has ended abruptly while
+    #           reading from the pipe, the join will hang forever.  The usual
+    #           solution for this in Python is calling  `q.cancel_join_thread`,
+    #           which prevents automatically joining it when finalizing
+    #           (exiting).
+    #
+    #           Nonetheless, `cancel_join_thread` must only be called when the
+    #           queue is **not** going to be read from or write into by another
+    #           process, because it may hold onto a lock or leave corrupted data
+    #           in the queue, leading other readers/writers to hang.
+    #
+    #           Hence,
+    #             + For worker processes, we only do so (for their output
+    #               queues, i.e., `worker_result_queue`) before exiting.
+    #             + For `pin_memory_thread`, its output queue `data_queue` is a
+    #               `queue.Queue` that does blocking `put` if the queue is full.
+    #               So there is no above problem, but as a result, in
+    #               `_pin_memory_loop`, we do need to  wrap the `put` in a loop
+    #               that breaks not only upon success, but also when the main
+    #               process stops reading, i.e., is shutting down.
+    #             + For loader process, we `cancel_join_thread()` for all
+    #               `_index_queues` because the whole purpose of workers and
+    #               `pin_memory_thread` is to serve the loader process.  If
+    #               loader process is already exiting, we don't really care if
+    #               the queues are corrupted.
+    #
+    #
+    # Now let's get back to 1:
+    #   how we gracefully exit the workers when the last reference to the
+    #   iterator is gone.
+    #
+    # To achieve this, we implement the following logic along with the design
+    # choices mentioned above:
+    #
+    # `workers_done_event`:
+    #   A `multiprocessing.Event` shared among the main process and all worker
+    #   processes. This is used to signal the workers that the iterator is
+    #   shutting down. After it is set, they will not send processed data to
+    #   queues anymore, and only wait for the final `None` before exiting.
+    #   `done_event` isn't strictly needed. I.e., we can just check for `None`
+    #   from the input queue, but it allows us to skip wasting resources
+    #   processing data if we are already shutting down.
+    #
+    # `pin_memory_thread_done_event`:
+    #   A `threading.Event` for a similar purpose to that of
+    #   `workers_done_event`, but is for the `pin_memory_thread`. The reason
+    #   that separate events are needed is that `pin_memory_thread` reads from
+    #   the output queue of the workers. But the workers, upon seeing that
+    #   `workers_done_event` is set, only wants to see the final `None`, and is
+    #   not required to flush all data in the output queue (e.g., it may call
+    #   `cancel_join_thread` on that queue if its `IterableDataset` iterator
+    #   happens to exhaust coincidentally, which is out of the control of the
+    #   main process). Thus, since we will exit `pin_memory_thread` before the
+    #   workers (see below), two separete events are used.
+    #
+    # NOTE: In short, the protocol is that the main process will set these
+    #       `done_event`s and then the corresponding processes/threads a `None`,
+    #       and that they may exit at any time after receiving the `None`.
+    #
+    # NOTE: Using `None` as the final signal is valid, since normal data will
+    #       always be a 2-tuple with the 1st element being the index of the data
+    #       transferred (different from dataset index/key), and the 2nd being
+    #       either the dataset key or the data sample (depending on which part
+    #       of the data model the queue is at).
+    #
+    # [ worker processes ]
+    #   While loader process is alive:
+    #     Get from `index_queue`.
+    #       If get anything else,
+    #          Check `workers_done_event`.
+    #            If set, continue to next iteration
+    #                    i.e., keep getting until see the `None`, then exit.
+    #            Otherwise, process data:
+    #                If is fetching from an `IterableDataset` and the iterator
+    #                    is exhausted, send an `_IterableDatasetStopIteration`
+    #                    object to signal iteration end. The main process, upon
+    #                    receiving such an object, will send `None` to this
+    #                    worker and not use the corresponding `index_queue`
+    #                    anymore.
+    #       If timed out,
+    #          No matter `workers_done_event` is set (still need to see `None`)
+    #          or not, must continue to next iteration.
+    #   (outside loop)
+    #   If `workers_done_event` is set,  (this can be False with `IterableDataset`)
+    #     `data_queue.cancel_join_thread()`.  (Everything is ending here:
+    #                                          main process won't read from it;
+    #                                          other workers will also call
+    #                                          `cancel_join_thread`.)
+    #
+    # [ pin_memory_thread ]
+    #   # No need to check main thread. If this thread is alive, the main loader
+    #   # thread must be alive, because this thread is set as daemonic.
+    #   While `pin_memory_thread_done_event` is not set:
+    #     Get from `index_queue`.
+    #       If timed out, continue to get in the next iteration.
+    #       Otherwise, process data.
+    #       While `pin_memory_thread_done_event` is not set:
+    #         Put processed data to `data_queue` (a `queue.Queue` with blocking put)
+    #         If timed out, continue to put in the next iteration.
+    #         Otherwise, break, i.e., continuing to the out loop.
+    #
+    #   NOTE: we don't check the status of the main thread because
+    #           1. if the process is killed by fatal signal, `pin_memory_thread`
+    #              ends.
+    #           2. in other cases, either the cleaning-up in __del__ or the
+    #              automatic exit of daemonic thread will take care of it.
+    #              This won't busy-wait either because `.get(timeout)` does not
+    #              busy-wait.
+    #
+    # [ main process ]
+    #   In the DataLoader Iter's `__del__`
+    #     b. Exit `pin_memory_thread`
+    #          i.   Set `pin_memory_thread_done_event`.
+    #          ii   Put `None` in `worker_result_queue`.
+    #          iii. Join the `pin_memory_thread`.
+    #          iv.  `worker_result_queue.cancel_join_thread()`.
+    #
+    #     c. Exit the workers.
+    #          i.   Set `workers_done_event`.
+    #          ii.  Put `None` in each worker's `index_queue`.
+    #          iii. Join the workers.
+    #          iv.  Call `.cancel_join_thread()` on each worker's `index_queue`.
+    #
+    #        NOTE: (c) is better placed after (b) because it may leave corrupted
+    #              data in `worker_result_queue`, which `pin_memory_thread`
+    #              reads from, in which case the `pin_memory_thread` can only
+    #              happen at timeing out, which is slow. Nonetheless, same thing
+    #              happens if a worker is killed by signal at unfortunate times,
+    #              but in other cases, we are better off having a non-corrupted
+    #              `worker_result_queue` for `pin_memory_thread`.
+    #
+    #   NOTE: If `pin_memory=False`, there is no `pin_memory_thread` and (b)
+    #         can be omitted
+    #
+    # NB: `done_event`s isn't strictly needed. E.g., we can just check for
+    #     `None` from `index_queue`, but it allows us to skip wasting resources
+    #     processing indices already in `index_queue` if we are already shutting
+    #     down.
+
+    def __init__(self, loader):
+        super().__init__(loader)
+
+        self._prefetch_factor = loader.prefetch_factor
+
+        assert self._num_workers > 0
+        assert self._prefetch_factor > 0
+
+        if loader.multiprocessing_context is None:
+            multiprocessing_context = multiprocessing
+        else:
+            multiprocessing_context = loader.multiprocessing_context
+
+        self._worker_init_fn = loader.worker_init_fn
+
+        # Adds forward compatibilities so classic DataLoader can work with DataPipes:
+        #   Additional worker init function will take care of sharding in MP and Distributed
+        if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):
+            self._worker_init_fn = functools.partial(
+                _sharding_worker_init_fn,
+                self._worker_init_fn,
+                self._world_size,
+                self._rank,
+            )
+
+        # No certainty which module multiprocessing_context is
+        self._worker_result_queue = multiprocessing_context.Queue()  # type: ignore[var-annotated]
+        self._worker_pids_set = False
+        self._shutdown = False
+        self._workers_done_event = multiprocessing_context.Event()
+
+        self._index_queues = []
+        self._workers = []
+        for i in range(self._num_workers):
+            # No certainty which module multiprocessing_context is
+            index_queue = multiprocessing_context.Queue()  # type: ignore[var-annotated]
+            # Need to `cancel_join_thread` here!
+            # See sections (2) and (3b) above.
+            index_queue.cancel_join_thread()
+            w = multiprocessing_context.Process(
+                target=_worker_loop,
+                args=(
+                    self._dataset_kind,
+                    self._dataset,
+                    index_queue,
+                    self._worker_result_queue,
+                    self._workers_done_event,
+                    self._auto_collation,
+                    self._collate_fn,
+                    self._drop_last,
+                    self._base_seed,
+                    self._worker_init_fn,
+                    i,
+                    self._num_workers,
+                    self._persistent_workers,
+                    self._shared_seed,
+                ),
+            )
+            w.daemon = True
+            # NB: Process.start() actually take some time as it needs to
+            #     start a process and pass the arguments over via a pipe.
+            #     Therefore, we only add a worker to self._workers list after
+            #     it started, so that we do not call .join() if program dies
+            #     before it starts, and __del__ tries to join but will get:
+            #     AssertionError: can only join a started process.
+            w.start()
+            self._index_queues.append(index_queue)
+            self._workers.append(w)
+
+        if self._pin_memory:
+            self._pin_memory_thread_done_event = threading.Event()
+
+            # Queue is not type-annotated
+            self._data_queue = queue.Queue()  # type: ignore[var-annotated]
+            if self._pin_memory_device == "xpu":
+                current_device = torch.xpu.current_device()  # type: ignore[attr-defined]
+            else:
+                current_device = torch.cuda.current_device()  # choose cuda for default
+            pin_memory_thread = threading.Thread(
+                target=_utils.pin_memory._pin_memory_loop,
+                args=(
+                    self._worker_result_queue,
+                    self._data_queue,
+                    current_device,
+                    self._pin_memory_thread_done_event,
+                    self._pin_memory_device,
+                ),
+            )
+            pin_memory_thread.daemon = True
+            pin_memory_thread.start()
+            # Similar to workers (see comment above), we only register
+            # pin_memory_thread once it is started.
+            self._pin_memory_thread = pin_memory_thread
+        else:
+            self._data_queue = self._worker_result_queue
+
+        # In some rare cases, persistent workers (daemonic processes)
+        # would be terminated before `__del__` of iterator is invoked
+        # when main process exits
+        # It would cause failure when pin_memory_thread tries to read
+        # corrupted data from worker_result_queue
+        # atexit is used to shutdown thread and child processes in the
+        # right sequence before main process exits
+        if self._persistent_workers and self._pin_memory:
+            import atexit
+
+            for w in self._workers:
+                atexit.register(_MultiProcessingDataLoaderIter._clean_up_worker, w)
+
+        # .pid can be None only before process is spawned (not the case, so ignore)
+        _utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self._workers))  # type: ignore[misc]
+        _utils.signal_handling._set_SIGCHLD_handler()
+        self._worker_pids_set = True
+        self._reset(loader, first_iter=True)
+
+    def _reset(self, loader, first_iter=False):
+        super()._reset(loader, first_iter)
+        self._send_idx = 0  # idx of the next task to be sent to workers
+        self._rcvd_idx = 0  # idx of the next task to be returned in __next__
+        # information about data not yet yielded, i.e., tasks w/ indices in range [rcvd_idx, send_idx).
+        # map: task idx => - (worker_id,)        if data isn't fetched (outstanding)
+        #                  \ (worker_id, data)   if data is already fetched (out-of-order)
+        self._task_info = {}
+        self._tasks_outstanding = (
+            0  # always equal to count(v for v in task_info.values() if len(v) == 1)
+        )
+        # A list of booleans representing whether each worker still has work to
+        # do, i.e., not having exhausted its iterable dataset object. It always
+        # contains all `True`s if not using an iterable-style dataset
+        # (i.e., if kind != Iterable).
+        # Not that this indicates that a worker still has work to do *for this epoch*.
+        # It does not mean that a worker is dead. In case of `_persistent_workers`,
+        # the worker will be reset to available in the next epoch.
+        self._workers_status = [True for i in range(self._num_workers)]
+        # Reset the worker queue cycle so it resumes next epoch at worker 0
+        self._worker_queue_idx_cycle = itertools.cycle(range(self._num_workers))
+        # We resume the prefetching in case it was enabled
+        if not first_iter:
+            for idx in range(self._num_workers):
+                self._index_queues[idx].put(
+                    _utils.worker._ResumeIteration(self._shared_seed)
+                )
+            resume_iteration_cnt = self._num_workers
+            while resume_iteration_cnt > 0:
+                return_idx, return_data = self._get_data()
+                if isinstance(return_idx, _utils.worker._ResumeIteration):
+                    assert return_data is None
+                    resume_iteration_cnt -= 1
+        # prime the prefetch loop
+        for _ in range(self._prefetch_factor * self._num_workers):
+            self._try_put_index()
+
+    def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):
+        # Tries to fetch data from `self._data_queue` once for a given timeout.
+        # This can also be used as inner loop of fetching without timeout, with
+        # the sender status as the loop condition.
+        #
+        # This raises a `RuntimeError` if any worker died expectedly. This error
+        # can come from either the SIGCHLD handler in `_utils/signal_handling.py`
+        # (only for non-Windows platforms), or the manual check below on errors
+        # and timeouts.
+        #
+        # Returns a 2-tuple:
+        #   (bool: whether successfully get data, any: data if successful else None)
+        try:
+            data = self._data_queue.get(timeout=timeout)
+            return (True, data)
+        except Exception as e:
+            # At timeout and error, we manually check whether any worker has
+            # failed. Note that this is the only mechanism for Windows to detect
+            # worker failures.
+            failed_workers = []
+            for worker_id, w in enumerate(self._workers):
+                if self._workers_status[worker_id] and not w.is_alive():
+                    failed_workers.append(w)
+                    self._mark_worker_as_unavailable(worker_id)
+            if len(failed_workers) > 0:
+                pids_str = ", ".join(str(w.pid) for w in failed_workers)
+                raise RuntimeError(
+                    "DataLoader worker (pid(s) {}) exited unexpectedly".format(pids_str)
+                ) from e
+            if isinstance(e, queue.Empty):
+                return (False, None)
+            import errno
+            import tempfile
+
+            try:
+                # Raise an exception if we are this close to the FDs limit.
+                # Apparently, trying to open only one file is not a sufficient
+                # test.
+                # See NOTE [ DataLoader on Linux and open files limit ]
+                fds_limit_margin = 10
+                fs = [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]
+            except OSError as e:
+                if e.errno == errno.EMFILE:
+                    raise RuntimeError(
+                        "Too many open files. Communication with the"
+                        " workers is no longer possible. Please increase the"
+                        " limit using `ulimit -n` in the shell or change the"
+                        " sharing strategy by calling"
+                        " `torch.multiprocessing.set_sharing_strategy('file_system')`"
+                        " at the beginning of your code"
+                    ) from None
+            raise
+
+    # NOTE [ DataLoader on Linux and open files limit ]
+    #
+    # On Linux when DataLoader is used with multiprocessing we pass the data between
+    # the root process and the workers through SHM files. We remove those files from
+    # the filesystem as soon as they are created and keep them alive by
+    # passing around their file descriptors through AF_UNIX sockets. (See
+    # docs/source/multiprocessing.rst and 'Multiprocessing Technical Notes` in
+    # the wiki (https://github.com/pytorch/pytorch/wiki).)
+    #
+    # This sometimes leads us to exceeding the open files limit. When that happens,
+    # and the offending file descriptor is coming over a socket, the `socket` Python
+    # package silently strips the file descriptor from the message, setting only the
+    # `MSG_CTRUNC` flag (which might be a bit misleading since the manpage says that
+    # it _indicates that some control data were discarded due to lack of space in
+    # the buffer for ancillary data_). This might reflect the C implementation of
+    # AF_UNIX sockets.
+    #
+    # This behaviour can be reproduced with the script and instructions at the
+    # bottom of this note.
+    #
+    # When that happens, the standard Python `multiprocessing` (and not
+    # `torch.multiprocessing`) raises a `RuntimeError: received 0 items of ancdata`
+    #
+    # Sometimes, instead of the FD being stripped, you may get an `OSError:
+    # Too many open files`, both in the script below and in DataLoader. However,
+    # this is rare and seems to be nondeterministic.
+    #
+    #
+    #   #!/usr/bin/env python3
+    #   import sys
+    #   import socket
+    #   import os
+    #   import array
+    #   import shutil
+    #   import socket
+    #
+    #
+    #   if len(sys.argv) != 4:
+    #       print("Usage: ", sys.argv[0], " tmp_dirname iteration (send|recv)")
+    #       sys.exit(1)
+    #
+    #   if __name__ == '__main__':
+    #       dirname = sys.argv[1]
+    #       sock_path = dirname + "/sock"
+    #       iterations = int(sys.argv[2])
+    #       def dummy_path(i):
+    #           return dirname + "/" + str(i) + ".dummy"
+    #
+    #
+    #       if sys.argv[3] == 'send':
+    #           while not os.path.exists(sock_path):
+    #               pass
+    #           client = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
+    #           client.connect(sock_path)
+    #           for i in range(iterations):
+    #               fd = os.open(dummy_path(i), os.O_WRONLY | os.O_CREAT)
+    #               ancdata = array.array('i', [fd])
+    #               msg = bytes([i % 256])
+    #               print("Sending fd ", fd, " (iteration #", i, ")")
+    #               client.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, ancdata)])
+    #
+    #
+    #       else:
+    #           assert sys.argv[3] == 'recv'
+    #
+    #           if os.path.exists(dirname):
+    #               raise Exception("Directory exists")
+    #
+    #           os.mkdir(dirname)
+    #
+    #           print("Opening socket...")
+    #           server = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
+    #           server.bind(sock_path)
+    #
+    #           print("Listening...")
+    #           for i in range(iterations):
+    #               a = array.array('i')
+    #               msg, ancdata, flags, addr = server.recvmsg(1, socket.CMSG_SPACE(a.itemsize))
+    #               assert(len(ancdata) == 1)
+    #               cmsg_level, cmsg_type, cmsg_data = ancdata[0]
+    #               a.frombytes(cmsg_data)
+    #               print("Received fd ", a[0], " (iteration #", i, ")")
+    #
+    #           shutil.rmtree(dirname)
+    #
+    # Steps to reproduce:
+    #
+    # 1. Run two shells and set lower file descriptor limit in the receiving one:
+    # (shell1) ulimit -n 1020
+    # (shell2) ulimit -n 1022
+    #
+    # 2. Run the script above with the `recv` option in the first shell
+    # (shell1) ./test_socket.py sock_tmp 1017 recv
+    #
+    # 3. Run the script with the `send` option in the second shell:
+    # (shell2) ./test_socket.py sock_tmp 1017 send
+
+    def _get_data(self):
+        # Fetches data from `self._data_queue`.
+        #
+        # We check workers' status every `MP_STATUS_CHECK_INTERVAL` seconds,
+        # which we achieve by running `self._try_get_data(timeout=MP_STATUS_CHECK_INTERVAL)`
+        # in a loop. This is the only mechanism to detect worker failures for
+        # Windows. For other platforms, a SIGCHLD handler is also used for
+        # worker failure detection.
+        #
+        # If `pin_memory=True`, we also need check if `pin_memory_thread` had
+        # died at timeouts.
+        if self._timeout > 0:
+            success, data = self._try_get_data(self._timeout)
+            if success:
+                return data
+            else:
+                raise RuntimeError(
+                    "DataLoader timed out after {} seconds".format(self._timeout)
+                )
+        elif self._pin_memory:
+            while self._pin_memory_thread.is_alive():
+                success, data = self._try_get_data()
+                if success:
+                    return data
+            else:
+                # while condition is false, i.e., pin_memory_thread died.
+                raise RuntimeError("Pin memory thread exited unexpectedly")
+            # In this case, `self._data_queue` is a `queue.Queue`,. But we don't
+            # need to call `.task_done()` because we don't use `.join()`.
+        else:
+            while True:
+                success, data = self._try_get_data()
+                if success:
+                    return data
+
+    def _next_data(self):
+        while True:
+            # If the worker responsible for `self._rcvd_idx` has already ended
+            # and was unable to fulfill this task (due to exhausting an `IterableDataset`),
+            # we try to advance `self._rcvd_idx` to find the next valid index.
+            #
+            # This part needs to run in the loop because both the `self._get_data()`
+            # call and `_IterableDatasetStopIteration` check below can mark
+            # extra worker(s) as dead.
+            while self._rcvd_idx < self._send_idx:
+                info = self._task_info[self._rcvd_idx]
+                worker_id = info[0]
+                if (
+                    len(info) == 2 or self._workers_status[worker_id]
+                ):  # has data or is still active
+                    break
+                del self._task_info[self._rcvd_idx]
+                self._rcvd_idx += 1
+            else:
+                # no valid `self._rcvd_idx` is found (i.e., didn't break)
+                if not self._persistent_workers:
+                    self._shutdown_workers()
+                raise StopIteration
+
+            # Now `self._rcvd_idx` is the batch index we want to fetch
+
+            # Check if the next sample has already been generated
+            if len(self._task_info[self._rcvd_idx]) == 2:
+                data = self._task_info.pop(self._rcvd_idx)[1]
+                return self._process_data(data)
+
+            assert not self._shutdown and self._tasks_outstanding > 0
+            idx, data = self._get_data()
+            self._tasks_outstanding -= 1
+            if self._dataset_kind == _DatasetKind.Iterable:
+                # Check for _IterableDatasetStopIteration
+                if isinstance(data, _utils.worker._IterableDatasetStopIteration):
+                    if self._persistent_workers:
+                        self._workers_status[data.worker_id] = False
+                    else:
+                        self._mark_worker_as_unavailable(data.worker_id)
+                    self._try_put_index()
+                    continue
+
+            if idx != self._rcvd_idx:
+                # store out-of-order samples
+                self._task_info[idx] += (data,)
+            else:
+                del self._task_info[idx]
+                return self._process_data(data)
+
+    def _try_put_index(self):
+        assert self._tasks_outstanding < self._prefetch_factor * self._num_workers
+
+        try:
+            index = self._next_index()
+        except StopIteration:
+            return
+        for _ in range(self._num_workers):  # find the next active worker, if any
+            worker_queue_idx = next(self._worker_queue_idx_cycle)
+            if self._workers_status[worker_queue_idx]:
+                break
+        else:
+            # not found (i.e., didn't break)
+            return
+
+        self._index_queues[worker_queue_idx].put((self._send_idx, index))
+        self._task_info[self._send_idx] = (worker_queue_idx,)
+        self._tasks_outstanding += 1
+        self._send_idx += 1
+
+    def _process_data(self, data):
+        self._rcvd_idx += 1
+        self._try_put_index()
+        if isinstance(data, ExceptionWrapper):
+            data.reraise()
+        return data
+
+    def _mark_worker_as_unavailable(self, worker_id, shutdown=False):
+        # Mark a worker as having finished its work e.g., due to
+        # exhausting an `IterableDataset`. This should be used only when this
+        # `_MultiProcessingDataLoaderIter` is going to continue running.
+
+        assert self._workers_status[worker_id] or (
+            self._persistent_workers and shutdown
+        )
+
+        # Signal termination to that specific worker.
+        q = self._index_queues[worker_id]
+        # Indicate that no more data will be put on this queue by the current
+        # process.
+        q.put(None)
+
+        # Note that we don't actually join the worker here, nor do we remove the
+        # worker's pid from C side struct because (1) joining may be slow, and
+        # (2) since we don't join, the worker may still raise error, and we
+        # prefer capturing those, rather than ignoring them, even though they
+        # are raised after the worker has finished its job.
+        # Joinning is deferred to `_shutdown_workers`, which it is called when
+        # all workers finish their jobs (e.g., `IterableDataset` replicas) or
+        # when this iterator is garbage collected.
+
+        self._workers_status[worker_id] = False
+
+        assert self._workers_done_event.is_set() == shutdown
+
+    def _shutdown_workers(self):
+        # Called when shutting down this `_MultiProcessingDataLoaderIter`.
+        # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on
+        # the logic of this function.
+        if (
+            _utils is None
+            or _utils.python_exit_status is True
+            or _utils.python_exit_status is None
+        ):
+            # See (2) of the note. If Python is shutting down, do no-op.
+            return
+        # Normal exit when last reference is gone / iterator is depleted.
+        # See (1) and the second half of the note.
+        if not self._shutdown:
+            self._shutdown = True
+            try:
+                # Normal exit when last reference is gone / iterator is depleted.
+                # See (1) and the second half of the note.
+
+                # Exit `pin_memory_thread` first because exiting workers may leave
+                # corrupted data in `worker_result_queue` which `pin_memory_thread`
+                # reads from.
+                if hasattr(self, "_pin_memory_thread"):
+                    # Use hasattr in case error happens before we set the attribute.
+                    self._pin_memory_thread_done_event.set()
+                    # Send something to pin_memory_thread in case it is waiting
+                    # so that it can wake up and check `pin_memory_thread_done_event`
+                    self._worker_result_queue.put((None, None))
+                    self._pin_memory_thread.join()
+                    self._worker_result_queue.cancel_join_thread()
+                    self._worker_result_queue.close()
+
+                # Exit workers now.
+                self._workers_done_event.set()
+                for worker_id in range(len(self._workers)):
+                    # Get number of workers from `len(self._workers)` instead of
+                    # `self._num_workers` in case we error before starting all
+                    # workers.
+                    # If we are using workers_status with persistent_workers
+                    # we have to shut it down because the worker is paused
+                    if self._persistent_workers or self._workers_status[worker_id]:
+                        self._mark_worker_as_unavailable(worker_id, shutdown=True)
+                for w in self._workers:
+                    # We should be able to join here, but in case anything went
+                    # wrong, we set a timeout and if the workers fail to join,
+                    # they are killed in the `finally` block.
+                    w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+                for q in self._index_queues:
+                    q.cancel_join_thread()
+                    q.close()
+            finally:
+                # Even though all this function does is putting into queues that
+                # we have called `cancel_join_thread` on, weird things can
+                # happen when a worker is killed by a signal, e.g., hanging in
+                # `Event.set()`. So we need to guard this with SIGCHLD handler,
+                # and remove pids from the C side data structure only at the
+                # end.
+                #
+                # FIXME: Unfortunately, for Windows, we are missing a worker
+                #        error detection mechanism here in this function, as it
+                #        doesn't provide a SIGCHLD handler.
+                if self._worker_pids_set:
+                    _utils.signal_handling._remove_worker_pids(id(self))
+                    self._worker_pids_set = False
+                for w in self._workers:
+                    if w.is_alive():
+                        # Existing mechanisms try to make the workers exit
+                        # peacefully, but in case that we unfortunately reach
+                        # here, which we shouldn't, (e.g., pytorch/pytorch#39570),
+                        # we kill the worker.
+                        w.terminate()
+
+    # staticmethod is used to remove reference to `_MultiProcessingDataLoaderIter`
+    @staticmethod
+    def _clean_up_worker(w):
+        try:
+            w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+        finally:
+            if w.is_alive():
+                w.terminate()
+
+    def __del__(self):
+        self._shutdown_workers()

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/random_resolution/_data_worker.py

@@ -0,0 +1,378 @@
+r""""This file is based on torch/utils/data/_utils/worker.py
+
+Contains definitions of the methods used by the _BaseDataLoaderIter workers.
+These **needs** to be in global scope since Py2 doesn't support serializing
+static methods.
+"""
+
+import os
+import queue
+import random
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional, Union
+
+import torch
+from torch._utils import ExceptionWrapper
+from torch.utils.data._utils import (HAS_NUMPY, IS_WINDOWS,
+                                     MP_STATUS_CHECK_INTERVAL, signal_handling)
+
+if TYPE_CHECKING:
+    from torch.utils.data import Dataset
+
+from .controller import RRSController
+
+if IS_WINDOWS:
+    import ctypes
+    from ctypes.wintypes import BOOL, DWORD, HANDLE
+
+    # On Windows, the parent ID of the worker process remains unchanged when the manager process
+    # is gone, and the only way to check it through OS is to let the worker have a process handle
+    # of the manager and ask if the process status has changed.
+    class ManagerWatchdog:
+        def __init__(self):
+            self.manager_pid = os.getppid()
+
+            # mypy cannot detect this code is windows only
+            self.kernel32 = ctypes.WinDLL("kernel32", use_last_error=True)  # type: ignore[attr-defined]
+            self.kernel32.OpenProcess.argtypes = (DWORD, BOOL, DWORD)
+            self.kernel32.OpenProcess.restype = HANDLE
+            self.kernel32.WaitForSingleObject.argtypes = (HANDLE, DWORD)
+            self.kernel32.WaitForSingleObject.restype = DWORD
+
+            # Value obtained from https://msdn.microsoft.com/en-us/library/ms684880.aspx
+            SYNCHRONIZE = 0x00100000
+            self.manager_handle = self.kernel32.OpenProcess(
+                SYNCHRONIZE, 0, self.manager_pid
+            )
+
+            if not self.manager_handle:
+                raise ctypes.WinError(ctypes.get_last_error())  # type: ignore[attr-defined]
+
+            self.manager_dead = False
+
+        def is_alive(self):
+            if not self.manager_dead:
+                # Value obtained from https://msdn.microsoft.com/en-us/library/windows/desktop/ms687032.aspx
+                self.manager_dead = (
+                    self.kernel32.WaitForSingleObject(self.manager_handle, 0) == 0
+                )
+            return not self.manager_dead
+
+else:
+
+    class ManagerWatchdog:  # type: ignore[no-redef]
+        def __init__(self):
+            self.manager_pid = os.getppid()
+            self.manager_dead = False
+
+        def is_alive(self):
+            if not self.manager_dead:
+                self.manager_dead = os.getppid() != self.manager_pid
+            return not self.manager_dead
+
+
+_worker_info = None
+
+
+class WorkerInfo:
+    id: int
+    num_workers: int
+    seed: int
+    dataset: "Dataset"
+    __initialized = False
+
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        self.__keys = tuple(kwargs.keys())
+        self.__initialized = True
+
+    def __setattr__(self, key, val):
+        if self.__initialized:
+            raise RuntimeError(
+                "Cannot assign attributes to {} objects".format(self.__class__.__name__)
+            )
+        return super().__setattr__(key, val)
+
+    def __repr__(self):
+        items = []
+        for k in self.__keys:
+            items.append("{}={}".format(k, getattr(self, k)))
+        return "{}({})".format(self.__class__.__name__, ", ".join(items))
+
+
+def get_worker_info() -> Optional[WorkerInfo]:
+    r"""Returns the information about the current
+    :class:`~torch.utils.data.DataLoader` iterator worker process.
+
+    When called in a worker, this returns an object guaranteed to have the
+    following attributes:
+
+    * :attr:`id`: the current worker id.
+    * :attr:`num_workers`: the total number of workers.
+    * :attr:`seed`: the random seed set for the current worker. This value is
+      determined by main process RNG and the worker id. See
+      :class:`~torch.utils.data.DataLoader`'s documentation for more details.
+    * :attr:`dataset`: the copy of the dataset object in **this** process. Note
+      that this will be a different object in a different process than the one
+      in the main process.
+
+    When called in the main process, this returns ``None``.
+
+    .. note::
+       When used in a :attr:`worker_init_fn` passed over to
+       :class:`~torch.utils.data.DataLoader`, this method can be useful to
+       set up each worker process differently, for instance, using ``worker_id``
+       to configure the ``dataset`` object to only read a specific fraction of a
+       sharded dataset, or use ``seed`` to seed other libraries used in dataset
+       code.
+    """
+    return _worker_info
+
+
+r"""Dummy class used to signal the end of an IterableDataset"""
+
+
+@dataclass(frozen=True)
+class _IterableDatasetStopIteration:
+    worker_id: int
+
+
+r"""Dummy class used to resume the fetching when worker reuse is enabled"""
+
+
+@dataclass(frozen=True)
+class _ResumeIteration:
+    seed: Optional[int] = None
+
+
+# The function `_generate_state` is adapted from `numpy.random.SeedSequence`
+# from https://github.com/numpy/numpy/blob/main/numpy/random/bit_generator.pyx
+# It's MIT licensed, here is the copyright:
+
+# Copyright (c) 2015 Melissa E. O'Neill
+# Copyright (c) 2019 NumPy Developers
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+
+# This function generates an array of int32 as the seed for
+# `numpy.random`, in order to prevent state collision due to same
+# seed and algorithm for `numpy.random` and `random` modules.
+# TODO: Implement `SeedSequence` like object for `torch.random`
+def _generate_state(base_seed, worker_id):
+    INIT_A = 0x43B0D7E5
+    MULT_A = 0x931E8875
+    INIT_B = 0x8B51F9DD
+    MULT_B = 0x58F38DED
+    MIX_MULT_L = 0xCA01F9DD
+    MIX_MULT_R = 0x4973F715
+    XSHIFT = 4 * 8 // 2
+    MASK32 = 0xFFFFFFFF
+
+    entropy = [worker_id, base_seed & MASK32, base_seed >> 32, 0]
+    pool = [0] * 4
+
+    hash_const_A = INIT_A
+
+    def hash(value):
+        nonlocal hash_const_A
+        value = (value ^ hash_const_A) & MASK32
+        hash_const_A = (hash_const_A * MULT_A) & MASK32
+        value = (value * hash_const_A) & MASK32
+        value = (value ^ (value >> XSHIFT)) & MASK32
+        return value
+
+    def mix(x, y):
+        result_x = (MIX_MULT_L * x) & MASK32
+        result_y = (MIX_MULT_R * y) & MASK32
+        result = (result_x - result_y) & MASK32
+        result = (result ^ (result >> XSHIFT)) & MASK32
+        return result
+
+    # Add in the entropy to the pool.
+    for i in range(len(pool)):
+        pool[i] = hash(entropy[i])
+
+    # Mix all bits together so late bits can affect earlier bits.
+    for i_src in range(len(pool)):
+        for i_dst in range(len(pool)):
+            if i_src != i_dst:
+                pool[i_dst] = mix(pool[i_dst], hash(pool[i_src]))
+
+    hash_const_B = INIT_B
+    state = []
+    for i_dst in range(4):
+        data_val = pool[i_dst]
+        data_val = (data_val ^ hash_const_B) & MASK32
+        hash_const_B = (hash_const_B * MULT_B) & MASK32
+        data_val = (data_val * hash_const_B) & MASK32
+        data_val = (data_val ^ (data_val >> XSHIFT)) & MASK32
+        state.append(data_val)
+    return state
+
+
+def _worker_loop(
+    dataset_kind,
+    dataset,
+    index_queue,
+    data_queue,
+    done_event,
+    auto_collation,
+    collate_fn,
+    drop_last,
+    base_seed,
+    init_fn,
+    worker_id,
+    num_workers,
+    persistent_workers,
+    shared_seed,
+):
+    # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
+    # logic of this function.
+
+    try:
+        # Initialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
+        # module's handlers are executed after Python returns from C low-level
+        # handlers, likely when the same fatal signal had already happened
+        # again.
+        # https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers
+        signal_handling._set_worker_signal_handlers()
+
+        torch.set_num_threads(1)
+        seed = base_seed + worker_id
+        random.seed(seed)
+        torch.manual_seed(seed)
+        if HAS_NUMPY:
+            np_seed = _generate_state(base_seed, worker_id)
+            import numpy as np
+
+            np.random.seed(np_seed)
+
+        from torch.utils.data import IterDataPipe
+        from torch.utils.data.graph_settings import apply_random_seed
+
+        shared_rng = torch.Generator()
+        if isinstance(dataset, IterDataPipe):
+            assert shared_seed is not None
+            shared_rng.manual_seed(shared_seed)
+            dataset = apply_random_seed(dataset, shared_rng)
+
+        global _worker_info
+        _worker_info = WorkerInfo(
+            id=worker_id, num_workers=num_workers, seed=seed, dataset=dataset
+        )
+
+        from torch.utils.data import _DatasetKind
+
+        init_exception = None
+
+        try:
+            if init_fn is not None:
+                init_fn(worker_id)
+
+            fetcher = _DatasetKind.create_fetcher(
+                dataset_kind, dataset, auto_collation, collate_fn, drop_last
+            )
+        except Exception:
+            init_exception = ExceptionWrapper(
+                where="in DataLoader worker process {}".format(worker_id)
+            )
+
+        # When using Iterable mode, some worker can exit earlier than others due
+        # to the IterableDataset behaving differently for different workers.
+        # When such things happen, an `_IterableDatasetStopIteration` object is
+        # sent over to the main process with the ID of this worker, so that the
+        # main process won't send more tasks to this worker, and will send
+        # `None` to this worker to properly exit it.
+        #
+        # Note that we cannot set `done_event` from a worker as it is shared
+        # among all processes. Instead, we set the `iteration_end` flag to
+        # signify that the iterator is exhausted. When either `done_event` or
+        # `iteration_end` is set, we skip all processing step and just wait for
+        # `None`.
+        iteration_end = False
+
+        watchdog = ManagerWatchdog()
+
+        while watchdog.is_alive():
+            try:
+                r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
+            except queue.Empty:
+                continue
+            if isinstance(r, _ResumeIteration):
+                # Acknowledge the main process
+                data_queue.put((r, None))
+                iteration_end = False
+
+                if isinstance(dataset, IterDataPipe):
+                    assert r.seed is not None
+                    shared_rng.manual_seed(r.seed)
+                    dataset = apply_random_seed(dataset, shared_rng)
+
+                # Recreate the fetcher for worker-reuse policy
+                fetcher = _DatasetKind.create_fetcher(
+                    dataset_kind, dataset, auto_collation, collate_fn, drop_last
+                )
+                continue
+            elif r is None:
+                # Received the final signal
+                assert done_event.is_set() or iteration_end
+                break
+            elif done_event.is_set() or iteration_end:
+                # `done_event` is set. But I haven't received the final signal
+                # (None) yet. I will keep continuing until get it, and skip the
+                # processing steps.
+                continue
+            idx, index = r
+            """ Added """
+            RRSController.sample_resolution(batch_id=idx)
+            """ Added """
+            data: Union[_IterableDatasetStopIteration, ExceptionWrapper]
+            if init_exception is not None:
+                data = init_exception
+                init_exception = None
+            else:
+                try:
+                    data = fetcher.fetch(index)
+                except Exception as e:
+                    if (
+                        isinstance(e, StopIteration)
+                        and dataset_kind == _DatasetKind.Iterable
+                    ):
+                        data = _IterableDatasetStopIteration(worker_id)
+                        # Set `iteration_end`
+                        #   (1) to save future `next(...)` calls, and
+                        #   (2) to avoid sending multiple `_IterableDatasetStopIteration`s.
+                        iteration_end = True
+                    else:
+                        # It is important that we don't store exc_info in a variable.
+                        # `ExceptionWrapper` does the correct thing.
+                        # See NOTE [ Python Traceback Reference Cycle Problem ]
+                        data = ExceptionWrapper(
+                            where="in DataLoader worker process {}".format(worker_id)
+                        )
+            data_queue.put((idx, data))
+            del data, idx, index, r  # save memory
+    except KeyboardInterrupt:
+        # Main process will raise KeyboardInterrupt anyways.
+        pass
+    if done_event.is_set():
+        data_queue.cancel_join_thread()
+        data_queue.close()

yolo-world-with-efficientvit-sam/efficientvit/apps/data_provider/random_resolution/controller.py

@@ -0,0 +1,94 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import copy
+
+import torch
+import torchvision.transforms as transforms
+import torchvision.transforms.functional as F
+
+from efficientvit.models.utils import torch_random_choices
+
+__all__ = [
+    "RRSController",
+    "get_interpolate",
+    "MyRandomResizedCrop",
+]
+
+
+class RRSController:
+    ACTIVE_SIZE = (224, 224)
+    IMAGE_SIZE_LIST = [(224, 224)]
+
+    CHOICE_LIST = None
+
+    @staticmethod
+    def get_candidates() -> list[tuple[int, int]]:
+        return copy.deepcopy(RRSController.IMAGE_SIZE_LIST)
+
+    @staticmethod
+    def sample_resolution(batch_id: int) -> None:
+        RRSController.ACTIVE_SIZE = RRSController.CHOICE_LIST[batch_id]
+
+    @staticmethod
+    def set_epoch(epoch: int, batch_per_epoch: int) -> None:
+        g = torch.Generator()
+        g.manual_seed(epoch)
+        RRSController.CHOICE_LIST = torch_random_choices(
+            RRSController.get_candidates(),
+            g,
+            batch_per_epoch,
+        )
+
+
+def get_interpolate(name: str) -> F.InterpolationMode:
+    mapping = {
+        "nearest": F.InterpolationMode.NEAREST,
+        "bilinear": F.InterpolationMode.BILINEAR,
+        "bicubic": F.InterpolationMode.BICUBIC,
+        "box": F.InterpolationMode.BOX,
+        "hamming": F.InterpolationMode.HAMMING,
+        "lanczos": F.InterpolationMode.LANCZOS,
+    }
+    if name in mapping:
+        return mapping[name]
+    elif name == "random":
+        return torch_random_choices(
+            [
+                F.InterpolationMode.NEAREST,
+                F.InterpolationMode.BILINEAR,
+                F.InterpolationMode.BICUBIC,
+                F.InterpolationMode.BOX,
+                F.InterpolationMode.HAMMING,
+                F.InterpolationMode.LANCZOS,
+            ],
+        )
+    else:
+        raise NotImplementedError
+
+
+class MyRandomResizedCrop(transforms.RandomResizedCrop):
+    def __init__(
+        self,
+        scale=(0.08, 1.0),
+        ratio=(3.0 / 4.0, 4.0 / 3.0),
+        interpolation: str = "random",
+    ):
+        super(MyRandomResizedCrop, self).__init__(224, scale, ratio)
+        self.interpolation = interpolation
+
+    def forward(self, img: torch.Tensor) -> torch.Tensor:
+        i, j, h, w = self.get_params(img, list(self.scale), list(self.ratio))
+        target_size = RRSController.ACTIVE_SIZE
+        return F.resized_crop(
+            img, i, j, h, w, list(target_size), get_interpolate(self.interpolation)
+        )
+
+    def __repr__(self) -> str:
+        format_string = self.__class__.__name__
+        format_string += f"(\n\tsize={RRSController.get_candidates()},\n"
+        format_string += f"\tscale={tuple(round(s, 4) for s in self.scale)},\n"
+        format_string += f"\tratio={tuple(round(r, 4) for r in self.ratio)},\n"
+        format_string += f"\tinterpolation={self.interpolation})"
+        return format_string

yolo-world-with-efficientvit-sam/efficientvit/apps/setup.py

@@ -0,0 +1,141 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import os
+import time
+from copy import deepcopy
+
+import torch.backends.cudnn
+import torch.distributed
+import torch.nn as nn
+
+from efficientvit.apps.data_provider import DataProvider
+from efficientvit.apps.trainer.run_config import RunConfig
+from efficientvit.apps.utils import (dist_init, dump_config,
+                                     get_dist_local_rank, get_dist_rank,
+                                     get_dist_size, init_modules, is_master,
+                                     load_config, partial_update_config,
+                                     zero_last_gamma)
+from efficientvit.models.utils import (build_kwargs_from_config,
+                                       load_state_dict_from_file)
+
+__all__ = [
+    "save_exp_config",
+    "setup_dist_env",
+    "setup_seed",
+    "setup_exp_config",
+    "setup_data_provider",
+    "setup_run_config",
+    "init_model",
+]
+
+
+def save_exp_config(exp_config: dict, path: str, name="config.yaml") -> None:
+    if not is_master():
+        return
+    dump_config(exp_config, os.path.join(path, name))
+
+
+def setup_dist_env(gpu: str or None = None) -> None:
+    if gpu is not None:
+        os.environ["CUDA_VISIBLE_DEVICES"] = gpu
+    if not torch.distributed.is_initialized():
+        dist_init()
+    torch.backends.cudnn.benchmark = True
+    torch.cuda.set_device(get_dist_local_rank())
+
+
+def setup_seed(manual_seed: int, resume: bool) -> None:
+    if resume:
+        manual_seed = int(time.time())
+    manual_seed = get_dist_rank() + manual_seed
+    torch.manual_seed(manual_seed)
+    torch.cuda.manual_seed_all(manual_seed)
+
+
+def setup_exp_config(
+    config_path: str, recursive=True, opt_args: dict or None = None
+) -> dict:
+    # load config
+    if not os.path.isfile(config_path):
+        raise ValueError(config_path)
+
+    fpaths = [config_path]
+    if recursive:
+        extension = os.path.splitext(config_path)[1]
+        while os.path.dirname(config_path) != config_path:
+            config_path = os.path.dirname(config_path)
+            fpath = os.path.join(config_path, "default" + extension)
+            if os.path.isfile(fpath):
+                fpaths.append(fpath)
+        fpaths = fpaths[::-1]
+
+    default_config = load_config(fpaths[0])
+    exp_config = deepcopy(default_config)
+    for fpath in fpaths[1:]:
+        partial_update_config(exp_config, load_config(fpath))
+    # update config via args
+    if opt_args is not None:
+        partial_update_config(exp_config, opt_args)
+
+    return exp_config
+
+
+def setup_data_provider(
+    exp_config: dict,
+    data_provider_classes: list[type[DataProvider]],
+    is_distributed: bool = True,
+) -> DataProvider:
+    dp_config = exp_config["data_provider"]
+    dp_config["num_replicas"] = get_dist_size() if is_distributed else None
+    dp_config["rank"] = get_dist_rank() if is_distributed else None
+    dp_config["test_batch_size"] = (
+        dp_config.get("test_batch_size", None) or dp_config["base_batch_size"] * 2
+    )
+    dp_config["batch_size"] = dp_config["train_batch_size"] = dp_config[
+        "base_batch_size"
+    ]
+
+    data_provider_lookup = {
+        provider.name: provider for provider in data_provider_classes
+    }
+    data_provider_class = data_provider_lookup[dp_config["dataset"]]
+
+    data_provider_kwargs = build_kwargs_from_config(dp_config, data_provider_class)
+    data_provider = data_provider_class(**data_provider_kwargs)
+    return data_provider
+
+
+def setup_run_config(exp_config: dict, run_config_cls: type[RunConfig]) -> RunConfig:
+    exp_config["run_config"]["init_lr"] = (
+        exp_config["run_config"]["base_lr"] * get_dist_size()
+    )
+
+    run_config = run_config_cls(**exp_config["run_config"])
+
+    return run_config
+
+
+def init_model(
+    network: nn.Module,
+    init_from: str or None = None,
+    backbone_init_from: str or None = None,
+    rand_init="trunc_normal",
+    last_gamma=None,
+) -> None:
+    # initialization
+    init_modules(network, init_type=rand_init)
+    # zero gamma of last bn in each block
+    if last_gamma is not None:
+        zero_last_gamma(network, last_gamma)
+
+    # load weight
+    if init_from is not None and os.path.isfile(init_from):
+        network.load_state_dict(load_state_dict_from_file(init_from))
+        print(f"Loaded init from {init_from}")
+    elif backbone_init_from is not None and os.path.isfile(backbone_init_from):
+        network.backbone.load_state_dict(load_state_dict_from_file(backbone_init_from))
+        print(f"Loaded backbone init from {backbone_init_from}")
+    else:
+        print(f"Random init ({rand_init}) with last gamma {last_gamma}")

yolo-world-with-efficientvit-sam/efficientvit/apps/trainer/__init__.py

@@ -0,0 +1,6 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+from .base import *
+from .run_config import *

yolo-world-with-efficientvit-sam/efficientvit/apps/trainer/base.py

@@ -0,0 +1,297 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import os
+
+import torch
+import torch.nn as nn
+
+from efficientvit.apps.data_provider import DataProvider, parse_image_size
+from efficientvit.apps.trainer.run_config import RunConfig
+from efficientvit.apps.utils import (EMA, dist_barrier, get_dist_local_rank,
+                                     is_master)
+from efficientvit.models.nn.norm import reset_bn
+from efficientvit.models.utils import is_parallel, load_state_dict_from_file
+
+__all__ = ["Trainer"]
+
+
+class Trainer:
+    def __init__(self, path: str, model: nn.Module, data_provider: DataProvider):
+        self.path = os.path.realpath(os.path.expanduser(path))
+        self.model = model.cuda()
+        self.data_provider = data_provider
+
+        self.ema = None
+
+        self.checkpoint_path = os.path.join(self.path, "checkpoint")
+        self.logs_path = os.path.join(self.path, "logs")
+        for path in [self.path, self.checkpoint_path, self.logs_path]:
+            os.makedirs(path, exist_ok=True)
+
+        self.best_val = 0.0
+        self.start_epoch = 0
+
+    @property
+    def network(self) -> nn.Module:
+        return self.model.module if is_parallel(self.model) else self.model
+
+    @property
+    def eval_network(self) -> nn.Module:
+        if self.ema is None:
+            model = self.model
+        else:
+            model = self.ema.shadows
+        model = model.module if is_parallel(model) else model
+        return model
+
+    def write_log(self, log_str, prefix="valid", print_log=True, mode="a") -> None:
+        if is_master():
+            fout = open(os.path.join(self.logs_path, f"{prefix}.log"), mode)
+            fout.write(log_str + "\n")
+            fout.flush()
+            fout.close()
+            if print_log:
+                print(log_str)
+
+    def save_model(
+        self,
+        checkpoint=None,
+        only_state_dict=True,
+        epoch=0,
+        model_name=None,
+    ) -> None:
+        if is_master():
+            if checkpoint is None:
+                if only_state_dict:
+                    checkpoint = {"state_dict": self.network.state_dict()}
+                else:
+                    checkpoint = {
+                        "state_dict": self.network.state_dict(),
+                        "epoch": epoch,
+                        "best_val": self.best_val,
+                        "optimizer": self.optimizer.state_dict(),
+                        "lr_scheduler": self.lr_scheduler.state_dict(),
+                        "ema": self.ema.state_dict() if self.ema is not None else None,
+                        "scaler": self.scaler.state_dict() if self.fp16 else None,
+                    }
+
+            model_name = model_name or "checkpoint.pt"
+
+            latest_fname = os.path.join(self.checkpoint_path, "latest.txt")
+            model_path = os.path.join(self.checkpoint_path, model_name)
+            with open(latest_fname, "w") as _fout:
+                _fout.write(model_path + "\n")
+            torch.save(checkpoint, model_path)
+
+    def load_model(self, model_fname=None) -> None:
+        latest_fname = os.path.join(self.checkpoint_path, "latest.txt")
+        if model_fname is None and os.path.exists(latest_fname):
+            with open(latest_fname, "r") as fin:
+                model_fname = fin.readline()
+                if len(model_fname) > 0 and model_fname[-1] == "\n":
+                    model_fname = model_fname[:-1]
+        try:
+            if model_fname is None:
+                model_fname = f"{self.checkpoint_path}/checkpoint.pt"
+            elif not os.path.exists(model_fname):
+                model_fname = f"{self.checkpoint_path}/{os.path.basename(model_fname)}"
+                if not os.path.exists(model_fname):
+                    model_fname = f"{self.checkpoint_path}/checkpoint.pt"
+            print(f"=> loading checkpoint {model_fname}")
+            checkpoint = load_state_dict_from_file(model_fname, False)
+        except Exception:
+            self.write_log(f"fail to load checkpoint from {self.checkpoint_path}")
+            return
+
+        # load checkpoint
+        self.network.load_state_dict(checkpoint["state_dict"], strict=False)
+        log = []
+        if "epoch" in checkpoint:
+            self.start_epoch = checkpoint["epoch"] + 1
+            self.run_config.update_global_step(self.start_epoch)
+            log.append(f"epoch={self.start_epoch - 1}")
+        if "best_val" in checkpoint:
+            self.best_val = checkpoint["best_val"]
+            log.append(f"best_val={self.best_val:.2f}")
+        if "optimizer" in checkpoint:
+            self.optimizer.load_state_dict(checkpoint["optimizer"])
+            log.append("optimizer")
+        if "lr_scheduler" in checkpoint:
+            self.lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
+            log.append("lr_scheduler")
+        if "ema" in checkpoint and self.ema is not None:
+            self.ema.load_state_dict(checkpoint["ema"])
+            log.append("ema")
+        if "scaler" in checkpoint and self.fp16:
+            self.scaler.load_state_dict(checkpoint["scaler"])
+            log.append("scaler")
+        self.write_log("Loaded: " + ", ".join(log))
+
+    """ validate """
+
+    def reset_bn(
+        self,
+        network: nn.Module or None = None,
+        subset_size: int = 16000,
+        subset_batch_size: int = 100,
+        data_loader=None,
+        progress_bar=False,
+    ) -> None:
+        network = network or self.network
+        if data_loader is None:
+            data_loader = []
+            for data in self.data_provider.build_sub_train_loader(
+                subset_size, subset_batch_size
+            ):
+                if isinstance(data, list):
+                    data_loader.append(data[0])
+                elif isinstance(data, dict):
+                    data_loader.append(data["data"])
+                elif isinstance(data, torch.Tensor):
+                    data_loader.append(data)
+                else:
+                    raise NotImplementedError
+
+        network.eval()
+        reset_bn(
+            network,
+            data_loader,
+            sync=True,
+            progress_bar=progress_bar,
+        )
+
+    def _validate(self, model, data_loader, epoch) -> dict[str, any]:
+        raise NotImplementedError
+
+    def validate(
+        self, model=None, data_loader=None, is_test=True, epoch=0
+    ) -> dict[str, any]:
+        model = model or self.eval_network
+        if data_loader is None:
+            if is_test:
+                data_loader = self.data_provider.test
+            else:
+                data_loader = self.data_provider.valid
+
+        model.eval()
+        return self._validate(model, data_loader, epoch)
+
+    def multires_validate(
+        self,
+        model=None,
+        data_loader=None,
+        is_test=True,
+        epoch=0,
+        eval_image_size=None,
+    ) -> dict[str, dict[str, any]]:
+        eval_image_size = eval_image_size or self.run_config.eval_image_size
+        eval_image_size = eval_image_size or self.data_provider.image_size
+        model = model or self.eval_network
+
+        if not isinstance(eval_image_size, list):
+            eval_image_size = [eval_image_size]
+
+        output_dict = {}
+        for r in eval_image_size:
+            self.data_provider.assign_active_image_size(parse_image_size(r))
+            if self.run_config.reset_bn:
+                self.reset_bn(
+                    network=model,
+                    subset_size=self.run_config.reset_bn_size,
+                    subset_batch_size=self.run_config.reset_bn_batch_size,
+                    progress_bar=True,
+                )
+            output_dict[f"r{r}"] = self.validate(model, data_loader, is_test, epoch)
+        return output_dict
+
+    """ training """
+
+    def prep_for_training(
+        self, run_config: RunConfig, ema_decay: float or None = None, fp16=False
+    ) -> None:
+        self.run_config = run_config
+        self.model = nn.parallel.DistributedDataParallel(
+            self.model.cuda(),
+            device_ids=[get_dist_local_rank()],
+            static_graph=True,
+        )
+
+        self.run_config.global_step = 0
+        self.run_config.batch_per_epoch = len(self.data_provider.train)
+        assert self.run_config.batch_per_epoch > 0, "Training set is empty"
+
+        # build optimizer
+        self.optimizer, self.lr_scheduler = self.run_config.build_optimizer(self.model)
+
+        if ema_decay is not None:
+            self.ema = EMA(self.network, ema_decay)
+
+        # fp16
+        self.fp16 = fp16
+        self.scaler = torch.cuda.amp.GradScaler(enabled=self.fp16)
+
+    def sync_model(self):
+        print("Sync model")
+        self.save_model(model_name="sync.pt")
+        dist_barrier()
+        checkpoint = torch.load(
+            os.path.join(self.checkpoint_path, "sync.pt"), map_location="cpu"
+        )
+        dist_barrier()
+        if is_master():
+            os.remove(os.path.join(self.checkpoint_path, "sync.pt"))
+        dist_barrier()
+
+        # load checkpoint
+        self.network.load_state_dict(checkpoint["state_dict"], strict=False)
+        if "optimizer" in checkpoint:
+            self.optimizer.load_state_dict(checkpoint["optimizer"])
+        if "lr_scheduler" in checkpoint:
+            self.lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
+        if "ema" in checkpoint and self.ema is not None:
+            self.ema.load_state_dict(checkpoint["ema"])
+        if "scaler" in checkpoint and self.fp16:
+            self.scaler.load_state_dict(checkpoint["scaler"])
+
+    def before_step(self, feed_dict: dict[str, any]) -> dict[str, any]:
+        for key in feed_dict:
+            if isinstance(feed_dict[key], torch.Tensor):
+                feed_dict[key] = feed_dict[key].cuda()
+        return feed_dict
+
+    def run_step(self, feed_dict: dict[str, any]) -> dict[str, any]:
+        raise NotImplementedError
+
+    def after_step(self) -> None:
+        self.scaler.unscale_(self.optimizer)
+        # gradient clip
+        if self.run_config.grad_clip is not None:
+            torch.nn.utils.clip_grad_value_(
+                self.model.parameters(), self.run_config.grad_clip
+            )
+        # update
+        self.scaler.step(self.optimizer)
+        self.scaler.update()
+
+        self.lr_scheduler.step()
+        self.run_config.step()
+        # update ema
+        if self.ema is not None:
+            self.ema.step(self.network, self.run_config.global_step)
+
+    def _train_one_epoch(self, epoch: int) -> dict[str, any]:
+        raise NotImplementedError
+
+    def train_one_epoch(self, epoch: int) -> dict[str, any]:
+        self.model.train()
+
+        self.data_provider.set_epoch(epoch)
+
+        train_info_dict = self._train_one_epoch(epoch)
+
+        return train_info_dict
+
+    def train(self) -> None:
+        raise NotImplementedError

yolo-world-with-efficientvit-sam/efficientvit/apps/trainer/run_config.py

@@ -0,0 +1,121 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import json
+
+import numpy as np
+import torch.nn as nn
+
+from efficientvit.apps.utils import CosineLRwithWarmup, build_optimizer
+
+__all__ = ["Scheduler", "RunConfig"]
+
+
+class Scheduler:
+    PROGRESS = 0
+
+
+class RunConfig:
+    n_epochs: int
+    init_lr: float
+    warmup_epochs: int
+    warmup_lr: float
+    lr_schedule_name: str
+    lr_schedule_param: dict
+    optimizer_name: str
+    optimizer_params: dict
+    weight_decay: float
+    no_wd_keys: list
+    grad_clip: float  # allow none to turn off grad clipping
+    reset_bn: bool
+    reset_bn_size: int
+    reset_bn_batch_size: int
+    eval_image_size: list  # allow none to use image_size in data_provider
+
+    @property
+    def none_allowed(self):
+        return ["grad_clip", "eval_image_size"]
+
+    def __init__(self, **kwargs):  # arguments must be passed as kwargs
+        for k, val in kwargs.items():
+            setattr(self, k, val)
+
+        # check that all relevant configs are there
+        annotations = {}
+        for clas in type(self).mro():
+            if hasattr(clas, "__annotations__"):
+                annotations.update(clas.__annotations__)
+        for k, k_type in annotations.items():
+            assert hasattr(
+                self, k
+            ), f"Key {k} with type {k_type} required for initialization."
+            attr = getattr(self, k)
+            if k in self.none_allowed:
+                k_type = (k_type, type(None))
+            assert isinstance(
+                attr, k_type
+            ), f"Key {k} must be type {k_type}, provided={attr}."
+
+        self.global_step = 0
+        self.batch_per_epoch = 1
+
+    def build_optimizer(self, network: nn.Module) -> tuple[any, any]:
+        r"""require setting 'batch_per_epoch' before building optimizer & lr_scheduler"""
+        param_dict = {}
+        for name, param in network.named_parameters():
+            if param.requires_grad:
+                opt_config = [self.weight_decay, self.init_lr]
+                if self.no_wd_keys is not None and len(self.no_wd_keys) > 0:
+                    if np.any([key in name for key in self.no_wd_keys]):
+                        opt_config[0] = 0
+                opt_key = json.dumps(opt_config)
+                param_dict[opt_key] = param_dict.get(opt_key, []) + [param]
+
+        net_params = []
+        for opt_key, param_list in param_dict.items():
+            wd, lr = json.loads(opt_key)
+            net_params.append({"params": param_list, "weight_decay": wd, "lr": lr})
+
+        optimizer = build_optimizer(
+            net_params, self.optimizer_name, self.optimizer_params, self.init_lr
+        )
+        # build lr scheduler
+        if self.lr_schedule_name == "cosine":
+            decay_steps = []
+            for epoch in self.lr_schedule_param.get("step", []):
+                decay_steps.append(epoch * self.batch_per_epoch)
+            decay_steps.append(self.n_epochs * self.batch_per_epoch)
+            decay_steps.sort()
+            lr_scheduler = CosineLRwithWarmup(
+                optimizer,
+                self.warmup_epochs * self.batch_per_epoch,
+                self.warmup_lr,
+                decay_steps,
+            )
+        else:
+            raise NotImplementedError
+        return optimizer, lr_scheduler
+
+    def update_global_step(self, epoch, batch_id=0) -> None:
+        self.global_step = epoch * self.batch_per_epoch + batch_id
+        Scheduler.PROGRESS = self.progress
+
+    @property
+    def progress(self) -> float:
+        warmup_steps = self.warmup_epochs * self.batch_per_epoch
+        steps = max(0, self.global_step - warmup_steps)
+        return steps / (self.n_epochs * self.batch_per_epoch)
+
+    def step(self) -> None:
+        self.global_step += 1
+        Scheduler.PROGRESS = self.progress
+
+    def get_remaining_epoch(self, epoch, post=True) -> int:
+        return self.n_epochs + self.warmup_epochs - epoch - int(post)
+
+    def epoch_format(self, epoch: int) -> str:
+        epoch_format = f"%.{len(str(self.n_epochs))}d"
+        epoch_format = f"[{epoch_format}/{epoch_format}]"
+        epoch_format = epoch_format % (epoch + 1 - self.warmup_epochs, self.n_epochs)
+        return epoch_format

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/__init__.py

@@ -0,0 +1,12 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+from .dist import *
+from .ema import *
+from .export import *
+from .init import *
+from .lr import *
+from .metric import *
+from .misc import *
+from .opt import *

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/dist.py

@@ -0,0 +1,73 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import os
+
+import torch
+import torch.distributed
+
+from efficientvit.models.utils.list import list_mean, list_sum
+
+__all__ = [
+    "dist_init",
+    "get_dist_rank",
+    "get_dist_size",
+    "is_master",
+    "dist_barrier",
+    "get_dist_local_rank",
+    "sync_tensor",
+]
+
+
+def dist_init() -> None:
+    try:
+        torch.distributed.init_process_group(backend="nccl")
+        assert torch.distributed.is_initialized()
+    except Exception:
+        # use torchpack
+        from torchpack import distributed as dist
+
+        dist.init()
+        os.environ["RANK"] = f"{dist.rank()}"
+        os.environ["WORLD_SIZE"] = f"{dist.size()}"
+        os.environ["LOCAL_RANK"] = f"{dist.local_rank()}"
+
+
+def get_dist_rank() -> int:
+    return int(os.environ["RANK"])
+
+
+def get_dist_size() -> int:
+    return int(os.environ["WORLD_SIZE"])
+
+
+def is_master() -> bool:
+    return get_dist_rank() == 0
+
+
+def dist_barrier() -> None:
+    torch.distributed.barrier()
+
+
+def get_dist_local_rank() -> int:
+    return int(os.environ["LOCAL_RANK"])
+
+
+def sync_tensor(
+    tensor: torch.Tensor or float, reduce="mean"
+) -> torch.Tensor or list[torch.Tensor]:
+    if not isinstance(tensor, torch.Tensor):
+        tensor = torch.Tensor(1).fill_(tensor).cuda()
+    tensor_list = [torch.empty_like(tensor) for _ in range(get_dist_size())]
+    torch.distributed.all_gather(tensor_list, tensor.contiguous(), async_op=False)
+    if reduce == "mean":
+        return list_mean(tensor_list)
+    elif reduce == "sum":
+        return list_sum(tensor_list)
+    elif reduce == "cat":
+        return torch.cat(tensor_list, dim=0)
+    elif reduce == "root":
+        return tensor_list[0]
+    else:
+        return tensor_list

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/ema.py

@@ -0,0 +1,50 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import copy
+import math
+
+import torch
+import torch.nn as nn
+
+from efficientvit.models.utils import is_parallel
+
+__all__ = ["EMA"]
+
+
+def update_ema(
+    ema: nn.Module, new_state_dict: dict[str, torch.Tensor], decay: float
+) -> None:
+    for k, v in ema.state_dict().items():
+        if v.dtype.is_floating_point:
+            v -= (1.0 - decay) * (v - new_state_dict[k].detach())
+
+
+class EMA:
+    def __init__(self, model: nn.Module, decay: float, warmup_steps=2000):
+        self.shadows = copy.deepcopy(
+            model.module if is_parallel(model) else model
+        ).eval()
+        self.decay = decay
+        self.warmup_steps = warmup_steps
+
+        for p in self.shadows.parameters():
+            p.requires_grad = False
+
+    def step(self, model: nn.Module, global_step: int) -> None:
+        with torch.no_grad():
+            msd = (model.module if is_parallel(model) else model).state_dict()
+            update_ema(
+                self.shadows,
+                msd,
+                self.decay * (1 - math.exp(-global_step / self.warmup_steps)),
+            )
+
+    def state_dict(self) -> dict[float, dict[str, torch.Tensor]]:
+        return {self.decay: self.shadows.state_dict()}
+
+    def load_state_dict(self, state_dict: dict[float, dict[str, torch.Tensor]]) -> None:
+        for decay in state_dict:
+            if decay == self.decay:
+                self.shadows.load_state_dict(state_dict[decay])

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/export.py

@@ -0,0 +1,47 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import io
+import os
+
+import onnx
+import torch
+import torch.nn as nn
+from onnxsim import simplify as simplify_func
+
+__all__ = ["export_onnx"]
+
+
+def export_onnx(
+    model: nn.Module, export_path: str, sample_inputs: any, simplify=True, opset=11
+) -> None:
+    """Export a model to a platform-specific onnx format.
+
+    Args:
+        model: a torch.nn.Module object.
+        export_path: export location.
+        sample_inputs: Any.
+        simplify: a flag to turn on onnx-simplifier
+        opset: int
+    """
+    model.eval()
+
+    buffer = io.BytesIO()
+    with torch.no_grad():
+        torch.onnx.export(model, sample_inputs, buffer, opset_version=opset)
+        buffer.seek(0, 0)
+        if simplify:
+            onnx_model = onnx.load_model(buffer)
+            onnx_model, success = simplify_func(onnx_model)
+            assert success
+            new_buffer = io.BytesIO()
+            onnx.save(onnx_model, new_buffer)
+            buffer = new_buffer
+            buffer.seek(0, 0)
+
+    if buffer.getbuffer().nbytes > 0:
+        save_dir = os.path.dirname(export_path)
+        os.makedirs(save_dir, exist_ok=True)
+        with open(export_path, "wb") as f:
+            f.write(buffer.read())

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/init.py

@@ -0,0 +1,68 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import torch
+import torch.nn as nn
+from torch.nn.modules.batchnorm import _BatchNorm
+
+__all__ = ["init_modules", "zero_last_gamma"]
+
+
+def init_modules(model: nn.Module or list[nn.Module], init_type="trunc_normal") -> None:
+    _DEFAULT_INIT_PARAM = {"trunc_normal": 0.02}
+
+    if isinstance(model, list):
+        for sub_module in model:
+            init_modules(sub_module, init_type)
+    else:
+        init_params = init_type.split("@")
+        init_params = float(init_params[1]) if len(init_params) > 1 else None
+
+        if init_type.startswith("trunc_normal"):
+            init_func = lambda param: nn.init.trunc_normal_(
+                param, std=(init_params or _DEFAULT_INIT_PARAM["trunc_normal"])
+            )
+        else:
+            raise NotImplementedError
+
+        for m in model.modules():
+            if isinstance(m, (nn.Conv2d, nn.Linear, nn.ConvTranspose2d)):
+                init_func(m.weight)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.Embedding):
+                init_func(m.weight)
+            elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            else:
+                weight = getattr(m, "weight", None)
+                bias = getattr(m, "bias", None)
+                if isinstance(weight, torch.nn.Parameter):
+                    init_func(weight)
+                if isinstance(bias, torch.nn.Parameter):
+                    bias.data.zero_()
+
+
+def zero_last_gamma(model: nn.Module, init_val=0) -> None:
+    import efficientvit.models.nn.ops as ops
+
+    for m in model.modules():
+        if isinstance(m, ops.ResidualBlock) and isinstance(
+            m.shortcut, ops.IdentityLayer
+        ):
+            if isinstance(m.main, (ops.DSConv, ops.MBConv, ops.FusedMBConv)):
+                parent_module = m.main.point_conv
+            elif isinstance(m.main, ops.ResBlock):
+                parent_module = m.main.conv2
+            elif isinstance(m.main, ops.ConvLayer):
+                parent_module = m.main
+            elif isinstance(m.main, (ops.LiteMLA)):
+                parent_module = m.main.proj
+            else:
+                parent_module = None
+            if parent_module is not None:
+                norm = getattr(parent_module, "norm", None)
+                if norm is not None:
+                    nn.init.constant_(norm.weight, init_val)

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/lr.py

@@ -0,0 +1,48 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import math
+
+import torch
+
+from efficientvit.models.utils.list import val2list
+
+__all__ = ["CosineLRwithWarmup"]
+
+
+class CosineLRwithWarmup(torch.optim.lr_scheduler._LRScheduler):
+    def __init__(
+        self,
+        optimizer: torch.optim.Optimizer,
+        warmup_steps: int,
+        warmup_lr: float,
+        decay_steps: int or list[int],
+        last_epoch: int = -1,
+    ) -> None:
+        self.warmup_steps = warmup_steps
+        self.warmup_lr = warmup_lr
+        self.decay_steps = val2list(decay_steps)
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self) -> list[float]:
+        if self.last_epoch < self.warmup_steps:
+            return [
+                (base_lr - self.warmup_lr) * (self.last_epoch + 1) / self.warmup_steps
+                + self.warmup_lr
+                for base_lr in self.base_lrs
+            ]
+        else:
+            current_steps = self.last_epoch - self.warmup_steps
+            decay_steps = [0] + self.decay_steps
+            idx = len(decay_steps) - 2
+            for i, decay_step in enumerate(decay_steps[:-1]):
+                if decay_step <= current_steps < decay_steps[i + 1]:
+                    idx = i
+                    break
+            current_steps -= decay_steps[idx]
+            decay_step = decay_steps[idx + 1] - decay_steps[idx]
+            return [
+                0.5 * base_lr * (1 + math.cos(math.pi * current_steps / decay_step))
+                for base_lr in self.base_lrs
+            ]

yolo-world-with-efficientvit-sam/efficientvit/apps/utils/metric.py

@@ -0,0 +1,37 @@
+# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
+# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
+# International Conference on Computer Vision (ICCV), 2023
+
+import torch
+
+from efficientvit.apps.utils.dist import sync_tensor
+
+__all__ = ["AverageMeter"]
+
+
+class AverageMeter:
+    """Computes and stores the average and current value."""
+
+    def __init__(self, is_distributed=True):
+        self.is_distributed = is_distributed
+        self.sum = 0
+        self.count = 0
+
+    def _sync(self, val: torch.Tensor or int or float) -> torch.Tensor or int or float:
+        return sync_tensor(val, reduce="sum") if self.is_distributed else val
+
+    def update(self, val: torch.Tensor or int or float, delta_n=1):
+        self.count += self._sync(delta_n)
+        self.sum += self._sync(val * delta_n)
+
+    def get_count(self) -> torch.Tensor or int or float:
+        return (
+            self.count.item()
+            if isinstance(self.count, torch.Tensor) and self.count.numel() == 1
+            else self.count
+        )
+
+    @property
+    def avg(self):
+        avg = -1 if self.count == 0 else self.sum / self.count
+        return avg.item() if isinstance(avg, torch.Tensor) and avg.numel() == 1 else avg