Import YOLOv5 dataloader (#94)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/ci.yaml

@@ -20,21 +20,21 @@ jobs:
       matrix:
         os: [ ubuntu-latest ]
         python-version: [ '3.10' ]
-        model: [ yolov5n ]
+        model: [ yolov8n ]
         torch: [ latest ]
 #        include:
 #          - os: ubuntu-latest
 #            python-version: '3.7'  # '3.6.8' min
-#            model: yolov5n
+#            model: yolov8n
 #          - os: ubuntu-latest
 #            python-version: '3.8'
-#            model: yolov5n
+#            model: yolov8n
 #          - os: ubuntu-latest
 #            python-version: '3.9'
-#            model: yolov5n
+#            model: yolov8n
 #          - os: ubuntu-latest
 #            python-version: '3.8'  # torch 1.7.0 requires python >=3.6, <=3.8
-#            model: yolov5n
+#            model: yolov8n
 #            torch: '1.7.0'  # min torch version CI https://pypi.org/project/torchvision/
     steps:
       - uses: actions/checkout@v3

ultralytics/yolo/data/dataloaders/v5augmentations.py

@@ -0,0 +1,402 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Image augmentation functions
+"""
+
+import math
+import random
+
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from ultralytics.yolo.utils import LOGGER, colorstr
+from ultralytics.yolo.utils.checks import check_version
+from ultralytics.yolo.utils.metrics import bbox_ioa
+from ultralytics.yolo.utils.ops import resample_segments, segment2box, xywhn2xyxy
+
+IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
+IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation
+
+
+class Albumentations:
+    # YOLOv5 Albumentations class (optional, only used if package is installed)
+    def __init__(self, size=640):
+        self.transform = None
+        prefix = colorstr('albumentations: ')
+        try:
+            import albumentations as A
+            check_version(A.__version__, '1.0.3', hard=True)  # version requirement
+
+            T = [
+                A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
+                A.Blur(p=0.01),
+                A.MedianBlur(p=0.01),
+                A.ToGray(p=0.01),
+                A.CLAHE(p=0.01),
+                A.RandomBrightnessContrast(p=0.0),
+                A.RandomGamma(p=0.0),
+                A.ImageCompression(quality_lower=75, p=0.0)]  # transforms
+            self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
+
+            LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
+        except ImportError:  # package not installed, skip
+            pass
+        except Exception as e:
+            LOGGER.info(f'{prefix}{e}')
+
+    def __call__(self, im, labels, p=1.0):
+        if self.transform and random.random() < p:
+            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
+            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
+        return im, labels
+
+
+def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
+    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
+    return TF.normalize(x, mean, std, inplace=inplace)
+
+
+def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
+    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
+    for i in range(3):
+        x[:, i] = x[:, i] * std[i] + mean[i]
+    return x
+
+
+def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
+    # HSV color-space augmentation
+    if hgain or sgain or vgain:
+        r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
+        hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
+        dtype = im.dtype  # uint8
+
+        x = np.arange(0, 256, dtype=r.dtype)
+        lut_hue = ((x * r[0]) % 180).astype(dtype)
+        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+
+        im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
+        cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed
+
+
+def hist_equalize(im, clahe=True, bgr=False):
+    # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
+    yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
+    if clahe:
+        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
+    else:
+        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
+    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
+
+
+def replicate(im, labels):
+    # Replicate labels
+    h, w = im.shape[:2]
+    boxes = labels[:, 1:].astype(int)
+    x1, y1, x2, y2 = boxes.T
+    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
+    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
+        x1b, y1b, x2b, y2b = boxes[i]
+        bh, bw = y2b - y1b, x2b - x1b
+        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y
+        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
+        im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b]  # im4[ymin:ymax, xmin:xmax]
+        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
+
+    return im, labels
+
+
+def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better val mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+    elif scaleFill:  # stretch
+        dw, dh = 0.0, 0.0
+        new_unpad = (new_shape[1], new_shape[0])
+        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, ratio, (dw, dh)
+
+
+def random_perspective(im,
+                       targets=(),
+                       segments=(),
+                       degrees=10,
+                       translate=.1,
+                       scale=.1,
+                       shear=10,
+                       perspective=0.0,
+                       border=(0, 0)):
+    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
+    # targets = [cls, xyxy]
+
+    height = im.shape[0] + border[0] * 2  # shape(h,w,c)
+    width = im.shape[1] + border[1] * 2
+
+    # Center
+    C = np.eye(3)
+    C[0, 2] = -im.shape[1] / 2  # x translation (pixels)
+    C[1, 2] = -im.shape[0] / 2  # y translation (pixels)
+
+    # Perspective
+    P = np.eye(3)
+    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
+    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
+
+    # Rotation and Scale
+    R = np.eye(3)
+    a = random.uniform(-degrees, degrees)
+    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
+    s = random.uniform(1 - scale, 1 + scale)
+    # s = 2 ** random.uniform(-scale, scale)
+    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+    # Shear
+    S = np.eye(3)
+    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
+    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
+
+    # Translation
+    T = np.eye(3)
+    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
+    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
+
+    # Combined rotation matrix
+    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
+    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
+        if perspective:
+            im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
+        else:  # affine
+            im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+    # Visualize
+    # import matplotlib.pyplot as plt
+    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+    # ax[0].imshow(im[:, :, ::-1])  # base
+    # ax[1].imshow(im2[:, :, ::-1])  # warped
+
+    # Transform label coordinates
+    n = len(targets)
+    if n:
+        use_segments = any(x.any() for x in segments)
+        new = np.zeros((n, 4))
+        if use_segments:  # warp segments
+            segments = resample_segments(segments)  # upsample
+            for i, segment in enumerate(segments):
+                xy = np.ones((len(segment), 3))
+                xy[:, :2] = segment
+                xy = xy @ M.T  # transform
+                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
+
+                # clip
+                new[i] = segment2box(xy, width, height)
+
+        else:  # warp boxes
+            xy = np.ones((n * 4, 3))
+            xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
+            xy = xy @ M.T  # transform
+            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
+
+            # create new boxes
+            x = xy[:, [0, 2, 4, 6]]
+            y = xy[:, [1, 3, 5, 7]]
+            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+
+            # clip
+            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
+            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
+
+        # filter candidates
+        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
+        targets = targets[i]
+        targets[:, 1:5] = new[i]
+
+    return im, targets
+
+
+def copy_paste(im, labels, segments, p=0.5):
+    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
+    n = len(segments)
+    if p and n:
+        h, w, c = im.shape  # height, width, channels
+        im_new = np.zeros(im.shape, np.uint8)
+
+        # calculate ioa first then select indexes randomly
+        boxes = np.stack([w - labels[:, 3], labels[:, 2], w - labels[:, 1], labels[:, 4]], axis=-1)  # (n, 4)
+        ioa = bbox_ioa(boxes, labels[:, 1:5])  # intersection over area
+        indexes = np.nonzero((ioa < 0.30).all(1))[0]  # (N, )
+        n = len(indexes)
+        for j in random.sample(list(indexes), k=round(p * n)):
+            l, box, s = labels[j], boxes[j], segments[j]
+            labels = np.concatenate((labels, [[l[0], *box]]), 0)
+            segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
+            cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
+
+        result = cv2.flip(im, 1)  # augment segments (flip left-right)
+        i = cv2.flip(im_new, 1).astype(bool)
+        im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug
+
+    return im, labels, segments
+
+
+def cutout(im, labels, p=0.5):
+    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
+    if random.random() < p:
+        h, w = im.shape[:2]
+        scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
+        for s in scales:
+            mask_h = random.randint(1, int(h * s))  # create random masks
+            mask_w = random.randint(1, int(w * s))
+
+            # box
+            xmin = max(0, random.randint(0, w) - mask_w // 2)
+            ymin = max(0, random.randint(0, h) - mask_h // 2)
+            xmax = min(w, xmin + mask_w)
+            ymax = min(h, ymin + mask_h)
+
+            # apply random color mask
+            im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
+
+            # return unobscured labels
+            if len(labels) and s > 0.03:
+                box = np.array([[xmin, ymin, xmax, ymax]], dtype=np.float32)
+                ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h))[0]  # intersection over area
+                labels = labels[ioa < 0.60]  # remove >60% obscured labels
+
+    return labels
+
+
+def mixup(im, labels, im2, labels2):
+    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
+    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
+    im = (im * r + im2 * (1 - r)).astype(np.uint8)
+    labels = np.concatenate((labels, labels2), 0)
+    return im, labels
+
+
+def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
+    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
+    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
+    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
+    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
+    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
+
+
+def classify_albumentations(
+        augment=True,
+        size=224,
+        scale=(0.08, 1.0),
+        ratio=(0.75, 1.0 / 0.75),  # 0.75, 1.33
+        hflip=0.5,
+        vflip=0.0,
+        jitter=0.4,
+        mean=IMAGENET_MEAN,
+        std=IMAGENET_STD,
+        auto_aug=False):
+    # YOLOv5 classification Albumentations (optional, only used if package is installed)
+    prefix = colorstr('albumentations: ')
+    try:
+        import albumentations as A
+        from albumentations.pytorch import ToTensorV2
+        check_version(A.__version__, '1.0.3', hard=True)  # version requirement
+        if augment:  # Resize and crop
+            T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
+            if auto_aug:
+                # TODO: implement AugMix, AutoAug & RandAug in albumentation
+                LOGGER.info(f'{prefix}auto augmentations are currently not supported')
+            else:
+                if hflip > 0:
+                    T += [A.HorizontalFlip(p=hflip)]
+                if vflip > 0:
+                    T += [A.VerticalFlip(p=vflip)]
+                if jitter > 0:
+                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    T += [A.ColorJitter(*color_jitter, 0)]
+        else:  # Use fixed crop for eval set (reproducibility)
+            T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
+        T += [A.Normalize(mean=mean, std=std), ToTensorV2()]  # Normalize and convert to Tensor
+        LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
+        return A.Compose(T)
+
+    except ImportError:  # package not installed, skip
+        LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
+    except Exception as e:
+        LOGGER.info(f'{prefix}{e}')
+
+
+def classify_transforms(size=224):
+    # Transforms to apply if albumentations not installed
+    assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
+    # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
+    return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
+
+
+class LetterBox:
+    # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    def __init__(self, size=(640, 640), auto=False, stride=32):
+        super().__init__()
+        self.h, self.w = (size, size) if isinstance(size, int) else size
+        self.auto = auto  # pass max size integer, automatically solve for short side using stride
+        self.stride = stride  # used with auto
+
+    def __call__(self, im):  # im = np.array HWC
+        imh, imw = im.shape[:2]
+        r = min(self.h / imh, self.w / imw)  # ratio of new/old
+        h, w = round(imh * r), round(imw * r)  # resized image
+        hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
+        top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
+        im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
+        im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
+        return im_out
+
+
+class CenterCrop:
+    # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
+    def __init__(self, size=640):
+        super().__init__()
+        self.h, self.w = (size, size) if isinstance(size, int) else size
+
+    def __call__(self, im):  # im = np.array HWC
+        imh, imw = im.shape[:2]
+        m = min(imh, imw)  # min dimension
+        top, left = (imh - m) // 2, (imw - m) // 2
+        return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
+
+
+class ToTensor:
+    # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    def __init__(self, half=False):
+        super().__init__()
+        self.half = half
+
+    def __call__(self, im):  # im = np.array HWC in BGR order
+        im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1])  # HWC to CHW -> BGR to RGB -> contiguous
+        im = torch.from_numpy(im)  # to torch
+        im = im.half() if self.half else im.float()  # uint8 to fp16/32
+        im /= 255.0  # 0-255 to 0.0-1.0
+        return im

ultralytics/yolo/engine/model.py

@@ -1,7 +1,7 @@
 import torch
 import yaml
 
-# from ultralytics import yolo
+from ultralytics import yolo  # (required for python usage)
 # from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
 from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
 from ultralytics.yolo.utils import LOGGER

ultralytics/yolo/engine/trainer.py

@@ -110,8 +110,9 @@ class BaseTrainer:
         world_size = torch.cuda.device_count()
         if world_size > 1 and "LOCAL_RANK" not in os.environ:
             command = generate_ddp_command(world_size, self)
+            print('DDP command: ', command)
             subprocess.Popen(command)
-            ddp_cleanup(command, self)
+            # ddp_cleanup(command, self)  # TODO: uncomment and fix
         else:
             self._do_train(int(os.getenv("RANK", -1)), world_size)
 
@@ -121,7 +122,7 @@ class BaseTrainer:
         torch.cuda.set_device(rank)
         self.device = torch.device('cuda', rank)
         self.console.info(f"RANK - WORLD_SIZE - DEVICE: {rank} - {world_size} - {self.device} ")
-        mp.use_start_method('spawn', force=True)
+        mp.set_start_method('spawn', force=True)
         dist.init_process_group("nccl" if dist.is_nccl_available() else "gloo", rank=rank, world_size=world_size)
 
     def _setup_train(self, rank, world_size):
@@ -195,6 +196,11 @@ class BaseTrainer:
             for i, batch in pbar:
                 self.trigger_callbacks("on_train_batch_start")
 
+                # update dataloader attributes (optional)
+                if epoch == (self.epochs - self.args.close_mosaic) and hasattr(self.train_loader.dataset, 'mosaic'):
+                    LOGGER.info("Closing dataloader mosaic")
+                    self.train_loader.dataset.mosaic = False
+
                 # warmup
                 ni = i + nb * epoch
                 if ni <= nw:
@@ -207,7 +213,7 @@ class BaseTrainer:
                         if 'momentum' in x:
                             x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
 
-                # Forward
+                # forward
                 with torch.cuda.amp.autocast(self.amp):
                     batch = self.preprocess_batch(batch)
                     preds = self.model(batch["img"])
@@ -217,10 +223,10 @@ class BaseTrainer:
                     self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
                         else self.loss_items
 
-                # Backward
+                # backward
                 self.scaler.scale(self.loss).backward()
 
-                # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
+                # optimize - https://pytorch.org/docs/master/notes/amp_examples.html
                 if ni - last_opt_step >= self.accumulate:
                     self.optimizer_step()
                     last_opt_step = ni

ultralytics/yolo/utils/__init__.py

@@ -6,13 +6,15 @@ import sys
 import threading
 from pathlib import Path
 
+import cv2
 import IPython
+import pandas as pd
 
 # Constants
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[2]  # YOLO
 RANK = int(os.getenv('RANK', -1))
-DATASETS_DIR = ROOT.parent / 'datasets'  # YOLOv5 datasets directory
+DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets'))  # global datasets directory
 NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
 AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
 FONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf
@@ -20,6 +22,14 @@ VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true'  # global ver
 TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}'  # tqdm bar format
 LOGGING_NAME = 'yolov5'
 
+# Settings
+# torch.set_printoptions(linewidth=320, precision=5, profile='long')
+# np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
+pd.options.display.max_columns = 10
+cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
+os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
+os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS)  # OpenMP (PyTorch and SciPy)
+
 
 def is_colab():
     # Is environment a Google Colab instance?

ultralytics/yolo/utils/configs/default.yaml

@@ -2,13 +2,13 @@
 # Default training settings and hyperparameters for medium-augmentation COCO training
 
 # Task and Mode
-task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
-mode: "train" # choice=['train', 'val', 'infer']
+task: "classify"  # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
+mode: "train"  # choice=['train', 'val', 'infer']
 
 # Train settings -------------------------------------------------------------------------------------------------------
 model: null  # i.e. yolov5s.pt, yolo.yaml
 data: null  # i.e. coco128.yaml
-epochs: 300
+epochs: 100
 batch_size: 16
 imgsz: 640
 nosave: False
@@ -42,10 +42,10 @@ noval: False
 save_json: False
 save_hybrid: False
 conf_thres: 0.001
-iou_thres: 0.6
+iou_thres: 0.7
 max_det: 300
 half: True
-dnn: False # use OpenCV DNN for ONNX inference
+dnn: False  # use OpenCV DNN for ONNX inference
 plots: True
 
 # Prediction settings:
@@ -56,9 +56,9 @@ save_conf: False
 save_crop: False
 hide_labels: False  # hide labels
 hide_conf: False
-vid_stride: 1 # video frame-rate stride
+vid_stride: 1  # video frame-rate stride
 line_thickness: 3  # bounding box thickness (pixels)
-update: False # Update all models
+update: False  # Update all models
 visualize: False
 augment: False
 agnostic_nms: False  # class-agnostic NMS
@@ -77,7 +77,7 @@ cls: 0.5  # cls loss gain (scale with pixels)
 dfl: 1.5  # dfl loss gain
 fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
 label_smoothing: 0.0
-nbs: 64 # nominal batch size
+nbs: 64  # nominal batch size
 hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
 hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
 hsv_v: 0.4  # image HSV-Value augmentation (fraction)
@@ -92,6 +92,9 @@ mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
 copy_paste: 0.0  # segment copy-paste (probability)
 
+# For debugging. Don't change
+v5loader: True
+
 # Hydra configs --------------------------------------------------------------------------------------------------------
 hydra:
   output_subdir: null  # disable hydra directory creation

ultralytics/yolo/utils/dist.py

@@ -47,7 +47,7 @@ def generate_ddp_command(world_size, trainer):
     if using_cli:
         file_name = generate_ddp_file(trainer)
     return [
-        sys.executable, "-m", "torch.distributed.launch", "--nproc_per_node", f"{world_size}", "--master_port",
+        sys.executable, "-m", "torch.distributed.run", "--nproc_per_node", f"{world_size}", "--master_port",
         f"{find_free_network_port()}", file_name] + sys.argv[1:]
 
 
@@ -55,7 +55,7 @@ def ddp_cleanup(command, trainer):
     # delete temp file if  created
     # TODO: this is a temp solution in case the file is deleted before DDP launching
     time.sleep(5)
-    tempfile_suffix = str(id(trainer)) + ".py"
+    tempfile_suffix = f"{id(trainer)}.py"
     if tempfile_suffix in "".join(command):
         for chunk in command:
             if tempfile_suffix in chunk:

ultralytics/yolo/v8/detect/train.py

@@ -4,7 +4,9 @@ import torch.nn as nn
 
 from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_dataloader
+from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
 from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
+from ultralytics.yolo.utils import colorstr
 from ultralytics.yolo.utils.loss import BboxLoss
 from ultralytics.yolo.utils.metrics import smooth_BCE
 from ultralytics.yolo.utils.modeling.tasks import DetectionModel
@@ -21,7 +23,22 @@ class DetectionTrainer(BaseTrainer):
         # TODO: manage splits differently
         # calculate stride - check if model is initialized
         gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
-        return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
+        return create_dataloader(path=dataset_path,
+                                 imgsz=self.args.imgsz,
+                                 batch_size=batch_size,
+                                 stride=gs,
+                                 hyp=dict(self.args),
+                                 augment=mode == "train",
+                                 cache=self.args.cache,
+                                 pad=0 if mode == "train" else 0.5,
+                                 rect=self.args.rect,
+                                 rank=rank,
+                                 workers=self.args.workers,
+                                 close_mosaic=self.args.close_mosaic != 0,
+                                 prefix=colorstr(f'{mode}: '),
+                                 shuffle=mode == "train",
+                                 seed=self.args.seed)[0] if self.args.v5loader else \
+            build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
 
     def preprocess_batch(self, batch):
         batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255

ultralytics/yolo/v8/models/yolov8m.yaml

@@ -0,0 +1,42 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.67  # model depth multiple
+width_multiple: 0.75  # layer channel multiple
+
+# YOLOv8.0m backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C2f, [128, True]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C2f, [256, True]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 6, C2f, [512, True]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C2f, [768, True]],
+   [-1, 1, SPPF, [768, 5]],  # 9
+  ]
+
+# YOLOv8.0m head
+head:
+  [[-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C2f, [512]],  # 13
+
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C2f, [256]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C2f, [512]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C2f, [768]],  # 23 (P5/32-large)
+
+   [[15, 18, 21], 1, Detect, [nc]],  # Detect(P3, P4, P5)
+  ]

ultralytics/yolo/v8/models/yolov8s.yaml

@@ -0,0 +1,42 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.50  # layer channel multiple
+
+# YOLOv8.0s backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C2f, [128, True]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C2f, [256, True]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 6, C2f, [512, True]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 3, C2f, [1024, True]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
+  ]
+
+# YOLOv8.0s head
+head:
+  [[-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C2f, [512]],  # 13
+
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C2f, [256]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C2f, [512]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C2f, [1024]],  # 23 (P5/32-large)
+
+   [[15, 18, 21], 1, Detect, [nc]],  # Detect(P3, P4, P5)
+  ]