Trainer + Dataloaders (#27)

Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Ayush Chaurasia <ayushchaurasia@Ayushs-MacBook-Pro.local>
Co-authored-by: Ayush Chaurasia <ayush.chuararsia@gmail.com>

ultralytics/yolo/data/augment.py

@@ -0,0 +1,785 @@
+import collections
+import math
+import random
+from copy import deepcopy
+
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as T
+
+from ..utils.general import LOGGER, check_version, colorstr, segment2box
+from ..utils.instance import Instances
+from ..utils.metrics import bbox_ioa
+from .utils import IMAGENET_MEAN, IMAGENET_STD, polygons2masks, polygons2masks_overlap
+
+
+# TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
+class BaseTransform:
+
+    def __init__(self) -> None:
+        pass
+
+    def apply_image(self, labels):
+        pass
+
+    def apply_instances(self, labels):
+        pass
+
+    def apply_semantic(self, labels):
+        pass
+
+    def __call__(self, labels):
+        self.apply_image(labels)
+        self.apply_instances(labels)
+        self.apply_semantic(labels)
+
+
+class Compose:
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, data):
+        for t in self.transforms:
+            data = t(data)
+        return data
+
+    def append(self, transform):
+        self.transforms.append(transform)
+
+    def tolist(self):
+        return self.transforms
+
+    def __repr__(self):
+        format_string = f"{self.__class__.__name__}("
+        for t in self.transforms:
+            format_string += "\n"
+            format_string += f"    {t}"
+        format_string += "\n)"
+        return format_string
+
+
+class BaseMixTransform:
+    """This implementation is from mmyolo"""
+
+    def __init__(self, pre_transform=None, p=0.0) -> None:
+        self.pre_transform = pre_transform
+        self.p = p
+
+    def __call__(self, labels):
+        if random.uniform(0, 1) > self.p:
+            return labels
+
+        assert "dataset" in labels
+        dataset = labels.pop("dataset")
+
+        # get index of one or three other images
+        indexes = self.get_indexes(dataset)
+        if not isinstance(indexes, collections.abc.Sequence):
+            indexes = [indexes]
+
+        # get images information will be used for Mosaic or MixUp
+        mix_labels = [deepcopy(dataset.get_label_info(index)) for index in indexes]
+
+        if self.pre_transform is not None:
+            for i, data in enumerate(mix_labels):
+                # pre_transform may also require dataset
+                data.update({"dataset": dataset})
+                # before Mosaic or MixUp need to go through
+                # the necessary pre_transform
+                _labels = self.pre_transform(data)
+                _labels.pop("dataset")
+                mix_labels[i] = _labels
+        labels["mix_labels"] = mix_labels
+
+        # Mosaic or MixUp
+        labels = self._mix_transform(labels)
+
+        if "mix_labels" in labels:
+            labels.pop("mix_labels")
+        labels["dataset"] = dataset
+
+        return labels
+
+    def _mix_transform(self, labels):
+        raise NotImplementedError
+
+    def get_indexes(self, dataset):
+        raise NotImplementedError
+
+
+class Mosaic(BaseMixTransform):
+    """Mosaic augmentation.
+    Args:
+        img_size (Sequence[int]): Image size after mosaic pipeline of single
+            image. The shape order should be (height, width).
+            Default to (640, 640).
+    """
+
+    def __init__(self, img_size=640, p=1.0, border=(0, 0)):
+        assert 0 <= p <= 1.0, "The probability should be in range [0, 1]. " f"got {p}."
+        super().__init__(pre_transform=None, p=p)
+        self.img_size = img_size
+        self.border = border
+
+    def get_indexes(self, dataset):
+        return [random.randint(0, len(dataset)) for _ in range(3)]
+
+    def _mix_transform(self, labels):
+        mosaic_labels = []
+        assert labels.get("rect_shape", None) is None, "rect and mosaic is exclusive."
+        assert len(labels.get("mix_labels", [])) > 0, "There are no other images for mosaic augment."
+        s = self.img_size
+        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border)  # mosaic center x, y
+        mix_labels = labels["mix_labels"]
+        for i in range(4):
+            labels_patch = deepcopy(labels) if i == 0 else deepcopy(mix_labels[i - 1])
+            # Load image
+            img = labels_patch["img"]
+            h, w = labels_patch["resized_shape"]
+
+            # place img in img4
+            if i == 0:  # top left
+                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
+                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
+                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
+            elif i == 1:  # top right
+                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+            elif i == 2:  # bottom left
+                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+            elif i == 3:  # bottom right
+                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
+            padw = x1a - x1b
+            padh = y1a - y1b
+
+            labels_patch = self._update_labels(labels_patch, padw, padh)
+            mosaic_labels.append(labels_patch)
+        final_labels = self._cat_labels(mosaic_labels)
+        final_labels["img"] = img4
+        return final_labels
+
+    def _update_labels(self, labels, padw, padh):
+        """Update labels"""
+        nh, nw = labels["img"].shape[:2]
+        labels["instances"].convert_bbox(format="xyxy")
+        labels["instances"].denormalize(nw, nh)
+        labels["instances"].add_padding(padw, padh)
+        return labels
+
+    def _cat_labels(self, mosaic_labels):
+        if len(mosaic_labels) == 0:
+            return {}
+        cls = []
+        instances = []
+        for labels in mosaic_labels:
+            cls.append(labels["cls"])
+            instances.append(labels["instances"])
+        final_labels = {
+            "ori_shape": (self.img_size * 2, self.img_size * 2),
+            "resized_shape": (self.img_size * 2, self.img_size * 2),
+            "im_file": mosaic_labels[0]["im_file"],
+            "cls": np.concatenate(cls, 0)}
+
+        final_labels["instances"] = Instances.concatenate(instances, axis=0)
+        final_labels["instances"].clip(self.img_size * 2, self.img_size * 2)
+        return final_labels
+
+
+class MixUp(BaseMixTransform):
+
+    def __init__(self, pre_transform=None, p=0.0) -> None:
+        super().__init__(pre_transform=pre_transform, p=p)
+
+    def get_indexes(self, dataset):
+        return random.randint(0, len(dataset))
+
+    def _mix_transform(self, labels):
+        im = labels["img"]
+        labels2 = labels["mix_labels"][0]
+        im2 = labels2["img"]
+        cls2 = labels2["cls"]
+        # 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)
+        cat_instances = Instances.concatenate([labels["instances"], labels2["instances"]], axis=0)
+        cls = labels["cls"]
+        labels["img"] = im
+        labels["instances"] = cat_instances
+        labels["cls"] = np.concatenate([cls, cls2], 0)
+        return labels
+
+
+class RandomPerspective:
+
+    def __init__(self, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, border=(0, 0)):
+        self.degrees = degrees
+        self.translate = translate
+        self.scale = scale
+        self.shear = shear
+        self.perspective = perspective
+        # mosaic border
+        self.border = border
+
+    def affine_transform(self, img):
+        # Center
+        C = np.eye(3)
+
+        C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
+        C[1, 2] = -img.shape[0] / 2  # y translation (pixels)
+
+        # Perspective
+        P = np.eye(3)
+        P[2, 0] = random.uniform(-self.perspective, self.perspective)  # x perspective (about y)
+        P[2, 1] = random.uniform(-self.perspective, self.perspective)  # y perspective (about x)
+
+        # Rotation and Scale
+        R = np.eye(3)
+        a = random.uniform(-self.degrees, self.degrees)
+        # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
+        s = random.uniform(1 - self.scale, 1 + self.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(-self.shear, self.shear) * math.pi / 180)  # x shear (deg)
+        S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180)  # y shear (deg)
+
+        # Translation
+        T = np.eye(3)
+        T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[0]  # x translation (pixels)
+        T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[1]  # y translation (pixels)
+
+        # Combined rotation matrix
+        M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
+        # affine image
+        if (self.border[0] != 0) or (self.border[1] != 0) or (M != np.eye(3)).any():  # image changed
+            if self.perspective:
+                img = cv2.warpPerspective(img, M, dsize=self.size, borderValue=(114, 114, 114))
+            else:  # affine
+                img = cv2.warpAffine(img, M[:2], dsize=self.size, borderValue=(114, 114, 114))
+        return img, M, s
+
+    def apply_bboxes(self, bboxes, M):
+        """apply affine to bboxes only.
+
+        Args:
+            bboxes(ndarray): list of bboxes, xyxy format, with shape (num_bboxes, 4).
+            M(ndarray): affine matrix.
+        Returns:
+            new_bboxes(ndarray): bboxes after affine, [num_bboxes, 4].
+        """
+        n = len(bboxes)
+        if n == 0:
+            return bboxes
+
+        xy = np.ones((n * 4, 3))
+        xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
+        xy = xy @ M.T  # transform
+        xy = (xy[:, :2] / xy[:, 2:3] if self.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]]
+        return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+
+    def apply_segments(self, segments, M):
+        """apply affine to segments and generate new bboxes from segments.
+
+        Args:
+            segments(ndarray): list of segments, [num_samples, 500, 2].
+            M(ndarray): affine matrix.
+        Returns:
+            new_segments(ndarray): list of segments after affine, [num_samples, 500, 2].
+            new_bboxes(ndarray): bboxes after affine, [N, 4].
+        """
+        n, num = segments.shape[:2]
+        if n == 0:
+            return [], segments
+
+        xy = np.ones((n * num, 3))
+        segments = segments.reshape(-1, 2)
+        xy[:, :2] = segments
+        xy = xy @ M.T  # transform
+        xy = xy[:, :2] / xy[:, 2:3]
+        segments = xy.reshape(n, -1, 2)
+        bboxes = np.stack([segment2box(xy, self.size[0], self.size[1]) for xy in segments], 0)
+        return bboxes, segments
+
+    def apply_keypoints(self, keypoints, M):
+        """apply affine to keypoints.
+
+        Args:
+            keypoints(ndarray): keypoints, [N, 17, 2].
+            M(ndarray): affine matrix.
+        Return:
+            new_keypoints(ndarray): keypoints after affine, [N, 17, 2].
+        """
+        n = len(keypoints)
+        if n == 0:
+            return keypoints
+        new_keypoints = np.ones((n * 17, 3))
+        new_keypoints[:, :2] = keypoints.reshape(n * 17, 2)  # num_kpt is hardcoded to 17
+        new_keypoints = new_keypoints @ M.T  # transform
+        new_keypoints = (new_keypoints[:, :2] / new_keypoints[:, 2:3]).reshape(n, 34)  # perspective rescale or affine
+        new_keypoints[keypoints.reshape(-1, 34) == 0] = 0
+        x_kpts = new_keypoints[:, list(range(0, 34, 2))]
+        y_kpts = new_keypoints[:, list(range(1, 34, 2))]
+
+        x_kpts[np.logical_or.reduce((x_kpts < 0, x_kpts > self.size[0], y_kpts < 0, y_kpts > self.size[1]))] = 0
+        y_kpts[np.logical_or.reduce((x_kpts < 0, x_kpts > self.size[0], y_kpts < 0, y_kpts > self.size[1]))] = 0
+        new_keypoints[:, list(range(0, 34, 2))] = x_kpts
+        new_keypoints[:, list(range(1, 34, 2))] = y_kpts
+        return new_keypoints.reshape(n, 17, 2)
+
+    def __call__(self, labels):
+        """
+        Affine images and targets.
+
+        Args:
+            img(ndarray): image.
+            labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
+        """
+        img = labels["img"]
+        cls = labels["cls"]
+        instances = labels["instances"]
+        # make sure the coord formats are right
+        instances.convert_bbox(format="xyxy")
+        instances.denormalize(*img.shape[:2][::-1])
+
+        self.size = img.shape[1] + self.border[1] * 2, img.shape[0] + self.border[0] * 2  # w, h
+        # M is affine matrix
+        # scale for func:`box_candidates`
+        img, M, scale = self.affine_transform(img)
+
+        bboxes = self.apply_bboxes(instances.bboxes, M)
+
+        segments = instances.segments
+        keypoints = instances.keypoints
+        # update bboxes if there are segments.
+        if segments is not None:
+            bboxes, segments = self.apply_segments(segments, M)
+
+        if keypoints is not None:
+            keypoints = self.apply_keypoints(keypoints, M)
+        new_instances = Instances(bboxes, segments, keypoints, bbox_format="xyxy", normalized=False)
+        new_instances.clip(*self.size)
+
+        # filter instances
+        instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
+        # make the bboxes have the same scale with new_bboxes
+        i = self.box_candidates(box1=instances.bboxes.T,
+                                box2=new_instances.bboxes.T,
+                                area_thr=0.01 if segments is not None else 0.10)
+        labels["instances"] = new_instances[i]
+        # clip
+        labels["cls"] = cls[i]
+        labels["img"] = img
+        return labels
+
+    def box_candidates(self, 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
+
+
+class RandomHSV:
+
+    def __init__(self, hgain=0.5, sgain=0.5, vgain=0.5) -> None:
+        self.hgain = hgain
+        self.sgain = sgain
+        self.vgain = vgain
+
+    def __call__(self, labels):
+        img = labels["img"]
+        if self.hgain or self.sgain or self.vgain:
+            r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain] + 1  # random gains
+            hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
+            dtype = img.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=img)  # no return needed
+        labels["img"] = img
+        return labels
+
+
+class RandomFlip:
+
+    def __init__(self, p=0.5, direction="horizontal") -> None:
+        assert direction in ["horizontal", "vertical"], f"Support direction `horizontal` or `vertical`, got {direction}"
+        assert 0 <= p <= 1.0
+
+        self.p = p
+        self.direction = direction
+
+    def __call__(self, labels):
+        img = labels["img"]
+        instances = labels["instances"]
+        instances.convert_bbox(format="xywh")
+        h, w = img.shape[:2]
+        h = 1 if instances.normalized else h
+        w = 1 if instances.normalized else w
+
+        # Flip up-down
+        if self.direction == "vertical" and random.random() < self.p:
+            img = np.flipud(img)
+            img = np.ascontiguousarray(img)
+            instances.flipud(h)
+        if self.direction == "horizontal" and random.random() < self.p:
+            img = np.fliplr(img)
+            img = np.ascontiguousarray(img)
+            instances.fliplr(w)
+        labels["img"] = img
+        labels["instances"] = instances
+        return labels
+
+
+class LetterBox:
+    """Resize image and padding for detection, instance segmentation, pose"""
+
+    def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, stride=32):
+        self.new_shape = new_shape
+        self.auto = auto
+        self.scaleFill = scaleFill
+        self.scaleup = scaleup
+        self.stride = stride
+
+    def __call__(self, labels):
+        img = labels["img"]
+        shape = img.shape[:2]  # current shape [height, width]
+        new_shape = labels.get("rect_shape", self.new_shape)
+        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 self.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 self.auto:  # minimum rectangle
+            dw, dh = np.mod(dw, self.stride), np.mod(dh, self.stride)  # wh padding
+        elif self.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
+            img = cv2.resize(img, 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))
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT,
+                                 value=(114, 114, 114))  # add border
+
+        labels = self._update_labels(labels, ratio, dw, dh)
+        labels["img"] = img
+        return labels
+
+    def _update_labels(self, labels, ratio, padw, padh):
+        """Update labels"""
+        labels["instances"].convert_bbox(format="xyxy")
+        labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
+        labels["instances"].scale(*ratio)
+        labels["instances"].add_padding(padw, padh)
+        return labels
+
+
+class CopyPaste:
+
+    def __init__(self, p=0.5) -> None:
+        self.p = p
+
+    def __call__(self, labels):
+        # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
+        im = labels["img"]
+        cls = labels["cls"]
+        bboxes = labels["instances"].bboxes
+        segments = labels["instances"].segments  # n, 1000, 2
+        keypoints = labels["instances"].keypoints
+        if self.p and segments is not None:
+            n = len(segments)
+            h, w, _ = im.shape  # height, width, channels
+            im_new = np.zeros(im.shape, np.uint8)
+            # TODO: this implement can be parallel since segments are ndarray, also might work with Instances inside
+            for j in random.sample(range(n), k=round(self.p * n)):
+                c, b, s = cls[j], bboxes[j], segments[j]
+                box = w - b[2], b[1], w - b[0], b[3]
+                ioa = bbox_ioa(box, bboxes)  # intersection over area
+                if (ioa < 0.30).all():  # allow 30% obscuration of existing labels
+                    bboxes = np.concatenate((bboxes, [box]), 0)
+                    cls = np.concatenate((cls, c[None]), axis=0)
+                    segments = np.concatenate((segments, np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)[None]), 0)
+                    if keypoints is not None:
+                        keypoints = np.concatenate(
+                            (keypoints, np.concatenate((w - keypoints[j][:, 0:1], keypoints[j][:, 1:2]), 1)), 0)
+                    cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
+
+            result = cv2.bitwise_and(src1=im, src2=im_new)
+            result = cv2.flip(result, 1)  # augment segments (flip left-right)
+            i = result > 0  # pixels to replace
+            # i[:, :] = result.max(2).reshape(h, w, 1)  # act over ch
+            im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug
+        labels["img"] = im
+        labels["cls"] = cls
+        labels["instances"].update(bboxes, segments, keypoints)
+        return labels
+
+
+class Albumentations:
+    # YOLOv5 Albumentations class (optional, only used if package is installed)
+    def __init__(self, p=1.0):
+        self.p = p
+        self.transform = None
+        prefix = colorstr("albumentations: ")
+        try:
+            import albumentations as A
+
+            check_version(A.__version__, "1.0.3", hard=True)  # version requirement
+
+            T = [
+                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, labels):
+        im = labels["img"]
+        cls = labels["cls"]
+        if len(cls):
+            labels["instances"].convert_bbox("xywh")
+            labels["instances"].normalize(*im.shape[:2][::-1])
+            bboxes = labels["instances"].bboxes
+            # TODO: add supports of segments and keypoints
+            if self.transform and random.random() < self.p:
+                new = self.transform(image=im, bboxes=bboxes, class_labels=cls)  # transformed
+            labels["img"] = new["image"]
+            labels["cls"] = np.array(new["class_labels"])
+            labels["instances"].update(bboxes=bboxes)
+        return labels
+
+
+# TODO: technically this is not an augmentation, maybe we should put this to another files
+class Format:
+
+    def __init__(self, bbox_format="xywh", normalize=True, mask=False, mask_ratio=4, mask_overlap=True, batch_idx=True):
+        self.bbox_format = bbox_format
+        self.normalize = normalize
+        self.mask = mask  # set False when training detection only
+        self.mask_ratio = mask_ratio
+        self.mask_overlap = mask_overlap
+        self.batch_idx = batch_idx  # keep the batch indexes
+
+    def __call__(self, labels):
+        img = labels["img"]
+        h, w = img.shape[:2]
+        cls = labels.pop("cls")
+        instances = labels.pop("instances")
+        instances.convert_bbox(format=self.bbox_format)
+        instances.denormalize(w, h)
+        nl = len(instances)
+
+        if instances.segments is not None and self.mask:
+            masks, instances, cls = self._format_segments(instances, cls, w, h)
+            labels["masks"] = (torch.from_numpy(masks) if nl else torch.zeros(
+                1 if self.mask_overlap else nl, img.shape[0] // self.mask_ratio, img.shape[1] // self.mask_ratio))
+        if self.normalize:
+            instances.normalize(w, h)
+        labels["img"] = self._format_img(img)
+        labels["cls"] = torch.from_numpy(cls) if nl else torch.zeros(nl)
+        labels["bboxes"] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
+        if instances.keypoints is not None:
+            labels["keypoints"] = torch.from_numpy(instances.keypoints) if nl else torch.zeros((nl, 17, 2))
+        # then we can use collate_fn
+        if self.batch_idx:
+            labels["batch_idx"] = torch.zeros(nl)
+        return labels
+
+    def _format_img(self, img):
+        if len(img.shape) < 3:
+            img = np.expand_dims(img, -1)
+        img = np.ascontiguousarray(img.transpose(2, 0, 1))
+        img = torch.from_numpy(img)
+        return img
+
+    def _format_segments(self, instances, cls, w, h):
+        """convert polygon points to bitmap"""
+        segments = instances.segments
+        if self.mask_overlap:
+            masks, sorted_idx = polygons2masks_overlap((h, w), segments, downsample_ratio=self.mask_ratio)
+            masks = masks[None]  # (640, 640) -> (1, 640, 640)
+            instances = instances[sorted_idx]
+            cls = cls[sorted_idx]
+        else:
+            masks = polygons2masks((h, w), segments, color=1, downsample_ratio=self.mask_ratio)
+
+        return masks, instances, cls
+
+
+def mosaic_transforms(img_size, hyp):
+    pre_transform = Compose([
+        Mosaic(img_size=img_size, p=hyp.mosaic, border=[-img_size // 2, -img_size // 2]),
+        CopyPaste(p=hyp.copy_paste),
+        RandomPerspective(
+            degrees=hyp.degrees,
+            translate=hyp.translate,
+            scale=hyp.scale,
+            shear=hyp.shear,
+            perspective=hyp.perspective,
+            border=[-img_size // 2, -img_size // 2],
+        ),])
+    transforms = Compose([
+        pre_transform,
+        MixUp(
+            pre_transform=pre_transform,
+            p=hyp.mixup,
+        ),
+        Albumentations(p=1.0),
+        RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
+        RandomFlip(direction="vertical", p=hyp.flipud),
+        RandomFlip(direction="horizontal", p=hyp.fliplr),])
+    return transforms
+
+
+def affine_transforms(img_size, hyp):
+    # rect, randomperspective, albumentation, hsv, flipud, fliplr
+    transforms = Compose([
+        LetterBox(new_shape=(img_size, img_size)),
+        RandomPerspective(
+            degrees=hyp.degrees,
+            translate=hyp.translate,
+            scale=hyp.scale,
+            shear=hyp.shear,
+            perspective=hyp.perspective,
+            border=[0, 0],
+        ),
+        Albumentations(p=1.0),
+        RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
+        RandomFlip(direction="vertical", p=hyp.flipud),
+        RandomFlip(direction="horizontal", p=hyp.fliplr),])
+    return transforms
+
+
+# Classification augmentations -------------------------------------------------------------------------------------------
+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)])
+
+
+def classify_albumentations(
+        augment=True,
+        size=224,
+        scale=(0.08, 1.0),
+        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)]
+            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
+        pass
+    except Exception as e:
+        LOGGER.info(f"{prefix}{e}")
+
+
+class ClassifyLetterBox:
+    # 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