transforms.py

import numpy as np
import oneflow as flow
import flowvision

from oneflow import nn, Tensor
from flowvision.transforms import functional as F
from flowvision.transforms import transforms as T
from typing import List, Tuple, Dict, Optional


def _flip_coco_person_keypoints(kps, width):
    flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
    flipped_data = kps[:, flip_inds]
    flipped_data[..., 0] = width - flipped_data[..., 0]
    # Maintain COCO convention that if visibility == 0, then x, y = 0
    inds = flipped_data[..., 2] == 0
    flipped_data[inds] = 0
    return flipped_data


class Compose:
    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, image, target):
        for t in self.transforms:
            image, target = t(image, target)
        return image, target


class RandomHorizontalFlip(T.RandomHorizontalFlip):
    def forward(
        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        if flow.rand(1) < self.p:
            image = F.hflip(image)
            if target is not None:
                width, _ = F._get_image_size(image)
                # TODO (shijie wang): support setitem combining index.
                # target["boxes"][:, [0, 2]] = width - target["boxes"][:, [2, 0]]
                temp = target["boxes"].clone()
                target["boxes"][:, 0] = width - temp[:, 2]
                target["boxes"][:, 2] = width - temp[:, 0]
                if "masks" in target:
                    target["masks"] = target["masks"].flip(-1)
                if "keypoints" in target:
                    keypoints = target["keypoints"]
                    keypoints = _flip_coco_person_keypoints(keypoints, width)
                    target["keypoints"] = keypoints
        return image, target


class ToTensor(nn.Module):
    def forward(
        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        image = F.pil_to_tensor(image)
        image = F.convert_image_dtype(image)
        return image, target


class RandomIoUCrop(nn.Module):
    def __init__(
        self,
        min_scale: float = 0.3,
        max_scale: float = 1.0,
        min_aspect_ratio: float = 0.5,
        max_aspect_ratio: float = 2.0,
        sampler_options: Optional[List[float]] = None,
        trials: int = 40,
    ):
        super().__init__()
        self.min_scale = min_scale
        self.max_scale = max_scale
        self.min_aspect_ratio = min_aspect_ratio
        self.max_aspect_ratio = max_aspect_ratio
        if sampler_options is None:
            sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
        self.options = sampler_options
        self.trials = trials

    def forward(
        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        if target is None:
            raise ValueError("The targets can't be None for this transform.")

        if isinstance(image, flow.Tensor):
            if image.ndimension() not in {2, 3}:
                raise ValueError(
                    "image should be 2/3 dimensional. Got {} dimensions.".format(
                        image.ndimension()
                    )
                )
            elif image.ndimension() == 2:
                image = image.unsqueeze(0)

        orig_w, orig_h = F._get_image_size(image)

        boxes_np = target["boxes"].numpy()

        while True:
            # sample on option
            idx = int(np.random.randint(low=0, high=len(self.options), size=(1,)))
            min_jaccard_overlap = self.options[idx]
            if (
                min_jaccard_overlap >= 1.0
            ):  # a value larger than 1 encodes the leave as-is option
                return image, target

            for _ in range(self.trials):
                # check the aspect ratio limitations
                r = self.min_scale + (self.max_scale - self.min_scale) * np.random.rand(
                    2
                )
                new_w = int(orig_w * r[0])
                new_h = int(orig_h * r[1])
                aspect_ratio = new_w / new_h
                if not (self.min_aspect_ratio <= aspect_ratio <= self.max_aspect_ratio):
                    continue

                # check for 0 area crops
                r = np.random.rand(2)
                left = int((orig_w - new_w) * r[0])
                top = int((orig_h - new_h) * r[1])
                right = left + new_w
                bottom = top + new_h
                if left == right or top == bottom:
                    continue

                # check for any valid boxes with centers within the crop area
                # cx = 0.5 * (boxes_flow[:, 0] + boxes_flow[:, 2])
                # cy = 0.5 * (boxes_flow[:, 1] + boxes_flow[:, 3])
                cx = 0.5 * (boxes_np[:, 0] + boxes_np[:, 2])
                cy = 0.5 * (boxes_np[:, 1] + boxes_np[:, 3])
                is_within_crop_area = (
                    (left < cx) & (cx < right) & (top < cy) & (cy < bottom)
                )
                if not is_within_crop_area.any():
                    continue

                # check at least 1 box with jaccard limitations
                boxes = boxes_np[is_within_crop_area]
                ious = flowvision.layers.blocks.box_iou_np(
                    boxes, np.array([[left, top, right, bottom]], dtype=np.float32,)
                )
                # boxes = targets["boxes"][is_within_crop_area]
                # ious = flowvision.layers.blocks.box_iou(
                #     boxes,
                #     flow.tensor(
                #         [[left, top, right, bottom]],
                #         dtype=boxes.dtype,
                #         device=boxes.device,
                #     ),
                # )
                if ious.max() < min_jaccard_overlap:
                    continue

                # keep only valid boxes and perform cropping
                target["boxes"] = flow.tensor(boxes)
                target["labels"] = target["labels"][flow.tensor(is_within_crop_area)]
                target["boxes"][:, 0::2] -= left
                target["boxes"][:, 1::2] -= top
                target["boxes"][:, 0::2].clamp(min=0, max=new_w)
                target["boxes"][:, 1::2].clamp(min=0, max=new_h)
                image = F.crop(image, top, left, new_h, new_w)

                return image, target


class RandomZoomOut(nn.Module):
    def __init__(
        self,
        fill: Optional[List[float]] = None,
        side_range: Tuple[float, float] = (1.0, 4.0),
        p: float = 0.5,
    ):
        super().__init__()
        if fill is None:
            fill = [0.0, 0.0, 0.0]
        self.fill = fill
        self.side_range = side_range
        if side_range[0] < 1.0 or side_range[0] > side_range[1]:
            raise ValueError(
                "Invalid canvas side range provided {}.".format(side_range)
            )
        self.p = p

    def _get_fill_value(self, is_pil):
        # type: (bool) -> int
        return tuple(int(x) for x in self.fill) if is_pil else 0

    def forward(
        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        if isinstance(image, flow.Tensor):
            if image.ndimension() not in {2, 3}:
                raise ValueError(
                    "image should be 2/3 dimensional. Got {} dimensions.".format(
                        image.ndimension()
                    )
                )
            elif image.ndimension() == 2:
                image = image.unsqueeze(0)

        if flow.rand(1) < self.p:
            return image, target

        orig_w, orig_h = F._get_image_size(image)

        r = self.side_range[0] + flow.rand(1) * (
            self.side_range[1] - self.side_range[0]
        )
        canvas_width = int((orig_w * r).item())
        canvas_height = int((orig_h * r).item())

        r = flow.rand(2)
        left = int(((canvas_width - orig_w) * r[0]).item())
        top = int(((canvas_height - orig_h) * r[1]).item())
        right = canvas_width - (left + orig_w)
        bottom = canvas_height - (top + orig_h)

        fill = self._get_fill_value(F._is_pil_image(image))

        image = F.pad(image, [left, top, right, bottom], fill=fill)
        if isinstance(image, flow.Tensor):
            v = flow.tensor(self.fill, device=image.device, dtype=image.dtype).view(
                -1, 1, 1
            )
            image[..., :top, :] = image[..., :, :left] = image[
                ..., (top + orig_h) :, :
            ] = image[..., :, (left + orig_w) :] = v

        if target is not None:
            target["boxes"][:, 0::2] += left
            target["boxes"][:, 1::2] += top

        return image, target


class RandomPhotometricDistort(nn.Module):
    def __init__(
        self,
        contrast: Tuple[float] = (0.5, 1.5),
        saturation: Tuple[float] = (0.5, 1.5),
        hue: Tuple[float] = (-0.05, 0.05),
        brightness: Tuple[float] = (0.875, 1.125),
        p: float = 0.5,
    ):
        super().__init__()
        self._brightness = T.ColorJitter(brightness=brightness)
        self._contrast = T.ColorJitter(contrast=contrast)
        self._hue = T.ColorJitter(hue=hue)
        self._saturation = T.ColorJitter(saturation=saturation)
        self.p = p

    def forward(
        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        if isinstance(image, flow.Tensor):
            if image.ndimension() not in (2, 3):
                raise ValueError(
                    "image should be 2/3 dimensional. Got {} dimensions.".format(
                        image.ndimension()
                    )
                )
            elif image.ndimension() == 2:
                image = image.unsqueeze(0)

        r = flow.rand(7)

        if r[0] < self.p:
            image = self._brightness(image)

        contrast_before = r[1] < 0.5
        if contrast_before:
            if r[2] < self.p:
                image = self._contrast(image)

        if r[3] < self.p:
            image = self._saturation(image)

        if r[4] < self.p:
            image = self._hue(image)

        if not contrast_before:
            if r[5] < self.p:
                image = self._contrast(image)

        if r[6] < self.p:
            channels = F._get_image_num_channels(image)
            permutation = flow.randperm(channels)

            is_pil = F._is_pil_image(image)
            if is_pil:
                image = F.pil_to_tensor(image)
                image = F.convert_image_dtype(image)
            image = image[..., permutation, :, :]
            if is_pil:
                image = F.to_pil_image(image)

        return image, target