image_processing_detr.py

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for DETR."""

import io
import pathlib
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    PaddingMode,
    center_to_corners_format,
    corners_to_center_format,
    id_to_rgb,
    pad,
    rescale,
    resize,
    rgb_to_id,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    AnnotationFormat,
    AnnotationType,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    infer_channel_dimension_format,
    is_scaled_image,
    make_list_of_images,
    to_numpy_array,
    valid_images,
    validate_annotations,
    validate_kwargs,
    validate_preprocess_arguments,
)
from ...utils import (
    TensorType,
    is_flax_available,
    is_jax_tensor,
    is_scipy_available,
    is_tf_available,
    is_tf_tensor,
    is_torch_available,
    is_torch_tensor,
    is_vision_available,
    logging,
)


if is_torch_available():
    import torch
    from torch import nn


if is_vision_available():
    import PIL


if is_scipy_available():
    import scipy.special
    import scipy.stats


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)


# From the original repo: https://github.com/facebookresearch/detr/blob/3af9fa878e73b6894ce3596450a8d9b89d918ca9/datasets/transforms.py#L76
def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
    """
    Computes the output image size given the input image size and the desired output size.

    Args:
        image_size (`Tuple[int, int]`):
            The input image size.
        size (`int`):
            The desired output size.
        max_size (`int`, *optional*):
            The maximum allowed output size.
    """
    height, width = image_size
    if max_size is not None:
        min_original_size = float(min((height, width)))
        max_original_size = float(max((height, width)))
        if max_original_size / min_original_size * size > max_size:
            size = int(round(max_size * min_original_size / max_original_size))

    if (height <= width and height == size) or (width <= height and width == size):
        return height, width

    if width < height:
        ow = size
        oh = int(size * height / width)
    else:
        oh = size
        ow = int(size * width / height)
    return (oh, ow)


def get_resize_output_image_size(
    input_image: np.ndarray,
    size: Union[int, Tuple[int, int], List[int]],
    max_size: Optional[int] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
    """
    Computes the output image size given the input image size and the desired output size. If the desired output size
    is a tuple or list, the output image size is returned as is. If the desired output size is an integer, the output
    image size is computed by keeping the aspect ratio of the input image size.

    Args:
        input_image (`np.ndarray`):
            The image to resize.
        size (`int` or `Tuple[int, int]` or `List[int]`):
            The desired output size.
        max_size (`int`, *optional*):
            The maximum allowed output size.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred from the input image.
    """
    image_size = get_image_size(input_image, input_data_format)
    if isinstance(size, (list, tuple)):
        return size

    return get_size_with_aspect_ratio(image_size, size, max_size)


def get_numpy_to_framework_fn(arr) -> Callable:
    """
    Returns a function that converts a numpy array to the framework of the input array.

    Args:
        arr (`np.ndarray`): The array to convert.
    """
    if isinstance(arr, np.ndarray):
        return np.array
    if is_tf_available() and is_tf_tensor(arr):
        import tensorflow as tf

        return tf.convert_to_tensor
    if is_torch_available() and is_torch_tensor(arr):
        import torch

        return torch.tensor
    if is_flax_available() and is_jax_tensor(arr):
        import jax.numpy as jnp

        return jnp.array
    raise ValueError(f"Cannot convert arrays of type {type(arr)}")


def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
    """
    Squeezes an array, but only if the axis specified has dim 1.
    """
    if axis is None:
        return arr.squeeze()

    try:
        return arr.squeeze(axis=axis)
    except ValueError:
        return arr


def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
    image_height, image_width = image_size
    norm_annotation = {}
    for key, value in annotation.items():
        if key == "boxes":
            boxes = value
            boxes = corners_to_center_format(boxes)
            boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32)
            norm_annotation[key] = boxes
        else:
            norm_annotation[key] = value
    return norm_annotation


# Copied from transformers.models.vilt.image_processing_vilt.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
    """
    Return the maximum value across all indices of an iterable of values.
    """
    return [max(values_i) for values_i in zip(*values)]


# Copied from transformers.models.vilt.image_processing_vilt.get_max_height_width
def get_max_height_width(
    images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
    """
    Get the maximum height and width across all images in a batch.
    """
    if input_data_format is None:
        input_data_format = infer_channel_dimension_format(images[0])

    if input_data_format == ChannelDimension.FIRST:
        _, max_height, max_width = max_across_indices([img.shape for img in images])
    elif input_data_format == ChannelDimension.LAST:
        max_height, max_width, _ = max_across_indices([img.shape for img in images])
    else:
        raise ValueError(f"Invalid channel dimension format: {input_data_format}")
    return (max_height, max_width)


# Copied from transformers.models.vilt.image_processing_vilt.make_pixel_mask
def make_pixel_mask(
    image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
    """
    Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.

    Args:
        image (`np.ndarray`):
            Image to make the pixel mask for.
        output_size (`Tuple[int, int]`):
            Output size of the mask.
    """
    input_height, input_width = get_image_size(image, channel_dim=input_data_format)
    mask = np.zeros(output_size, dtype=np.int64)
    mask[:input_height, :input_width] = 1
    return mask


# inspired by https://github.com/facebookresearch/detr/blob/master/datasets/coco.py#L33
def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
    """
    Convert a COCO polygon annotation to a mask.

    Args:
        segmentations (`List[List[float]]`):
            List of polygons, each polygon represented by a list of x-y coordinates.
        height (`int`):
            Height of the mask.
        width (`int`):
            Width of the mask.
    """
    try:
        from pycocotools import mask as coco_mask
    except ImportError:
        raise ImportError("Pycocotools is not installed in your environment.")

    masks = []
    for polygons in segmentations:
        rles = coco_mask.frPyObjects(polygons, height, width)
        mask = coco_mask.decode(rles)
        if len(mask.shape) < 3:
            mask = mask[..., None]
        mask = np.asarray(mask, dtype=np.uint8)
        mask = np.any(mask, axis=2)
        masks.append(mask)
    if masks:
        masks = np.stack(masks, axis=0)
    else:
        masks = np.zeros((0, height, width), dtype=np.uint8)

    return masks


# inspired by https://github.com/facebookresearch/detr/blob/master/datasets/coco.py#L50
def prepare_coco_detection_annotation(
    image,
    target,
    return_segmentation_masks: bool = False,
    input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
    """
    Convert the target in COCO format into the format expected by DETR.
    """
    image_height, image_width = get_image_size(image, channel_dim=input_data_format)

    image_id = target["image_id"]
    image_id = np.asarray([image_id], dtype=np.int64)

    # Get all COCO annotations for the given image.
    annotations = target["annotations"]
    annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0]

    classes = [obj["category_id"] for obj in annotations]
    classes = np.asarray(classes, dtype=np.int64)

    # for conversion to coco api
    area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32)
    iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=np.int64)

    boxes = [obj["bbox"] for obj in annotations]
    # guard against no boxes via resizing
    boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
    boxes[:, 2:] += boxes[:, :2]
    boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width)
    boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height)

    keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])

    new_target = {}
    new_target["image_id"] = image_id
    new_target["class_labels"] = classes[keep]
    new_target["boxes"] = boxes[keep]
    new_target["area"] = area[keep]
    new_target["iscrowd"] = iscrowd[keep]
    new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64)

    if annotations and "keypoints" in annotations[0]:
        keypoints = [obj["keypoints"] for obj in annotations]
        # Converting the filtered keypoints list to a numpy array
        keypoints = np.asarray(keypoints, dtype=np.float32)
        # Apply the keep mask here to filter the relevant annotations
        keypoints = keypoints[keep]
        num_keypoints = keypoints.shape[0]
        keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
        new_target["keypoints"] = keypoints

    if return_segmentation_masks:
        segmentation_masks = [obj["segmentation"] for obj in annotations]
        masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width)
        new_target["masks"] = masks[keep]

    return new_target


def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
    """
    Compute the bounding boxes around the provided panoptic segmentation masks.

    Args:
        masks: masks in format `[number_masks, height, width]` where N is the number of masks

    Returns:
        boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
    """
    if masks.size == 0:
        return np.zeros((0, 4))

    h, w = masks.shape[-2:]
    y = np.arange(0, h, dtype=np.float32)
    x = np.arange(0, w, dtype=np.float32)
    # see https://github.com/pytorch/pytorch/issues/50276
    y, x = np.meshgrid(y, x, indexing="ij")

    x_mask = masks * np.expand_dims(x, axis=0)
    x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
    x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
    x_min = x.filled(fill_value=1e8)
    x_min = x_min.reshape(x_min.shape[0], -1).min(-1)

    y_mask = masks * np.expand_dims(y, axis=0)
    y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
    y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
    y_min = y.filled(fill_value=1e8)
    y_min = y_min.reshape(y_min.shape[0], -1).min(-1)

    return np.stack([x_min, y_min, x_max, y_max], 1)


def prepare_coco_panoptic_annotation(
    image: np.ndarray,
    target: Dict,
    masks_path: Union[str, pathlib.Path],
    return_masks: bool = True,
    input_data_format: Union[ChannelDimension, str] = None,
) -> Dict:
    """
    Prepare a coco panoptic annotation for DETR.
    """
    image_height, image_width = get_image_size(image, channel_dim=input_data_format)
    annotation_path = pathlib.Path(masks_path) / target["file_name"]

    new_target = {}
    new_target["image_id"] = np.asarray([target["image_id"] if "image_id" in target else target["id"]], dtype=np.int64)
    new_target["size"] = np.asarray([image_height, image_width], dtype=np.int64)
    new_target["orig_size"] = np.asarray([image_height, image_width], dtype=np.int64)

    if "segments_info" in target:
        masks = np.asarray(PIL.Image.open(annotation_path), dtype=np.uint32)
        masks = rgb_to_id(masks)

        ids = np.array([segment_info["id"] for segment_info in target["segments_info"]])
        masks = masks == ids[:, None, None]
        masks = masks.astype(np.uint8)
        if return_masks:
            new_target["masks"] = masks
        new_target["boxes"] = masks_to_boxes(masks)
        new_target["class_labels"] = np.array(
            [segment_info["category_id"] for segment_info in target["segments_info"]], dtype=np.int64
        )
        new_target["iscrowd"] = np.asarray(
            [segment_info["iscrowd"] for segment_info in target["segments_info"]], dtype=np.int64
        )
        new_target["area"] = np.asarray(
            [segment_info["area"] for segment_info in target["segments_info"]], dtype=np.float32
        )

    return new_target


def get_segmentation_image(
    masks: np.ndarray, input_size: Tuple, target_size: Tuple, stuff_equiv_classes, deduplicate=False
):
    h, w = input_size
    final_h, final_w = target_size

    m_id = scipy.special.softmax(masks.transpose(0, 1), -1)

    if m_id.shape[-1] == 0:
        # We didn't detect any mask :(
        m_id = np.zeros((h, w), dtype=np.int64)
    else:
        m_id = m_id.argmax(-1).reshape(h, w)

    if deduplicate:
        # Merge the masks corresponding to the same stuff class
        for equiv in stuff_equiv_classes.values():
            for eq_id in equiv:
                m_id[m_id == eq_id] = equiv[0]

    seg_img = id_to_rgb(m_id)
    seg_img = resize(seg_img, (final_w, final_h), resample=PILImageResampling.NEAREST)
    return seg_img


def get_mask_area(seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int) -> np.ndarray:
    final_h, final_w = target_size
    np_seg_img = seg_img.astype(np.uint8)
    np_seg_img = np_seg_img.reshape(final_h, final_w, 3)
    m_id = rgb_to_id(np_seg_img)
    area = [(m_id == i).sum() for i in range(n_classes)]
    return area


def score_labels_from_class_probabilities(logits: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    probs = scipy.special.softmax(logits, axis=-1)
    labels = probs.argmax(-1, keepdims=True)
    scores = np.take_along_axis(probs, labels, axis=-1)
    scores, labels = scores.squeeze(-1), labels.squeeze(-1)
    return scores, labels


def post_process_panoptic_sample(
    out_logits: np.ndarray,
    masks: np.ndarray,
    boxes: np.ndarray,
    processed_size: Tuple[int, int],
    target_size: Tuple[int, int],
    is_thing_map: Dict,
    threshold=0.85,
) -> Dict:
    """
    Converts the output of [`DetrForSegmentation`] into panoptic segmentation predictions for a single sample.

    Args:
        out_logits (`torch.Tensor`):
            The logits for this sample.
        masks (`torch.Tensor`):
            The predicted segmentation masks for this sample.
        boxes (`torch.Tensor`):
            The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y,
            width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding).
        processed_size (`Tuple[int, int]`):
            The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size
            after data augmentation but before batching.
        target_size (`Tuple[int, int]`):
            The target size of the image, `(height, width)` corresponding to the requested final size of the
            prediction.
        is_thing_map (`Dict`):
            A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not.
        threshold (`float`, *optional*, defaults to 0.85):
            The threshold used to binarize the segmentation masks.
    """
    # we filter empty queries and detection below threshold
    scores, labels = score_labels_from_class_probabilities(out_logits)
    keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold)

    cur_scores = scores[keep]
    cur_classes = labels[keep]
    cur_boxes = center_to_corners_format(boxes[keep])

    if len(cur_boxes) != len(cur_classes):
        raise ValueError("Not as many boxes as there are classes")

    cur_masks = masks[keep]
    cur_masks = resize(cur_masks[:, None], processed_size, resample=PILImageResampling.BILINEAR)
    cur_masks = safe_squeeze(cur_masks, 1)
    b, h, w = cur_masks.shape

    # It may be that we have several predicted masks for the same stuff class.
    # In the following, we track the list of masks ids for each stuff class (they are merged later on)
    cur_masks = cur_masks.reshape(b, -1)
    stuff_equiv_classes = defaultdict(list)
    for k, label in enumerate(cur_classes):
        if not is_thing_map[label]:
            stuff_equiv_classes[label].append(k)

    seg_img = get_segmentation_image(cur_masks, processed_size, target_size, stuff_equiv_classes, deduplicate=True)
    area = get_mask_area(cur_masks, processed_size, n_classes=len(cur_scores))

    # We filter out any mask that is too small
    if cur_classes.size() > 0:
        # We know filter empty masks as long as we find some
        filtered_small = np.array([a <= 4 for a in area], dtype=bool)
        while filtered_small.any():
            cur_masks = cur_masks[~filtered_small]
            cur_scores = cur_scores[~filtered_small]
            cur_classes = cur_classes[~filtered_small]
            seg_img = get_segmentation_image(cur_masks, (h, w), target_size, stuff_equiv_classes, deduplicate=True)
            area = get_mask_area(seg_img, target_size, n_classes=len(cur_scores))
            filtered_small = np.array([a <= 4 for a in area], dtype=bool)
    else:
        cur_classes = np.ones((1, 1), dtype=np.int64)

    segments_info = [
        {"id": i, "isthing": is_thing_map[cat], "category_id": int(cat), "area": a}
        for i, (cat, a) in enumerate(zip(cur_classes, area))
    ]
    del cur_classes

    with io.BytesIO() as out:
        PIL.Image.fromarray(seg_img).save(out, format="PNG")
        predictions = {"png_string": out.getvalue(), "segments_info": segments_info}

    return predictions


def resize_annotation(
    annotation: Dict[str, Any],
    orig_size: Tuple[int, int],
    target_size: Tuple[int, int],
    threshold: float = 0.5,
    resample: PILImageResampling = PILImageResampling.NEAREST,
):
    """
    Resizes an annotation to a target size.

    Args:
        annotation (`Dict[str, Any]`):
            The annotation dictionary.
        orig_size (`Tuple[int, int]`):
            The original size of the input image.
        target_size (`Tuple[int, int]`):
            The target size of the image, as returned by the preprocessing `resize` step.
        threshold (`float`, *optional*, defaults to 0.5):
            The threshold used to binarize the segmentation masks.
        resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
            The resampling filter to use when resizing the masks.
    """
    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size))
    ratio_height, ratio_width = ratios

    new_annotation = {}
    new_annotation["size"] = target_size

    for key, value in annotation.items():
        if key == "boxes":
            boxes = value
            scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
            new_annotation["boxes"] = scaled_boxes
        elif key == "area":
            area = value
            scaled_area = area * (ratio_width * ratio_height)
            new_annotation["area"] = scaled_area
        elif key == "masks":
            masks = value[:, None]
            masks = np.array([resize(mask, target_size, resample=resample) for mask in masks])
            masks = masks.astype(np.float32)
            masks = masks[:, 0] > threshold
            new_annotation["masks"] = masks
        elif key == "size":
            new_annotation["size"] = target_size
        else:
            new_annotation[key] = value

    return new_annotation


# TODO - (Amy) make compatible with other frameworks
def binary_mask_to_rle(mask):
    """
    Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.

    Args:
        mask (`torch.Tensor` or `numpy.array`):
            A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
            segment_id or class_id.
    Returns:
        `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
        format.
    """
    if is_torch_tensor(mask):
        mask = mask.numpy()

    pixels = mask.flatten()
    pixels = np.concatenate([[0], pixels, [0]])
    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
    runs[1::2] -= runs[::2]
    return list(runs)


# TODO - (Amy) make compatible with other frameworks
def convert_segmentation_to_rle(segmentation):
    """
    Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.

    Args:
        segmentation (`torch.Tensor` or `numpy.array`):
            A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
    Returns:
        `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
    """
    segment_ids = torch.unique(segmentation)

    run_length_encodings = []
    for idx in segment_ids:
        mask = torch.where(segmentation == idx, 1, 0)
        rle = binary_mask_to_rle(mask)
        run_length_encodings.append(rle)

    return run_length_encodings


def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
    """
    Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
    `labels`.

    Args:
        masks (`torch.Tensor`):
            A tensor of shape `(num_queries, height, width)`.
        scores (`torch.Tensor`):
            A tensor of shape `(num_queries)`.
        labels (`torch.Tensor`):
            A tensor of shape `(num_queries)`.
        object_mask_threshold (`float`):
            A number between 0 and 1 used to binarize the masks.
    Raises:
        `ValueError`: Raised when the first dimension doesn't match in all input tensors.
    Returns:
        `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
        < `object_mask_threshold`.
    """
    if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
        raise ValueError("mask, scores and labels must have the same shape!")

    to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)

    return masks[to_keep], scores[to_keep], labels[to_keep]


def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
    # Get the mask associated with the k class
    mask_k = mask_labels == k
    mask_k_area = mask_k.sum()

    # Compute the area of all the stuff in query k
    original_area = (mask_probs[k] >= mask_threshold).sum()
    mask_exists = mask_k_area > 0 and original_area > 0

    # Eliminate disconnected tiny segments
    if mask_exists:
        area_ratio = mask_k_area / original_area
        if not area_ratio.item() > overlap_mask_area_threshold:
            mask_exists = False

    return mask_exists, mask_k


def compute_segments(
    mask_probs,
    pred_scores,
    pred_labels,
    mask_threshold: float = 0.5,
    overlap_mask_area_threshold: float = 0.8,
    label_ids_to_fuse: Optional[Set[int]] = None,
    target_size: Tuple[int, int] = None,
):
    height = mask_probs.shape[1] if target_size is None else target_size[0]
    width = mask_probs.shape[2] if target_size is None else target_size[1]

    segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
    segments: List[Dict] = []

    if target_size is not None:
        mask_probs = nn.functional.interpolate(
            mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
        )[0]

    current_segment_id = 0

    # Weigh each mask by its prediction score
    mask_probs *= pred_scores.view(-1, 1, 1)
    mask_labels = mask_probs.argmax(0)  # [height, width]

    # Keep track of instances of each class
    stuff_memory_list: Dict[str, int] = {}
    for k in range(pred_labels.shape[0]):
        pred_class = pred_labels[k].item()
        should_fuse = pred_class in label_ids_to_fuse

        # Check if mask exists and large enough to be a segment
        mask_exists, mask_k = check_segment_validity(
            mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
        )

        if mask_exists:
            if pred_class in stuff_memory_list:
                current_segment_id = stuff_memory_list[pred_class]
            else:
                current_segment_id += 1

            # Add current object segment to final segmentation map
            segmentation[mask_k] = current_segment_id
            segment_score = round(pred_scores[k].item(), 6)
            segments.append(
                {
                    "id": current_segment_id,
                    "label_id": pred_class,
                    "was_fused": should_fuse,
                    "score": segment_score,
                }
            )
            if should_fuse:
                stuff_memory_list[pred_class] = current_segment_id

    return segmentation, segments


class DetrImageProcessor(BaseImageProcessor):
    r"""
    Constructs a Detr image processor.

    Args:
        format (`str`, *optional*, defaults to `"coco_detection"`):
            Data format of the annotations. One of "coco_detection" or "coco_panoptic".
        do_resize (`bool`, *optional*, defaults to `True`):
            Controls whether to resize the image's `(height, width)` dimensions to the specified `size`. Can be
            overridden by the `do_resize` parameter in the `preprocess` method.
        size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
            Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter
            in the `preprocess` method.
        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            Resampling filter to use if resizing the image.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
            `do_rescale` parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
            `preprocess` method.
        do_normalize (`bool`, *optional*, defaults to True):
            Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
            `preprocess` method.
        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
            Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
            channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
            Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
            for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
        do_convert_annotations (`bool`, *optional*, defaults to `True`):
            Controls whether to convert the annotations to the format expected by the DETR model. Converts the
            bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`.
            Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method.
        do_pad (`bool`, *optional*, defaults to `True`):
            Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess`
            method. If `True` will pad the images in the batch to the largest height and width in the batch.
            Padding will be applied to the bottom and right of the image with zeros.
    """

    model_input_names = ["pixel_values", "pixel_mask"]

    def __init__(
        self,
        format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Union[float, List[float]] = None,
        image_std: Union[float, List[float]] = None,
        do_convert_annotations: Optional[bool] = None,
        do_pad: bool = True,
        **kwargs,
    ) -> None:
        if "pad_and_return_pixel_mask" in kwargs:
            do_pad = kwargs.pop("pad_and_return_pixel_mask")

        if "max_size" in kwargs:
            logger.warning_once(
                "The `max_size` parameter is deprecated and will be removed in v4.26. "
                "Please specify in `size['longest_edge'] instead`.",
            )
            max_size = kwargs.pop("max_size")
        else:
            max_size = None if size is None else 1333

        size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
        size = get_size_dict(size, max_size=max_size, default_to_square=False)

        # Backwards compatibility
        if do_convert_annotations is None:
            do_convert_annotations = do_normalize

        super().__init__(**kwargs)
        self.format = format
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.do_convert_annotations = do_convert_annotations
        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
        self.do_pad = do_pad
        self._valid_processor_keys = [
            "images",
            "annotations",
            "return_segmentation_masks",
            "masks_path",
            "do_resize",
            "size",
            "resample",
            "do_rescale",
            "rescale_factor",
            "do_normalize",
            "do_convert_annotations",
            "image_mean",
            "image_std",
            "do_pad",
            "format",
            "return_tensors",
            "data_format",
            "input_data_format",
        ]

    @classmethod
    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
        """
        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
        created using from_dict and kwargs e.g. `DetrImageProcessor.from_pretrained(checkpoint, size=600,
        max_size=800)`
        """
        image_processor_dict = image_processor_dict.copy()
        if "max_size" in kwargs:
            image_processor_dict["max_size"] = kwargs.pop("max_size")
        if "pad_and_return_pixel_mask" in kwargs:
            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
        return super().from_dict(image_processor_dict, **kwargs)

    def prepare_annotation(
        self,
        image: np.ndarray,
        target: Dict,
        format: Optional[AnnotationFormat] = None,
        return_segmentation_masks: bool = None,
        masks_path: Optional[Union[str, pathlib.Path]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> Dict:
        """
        Prepare an annotation for feeding into DETR model.
        """
        format = format if format is not None else self.format

        if format == AnnotationFormat.COCO_DETECTION:
            return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
            target = prepare_coco_detection_annotation(
                image, target, return_segmentation_masks, input_data_format=input_data_format
            )
        elif format == AnnotationFormat.COCO_PANOPTIC:
            return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks
            target = prepare_coco_panoptic_annotation(
                image,
                target,
                masks_path=masks_path,
                return_masks=return_segmentation_masks,
                input_data_format=input_data_format,
            )
        else:
            raise ValueError(f"Format {format} is not supported.")
        return target

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: Optional[ChannelDimension] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
        int, smaller edge of the image will be matched to this number.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary containing the size to resize to. Can contain the keys `shortest_edge` and `longest_edge` or
                `height` and `width`.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                Resampling filter to use if resizing the image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
        """
        if "max_size" in kwargs:
            logger.warning_once(
                "The `max_size` parameter is deprecated and will be removed in v4.26. "
                "Please specify in `size['longest_edge'] instead`.",
            )
            max_size = kwargs.pop("max_size")
        else:
            max_size = None
        size = get_size_dict(size, max_size=max_size, default_to_square=False)
        if "shortest_edge" in size and "longest_edge" in size:
            size = get_resize_output_image_size(
                image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format
            )
        elif "height" in size and "width" in size:
            size = (size["height"], size["width"])
        else:
            raise ValueError(
                "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
                f" {size.keys()}."
            )
        image = resize(
            image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
        )
        return image

    def resize_annotation(
        self,
        annotation,
        orig_size,
        size,
        resample: PILImageResampling = PILImageResampling.NEAREST,
    ) -> Dict:
        """
        Resize the annotation to match the resized image. If size is an int, smaller edge of the mask will be matched
        to this number.
        """
        return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)

    # TODO (Amy) - update to use `rescale_factor` instead of `scale`
    def rescale(
        self,
        image: np.ndarray,
        rescale_factor: float,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        """
        Rescale the image by the given factor. image = image * rescale_factor.

        Args:
            image (`np.ndarray`):
                Image to rescale.
            rescale_factor (`float`):
                The value to use for rescaling.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            input_data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format for the input image. If unset, is inferred from the input image. Can be
                one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
        """
        return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)

    def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
        """
        Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
        `[center_x, center_y, width, height]` format and from absolute to relative pixel values.
        """
        return normalize_annotation(annotation, image_size=image_size)

    def _update_annotation_for_padded_image(
        self,
        annotation: Dict,
        input_image_size: Tuple[int, int],
        output_image_size: Tuple[int, int],
        padding,
        update_bboxes,
    ) -> Dict:
        """
        Update the annotation for a padded image.
        """
        new_annotation = {}
        new_annotation["size"] = output_image_size

        for key, value in annotation.items():
            if key == "masks":
                masks = value
                masks = pad(
                    masks,
                    padding,
                    mode=PaddingMode.CONSTANT,
                    constant_values=0,
                    input_data_format=ChannelDimension.FIRST,
                )
                masks = safe_squeeze(masks, 1)
                new_annotation["masks"] = masks
            elif key == "boxes" and update_bboxes:
                boxes = value
                boxes *= np.asarray(
                    [
                        input_image_size[1] / output_image_size[1],
                        input_image_size[0] / output_image_size[0],
                        input_image_size[1] / output_image_size[1],
                        input_image_size[0] / output_image_size[0],
                    ]
                )
                new_annotation["boxes"] = boxes
            elif key == "size":
                new_annotation["size"] = output_image_size
            else:
                new_annotation[key] = value
        return new_annotation

    def _pad_image(
        self,
        image: np.ndarray,
        output_size: Tuple[int, int],
        annotation: Optional[Dict[str, Any]] = None,
        constant_values: Union[float, Iterable[float]] = 0,
        data_format: Optional[ChannelDimension] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        update_bboxes: bool = True,
    ) -> np.ndarray:
        """
        Pad an image with zeros to the given size.
        """
        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
        output_height, output_width = output_size

        pad_bottom = output_height - input_height
        pad_right = output_width - input_width
        padding = ((0, pad_bottom), (0, pad_right))
        padded_image = pad(
            image,
            padding,
            mode=PaddingMode.CONSTANT,
            constant_values=constant_values,
            data_format=data_format,
            input_data_format=input_data_format,
        )
        if annotation is not None:
            annotation = self._update_annotation_for_padded_image(
                annotation, (input_height, input_width), (output_height, output_width), padding, update_bboxes
            )
        return padded_image, annotation

    def pad(
        self,
        images: List[np.ndarray],
        annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
        constant_values: Union[float, Iterable[float]] = 0,
        return_pixel_mask: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        update_bboxes: bool = True,
    ) -> BatchFeature:
        """
        Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
        in the batch and optionally returns their corresponding pixel mask.

        Args:
            images (List[`np.ndarray`]):
                Images to pad.
            annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
                Annotations to transform according to the padding that is applied to the images.
            constant_values (`float` or `Iterable[float]`, *optional*):
                The value to use for the padding if `mode` is `"constant"`.
            return_pixel_mask (`bool`, *optional*, defaults to `True`):
                Whether to return a pixel mask.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
            update_bboxes (`bool`, *optional*, defaults to `True`):
                Whether to update the bounding boxes in the annotations to match the padded images. If the
                bounding boxes have not been converted to relative coordinates and `(centre_x, centre_y, width, height)`
                format, the bounding boxes will not be updated.
        """
        pad_size = get_max_height_width(images, input_data_format=input_data_format)

        annotation_list = annotations if annotations is not None else [None] * len(images)
        padded_images = []
        padded_annotations = []
        for image, annotation in zip(images, annotation_list):
            padded_image, padded_annotation = self._pad_image(
                image,
                pad_size,
                annotation,
                constant_values=constant_values,
                data_format=data_format,
                input_data_format=input_data_format,
                update_bboxes=update_bboxes,
            )
            padded_images.append(padded_image)
            padded_annotations.append(padded_annotation)

        data = {"pixel_values": padded_images}

        if return_pixel_mask:
            masks = [
                make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format)
                for image in images
            ]
            data["pixel_mask"] = masks

        encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)

        if annotations is not None:
            encoded_inputs["labels"] = [
                BatchFeature(annotation, tensor_type=return_tensors) for annotation in padded_annotations
            ]

        return encoded_inputs

    def preprocess(
        self,
        images: ImageInput,
        annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
        return_segmentation_masks: bool = None,
        masks_path: Optional[Union[str, pathlib.Path]] = None,
        do_resize: Optional[bool] = None,
        size: Optional[Dict[str, int]] = None,
        resample=None,  # PILImageResampling
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[Union[int, float]] = None,
        do_normalize: Optional[bool] = None,
        do_convert_annotations: Optional[bool] = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_pad: Optional[bool] = None,
        format: Optional[Union[str, AnnotationFormat]] = None,
        return_tensors: Optional[Union[TensorType, str]] = None,
        data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess an image or a batch of images so that it can be used by the model.

        Args:
            images (`ImageInput`):
                Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
                from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
            annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
                List of annotations associated with the image or batch of images. If annotation is for object
                detection, the annotations should be a dictionary with the following keys:
                - "image_id" (`int`): The image id.
                - "annotations" (`List[Dict]`): List of annotations for an image. Each annotation should be a
                  dictionary. An image can have no annotations, in which case the list should be empty.
                If annotation is for segmentation, the annotations should be a dictionary with the following keys:
                - "image_id" (`int`): The image id.
                - "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary.
                  An image can have no segments, in which case the list should be empty.
                - "file_name" (`str`): The file name of the image.
            return_segmentation_masks (`bool`, *optional*, defaults to self.return_segmentation_masks):
                Whether to return segmentation masks.
            masks_path (`str` or `pathlib.Path`, *optional*):
                Path to the directory containing the segmentation masks.
            do_resize (`bool`, *optional*, defaults to self.do_resize):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to self.size):
                Size of the image after resizing.
            resample (`PILImageResampling`, *optional*, defaults to self.resample):
                Resampling filter to use when resizing the image.
            do_rescale (`bool`, *optional*, defaults to self.do_rescale):
                Whether to rescale the image.
            rescale_factor (`float`, *optional*, defaults to self.rescale_factor):
                Rescale factor to use when rescaling the image.
            do_normalize (`bool`, *optional*, defaults to self.do_normalize):
                Whether to normalize the image.
            do_convert_annotations (`bool`, *optional*, defaults to self.do_convert_annotations):
                Whether to convert the annotations to the format expected by the model. Converts the bounding
                boxes from the format `(top_left_x, top_left_y, width, height)` to `(center_x, center_y, width, height)`
                and in relative coordinates.
            image_mean (`float` or `List[float]`, *optional*, defaults to self.image_mean):
                Mean to use when normalizing the image.
            image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
                Standard deviation to use when normalizing the image.
            do_pad (`bool`, *optional*, defaults to self.do_pad):
                Whether to pad the image. If `True` will pad the images in the batch to the largest image in the batch
                and create a pixel mask. Padding will be applied to the bottom and right of the image with zeros.
            format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                Format of the annotations.
            return_tensors (`str` or `TensorType`, *optional*, defaults to self.return_tensors):
                Type of tensors to return. If `None`, will return the list of images.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - Unset: Use the channel dimension format of the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        if "pad_and_return_pixel_mask" in kwargs:
            logger.warning_once(
                "The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
                "use `do_pad` instead."
            )
            do_pad = kwargs.pop("pad_and_return_pixel_mask")

        max_size = None
        if "max_size" in kwargs:
            logger.warning_once(
                "The `max_size` argument is deprecated and will be removed in a future version, use"
                " `size['longest_edge']` instead."
            )
            size = kwargs.pop("max_size")

        do_resize = self.do_resize if do_resize is None else do_resize
        size = self.size if size is None else size
        size = get_size_dict(size=size, max_size=max_size, default_to_square=False)
        resample = self.resample if resample is None else resample
        do_rescale = self.do_rescale if do_rescale is None else do_rescale
        rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor
        do_normalize = self.do_normalize if do_normalize is None else do_normalize
        image_mean = self.image_mean if image_mean is None else image_mean
        image_std = self.image_std if image_std is None else image_std
        do_convert_annotations = (
            self.do_convert_annotations if do_convert_annotations is None else do_convert_annotations
        )
        do_pad = self.do_pad if do_pad is None else do_pad
        format = self.format if format is None else format

        images = make_list_of_images(images)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )
        validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)

        # Here, the pad() method pads to the maximum of (width, height). It does not need to be validated.
        validate_preprocess_arguments(
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            do_resize=do_resize,
            size=size,
            resample=resample,
        )

        if annotations is not None and isinstance(annotations, dict):
            annotations = [annotations]

        if annotations is not None and len(images) != len(annotations):
            raise ValueError(
                f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
            )

        format = AnnotationFormat(format)
        if annotations is not None:
            validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations)

        if (
            masks_path is not None
            and format == AnnotationFormat.COCO_PANOPTIC
            and not isinstance(masks_path, (pathlib.Path, str))
        ):
            raise ValueError(
                "The path to the directory containing the mask PNG files should be provided as a"
                f" `pathlib.Path` or string object, but is {type(masks_path)} instead."
            )

        # All transformations expect numpy arrays
        images = [to_numpy_array(image) for image in images]

        if is_scaled_image(images[0]) and do_rescale:
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )

        if input_data_format is None:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(images[0])

        # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
        if annotations is not None:
            prepared_images = []
            prepared_annotations = []
            for image, target in zip(images, annotations):
                target = self.prepare_annotation(
                    image,
                    target,
                    format,
                    return_segmentation_masks=return_segmentation_masks,
                    masks_path=masks_path,
                    input_data_format=input_data_format,
                )
                prepared_images.append(image)
                prepared_annotations.append(target)
            images = prepared_images
            annotations = prepared_annotations
            del prepared_images, prepared_annotations

        # transformations
        if do_resize:
            if annotations is not None:
                resized_images, resized_annotations = [], []
                for image, target in zip(images, annotations):
                    orig_size = get_image_size(image, input_data_format)
                    resized_image = self.resize(
                        image, size=size, max_size=max_size, resample=resample, input_data_format=input_data_format
                    )
                    resized_annotation = self.resize_annotation(
                        target, orig_size, get_image_size(resized_image, input_data_format)
                    )
                    resized_images.append(resized_image)
                    resized_annotations.append(resized_annotation)
                images = resized_images
                annotations = resized_annotations
                del resized_images, resized_annotations
            else:
                images = [
                    self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
                    for image in images
                ]

        if do_rescale:
            images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]

        if do_normalize:
            images = [
                self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
            ]

        if do_convert_annotations and annotations is not None:
            annotations = [
                self.normalize_annotation(annotation, get_image_size(image, input_data_format))
                for annotation, image in zip(annotations, images)
            ]

        if do_pad:
            # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
            encoded_inputs = self.pad(
                images,
                annotations=annotations,
                return_pixel_mask=True,
                data_format=data_format,
                input_data_format=input_data_format,
                update_bboxes=do_convert_annotations,
                return_tensors=return_tensors,
            )
        else:
            images = [
                to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
                for image in images
            ]
            encoded_inputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
            if annotations is not None:
                encoded_inputs["labels"] = [
                    BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations
                ]

        return encoded_inputs

    # POSTPROCESSING METHODS - TODO: add support for other frameworks
    # inspired by https://github.com/facebookresearch/detr/blob/master/models/detr.py#L258
    def post_process(self, outputs, target_sizes):
        """
        Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format. Only supports PyTorch.

        Args:
            outputs ([`DetrObjectDetectionOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
                Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
                original image size (before any data augmentation). For visualization, this should be the image size
                after data augment, but before padding.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model.
        """
        logger.warning_once(
            "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
        )

        out_logits, out_bbox = outputs.logits, outputs.pred_boxes

        if len(out_logits) != len(target_sizes):
            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
        if target_sizes.shape[1] != 2:
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        prob = nn.functional.softmax(out_logits, -1)
        scores, labels = prob[..., :-1].max(-1)

        # convert to [x0, y0, x1, y1] format
        boxes = center_to_corners_format(out_bbox)
        # and from relative [0, 1] to absolute [0, height] coordinates
        img_h, img_w = target_sizes.unbind(1)
        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
        boxes = boxes * scale_fct[:, None, :]

        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
        return results

    def post_process_segmentation(self, outputs, target_sizes, threshold=0.9, mask_threshold=0.5):
        """
        Converts the output of [`DetrForSegmentation`] into image segmentation predictions. Only supports PyTorch.

        Args:
            outputs ([`DetrSegmentationOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
                Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction.
            threshold (`float`, *optional*, defaults to 0.9):
                Threshold to use to filter out queries.
            mask_threshold (`float`, *optional*, defaults to 0.5):
                Threshold to use when turning the predicted masks into binary values.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, and masks for an image
            in the batch as predicted by the model.
        """
        logger.warning_once(
            "`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
            " `post_process_semantic_segmentation`.",
        )
        out_logits, raw_masks = outputs.logits, outputs.pred_masks
        empty_label = out_logits.shape[-1] - 1
        preds = []

        def to_tuple(tup):
            if isinstance(tup, tuple):
                return tup
            return tuple(tup.cpu().tolist())

        for cur_logits, cur_masks, size in zip(out_logits, raw_masks, target_sizes):
            # we filter empty queries and detection below threshold
            cur_scores, cur_labels = cur_logits.softmax(-1).max(-1)
            keep = cur_labels.ne(empty_label) & (cur_scores > threshold)
            cur_scores = cur_scores[keep]
            cur_labels = cur_labels[keep]
            cur_masks = cur_masks[keep]
            cur_masks = nn.functional.interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
            cur_masks = (cur_masks.sigmoid() > mask_threshold) * 1

            predictions = {"scores": cur_scores, "labels": cur_labels, "masks": cur_masks}
            preds.append(predictions)
        return preds

    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L218
    def post_process_instance(self, results, outputs, orig_target_sizes, max_target_sizes, threshold=0.5):
        """
        Converts the output of [`DetrForSegmentation`] into actual instance segmentation predictions. Only supports
        PyTorch.

        Args:
            results (`List[Dict]`):
                Results list obtained by [`~DetrImageProcessor.post_process`], to which "masks" results will be added.
            outputs ([`DetrSegmentationOutput`]):
                Raw outputs of the model.
            orig_target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
                Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
                image size (before any data augmentation).
            max_target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
                Tensor containing the maximum size (h, w) of each image of the batch. For evaluation, this must be the
                original image size (before any data augmentation).
            threshold (`float`, *optional*, defaults to 0.5):
                Threshold to use when turning the predicted masks into binary values.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, boxes and masks for an
            image in the batch as predicted by the model.
        """
        logger.warning_once(
            "`post_process_instance` is deprecated and will be removed in v5 of Transformers, please use"
            " `post_process_instance_segmentation`.",
        )

        if len(orig_target_sizes) != len(max_target_sizes):
            raise ValueError("Make sure to pass in as many orig_target_sizes as max_target_sizes")
        max_h, max_w = max_target_sizes.max(0)[0].tolist()
        outputs_masks = outputs.pred_masks.squeeze(2)
        outputs_masks = nn.functional.interpolate(
            outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False
        )
        outputs_masks = (outputs_masks.sigmoid() > threshold).cpu()

        for i, (cur_mask, t, tt) in enumerate(zip(outputs_masks, max_target_sizes, orig_target_sizes)):
            img_h, img_w = t[0], t[1]
            results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1)
            results[i]["masks"] = nn.functional.interpolate(
                results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest"
            ).byte()

        return results

    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L241
    def post_process_panoptic(self, outputs, processed_sizes, target_sizes=None, is_thing_map=None, threshold=0.85):
        """
        Converts the output of [`DetrForSegmentation`] into actual panoptic predictions. Only supports PyTorch.

        Args:
            outputs ([`DetrSegmentationOutput`]):
                Raw outputs of the model.
            processed_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`):
                Torch Tensor (or list) containing the size (h, w) of each image of the batch, i.e. the size after data
                augmentation but before batching.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`, *optional*):
                Torch Tensor (or list) corresponding to the requested final size `(height, width)` of each prediction.
                If left to None, it will default to the `processed_sizes`.
            is_thing_map (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
                Dictionary mapping class indices to either True or False, depending on whether or not they are a thing.
                If not set, defaults to the `is_thing_map` of COCO panoptic.
            threshold (`float`, *optional*, defaults to 0.85):
                Threshold to use to filter out queries.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing a PNG string and segments_info values for
            an image in the batch as predicted by the model.
        """
        logger.warning_once(
            "`post_process_panoptic is deprecated and will be removed in v5 of Transformers, please use"
            " `post_process_panoptic_segmentation`.",
        )
        if target_sizes is None:
            target_sizes = processed_sizes
        if len(processed_sizes) != len(target_sizes):
            raise ValueError("Make sure to pass in as many processed_sizes as target_sizes")

        if is_thing_map is None:
            # default to is_thing_map of COCO panoptic
            is_thing_map = {i: i <= 90 for i in range(201)}

        out_logits, raw_masks, raw_boxes = outputs.logits, outputs.pred_masks, outputs.pred_boxes
        if not len(out_logits) == len(raw_masks) == len(target_sizes):
            raise ValueError(
                "Make sure that you pass in as many target sizes as the batch dimension of the logits and masks"
            )
        empty_label = out_logits.shape[-1] - 1
        preds = []

        def to_tuple(tup):
            if isinstance(tup, tuple):
                return tup
            return tuple(tup.cpu().tolist())

        for cur_logits, cur_masks, cur_boxes, size, target_size in zip(
            out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes
        ):
            # we filter empty queries and detection below threshold
            cur_scores, cur_labels = cur_logits.softmax(-1).max(-1)
            keep = cur_labels.ne(empty_label) & (cur_scores > threshold)
            cur_scores = cur_scores[keep]
            cur_labels = cur_labels[keep]
            cur_masks = cur_masks[keep]
            cur_masks = nn.functional.interpolate(cur_masks[:, None], to_tuple(size), mode="bilinear").squeeze(1)
            cur_boxes = center_to_corners_format(cur_boxes[keep])

            h, w = cur_masks.shape[-2:]
            if len(cur_boxes) != len(cur_labels):
                raise ValueError("Not as many boxes as there are classes")

            # It may be that we have several predicted masks for the same stuff class.
            # In the following, we track the list of masks ids for each stuff class (they are merged later on)
            cur_masks = cur_masks.flatten(1)
            stuff_equiv_classes = defaultdict(lambda: [])
            for k, label in enumerate(cur_labels):
                if not is_thing_map[label.item()]:
                    stuff_equiv_classes[label.item()].append(k)

            def get_ids_area(masks, scores, dedup=False):
                # This helper function creates the final panoptic segmentation image
                # It also returns the area of the masks that appears on the image

                m_id = masks.transpose(0, 1).softmax(-1)

                if m_id.shape[-1] == 0:
                    # We didn't detect any mask :(
                    m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)
                else:
                    m_id = m_id.argmax(-1).view(h, w)

                if dedup:
                    # Merge the masks corresponding to the same stuff class
                    for equiv in stuff_equiv_classes.values():
                        if len(equiv) > 1:
                            for eq_id in equiv:
                                m_id.masked_fill_(m_id.eq(eq_id), equiv[0])

                final_h, final_w = to_tuple(target_size)

                seg_img = PIL.Image.fromarray(id_to_rgb(m_id.view(h, w).cpu().numpy()))
                seg_img = seg_img.resize(size=(final_w, final_h), resample=PILImageResampling.NEAREST)

                np_seg_img = torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes()))
                np_seg_img = np_seg_img.view(final_h, final_w, 3)
                np_seg_img = np_seg_img.numpy()

                m_id = torch.from_numpy(rgb_to_id(np_seg_img))

                area = []
                for i in range(len(scores)):
                    area.append(m_id.eq(i).sum().item())
                return area, seg_img

            area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)
            if cur_labels.numel() > 0:
                # We know filter empty masks as long as we find some
                while True:
                    filtered_small = torch.as_tensor(
                        [area[i] <= 4 for i, c in enumerate(cur_labels)], dtype=torch.bool, device=keep.device
                    )
                    if filtered_small.any().item():
                        cur_scores = cur_scores[~filtered_small]
                        cur_labels = cur_labels[~filtered_small]
                        cur_masks = cur_masks[~filtered_small]
                        area, seg_img = get_ids_area(cur_masks, cur_scores)
                    else:
                        break

            else:
                cur_labels = torch.ones(1, dtype=torch.long, device=cur_labels.device)

            segments_info = []
            for i, a in enumerate(area):
                cat = cur_labels[i].item()
                segments_info.append({"id": i, "isthing": is_thing_map[cat], "category_id": cat, "area": a})
            del cur_labels

            with io.BytesIO() as out:
                seg_img.save(out, format="PNG")
                predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
            preds.append(predictions)
        return preds

    # inspired by https://github.com/facebookresearch/detr/blob/master/models/detr.py#L258
    def post_process_object_detection(
        self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
    ):
        """
        Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format. Only supports PyTorch.

        Args:
            outputs ([`DetrObjectDetectionOutput`]):
                Raw outputs of the model.
            threshold (`float`, *optional*):
                Score threshold to keep object detection predictions.
            target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
                Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
                `(height, width)` of each image in the batch. If unset, predictions will not be resized.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model.
        """
        out_logits, out_bbox = outputs.logits, outputs.pred_boxes

        if target_sizes is not None:
            if len(out_logits) != len(target_sizes):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
                )

        prob = nn.functional.softmax(out_logits, -1)
        scores, labels = prob[..., :-1].max(-1)

        # Convert to [x0, y0, x1, y1] format
        boxes = center_to_corners_format(out_bbox)

        # Convert from relative [0, 1] to absolute [0, height] coordinates
        if target_sizes is not None:
            if isinstance(target_sizes, List):
                img_h = torch.Tensor([i[0] for i in target_sizes])
                img_w = torch.Tensor([i[1] for i in target_sizes])
            else:
                img_h, img_w = target_sizes.unbind(1)

            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
            boxes = boxes * scale_fct[:, None, :]

        results = []
        for s, l, b in zip(scores, labels, boxes):
            score = s[s > threshold]
            label = l[s > threshold]
            box = b[s > threshold]
            results.append({"scores": score, "labels": label, "boxes": box})

        return results

    def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
        """
        Converts the output of [`DetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.

        Args:
            outputs ([`DetrForSegmentation`]):
                Raw outputs of the model.
            target_sizes (`List[Tuple[int, int]]`, *optional*):
                A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the
                batch. If unset, predictions will not be resized.
        Returns:
            `List[torch.Tensor]`:
                A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
                corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
                `torch.Tensor` correspond to a semantic class id.
        """
        class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
        masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]

        # Remove the null class `[..., :-1]`
        masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
        masks_probs = masks_queries_logits.sigmoid()  # [batch_size, num_queries, height, width]

        # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
        segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
        batch_size = class_queries_logits.shape[0]

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
            if batch_size != len(target_sizes):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
                )

            semantic_segmentation = []
            for idx in range(batch_size):
                resized_logits = nn.functional.interpolate(
                    segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
                )
                semantic_map = resized_logits[0].argmax(dim=0)
                semantic_segmentation.append(semantic_map)
        else:
            semantic_segmentation = segmentation.argmax(dim=1)
            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

        return semantic_segmentation

    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L218
    def post_process_instance_segmentation(
        self,
        outputs,
        threshold: float = 0.5,
        mask_threshold: float = 0.5,
        overlap_mask_area_threshold: float = 0.8,
        target_sizes: Optional[List[Tuple[int, int]]] = None,
        return_coco_annotation: Optional[bool] = False,
    ) -> List[Dict]:
        """
        Converts the output of [`DetrForSegmentation`] into instance segmentation predictions. Only supports PyTorch.

        Args:
            outputs ([`DetrForSegmentation`]):
                Raw outputs of the model.
            threshold (`float`, *optional*, defaults to 0.5):
                The probability score threshold to keep predicted instance masks.
            mask_threshold (`float`, *optional*, defaults to 0.5):
                Threshold to use when turning the predicted masks into binary values.
            overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
                The overlap mask area threshold to merge or discard small disconnected parts within each binary
                instance mask.
            target_sizes (`List[Tuple]`, *optional*):
                List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
                final size (height, width) of each prediction. If unset, predictions will not be resized.
            return_coco_annotation (`bool`, *optional*):
                Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
                format.
        Returns:
            `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
            - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
              `List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
              `True`. Set to `None` if no mask if found above `threshold`.
            - **segments_info** -- A dictionary that contains additional information on each segment.
                - **id** -- An integer representing the `segment_id`.
                - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
                - **score** -- Prediction score of segment with `segment_id`.
        """
        class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
        masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]

        batch_size = class_queries_logits.shape[0]
        num_labels = class_queries_logits.shape[-1] - 1

        mask_probs = masks_queries_logits.sigmoid()  # [batch_size, num_queries, height, width]

        # Predicted label and score of each query (batch_size, num_queries)
        pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)

        # Loop over items in batch size
        results: List[Dict[str, TensorType]] = []

        for i in range(batch_size):
            mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
                mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
            )

            # No mask found
            if mask_probs_item.shape[0] <= 0:
                height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
                segmentation = torch.zeros((height, width)) - 1
                results.append({"segmentation": segmentation, "segments_info": []})
                continue

            # Get segmentation map and segment information of batch item
            target_size = target_sizes[i] if target_sizes is not None else None
            segmentation, segments = compute_segments(
                mask_probs=mask_probs_item,
                pred_scores=pred_scores_item,
                pred_labels=pred_labels_item,
                mask_threshold=mask_threshold,
                overlap_mask_area_threshold=overlap_mask_area_threshold,
                label_ids_to_fuse=[],
                target_size=target_size,
            )

            # Return segmentation map in run-length encoding (RLE) format
            if return_coco_annotation:
                segmentation = convert_segmentation_to_rle(segmentation)

            results.append({"segmentation": segmentation, "segments_info": segments})
        return results

    # inspired by https://github.com/facebookresearch/detr/blob/master/models/segmentation.py#L241
    def post_process_panoptic_segmentation(
        self,
        outputs,
        threshold: float = 0.5,
        mask_threshold: float = 0.5,
        overlap_mask_area_threshold: float = 0.8,
        label_ids_to_fuse: Optional[Set[int]] = None,
        target_sizes: Optional[List[Tuple[int, int]]] = None,
    ) -> List[Dict]:
        """
        Converts the output of [`DetrForSegmentation`] into image panoptic segmentation predictions. Only supports
        PyTorch.

        Args:
            outputs ([`DetrForSegmentation`]):
                The outputs from [`DetrForSegmentation`].
            threshold (`float`, *optional*, defaults to 0.5):
                The probability score threshold to keep predicted instance masks.
            mask_threshold (`float`, *optional*, defaults to 0.5):
                Threshold to use when turning the predicted masks into binary values.
            overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
                The overlap mask area threshold to merge or discard small disconnected parts within each binary
                instance mask.
            label_ids_to_fuse (`Set[int]`, *optional*):
                The labels in this state will have all their instances be fused together. For instance we could say
                there can only be one sky in an image, but several persons, so the label ID for sky would be in that
                set, but not the one for person.
            target_sizes (`List[Tuple]`, *optional*):
                List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
                final size (height, width) of each prediction in batch. If unset, predictions will not be resized.
        Returns:
            `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
            - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id` or
              `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized to
              the corresponding `target_sizes` entry.
            - **segments_info** -- A dictionary that contains additional information on each segment.
                - **id** -- an integer representing the `segment_id`.
                - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
                - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
                  Multiple instances of the same class / label were fused and assigned a single `segment_id`.
                - **score** -- Prediction score of segment with `segment_id`.
        """

        if label_ids_to_fuse is None:
            logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
            label_ids_to_fuse = set()

        class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
        masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]

        batch_size = class_queries_logits.shape[0]
        num_labels = class_queries_logits.shape[-1] - 1

        mask_probs = masks_queries_logits.sigmoid()  # [batch_size, num_queries, height, width]

        # Predicted label and score of each query (batch_size, num_queries)
        pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)

        # Loop over items in batch size
        results: List[Dict[str, TensorType]] = []

        for i in range(batch_size):
            mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
                mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
            )

            # No mask found
            if mask_probs_item.shape[0] <= 0:
                height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
                segmentation = torch.zeros((height, width)) - 1
                results.append({"segmentation": segmentation, "segments_info": []})
                continue

            # Get segmentation map and segment information of batch item
            target_size = target_sizes[i] if target_sizes is not None else None
            segmentation, segments = compute_segments(
                mask_probs=mask_probs_item,
                pred_scores=pred_scores_item,
                pred_labels=pred_labels_item,
                mask_threshold=mask_threshold,
                overlap_mask_area_threshold=overlap_mask_area_threshold,
                label_ids_to_fuse=label_ids_to_fuse,
                target_size=target_size,
            )

            results.append({"segmentation": segmentation, "segments_info": segments})
        return results