diff --git a/docs/source/en/model_doc/yolos.md b/docs/source/en/model_doc/yolos.md index 2a0f5d23fa..c4a9e21228 100644 --- a/docs/source/en/model_doc/yolos.md +++ b/docs/source/en/model_doc/yolos.md @@ -92,6 +92,11 @@ Use [`YolosImageProcessor`] for preparing images (and optional targets) for the [[autodoc]] YolosImageProcessor - preprocess + +## YolosImageProcessorFast + +[[autodoc]] YolosImageProcessorFast + - preprocess - pad - post_process_object_detection diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py index a536f5b45e..b5a87308e4 100644 --- a/src/transformers/models/auto/image_processing_auto.py +++ b/src/transformers/models/auto/image_processing_auto.py @@ -167,7 +167,7 @@ else: ("vit_msn", ("ViTImageProcessor", "ViTImageProcessorFast")), ("vitmatte", ("VitMatteImageProcessor",)), ("xclip", ("CLIPImageProcessor", "CLIPImageProcessorFast")), - ("yolos", ("YolosImageProcessor",)), + ("yolos", ("YolosImageProcessor", "YolosImageProcessorFast")), ("zoedepth", ("ZoeDepthImageProcessor",)), ] ) diff --git a/src/transformers/models/yolos/__init__.py b/src/transformers/models/yolos/__init__.py index 5739fc56fd..c7ce96987e 100644 --- a/src/transformers/models/yolos/__init__.py +++ b/src/transformers/models/yolos/__init__.py @@ -21,6 +21,7 @@ if TYPE_CHECKING: from .configuration_yolos import * from .feature_extraction_yolos import * from .image_processing_yolos import * + from .image_processing_yolos_fast import * from .modeling_yolos import * else: import sys diff --git a/src/transformers/models/yolos/image_processing_yolos_fast.py b/src/transformers/models/yolos/image_processing_yolos_fast.py new file mode 100644 index 0000000000..d048b3bb0e --- /dev/null +++ b/src/transformers/models/yolos/image_processing_yolos_fast.py @@ -0,0 +1,893 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/yolos/modular_yolos.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_yolos.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +import pathlib +from typing import Any, Dict, List, Optional, Tuple, Union + +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_processing_utils_fast import ( + BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, + BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, + BaseImageProcessorFast, + DefaultFastImageProcessorKwargs, + SizeDict, + get_image_size_for_max_height_width, + get_max_height_width, + safe_squeeze, +) +from ...image_transforms import center_to_corners_format, corners_to_center_format +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + AnnotationFormat, + AnnotationType, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + validate_annotations, +) +from ...processing_utils import Unpack +from ...utils import ( + TensorType, + add_start_docstrings, + is_torch_available, + is_torchvision_available, + is_torchvision_v2_available, + logging, +) +from ...utils.import_utils import requires + + +if is_torch_available(): + import torch + + +if is_torchvision_v2_available(): + from torchvision.io import read_image + from torchvision.transforms.v2 import functional as F +elif is_torchvision_available(): + from torchvision.io import read_image + from torchvision.transforms import functional as F + + +logger = logging.get_logger(__name__) + + +class YolosFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): + format: Optional[Union[str, AnnotationFormat]] + do_convert_annotations: Optional[bool] + do_pad: Optional[bool] + pad_size: Optional[Dict[str, int]] + return_segmentation_masks: Optional[bool] + + +SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC) + + +# inspired by https://github.com/facebookresearch/yolos/blob/master/datasets/coco.py#L33 +def convert_coco_poly_to_mask(segmentations, height: int, width: int, device: torch.device) -> torch.Tensor: + """ + 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 = torch.as_tensor(mask, dtype=torch.uint8, device=device) + mask = torch.any(mask, axis=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, axis=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8, device=device) + + return masks + + +# inspired by https://github.com/facebookresearch/yolos/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 YOLOS. + """ + image_height, image_width = image.size()[-2:] + + image_id = target["image_id"] + image_id = torch.as_tensor([image_id], dtype=torch.int64, device=image.device) + + # Get all COCO annotations for the given image. + annotations = target["annotations"] + classes = [] + area = [] + boxes = [] + keypoints = [] + for obj in annotations: + if "iscrowd" not in obj or obj["iscrowd"] == 0: + classes.append(obj["category_id"]) + area.append(obj["area"]) + boxes.append(obj["bbox"]) + if "keypoints" in obj: + keypoints.append(obj["keypoints"]) + + classes = torch.as_tensor(classes, dtype=torch.int64, device=image.device) + area = torch.as_tensor(area, dtype=torch.float32, device=image.device) + iscrowd = torch.zeros_like(classes, dtype=torch.int64, device=image.device) + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32, device=image.device).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 = { + "image_id": image_id, + "class_labels": classes[keep], + "boxes": boxes[keep], + "area": area[keep], + "iscrowd": iscrowd[keep], + "orig_size": torch.as_tensor([int(image_height), int(image_width)], dtype=torch.int64, device=image.device), + } + + if keypoints: + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=image.device) + # 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, device=image.device) + new_target["masks"] = masks[keep] + + return new_target + + +def masks_to_boxes(masks: torch.Tensor) -> torch.Tensor: + """ + 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.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + y = torch.arange(0, h, dtype=torch.float32, device=masks.device) + x = torch.arange(0, w, dtype=torch.float32, device=masks.device) + # see https://github.com/pytorch/pytorch/issues/50276 + y, x = torch.meshgrid(y, x, indexing="ij") + + x_mask = masks * torch.unsqueeze(x, 0) + x_max = x_mask.view(x_mask.shape[0], -1).max(-1)[0] + x_min = ( + torch.where(masks, x.unsqueeze(0), torch.tensor(1e8, device=masks.device)).view(masks.shape[0], -1).min(-1)[0] + ) + + y_mask = masks * torch.unsqueeze(y, 0) + y_max = y_mask.view(y_mask.shape[0], -1).max(-1)[0] + y_min = ( + torch.where(masks, y.unsqueeze(0), torch.tensor(1e8, device=masks.device)).view(masks.shape[0], -1).min(-1)[0] + ) + + return torch.stack([x_min, y_min, x_max, y_max], 1) + + +# 2 functions below adapted from https://github.com/cocodataset/panopticapi/blob/master/panopticapi/utils.py +# Copyright (c) 2018, Alexander Kirillov +# All rights reserved. +def rgb_to_id(color): + """ + Converts RGB color to unique ID. + """ + if isinstance(color, torch.Tensor) and len(color.shape) == 3: + if color.dtype == torch.uint8: + color = color.to(torch.int32) + return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2] + return int(color[0] + 256 * color[1] + 256 * 256 * color[2]) + + +def prepare_coco_panoptic_annotation( + image: torch.Tensor, + 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 YOLOS. + """ + 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"] = torch.as_tensor( + [target["image_id"] if "image_id" in target else target["id"]], dtype=torch.int64, device=image.device + ) + new_target["size"] = torch.as_tensor([image_height, image_width], dtype=torch.int64, device=image.device) + new_target["orig_size"] = torch.as_tensor([image_height, image_width], dtype=torch.int64, device=image.device) + + if "segments_info" in target: + masks = read_image(annotation_path).permute(1, 2, 0).to(dtype=torch.int32, device=image.device) + masks = rgb_to_id(masks) + + ids = torch.as_tensor([segment_info["id"] for segment_info in target["segments_info"]], device=image.device) + masks = masks == ids[:, None, None] + masks = masks.to(torch.bool) + if return_masks: + new_target["masks"] = masks + new_target["boxes"] = masks_to_boxes(masks) + new_target["class_labels"] = torch.as_tensor( + [segment_info["category_id"] for segment_info in target["segments_info"]], + dtype=torch.int64, + device=image.device, + ) + new_target["iscrowd"] = torch.as_tensor( + [segment_info["iscrowd"] for segment_info in target["segments_info"]], + dtype=torch.int64, + device=image.device, + ) + new_target["area"] = torch.as_tensor( + [segment_info["area"] for segment_info in target["segments_info"]], + dtype=torch.float32, + device=image.device, + ) + + return new_target + + +def get_size_with_aspect_ratio( + image_size: Tuple[int, int], size: int, max_size: Optional[int] = None, mod_size: int = 16 +) -> Tuple[int, int]: + """ + Computes the output image size given the input image size and the desired output size with multiple of divisible_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. + mod_size (`int`, *optional*): + The size to make multiple of mod_size. + """ + height, width = image_size + raw_size = None + 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: + raw_size = max_size * min_original_size / max_original_size + size = int(round(raw_size)) + + if width < height: + ow = size + if max_size is not None and raw_size is not None: + oh = int(raw_size * height / width) + else: + oh = int(size * height / width) + elif (height <= width and height == size) or (width <= height and width == size): + oh, ow = height, width + else: + oh = size + if max_size is not None and raw_size is not None: + ow = int(raw_size * width / height) + else: + ow = int(size * width / height) + + if mod_size is not None: + ow_mod = torch.remainder(torch.tensor(ow), mod_size).item() + oh_mod = torch.remainder(torch.tensor(oh), mod_size).item() + ow = ow - ow_mod + oh = oh - oh_mod + + return (oh, ow) + + +@add_start_docstrings( + "Constructs a fast Yolos image processor.", + BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, + """ + format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`): + Data format of the annotations. One of "coco_detection" or "coco_panoptic". + do_convert_annotations (`bool`, *optional*, defaults to `True`): + Controls whether to convert the annotations to the format expected by the YOLOS 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`, padding will be applied to the bottom and right of the image with zeros. + If `pad_size` is provided, the image will be padded to the specified dimensions. + Otherwise, the image will be padded to the maximum height and width of the batch. + pad_size (`Dict[str, int]`, *optional*): + The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size + provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest + height and width in the batch. + return_segmentation_masks (`bool`, *optional*, defaults to `False`): + Whether to return segmentation masks. + """, +) +@requires(backends=("torchvision", "torch")) +class YolosImageProcessorFast(BaseImageProcessorFast): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + format = AnnotationFormat.COCO_DETECTION + do_resize = True + do_rescale = True + do_normalize = True + do_pad = True + size = {"shortest_edge": 800, "longest_edge": 1333} + default_to_square = False + model_input_names = ["pixel_values", "pixel_mask"] + valid_kwargs = YolosFastImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[YolosFastImageProcessorKwargs]) -> None: + if "pad_and_return_pixel_mask" in kwargs: + kwargs["do_pad"] = kwargs.pop("pad_and_return_pixel_mask") + + size = kwargs.pop("size", None) + 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} + self.size = get_size_dict(size, max_size=max_size, default_to_square=False) + + # Backwards compatibility + do_convert_annotations = kwargs.get("do_convert_annotations", None) + do_normalize = kwargs.get("do_normalize", None) + if do_convert_annotations is None and getattr(self, "do_convert_annotations", None) is None: + self.do_convert_annotations = do_normalize if do_normalize is not None else self.do_normalize + + super().__init__(**kwargs) + + @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. `YolosImageProcessorFast.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: torch.Tensor, + target: Dict, + format: Optional[AnnotationFormat] = None, + return_segmentation_masks: Optional[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 YOLOS 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: torch.Tensor, + size: SizeDict, + interpolation: "F.InterpolationMode" = None, + **kwargs, + ) -> torch.Tensor: + """ + 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 (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Size of the image's `(height, width)` dimensions after resizing. Available options are: + - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. + Do NOT keep the aspect ratio. + - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting + the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge + less or equal to `longest_edge`. + - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the + aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to + `max_width`. + interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): + Resampling filter to use if resizing the image. + """ + interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR + if size.shortest_edge and size.longest_edge: + # Resize the image so that the shortest edge or the longest edge is of the given size + # while maintaining the aspect ratio of the original image. + new_size = get_size_with_aspect_ratio( + image.size()[-2:], + size["shortest_edge"], + size["longest_edge"], + ) + elif size.max_height and size.max_width: + new_size = get_image_size_for_max_height_width(image.size()[-2:], size["max_height"], size["max_width"]) + elif size.height and size.width: + new_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 = F.resize( + image, + size=new_size, + interpolation=interpolation, + **kwargs, + ) + return image + + def resize_annotation( + self, + annotation: Dict[str, Any], + orig_size: Tuple[int, int], + target_size: Tuple[int, int], + threshold: float = 0.5, + interpolation: "F.InterpolationMode" = None, + ): + """ + 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 (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`): + The resampling filter to use when resizing the masks. + """ + interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST + ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)] + + new_annotation = {} + new_annotation["size"] = target_size + + for key, value in annotation.items(): + if key == "boxes": + boxes = value + scaled_boxes = boxes * torch.as_tensor( + [ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32, device=boxes.device + ) + 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 = [F.resize(mask, target_size, interpolation=interpolation) for mask in masks] + masks = torch.stack(masks).to(torch.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 + + def normalize_annotation(self, 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 /= torch.as_tensor( + [image_width, image_height, image_width, image_height], dtype=torch.float32, device=boxes.device + ) + norm_annotation[key] = boxes + else: + norm_annotation[key] = value + return norm_annotation + + 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 + ratio_height, ratio_width = (input / output for output, input in zip(output_image_size, input_image_size)) + + for key, value in annotation.items(): + if key == "masks": + masks = value + masks = F.pad( + masks, + padding, + fill=0, + ) + masks = safe_squeeze(masks, 1) + new_annotation["masks"] = masks + elif key == "boxes" and update_bboxes: + boxes = value + boxes *= torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height], device=boxes.device) + new_annotation["boxes"] = boxes + elif key == "size": + new_annotation["size"] = output_image_size + else: + new_annotation[key] = value + return new_annotation + + def pad( + self, + image: torch.Tensor, + padded_size: Tuple[int, int], + annotation: Optional[Dict[str, Any]] = None, + update_bboxes: bool = True, + fill: int = 0, + ): + original_size = image.size()[-2:] + padding_bottom = padded_size[0] - original_size[0] + padding_right = padded_size[1] - original_size[1] + if padding_bottom < 0 or padding_right < 0: + raise ValueError( + f"Padding dimensions are negative. Please make sure that the padded size is larger than the " + f"original size. Got padded size: {padded_size}, original size: {original_size}." + ) + if original_size != padded_size: + padding = [0, 0, padding_right, padding_bottom] + image = F.pad(image, padding, fill=fill) + if annotation is not None: + annotation = self._update_annotation_for_padded_image( + annotation, original_size, padded_size, padding, update_bboxes + ) + + # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + pixel_mask = torch.zeros(padded_size, dtype=torch.int64, device=image.device) + pixel_mask[: original_size[0], : original_size[1]] = 1 + + return image, pixel_mask, annotation + + @add_start_docstrings( + BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, + """ + 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. + format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`): + Data format of the annotations. One of "coco_detection" or "coco_panoptic". + do_convert_annotations (`bool`, *optional*, defaults to `True`): + Controls whether to convert the annotations to the format expected by the YOLOS 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`, padding will be applied to the bottom and right of the image with zeros. + If `pad_size` is provided, the image will be padded to the specified dimensions. + Otherwise, the image will be padded to the maximum height and width of the batch. + pad_size (`Dict[str, int]`, *optional*): + The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size + provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest + height and width in the batch. + return_segmentation_masks (`bool`, *optional*, defaults to `False`): + Whether to return segmentation masks. + masks_path (`str` or `pathlib.Path`, *optional*): + Path to the directory containing the segmentation masks. + """, + ) + def preprocess( + self, + images: ImageInput, + annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None, + masks_path: Optional[Union[str, pathlib.Path]] = None, + **kwargs: Unpack[YolosFastImageProcessorKwargs], + ) -> BatchFeature: + if "pad_and_return_pixel_mask" in kwargs: + kwargs["do_pad"] = kwargs.pop("pad_and_return_pixel_mask") + logger.warning_once( + "The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, " + "use `do_pad` instead." + ) + + 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." + ) + kwargs["size"] = kwargs.pop("max_size") + + return super().preprocess(images, annotations=annotations, masks_path=masks_path, **kwargs) + + def _preprocess( + self, + images: List["torch.Tensor"], + annotations: Optional[Union[AnnotationType, List[AnnotationType]]], + return_segmentation_masks: bool, + masks_path: Optional[Union[str, pathlib.Path]], + do_resize: bool, + size: SizeDict, + interpolation: Optional["F.InterpolationMode"], + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + do_convert_annotations: bool, + image_mean: Optional[Union[float, List[float]]], + image_std: Optional[Union[float, List[float]]], + do_pad: bool, + pad_size: Optional[Dict[str, int]], + format: Optional[Union[str, AnnotationFormat]], + return_tensors: Optional[Union[str, TensorType]], + ) -> BatchFeature: + """ + Preprocess an image or a batch of images so that it can be used by the model. + """ + 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." + ) + + data = {} + + processed_images = [] + processed_annotations = [] + pixel_masks = [] # Initialize pixel_masks here + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # prepare (COCO annotations as a list of Dict -> YOLOS target as a single Dict per image) + if annotations is not None: + annotation = self.prepare_annotation( + image, + annotation, + format, + return_segmentation_masks=return_segmentation_masks, + masks_path=masks_path, + input_data_format=ChannelDimension.FIRST, + ) + + if do_resize: + resized_image = self.resize(image, size=size, interpolation=interpolation) + if annotations is not None: + annotation = self.resize_annotation( + annotation, + orig_size=image.size()[-2:], + target_size=resized_image.size()[-2:], + ) + image = resized_image + # Fused rescale and normalize + image = self.rescale_and_normalize(image, do_rescale, rescale_factor, do_normalize, image_mean, image_std) + if do_convert_annotations and annotations is not None: + annotation = self.normalize_annotation(annotation, get_image_size(image, ChannelDimension.FIRST)) + + processed_images.append(image) + processed_annotations.append(annotation) + images = processed_images + annotations = processed_annotations if annotations is not None else None + + if do_pad: + # depends on all resized image shapes so we need another loop + if pad_size is not None: + padded_size = (pad_size["height"], pad_size["width"]) + else: + padded_size = get_max_height_width(images) + + padded_images = [] + padded_annotations = [] + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...} + if padded_size == image.size()[-2:]: + padded_images.append(image) + pixel_masks.append(torch.ones(padded_size, dtype=torch.int64, device=image.device)) + padded_annotations.append(annotation) + continue + image, pixel_mask, annotation = self.pad( + image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations + ) + padded_images.append(image) + padded_annotations.append(annotation) + pixel_masks.append(pixel_mask) + images = padded_images + annotations = padded_annotations if annotations is not None else None + data.update({"pixel_mask": torch.stack(pixel_masks, dim=0)}) + + data.update({"pixel_values": torch.stack(images, dim=0)}) + encoded_inputs = BatchFeature(data, 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 + + def post_process(self, outputs, target_sizes): + """ + Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, + top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`YolosObjectDetectionOutput`]): + 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 = out_logits.sigmoid() + topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1) + scores = topk_values + topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor") + labels = topk_indexes % out_logits.shape[2] + boxes = center_to_corners_format(out_bbox) + boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + # 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) + 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_object_detection( + self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100 + ): + """ + Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, + top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`YolosObjectDetectionOutput`]): + 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 left to None, predictions will not be resized. + top_k (`int`, *optional*, defaults to 100): + Keep only top k bounding boxes before filtering by thresholding. + + 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 = out_logits.sigmoid() + prob = prob.view(out_logits.shape[0], -1) + k_value = min(top_k, prob.size(1)) + topk_values, topk_indexes = torch.topk(prob, k_value, dim=1) + scores = topk_values + topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor") + labels = topk_indexes % out_logits.shape[2] + boxes = center_to_corners_format(out_bbox) + boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + # and 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 + + +__all__ = ["YolosImageProcessorFast"] diff --git a/src/transformers/models/yolos/modular_yolos.py b/src/transformers/models/yolos/modular_yolos.py new file mode 100644 index 0000000000..5ffdbe2e2b --- /dev/null +++ b/src/transformers/models/yolos/modular_yolos.py @@ -0,0 +1,193 @@ +from typing import List, Optional, Tuple, Union + +from transformers.models.detr.image_processing_detr_fast import DetrImageProcessorFast + +from ...image_transforms import center_to_corners_format +from ...utils import ( + TensorType, + is_torch_available, + logging, +) + + +if is_torch_available(): + import torch + + +logger = logging.get_logger(__name__) + + +def get_size_with_aspect_ratio( + image_size: Tuple[int, int], size: int, max_size: Optional[int] = None, mod_size: int = 16 +) -> Tuple[int, int]: + """ + Computes the output image size given the input image size and the desired output size with multiple of divisible_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. + mod_size (`int`, *optional*): + The size to make multiple of mod_size. + """ + height, width = image_size + raw_size = None + 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: + raw_size = max_size * min_original_size / max_original_size + size = int(round(raw_size)) + + if width < height: + ow = size + if max_size is not None and raw_size is not None: + oh = int(raw_size * height / width) + else: + oh = int(size * height / width) + elif (height <= width and height == size) or (width <= height and width == size): + oh, ow = height, width + else: + oh = size + if max_size is not None and raw_size is not None: + ow = int(raw_size * width / height) + else: + ow = int(size * width / height) + + if mod_size is not None: + ow_mod = torch.remainder(torch.tensor(ow), mod_size).item() + oh_mod = torch.remainder(torch.tensor(oh), mod_size).item() + ow = ow - ow_mod + oh = oh - oh_mod + + return (oh, ow) + + +class YolosImageProcessorFast(DetrImageProcessorFast): + def post_process(self, outputs, target_sizes): + """ + Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, + top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`YolosObjectDetectionOutput`]): + 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 = out_logits.sigmoid() + topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1) + scores = topk_values + topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor") + labels = topk_indexes % out_logits.shape[2] + boxes = center_to_corners_format(out_bbox) + boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + # 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) + 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_object_detection( + self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100 + ): + """ + Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, + top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`YolosObjectDetectionOutput`]): + 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 left to None, predictions will not be resized. + top_k (`int`, *optional*, defaults to 100): + Keep only top k bounding boxes before filtering by thresholding. + + 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 = out_logits.sigmoid() + prob = prob.view(out_logits.shape[0], -1) + k_value = min(top_k, prob.size(1)) + topk_values, topk_indexes = torch.topk(prob, k_value, dim=1) + scores = topk_values + topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor") + labels = topk_indexes % out_logits.shape[2] + boxes = center_to_corners_format(out_bbox) + boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + # and 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_segmentation(): + raise NotImplementedError("Segmentation post-processing is not implemented for Deformable DETR yet.") + + def post_process_instance(): + raise NotImplementedError("Instance post-processing is not implemented for Deformable DETR yet.") + + def post_process_panoptic(): + raise NotImplementedError("Panoptic post-processing is not implemented for Deformable DETR yet.") + + def post_process_instance_segmentation(): + raise NotImplementedError("Segmentation post-processing is not implemented for Deformable DETR yet.") + + def post_process_semantic_segmentation(): + raise NotImplementedError("Semantic segmentation post-processing is not implemented for Deformable DETR yet.") + + def post_process_panoptic_segmentation(): + raise NotImplementedError("Panoptic segmentation post-processing is not implemented for Deformable DETR yet.") + + +__all__ = ["YolosImageProcessorFast"] diff --git a/tests/models/yolos/test_image_processing_yolos.py b/tests/models/yolos/test_image_processing_yolos.py index 69748d9e55..2ef2f197ae 100644 --- a/tests/models/yolos/test_image_processing_yolos.py +++ b/tests/models/yolos/test_image_processing_yolos.py @@ -21,7 +21,7 @@ import numpy as np from parameterized import parameterized from transformers.testing_utils import require_torch, require_vision, slow -from transformers.utils import is_torch_available, is_vision_available +from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs @@ -34,6 +34,9 @@ if is_vision_available(): from transformers import YolosImageProcessor + if is_torchvision_available(): + from transformers import YolosImageProcessorFast + class YolosImageProcessingTester: def __init__( @@ -143,6 +146,7 @@ class YolosImageProcessingTester: @require_vision class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase): image_processing_class = YolosImageProcessor if is_vision_available() else None + fast_image_processing_class = YolosImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() @@ -153,23 +157,25 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): - image_processing = self.image_processing_class(**self.image_processor_dict) - self.assertTrue(hasattr(image_processing, "image_mean")) - self.assertTrue(hasattr(image_processing, "image_std")) - self.assertTrue(hasattr(image_processing, "do_normalize")) - self.assertTrue(hasattr(image_processing, "do_resize")) - self.assertTrue(hasattr(image_processing, "size")) + for image_processing_class in self.image_processor_list: + image_processing = image_processing_class(**self.image_processor_dict) + self.assertTrue(hasattr(image_processing, "image_mean")) + self.assertTrue(hasattr(image_processing, "image_std")) + self.assertTrue(hasattr(image_processing, "do_normalize")) + self.assertTrue(hasattr(image_processing, "do_resize")) + self.assertTrue(hasattr(image_processing, "size")) def test_image_processor_from_dict_with_kwargs(self): - image_processor = self.image_processing_class.from_dict(self.image_processor_dict) - self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333}) - self.assertEqual(image_processor.do_pad, True) + for image_processing_class in self.image_processor_list: + image_processor = image_processing_class.from_dict(self.image_processor_dict) + self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333}) + self.assertEqual(image_processor.do_pad, True) - image_processor = self.image_processing_class.from_dict( - self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False - ) - self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) - self.assertEqual(image_processor.do_pad, False) + image_processor = image_processing_class.from_dict( + self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False + ) + self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) + self.assertEqual(image_processor.do_pad, False) def test_equivalence_padding(self): # Initialize image_processings @@ -199,21 +205,22 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix ] ) def test_resize_max_size_respected(self, image_size, longest_edge, shortest_edge): - image_processor = self.image_processing_class(**self.image_processor_dict) + for image_processing_class in self.image_processor_list: + image_processor = image_processing_class(**self.image_processor_dict) - # create torch tensors as image - image = torch.randint(0, 256, image_size, dtype=torch.uint8) - processed_image = image_processor( - image, - size={"longest_edge": longest_edge, "shortest_edge": shortest_edge}, - do_pad=False, - return_tensors="pt", - )["pixel_values"] + # create torch tensors as image + image = torch.randint(0, 256, image_size, dtype=torch.uint8) + processed_image = image_processor( + image, + size={"longest_edge": longest_edge, "shortest_edge": shortest_edge}, + do_pad=False, + return_tensors="pt", + )["pixel_values"] - shape = list(processed_image.shape[-2:]) - max_size, min_size = max(shape), min(shape) - self.assertTrue(max_size <= 1333, f"Expected max_size <= 1333, got image shape {shape}") - self.assertTrue(min_size <= 800, f"Expected min_size <= 800, got image shape {shape}") + shape = list(processed_image.shape[-2:]) + max_size, min_size = max(shape), min(shape) + self.assertTrue(max_size <= 1333, f"Expected max_size <= 1333, got image shape {shape}") + self.assertTrue(min_size <= 800, f"Expected min_size <= 800, got image shape {shape}") @slow def test_call_pytorch_with_coco_detection_annotations(self): @@ -224,40 +231,41 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix target = {"image_id": 39769, "annotations": target} - # encode them - image_processing = YolosImageProcessor.from_pretrained("hustvl/yolos-small") - encoding = image_processing(images=image, annotations=target, return_tensors="pt") + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class.from_pretrained("hustvl/yolos-small") + encoding = image_processing(images=image, annotations=target, return_tensors="pt") - # verify pixel values - expected_shape = torch.Size([1, 3, 800, 1056]) - self.assertEqual(encoding["pixel_values"].shape, expected_shape) + # verify pixel values + expected_shape = torch.Size([1, 3, 800, 1056]) + self.assertEqual(encoding["pixel_values"].shape, expected_shape) - expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) - torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4) + expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) + torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4) - # verify area - expected_area = torch.tensor([5832.7256, 11144.6689, 484763.2500, 829269.8125, 146579.4531, 164177.6250]) - torch.testing.assert_close(encoding["labels"][0]["area"], expected_area) - # verify boxes - expected_boxes_shape = torch.Size([6, 4]) - self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) - expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]) - torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3) - # verify image_id - expected_image_id = torch.tensor([39769]) - torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id) - # verify is_crowd - expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) - torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd) - # verify class_labels - expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) - torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels) - # verify orig_size - expected_orig_size = torch.tensor([480, 640]) - torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size) - # verify size - expected_size = torch.tensor([800, 1056]) - torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) + # verify area + expected_area = torch.tensor([5832.7256, 11144.6689, 484763.2500, 829269.8125, 146579.4531, 164177.6250]) + torch.testing.assert_close(encoding["labels"][0]["area"], expected_area) + # verify boxes + expected_boxes_shape = torch.Size([6, 4]) + self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) + expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]) + torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3) + # verify image_id + expected_image_id = torch.tensor([39769]) + torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id) + # verify is_crowd + expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) + torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd) + # verify class_labels + expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) + torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels) + # verify orig_size + expected_orig_size = torch.tensor([480, 640]) + torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size) + # verify size + expected_size = torch.tensor([800, 1056]) + torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) @slow def test_call_pytorch_with_coco_panoptic_annotations(self): @@ -270,43 +278,45 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") - # encode them - image_processing = YolosImageProcessor(format="coco_panoptic") - encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt") + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class(format="coco_panoptic") + encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt") - # verify pixel values - expected_shape = torch.Size([1, 3, 800, 1056]) - self.assertEqual(encoding["pixel_values"].shape, expected_shape) + # verify pixel values + expected_shape = torch.Size([1, 3, 800, 1056]) + self.assertEqual(encoding["pixel_values"].shape, expected_shape) - expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) - torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4) + expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) + torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4) - # verify area - expected_area = torch.tensor([146591.5000, 163974.2500, 480092.2500, 11187.0000, 5824.5000, 7562.5000]) - torch.testing.assert_close(encoding["labels"][0]["area"], expected_area) - # verify boxes - expected_boxes_shape = torch.Size([6, 4]) - self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) - expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]) - torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3) - # verify image_id - expected_image_id = torch.tensor([39769]) - torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id) - # verify is_crowd - expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) - torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd) - # verify class_labels - expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) - torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels) - # verify masks - expected_masks_sum = 815161 - self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum) - # verify orig_size - expected_orig_size = torch.tensor([480, 640]) - torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size) - # verify size - expected_size = torch.tensor([800, 1056]) - torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) + # verify area + expected_area = torch.tensor([146591.5000, 163974.2500, 480092.2500, 11187.0000, 5824.5000, 7562.5000]) + torch.testing.assert_close(encoding["labels"][0]["area"], expected_area) + # verify boxes + expected_boxes_shape = torch.Size([6, 4]) + self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) + expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]) + torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3) + # verify image_id + expected_image_id = torch.tensor([39769]) + torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id) + # verify is_crowd + expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) + torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd) + # verify class_labels + expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) + torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels) + # verify masks + expected_masks_sum = 815161 + relative_error = torch.abs(encoding["labels"][0]["masks"].sum() - expected_masks_sum) / expected_masks_sum + self.assertTrue(relative_error < 1e-3) + # verify orig_size + expected_orig_size = torch.tensor([480, 640]) + torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size) + # verify size + expected_size = torch.tensor([800, 1056]) + torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) # Output size is slight different from DETR as yolos takes mod of 16 @slow @@ -336,96 +346,97 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix images = [image_0, image_1] annotations = [annotations_0, annotations_1] - image_processing = YolosImageProcessor() - encoding = image_processing( - images=images, - annotations=annotations, - return_segmentation_masks=True, - return_tensors="pt", # do_convert_annotations=True - ) + for image_processing_class in self.image_processor_list: + image_processing = image_processing_class() + encoding = image_processing( + images=images, + annotations=annotations, + return_segmentation_masks=True, + return_tensors="pt", # do_convert_annotations=True + ) - # Check the pixel values have been padded - postprocessed_height, postprocessed_width = 800, 1056 - expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) - self.assertEqual(encoding["pixel_values"].shape, expected_shape) + # Check the pixel values have been padded + postprocessed_height, postprocessed_width = 800, 1056 + expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) + self.assertEqual(encoding["pixel_values"].shape, expected_shape) - # Check the bounding boxes have been adjusted for padded images - self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) - self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) - expected_boxes_0 = torch.tensor( - [ - [0.6879, 0.4609, 0.0755, 0.3691], - [0.2118, 0.3359, 0.2601, 0.1566], - [0.5011, 0.5000, 0.9979, 1.0000], - [0.5010, 0.5020, 0.9979, 0.9959], - [0.3284, 0.5944, 0.5884, 0.8112], - [0.8394, 0.5445, 0.3213, 0.9110], - ] - ) - expected_boxes_1 = torch.tensor( - [ - [0.4169, 0.2765, 0.0458, 0.2215], - [0.1284, 0.2016, 0.1576, 0.0940], - [0.3792, 0.4933, 0.7559, 0.9865], - [0.3794, 0.5002, 0.7563, 0.9955], - [0.1990, 0.5456, 0.3566, 0.8646], - [0.5845, 0.4115, 0.3462, 0.7161], - ] - ) - torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3, atol=1e-3) - torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3, atol=1e-3) + # Check the bounding boxes have been adjusted for padded images + self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) + self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) + expected_boxes_0 = torch.tensor( + [ + [0.6879, 0.4609, 0.0755, 0.3691], + [0.2118, 0.3359, 0.2601, 0.1566], + [0.5011, 0.5000, 0.9979, 1.0000], + [0.5010, 0.5020, 0.9979, 0.9959], + [0.3284, 0.5944, 0.5884, 0.8112], + [0.8394, 0.5445, 0.3213, 0.9110], + ] + ) + expected_boxes_1 = torch.tensor( + [ + [0.4169, 0.2765, 0.0458, 0.2215], + [0.1284, 0.2016, 0.1576, 0.0940], + [0.3792, 0.4933, 0.7559, 0.9865], + [0.3794, 0.5002, 0.7563, 0.9955], + [0.1990, 0.5456, 0.3566, 0.8646], + [0.5845, 0.4115, 0.3462, 0.7161], + ] + ) + torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3, atol=1e-3) + torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3, atol=1e-3) - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1056])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1056])) + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1056])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1056])) - # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height - # format and not in the range [0, 1] - encoding = image_processing( - images=images, - annotations=annotations, - return_segmentation_masks=True, - do_convert_annotations=False, - return_tensors="pt", - ) - self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) - self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) - # Convert to absolute coordinates - unnormalized_boxes_0 = torch.vstack( - [ - expected_boxes_0[:, 0] * postprocessed_width, - expected_boxes_0[:, 1] * postprocessed_height, - expected_boxes_0[:, 2] * postprocessed_width, - expected_boxes_0[:, 3] * postprocessed_height, - ] - ).T - unnormalized_boxes_1 = torch.vstack( - [ - expected_boxes_1[:, 0] * postprocessed_width, - expected_boxes_1[:, 1] * postprocessed_height, - expected_boxes_1[:, 2] * postprocessed_width, - expected_boxes_1[:, 3] * postprocessed_height, - ] - ).T - # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max - expected_boxes_0 = torch.vstack( - [ - unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, - unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, - unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, - unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, - ] - ).T - expected_boxes_1 = torch.vstack( - [ - unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, - unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, - unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, - unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, - ] - ).T - torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1, atol=1) - torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1, atol=1) + # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height + # format and not in the range [0, 1] + encoding = image_processing( + images=images, + annotations=annotations, + return_segmentation_masks=True, + do_convert_annotations=False, + return_tensors="pt", + ) + self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) + self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) + # Convert to absolute coordinates + unnormalized_boxes_0 = torch.vstack( + [ + expected_boxes_0[:, 0] * postprocessed_width, + expected_boxes_0[:, 1] * postprocessed_height, + expected_boxes_0[:, 2] * postprocessed_width, + expected_boxes_0[:, 3] * postprocessed_height, + ] + ).T + unnormalized_boxes_1 = torch.vstack( + [ + expected_boxes_1[:, 0] * postprocessed_width, + expected_boxes_1[:, 1] * postprocessed_height, + expected_boxes_1[:, 2] * postprocessed_width, + expected_boxes_1[:, 3] * postprocessed_height, + ] + ).T + # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max + expected_boxes_0 = torch.vstack( + [ + unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, + unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, + unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, + unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, + ] + ).T + expected_boxes_1 = torch.vstack( + [ + unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, + unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, + unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, + unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, + ] + ).T + torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1, atol=1) + torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1, atol=1) # Output size is slight different from DETR as yolos takes mod of 16 def test_batched_coco_panoptic_annotations(self): @@ -457,98 +468,100 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix annotations = [annotation_0, annotation_1] # encode them - image_processing = YolosImageProcessor(format="coco_panoptic") - encoding = image_processing( - images=images, - annotations=annotations, - masks_path=masks_path, - return_tensors="pt", - return_segmentation_masks=True, - ) + for image_processing_class in self.image_processor_list: + image_processing = image_processing_class() + image_processing = YolosImageProcessor(format="coco_panoptic") + encoding = image_processing( + images=images, + annotations=annotations, + masks_path=masks_path, + return_tensors="pt", + return_segmentation_masks=True, + ) - # Check the pixel values have been padded - postprocessed_height, postprocessed_width = 800, 1056 - expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) - self.assertEqual(encoding["pixel_values"].shape, expected_shape) + # Check the pixel values have been padded + postprocessed_height, postprocessed_width = 800, 1056 + expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) + self.assertEqual(encoding["pixel_values"].shape, expected_shape) - # Check the bounding boxes have been adjusted for padded images - self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) - self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) - expected_boxes_0 = torch.tensor( - [ - [0.2625, 0.5437, 0.4688, 0.8625], - [0.7719, 0.4104, 0.4531, 0.7125], - [0.5000, 0.4927, 0.9969, 0.9854], - [0.1688, 0.2000, 0.2063, 0.0917], - [0.5492, 0.2760, 0.0578, 0.2187], - [0.4992, 0.4990, 0.9984, 0.9979], - ] - ) - expected_boxes_1 = torch.tensor( - [ - [0.1591, 0.3262, 0.2841, 0.5175], - [0.4678, 0.2463, 0.2746, 0.4275], - [0.3030, 0.2956, 0.6042, 0.5913], - [0.1023, 0.1200, 0.1250, 0.0550], - [0.3329, 0.1656, 0.0350, 0.1312], - [0.3026, 0.2994, 0.6051, 0.5987], - ] - ) - torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3, atol=1e-3) - torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3, atol=1e-3) + # Check the bounding boxes have been adjusted for padded images + self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) + self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) + expected_boxes_0 = torch.tensor( + [ + [0.2625, 0.5437, 0.4688, 0.8625], + [0.7719, 0.4104, 0.4531, 0.7125], + [0.5000, 0.4927, 0.9969, 0.9854], + [0.1688, 0.2000, 0.2063, 0.0917], + [0.5492, 0.2760, 0.0578, 0.2187], + [0.4992, 0.4990, 0.9984, 0.9979], + ] + ) + expected_boxes_1 = torch.tensor( + [ + [0.1591, 0.3262, 0.2841, 0.5175], + [0.4678, 0.2463, 0.2746, 0.4275], + [0.3030, 0.2956, 0.6042, 0.5913], + [0.1023, 0.1200, 0.1250, 0.0550], + [0.3329, 0.1656, 0.0350, 0.1312], + [0.3026, 0.2994, 0.6051, 0.5987], + ] + ) + torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3, atol=1e-3) + torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3, atol=1e-3) - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1056])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1056])) + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1056])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1056])) - # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height - # format and not in the range [0, 1] - encoding = image_processing( - images=images, - annotations=annotations, - masks_path=masks_path, - return_segmentation_masks=True, - do_convert_annotations=False, - return_tensors="pt", - ) - self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) - self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) - # Convert to absolute coordinates - unnormalized_boxes_0 = torch.vstack( - [ - expected_boxes_0[:, 0] * postprocessed_width, - expected_boxes_0[:, 1] * postprocessed_height, - expected_boxes_0[:, 2] * postprocessed_width, - expected_boxes_0[:, 3] * postprocessed_height, - ] - ).T - unnormalized_boxes_1 = torch.vstack( - [ - expected_boxes_1[:, 0] * postprocessed_width, - expected_boxes_1[:, 1] * postprocessed_height, - expected_boxes_1[:, 2] * postprocessed_width, - expected_boxes_1[:, 3] * postprocessed_height, - ] - ).T - # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max - expected_boxes_0 = torch.vstack( - [ - unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, - unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, - unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, - unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, - ] - ).T - expected_boxes_1 = torch.vstack( - [ - unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, - unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, - unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, - unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, - ] - ).T - torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1) - torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1) + # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height + # format and not in the range [0, 1] + encoding = image_processing( + images=images, + annotations=annotations, + masks_path=masks_path, + return_segmentation_masks=True, + do_convert_annotations=False, + return_tensors="pt", + ) + self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) + self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) + # Convert to absolute coordinates + unnormalized_boxes_0 = torch.vstack( + [ + expected_boxes_0[:, 0] * postprocessed_width, + expected_boxes_0[:, 1] * postprocessed_height, + expected_boxes_0[:, 2] * postprocessed_width, + expected_boxes_0[:, 3] * postprocessed_height, + ] + ).T + unnormalized_boxes_1 = torch.vstack( + [ + expected_boxes_1[:, 0] * postprocessed_width, + expected_boxes_1[:, 1] * postprocessed_height, + expected_boxes_1[:, 2] * postprocessed_width, + expected_boxes_1[:, 3] * postprocessed_height, + ] + ).T + # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max + expected_boxes_0 = torch.vstack( + [ + unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, + unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, + unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, + unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, + ] + ).T + expected_boxes_1 = torch.vstack( + [ + unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, + unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, + unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, + unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, + ] + ).T + torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1) + torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->Yolos def test_max_width_max_height_resizing_and_pad_strategy(self):