diff --git a/docs/source/en/model_doc/detr.md b/docs/source/en/model_doc/detr.md index 0aeaf8e76937..43c6e6d17e2f 100644 --- a/docs/source/en/model_doc/detr.md +++ b/docs/source/en/model_doc/detr.md @@ -181,6 +181,15 @@ If you're interested in submitting a resource to be included here, please feel f - post_process_instance_segmentation - post_process_panoptic_segmentation +## DetrImageProcessorFast + +[[autodoc]] DetrImageProcessorFast + - preprocess + - post_process_object_detection + - post_process_semantic_segmentation + - post_process_instance_segmentation + - post_process_panoptic_segmentation + ## DetrFeatureExtractor [[autodoc]] DetrFeatureExtractor diff --git a/docs/source/ja/model_doc/detr.md b/docs/source/ja/model_doc/detr.md index 1b9e64eb5486..3342b123a01b 100644 --- a/docs/source/ja/model_doc/detr.md +++ b/docs/source/ja/model_doc/detr.md @@ -184,6 +184,15 @@ DETR の使用を開始するのに役立つ公式 Hugging Face およびコミ - post_process_instance_segmentation - post_process_panoptic_segmentation +## DetrImageProcessorFast + +[[autodoc]] DetrImageProcessorFast + - preprocess + - post_process_object_detection + - post_process_semantic_segmentation + - post_process_instance_segmentation + - post_process_panoptic_segmentation + ## DetrFeatureExtractor [[autodoc]] DetrFeatureExtractor diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index a926a848c3b5..7f408859c539 100755 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -1191,7 +1191,7 @@ _import_structure["models.deprecated.efficientformer"].append("EfficientFormerImageProcessor") _import_structure["models.deprecated.tvlt"].append("TvltImageProcessor") _import_structure["models.deprecated.vit_hybrid"].extend(["ViTHybridImageProcessor"]) - _import_structure["models.detr"].extend(["DetrFeatureExtractor", "DetrImageProcessor"]) + _import_structure["models.detr"].extend(["DetrFeatureExtractor", "DetrImageProcessor", "DetrImageProcessorFast"]) _import_structure["models.donut"].extend(["DonutFeatureExtractor", "DonutImageProcessor"]) _import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"]) _import_structure["models.efficientnet"].append("EfficientNetImageProcessor") @@ -6090,7 +6090,7 @@ from .models.deprecated.efficientformer import EfficientFormerImageProcessor from .models.deprecated.tvlt import TvltImageProcessor from .models.deprecated.vit_hybrid import ViTHybridImageProcessor - from .models.detr import DetrFeatureExtractor, DetrImageProcessor + from .models.detr import DetrFeatureExtractor, DetrImageProcessor, DetrImageProcessorFast from .models.donut import DonutFeatureExtractor, DonutImageProcessor from .models.dpt import DPTFeatureExtractor, DPTImageProcessor from .models.efficientnet import EfficientNetImageProcessor diff --git a/src/transformers/image_transforms.py b/src/transformers/image_transforms.py index 81e8d9185623..e7d3a5abb7a8 100644 --- a/src/transformers/image_transforms.py +++ b/src/transformers/image_transforms.py @@ -32,6 +32,7 @@ is_tf_available, is_torch_available, is_torchvision_available, + is_torchvision_v2_available, is_vision_available, requires_backends, ) @@ -51,7 +52,9 @@ if is_flax_available(): import jax.numpy as jnp -if is_torchvision_available(): +if is_torchvision_v2_available(): + from torchvision.transforms.v2 import functional as F +elif is_torchvision_available(): from torchvision.transforms import functional as F diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py index ef40798484ef..d181afeb2d4d 100644 --- a/src/transformers/models/auto/image_processing_auto.py +++ b/src/transformers/models/auto/image_processing_auto.py @@ -72,7 +72,7 @@ ("deit", ("DeiTImageProcessor",)), ("depth_anything", ("DPTImageProcessor",)), ("deta", ("DetaImageProcessor",)), - ("detr", ("DetrImageProcessor",)), + ("detr", ("DetrImageProcessor", "DetrImageProcessorFast")), ("dinat", ("ViTImageProcessor", "ViTImageProcessorFast")), ("dinov2", ("BitImageProcessor",)), ("donut-swin", ("DonutImageProcessor",)), diff --git a/src/transformers/models/detr/__init__.py b/src/transformers/models/detr/__init__.py index 422fe98230be..cc6687ff8bb4 100644 --- a/src/transformers/models/detr/__init__.py +++ b/src/transformers/models/detr/__init__.py @@ -27,6 +27,7 @@ else: _import_structure["feature_extraction_detr"] = ["DetrFeatureExtractor"] _import_structure["image_processing_detr"] = ["DetrImageProcessor"] + _import_structure["image_processing_detr_fast"] = ["DetrImageProcessorFast"] try: if not is_torch_available(): @@ -53,6 +54,7 @@ else: from .feature_extraction_detr import DetrFeatureExtractor from .image_processing_detr import DetrImageProcessor + from .image_processing_detr_fast import DetrImageProcessorFast try: if not is_torch_available(): diff --git a/src/transformers/models/detr/image_processing_detr_fast.py b/src/transformers/models/detr/image_processing_detr_fast.py new file mode 100644 index 000000000000..97940ab3132d --- /dev/null +++ b/src/transformers/models/detr/image_processing_detr_fast.py @@ -0,0 +1,1546 @@ +# coding=utf-8 +# Copyright 2024 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. +"""Fast Image processor class for DETR.""" + +import functools +import io +import pathlib +from collections import defaultdict +from typing import Any, Dict, List, Optional, Set, Tuple, Union + +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_processing_utils_fast import BaseImageProcessorFast, SizeDict +from ...image_transforms import ( + center_to_corners_format, + corners_to_center_format, + id_to_rgb, +) +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + AnnotationFormat, + AnnotationType, + ChannelDimension, + ImageInput, + ImageType, + PILImageResampling, + get_image_size, + get_image_type, + infer_channel_dimension_format, + make_list_of_images, + pil_torch_interpolation_mapping, + validate_annotations, + validate_kwargs, +) +from ...utils import ( + TensorType, + is_torch_available, + is_torchvision_available, + is_torchvision_v2_available, + is_vision_available, + logging, +) +from .image_processing_detr import ( + compute_segments, + convert_segmentation_to_rle, + get_size_with_aspect_ratio, + max_across_indices, + remove_low_and_no_objects, +) + + +if is_torch_available(): + import torch + from torch import nn + +if is_vision_available(): + import PIL + + +if is_torchvision_available(): + from torchvision.io import read_image + + from ...image_utils import pil_torch_interpolation_mapping + + if is_torchvision_v2_available(): + from torchvision.transforms.v2 import functional as F + else: + from torchvision.transforms import functional as F + + +logger = logging.get_logger(__name__) + +SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC) + + +def get_image_size_for_max_height_width( + image_size: Tuple[int, int], + max_height: int, + max_width: int, +) -> Tuple[int, int]: + """ + Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio. + Important, even if image_height < max_height and image_width < max_width, the image will be resized + to at least one of the edges be equal to max_height or max_width. + + For example: + - input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50) + - input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400) + + Args: + image_size (`Tuple[int, int]`): + The image to resize. + max_height (`int`): + The maximum allowed height. + max_width (`int`): + The maximum allowed width. + """ + height, width = image_size + height_scale = max_height / height + width_scale = max_width / width + min_scale = min(height_scale, width_scale) + new_height = int(height * min_scale) + new_width = int(width * min_scale) + return new_height, new_width + + +def safe_squeeze(tensor: torch.Tensor, axis: Optional[int] = None) -> torch.Tensor: + """ + Squeezes a tensor, but only if the axis specified has dim 1. + """ + if axis is None: + return tensor.squeeze() + + try: + return tensor.squeeze(axis=axis) + except ValueError: + return tensor + + +def get_max_height_width(images: List[torch.Tensor]) -> Tuple[int]: + """ + Get the maximum height and width across all images in a batch. + """ + + _, max_height, max_width = max_across_indices([img.shape for img in images]) + + return (max_height, max_width) + + +# inspired by https://github.com/facebookresearch/detr/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/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 = 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"] + annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0] + + classes = [obj["category_id"] for obj in annotations] + classes = torch.as_tensor(classes, dtype=torch.int64, device=image.device) + + # for conversion to coco api + area = torch.as_tensor([obj["area"] for obj in annotations], dtype=torch.float32, device=image.device) + iscrowd = torch.as_tensor( + [obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=torch.int64, device=image.device + ) + + boxes = [obj["bbox"] for obj in annotations] + # 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 = {} + 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"] = torch.as_tensor( + [int(image_height), int(image_width)], dtype=torch.int64, device=image.device + ) + + if annotations and "keypoints" in annotations[0]: + keypoints = [obj["keypoints"] for obj in annotations] + # Converting the filtered keypoints list to a numpy array + 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 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"] = 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(torch.int32).to(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 + + +class DetrImageProcessorFast(BaseImageProcessorFast): + r""" + Constructs a fast Detr image processor. + + Args: + format (`str`, *optional*, defaults to `AnnotationFormat.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. 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`. + 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`, 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. + """ + + 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: [Union[PILImageResampling, F.InterpolationMode]] = 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, + pad_size: Optional[Dict[str, int]] = None, + **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.pad_size = pad_size + 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", + "pad_size", + "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: torch.Tensor, + 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: torch.Tensor, + size: SizeDict, + interpolation: F.InterpolationMode = F.InterpolationMode.BILINEAR, + **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. + """ + 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 = F.InterpolationMode.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 (`InterpolationMode`, defaults to `InterpolationMode.NEAREST`): + The resampling filter to use when resizing the masks. + """ + 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 + + @functools.lru_cache(maxsize=1) + def _validate_input_arguments( + self, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: Union[float, List[float]], + image_std: Union[float, List[float]], + do_resize: bool, + size: Dict[str, int], + resample: "PILImageResampling", + data_format: Union[str, ChannelDimension], + return_tensors: Union[TensorType, str], + ): + if return_tensors != "pt": + raise ValueError("Only returning PyTorch tensors is currently supported.") + + if data_format != ChannelDimension.FIRST: + raise ValueError("Only channel first data format is currently supported.") + + if do_resize and None in (size, resample): + raise ValueError("Size and resample must be specified if do_resize is True.") + + if do_rescale and rescale_factor is None: + raise ValueError("Rescale factor must be specified if do_rescale is True.") + + if do_normalize and None in (image_mean, image_std): + raise ValueError("Image mean and standard deviation must be specified if do_normalize is True.") + + 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: Optional[Union[PILImageResampling, F.InterpolationMode]] = None, + 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, + pad_size: Optional[Dict[str, int]] = 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'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`. + resample (`PILImageResampling` or `InterpolationMode`, *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`, 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. + 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. + 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. + """ + 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") + + 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, 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 + pad_size = self.pad_size if pad_size is None else pad_size + format = self.format if format is None else format + device = kwargs.pop("device", None) + + # Make hashable for cache + size = SizeDict(**size) + image_mean = tuple(image_mean) if isinstance(image_mean, list) else image_mean + image_std = tuple(image_std) if isinstance(image_std, list) else image_std + + images = make_list_of_images(images) + image_type = get_image_type(images[0]) + + if image_type not in [ImageType.PIL, ImageType.TORCH, ImageType.NUMPY]: + raise ValueError(f"Unsupported input image type {image_type}") + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + self._validate_input_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, + return_tensors=return_tensors, + data_format=data_format, + ) + + 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 = {} + if image_type == ImageType.PIL: + images = [F.pil_to_tensor(image) for image in images] + elif image_type == ImageType.NUMPY: + # not using F.to_tensor as it doesn't handle (C, H, W) numpy arrays + images = [torch.from_numpy(image).contiguous() for image in images] + + if device is not None: + images = [image.to(device) for image in images] + + # We assume that all images have the same channel dimension format. + if input_data_format is None: + input_data_format = infer_channel_dimension_format(images[0]) + if input_data_format == ChannelDimension.LAST: + images = [image.permute(2, 0, 1).contiguous() for image in images] + + # 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 + + if do_resize: + if isinstance(resample, (PILImageResampling, int)): + interpolation = pil_torch_interpolation_mapping[resample] + else: + interpolation = resample + resized_images = [self.resize(image, size=size, interpolation=interpolation) for image in images] + if annotations is not None: + for i, (image, target) in enumerate(zip(resized_images, annotations)): + annotations[i] = self.resize_annotation( + target, + orig_size=images[i].size()[-2:], + target_size=image.size()[-2:], + ) + images = resized_images + del resized_images + + if do_rescale and do_normalize: + # fused rescale and normalize + new_mean = torch.tensor(image_mean, device=images[0].device) * (1.0 / rescale_factor) + new_std = torch.tensor(image_std, device=images[0].device) * (1.0 / rescale_factor) + images = [F.normalize(image.to(dtype=torch.float32), new_mean, new_std) for image in images] + elif do_rescale: + images = [image * rescale_factor for image in images] + elif do_normalize: + images = [F.normalize(image, image_mean, image_std) 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': ...} + if pad_size is not None: + padded_size = (pad_size["height"], pad_size["width"]) + else: + padded_size = get_max_height_width(images) + + annotation_list = annotations if annotations is not None else [None] * len(images) + padded_images = [] + pixel_masks = [] + padded_annotations = [] + for image, annotation in zip(images, annotation_list): + 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 + padded_image, pixel_mask, padded_annotation = self.pad( + image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations + ) + padded_images.append(padded_image) + pixel_masks.append(pixel_mask) + padded_annotations.append(padded_annotation) + images = padded_images + if annotations is not None: + annotations = padded_annotations + del padded_images, padded_annotations + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_segmentation + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_instance + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_panoptic + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_object_detection + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_semantic_segmentation + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_instance_segmentation + 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 + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_panoptic_segmentation + 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 diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py index 2876eef9ea02..a781389c2fbd 100755 --- a/src/transformers/utils/__init__.py +++ b/src/transformers/utils/__init__.py @@ -225,6 +225,7 @@ is_torchdynamo_available, is_torchdynamo_compiling, is_torchvision_available, + is_torchvision_v2_available, is_training_run_on_sagemaker, is_uroman_available, is_vision_available, diff --git a/src/transformers/utils/dummy_vision_objects.py b/src/transformers/utils/dummy_vision_objects.py index d2ccaeaaed23..d7f87717ca83 100644 --- a/src/transformers/utils/dummy_vision_objects.py +++ b/src/transformers/utils/dummy_vision_objects.py @@ -191,6 +191,13 @@ def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) +class DetrImageProcessorFast(metaclass=DummyObject): + _backends = ["vision"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["vision"]) + + class DonutFeatureExtractor(metaclass=DummyObject): _backends = ["vision"] diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py index ed95e4b5570e..173aee9b1ac7 100755 --- a/src/transformers/utils/import_utils.py +++ b/src/transformers/utils/import_utils.py @@ -186,7 +186,7 @@ def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[ _torchaudio_available = _is_package_available("torchaudio") _torchao_available = _is_package_available("torchao") _torchdistx_available = _is_package_available("torchdistx") -_torchvision_available = _is_package_available("torchvision") +_torchvision_available, _torchvision_version = _is_package_available("torchvision", return_version=True) _mlx_available = _is_package_available("mlx") _hqq_available, _hqq_version = _is_package_available("hqq", return_version=True) _tiktoken_available = _is_package_available("tiktoken") @@ -362,6 +362,14 @@ def is_torchvision_available(): return _torchvision_available +def is_torchvision_v2_available(): + if not is_torchvision_available(): + return False + + # NOTE: We require torchvision>=0.15 as v2 transforms are available from this version: https://pytorch.org/vision/stable/transforms.html#v1-or-v2-which-one-should-i-use + return version.parse(_torchvision_version) >= version.parse("0.15") + + def is_galore_torch_available(): return _galore_torch_available diff --git a/tests/models/conditional_detr/test_image_processing_conditional_detr.py b/tests/models/conditional_detr/test_image_processing_conditional_detr.py index 99a06613e141..32b135bcd220 100644 --- a/tests/models/conditional_detr/test_image_processing_conditional_detr.py +++ b/tests/models/conditional_detr/test_image_processing_conditional_detr.py @@ -282,96 +282,97 @@ def test_batched_coco_detection_annotations(self): images = [image_0, image_1] annotations = [annotations_0, annotations_1] - image_processing = ConditionalDetrImageProcessor() - 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, 1066 - 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( - [ - [0.4130, 0.2765, 0.0453, 0.2215], - [0.1272, 0.2016, 0.1561, 0.0940], - [0.3757, 0.4933, 0.7488, 0.9865], - [0.3759, 0.5002, 0.7492, 0.9955], - [0.1971, 0.5456, 0.3532, 0.8646], - [0.5790, 0.4115, 0.3430, 0.7161], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + 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, 1066 + 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( + [ + [0.4130, 0.2765, 0.0453, 0.2215], + [0.1272, 0.2016, 0.1561, 0.0940], + [0.3757, 0.4933, 0.7488, 0.9865], + [0.3759, 0.5002, 0.7492, 0.9955], + [0.1971, 0.5456, 0.3532, 0.8646], + [0.5790, 0.4115, 0.3430, 0.7161], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->ConditionalDetr def test_batched_coco_panoptic_annotations(self): @@ -402,146 +403,148 @@ def test_batched_coco_panoptic_annotations(self): images = [image_0, image_1] annotations = [annotation_0, annotation_1] - # encode them - image_processing = ConditionalDetrImageProcessor(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, 1066 - 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( - [ - [0.1576, 0.3262, 0.2814, 0.5175], - [0.4634, 0.2463, 0.2720, 0.4275], - [0.3002, 0.2956, 0.5985, 0.5913], - [0.1013, 0.1200, 0.1238, 0.0550], - [0.3297, 0.1656, 0.0347, 0.1312], - [0.2997, 0.2994, 0.5994, 0.5987], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class(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, 1066 + 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( + [ + [0.1576, 0.3262, 0.2814, 0.5175], + [0.4634, 0.2463, 0.2720, 0.4275], + [0.3002, 0.2956, 0.5985, 0.5913], + [0.1013, 0.1200, 0.1238, 0.0550], + [0.3297, 0.1656, 0.0347, 0.1312], + [0.2997, 0.2994, 0.5994, 0.5987], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->ConditionalDetr def test_max_width_max_height_resizing_and_pad_strategy(self): - image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) - - # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 - image_processor = ConditionalDetrImageProcessor( - size={"max_height": 100, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) - - # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 - image_processor = ConditionalDetrImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - - # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 - image_processor = ConditionalDetrImageProcessor( - size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) - - # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 - image_processor = ConditionalDetrImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=True, - pad_size={"height": 301, "width": 101}, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) - - ### Check for batch - image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) - - # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 - image_processor = ConditionalDetrImageProcessor( - size={"max_height": 150, "max_width": 100}, - do_pad=True, - pad_size={"height": 150, "width": 100}, - ) - inputs = image_processor(images=[image_1, image_2], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) + for image_processing_class in self.image_processor_list: + image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) + + # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) + + # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + + # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) + + # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=True, + pad_size={"height": 301, "width": 101}, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) + + ### Check for batch + image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) + + # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 + image_processor = image_processing_class( + size={"max_height": 150, "max_width": 100}, + do_pad=True, + pad_size={"height": 150, "width": 100}, + ) + inputs = image_processor(images=[image_1, image_2], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) def test_longest_edge_shortest_edge_resizing_strategy(self): image_1 = torch.ones([958, 653, 3], dtype=torch.uint8) diff --git a/tests/models/deformable_detr/test_image_processing_deformable_detr.py b/tests/models/deformable_detr/test_image_processing_deformable_detr.py index 41e5a81e2f93..29dd0556afcd 100644 --- a/tests/models/deformable_detr/test_image_processing_deformable_detr.py +++ b/tests/models/deformable_detr/test_image_processing_deformable_detr.py @@ -284,96 +284,97 @@ def test_batched_coco_detection_annotations(self): images = [image_0, image_1] annotations = [annotations_0, annotations_1] - image_processing = DeformableDetrImageProcessor() - 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, 1066 - 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( - [ - [0.4130, 0.2765, 0.0453, 0.2215], - [0.1272, 0.2016, 0.1561, 0.0940], - [0.3757, 0.4933, 0.7488, 0.9865], - [0.3759, 0.5002, 0.7492, 0.9955], - [0.1971, 0.5456, 0.3532, 0.8646], - [0.5790, 0.4115, 0.3430, 0.7161], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + 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, 1066 + 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( + [ + [0.4130, 0.2765, 0.0453, 0.2215], + [0.1272, 0.2016, 0.1561, 0.0940], + [0.3757, 0.4933, 0.7488, 0.9865], + [0.3759, 0.5002, 0.7492, 0.9955], + [0.1971, 0.5456, 0.3532, 0.8646], + [0.5790, 0.4115, 0.3430, 0.7161], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->DeformableDetr def test_batched_coco_panoptic_annotations(self): @@ -404,146 +405,148 @@ def test_batched_coco_panoptic_annotations(self): images = [image_0, image_1] annotations = [annotation_0, annotation_1] - # encode them - image_processing = DeformableDetrImageProcessor(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, 1066 - 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( - [ - [0.1576, 0.3262, 0.2814, 0.5175], - [0.4634, 0.2463, 0.2720, 0.4275], - [0.3002, 0.2956, 0.5985, 0.5913], - [0.1013, 0.1200, 0.1238, 0.0550], - [0.3297, 0.1656, 0.0347, 0.1312], - [0.2997, 0.2994, 0.5994, 0.5987], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class(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, 1066 + 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( + [ + [0.1576, 0.3262, 0.2814, 0.5175], + [0.4634, 0.2463, 0.2720, 0.4275], + [0.3002, 0.2956, 0.5985, 0.5913], + [0.1013, 0.1200, 0.1238, 0.0550], + [0.3297, 0.1656, 0.0347, 0.1312], + [0.2997, 0.2994, 0.5994, 0.5987], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->DeformableDetr def test_max_width_max_height_resizing_and_pad_strategy(self): - image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) - - # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 - image_processor = DeformableDetrImageProcessor( - size={"max_height": 100, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) - - # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 - image_processor = DeformableDetrImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - - # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 - image_processor = DeformableDetrImageProcessor( - size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) - - # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 - image_processor = DeformableDetrImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=True, - pad_size={"height": 301, "width": 101}, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) - - ### Check for batch - image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) - - # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 - image_processor = DeformableDetrImageProcessor( - size={"max_height": 150, "max_width": 100}, - do_pad=True, - pad_size={"height": 150, "width": 100}, - ) - inputs = image_processor(images=[image_1, image_2], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) + for image_processing_class in self.image_processor_list: + image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) + + # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) + + # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + + # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) + + # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=True, + pad_size={"height": 301, "width": 101}, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) + + ### Check for batch + image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) + + # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 + image_processor = image_processing_class( + size={"max_height": 150, "max_width": 100}, + do_pad=True, + pad_size={"height": 150, "width": 100}, + ) + inputs = image_processor(images=[image_1, image_2], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) def test_longest_edge_shortest_edge_resizing_strategy(self): image_1 = torch.ones([958, 653, 3], dtype=torch.uint8) diff --git a/tests/models/detr/test_image_processing_detr.py b/tests/models/detr/test_image_processing_detr.py index 4174df0f8cc7..976b306115b6 100644 --- a/tests/models/detr/test_image_processing_detr.py +++ b/tests/models/detr/test_image_processing_detr.py @@ -19,8 +19,8 @@ import numpy as np -from transformers.testing_utils import require_torch, require_vision, slow -from transformers.utils import is_torch_available, is_vision_available +from transformers.testing_utils import require_torch, require_torch_gpu, require_vision, slow +from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs @@ -33,6 +33,9 @@ from transformers import DetrImageProcessor + if is_torchvision_available(): + from transformers import DetrImageProcessorFast + class DetrImageProcessingTester(unittest.TestCase): def __init__( @@ -51,6 +54,7 @@ def __init__( image_std=[0.5, 0.5, 0.5], do_pad=True, ): + super().__init__() # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333} self.parent = parent @@ -132,6 +136,7 @@ def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=F @require_vision class DetrImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase): image_processing_class = DetrImageProcessor if is_vision_available() else None + fast_image_processing_class = DetrImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() @@ -142,26 +147,28 @@ def image_processor_dict(self): 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_rescale")) - self.assertTrue(hasattr(image_processing, "rescale_factor")) - self.assertTrue(hasattr(image_processing, "do_resize")) - self.assertTrue(hasattr(image_processing, "size")) - self.assertTrue(hasattr(image_processing, "do_pad")) + 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_rescale")) + self.assertTrue(hasattr(image_processing, "rescale_factor")) + self.assertTrue(hasattr(image_processing, "do_resize")) + self.assertTrue(hasattr(image_processing, "size")) + self.assertTrue(hasattr(image_processing, "do_pad")) 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_should_raise_if_annotation_format_invalid(self): image_processor_dict = self.image_processor_tester.prepare_image_processor_dict() @@ -178,12 +185,13 @@ def test_should_raise_if_annotation_format_invalid(self): } image_processor_params = {**image_processor_dict, **{"format": "_INVALID_FORMAT_"}} - image_processor = self.image_processing_class(**image_processor_params) + for image_processing_class in self.image_processor_list: + image_processor = image_processing_class(**image_processor_params) - with self.assertRaises(ValueError) as e: - image_processor(**params) + with self.assertRaises(ValueError) as e: + image_processor(**params) - self.assertTrue(str(e.exception).startswith("_INVALID_FORMAT_ is not a valid AnnotationFormat")) + self.assertTrue(str(e.exception).startswith("_INVALID_FORMAT_ is not a valid AnnotationFormat")) def test_valid_coco_detection_annotations(self): # prepare image and target @@ -193,32 +201,33 @@ def test_valid_coco_detection_annotations(self): params = {"image_id": 39769, "annotations": target} - # encode them - image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class.from_pretrained("facebook/detr-resnet-50") - # legal encodings (single image) - _ = image_processing(images=image, annotations=params, return_tensors="pt") - _ = image_processing(images=image, annotations=[params], return_tensors="pt") + # legal encodings (single image) + _ = image_processing(images=image, annotations=params, return_tensors="pt") + _ = image_processing(images=image, annotations=[params], return_tensors="pt") - # legal encodings (batch of one image) - _ = image_processing(images=[image], annotations=params, return_tensors="pt") - _ = image_processing(images=[image], annotations=[params], return_tensors="pt") + # legal encodings (batch of one image) + _ = image_processing(images=[image], annotations=params, return_tensors="pt") + _ = image_processing(images=[image], annotations=[params], return_tensors="pt") - # legal encoding (batch of more than one image) - n = 5 - _ = image_processing(images=[image] * n, annotations=[params] * n, return_tensors="pt") + # legal encoding (batch of more than one image) + n = 5 + _ = image_processing(images=[image] * n, annotations=[params] * n, return_tensors="pt") - # example of an illegal encoding (missing the 'image_id' key) - with self.assertRaises(ValueError) as e: - image_processing(images=image, annotations={"annotations": target}, return_tensors="pt") + # example of an illegal encoding (missing the 'image_id' key) + with self.assertRaises(ValueError) as e: + image_processing(images=image, annotations={"annotations": target}, return_tensors="pt") - self.assertTrue(str(e.exception).startswith("Invalid COCO detection annotations")) + self.assertTrue(str(e.exception).startswith("Invalid COCO detection annotations")) - # example of an illegal encoding (unequal lengths of images and annotations) - with self.assertRaises(ValueError) as e: - image_processing(images=[image] * n, annotations=[params] * (n - 1), return_tensors="pt") + # example of an illegal encoding (unequal lengths of images and annotations) + with self.assertRaises(ValueError) as e: + image_processing(images=[image] * n, annotations=[params] * (n - 1), return_tensors="pt") - self.assertTrue(str(e.exception) == "The number of images (5) and annotations (4) do not match.") + self.assertTrue(str(e.exception) == "The number of images (5) and annotations (4) do not match.") @slow def test_call_pytorch_with_coco_detection_annotations(self): @@ -229,40 +238,41 @@ def test_call_pytorch_with_coco_detection_annotations(self): target = {"image_id": 39769, "annotations": target} - # encode them - image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") - encoding = image_processing(images=image, annotations=target, return_tensors="pt") - - # verify pixel values - expected_shape = torch.Size([1, 3, 800, 1066]) - self.assertEqual(encoding["pixel_values"].shape, expected_shape) - - expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) - self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) - - # verify area - expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) - self.assertTrue(torch.allclose(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]) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) - # verify image_id - expected_image_id = torch.tensor([39769]) - self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) - # verify is_crowd - expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) - self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) - # verify class_labels - expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) - self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) - # verify orig_size - expected_orig_size = torch.tensor([480, 640]) - self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) - # verify size - expected_size = torch.tensor([800, 1066]) - self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class.from_pretrained("facebook/detr-resnet-50") + encoding = image_processing(images=image, annotations=target, return_tensors="pt") + + # verify pixel values + expected_shape = torch.Size([1, 3, 800, 1066]) + self.assertEqual(encoding["pixel_values"].shape, expected_shape) + + expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) + self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) + + # verify area + expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) + self.assertTrue(torch.allclose(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]) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) + # verify image_id + expected_image_id = torch.tensor([39769]) + self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) + # verify is_crowd + expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) + self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) + # verify class_labels + expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) + self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) + # verify orig_size + expected_orig_size = torch.tensor([480, 640]) + self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) + # verify size + expected_size = torch.tensor([800, 1066]) + self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) @slow def test_call_pytorch_with_coco_panoptic_annotations(self): @@ -275,43 +285,45 @@ def test_call_pytorch_with_coco_panoptic_annotations(self): masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") - # encode them - image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-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, 1066]) - self.assertEqual(encoding["pixel_values"].shape, expected_shape) - - expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) - self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) - - # verify area - expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) - self.assertTrue(torch.allclose(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]) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) - # verify image_id - expected_image_id = torch.tensor([39769]) - self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) - # verify is_crowd - expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) - self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) - # verify class_labels - expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) - self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) - # verify masks - expected_masks_sum = 822873 - self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum) - # verify orig_size - expected_orig_size = torch.tensor([480, 640]) - self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) - # verify size - expected_size = torch.tensor([800, 1066]) - self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class.from_pretrained("facebook/detr-resnet-50-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, 1066]) + self.assertEqual(encoding["pixel_values"].shape, expected_shape) + + expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) + self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) + + # verify area + expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) + self.assertTrue(torch.allclose(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]) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) + # verify image_id + expected_image_id = torch.tensor([39769]) + self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) + # verify is_crowd + expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) + self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) + # verify class_labels + expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) + self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) + # verify masks + expected_masks_sum = 822873 + 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]) + self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) + # verify size + expected_size = torch.tensor([800, 1066]) + self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) @slow def test_batched_coco_detection_annotations(self): @@ -340,96 +352,97 @@ def test_batched_coco_detection_annotations(self): images = [image_0, image_1] annotations = [annotations_0, annotations_1] - image_processing = DetrImageProcessor() - 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, 1066 - 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( - [ - [0.4130, 0.2765, 0.0453, 0.2215], - [0.1272, 0.2016, 0.1561, 0.0940], - [0.3757, 0.4933, 0.7488, 0.9865], - [0.3759, 0.5002, 0.7492, 0.9955], - [0.1971, 0.5456, 0.3532, 0.8646], - [0.5790, 0.4115, 0.3430, 0.7161], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + 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, 1066 + 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( + [ + [0.4130, 0.2765, 0.0453, 0.2215], + [0.1272, 0.2016, 0.1561, 0.0940], + [0.3757, 0.4933, 0.7488, 0.9865], + [0.3759, 0.5002, 0.7492, 0.9955], + [0.1971, 0.5456, 0.3532, 0.8646], + [0.5790, 0.4115, 0.3430, 0.7161], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) def test_batched_coco_panoptic_annotations(self): # prepare image, target and masks_path @@ -459,194 +472,318 @@ def test_batched_coco_panoptic_annotations(self): images = [image_0, image_1] annotations = [annotation_0, annotation_1] - # encode them - image_processing = DetrImageProcessor(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: + # encode them + image_processing = image_processing_class(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, 1066 + 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( + [ + [0.1576, 0.3262, 0.2814, 0.5175], + [0.4634, 0.2463, 0.2720, 0.4275], + [0.3002, 0.2956, 0.5985, 0.5913], + [0.1013, 0.1200, 0.1238, 0.0550], + [0.3297, 0.1656, 0.0347, 0.1312], + [0.2997, 0.2994, 0.5994, 0.5987], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) - # Check the pixel values have been padded - postprocessed_height, postprocessed_width = 800, 1066 - 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], - ] + def test_max_width_max_height_resizing_and_pad_strategy(self): + for image_processing_class in self.image_processor_list: + image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) + + # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) + + # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + + # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) + + # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=True, + pad_size={"height": 301, "width": 101}, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) + + ### Check for batch + image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) + + # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 + image_processor = image_processing_class( + size={"max_height": 150, "max_width": 100}, + do_pad=True, + pad_size={"height": 150, "width": 100}, + ) + inputs = image_processor(images=[image_1, image_2], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) + + def test_longest_edge_shortest_edge_resizing_strategy(self): + for image_processing_class in self.image_processor_list: + image_1 = torch.ones([958, 653, 3], dtype=torch.uint8) + + # max size is set; width < height; + # do_pad=False, longest_edge=640, shortest_edge=640, image=958x653 -> 640x436 + image_processor = image_processing_class( + size={"longest_edge": 640, "shortest_edge": 640}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 640, 436])) + + image_2 = torch.ones([653, 958, 3], dtype=torch.uint8) + # max size is set; height < width; + # do_pad=False, longest_edge=640, shortest_edge=640, image=653x958 -> 436x640 + image_processor = image_processing_class( + size={"longest_edge": 640, "shortest_edge": 640}, + do_pad=False, + ) + inputs = image_processor(images=[image_2], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 436, 640])) + + image_3 = torch.ones([100, 120, 3], dtype=torch.uint8) + # max size is set; width == size; height > max_size; + # do_pad=False, longest_edge=118, shortest_edge=100, image=120x100 -> 118x98 + image_processor = image_processing_class( + size={"longest_edge": 118, "shortest_edge": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_3], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 98, 118])) + + image_4 = torch.ones([128, 50, 3], dtype=torch.uint8) + # max size is set; height == size; width < max_size; + # do_pad=False, longest_edge=256, shortest_edge=50, image=50x128 -> 50x128 + image_processor = image_processing_class( + size={"longest_edge": 256, "shortest_edge": 50}, + do_pad=False, + ) + inputs = image_processor(images=[image_4], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 128, 50])) + + image_5 = torch.ones([50, 50, 3], dtype=torch.uint8) + # max size is set; height == width; width < max_size; + # do_pad=False, longest_edge=117, shortest_edge=50, image=50x50 -> 50x50 + image_processor = image_processing_class( + size={"longest_edge": 117, "shortest_edge": 50}, + do_pad=False, + ) + inputs = image_processor(images=[image_5], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 50, 50])) + + @slow + @require_torch_gpu + def test_fast_processor_equivalence_cpu_gpu_coco_detection_annotations(self): + # prepare image and target + image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") + with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f: + target = json.loads(f.read()) + + target = {"image_id": 39769, "annotations": target} + + processor = self.image_processor_list[1].from_pretrained("facebook/detr-resnet-50") + # 1. run processor on CPU + encoding_cpu = processor(images=image, annotations=target, return_tensors="pt", device="cpu") + # 2. run processor on GPU + encoding_gpu = processor(images=image, annotations=target, return_tensors="pt", device="cuda") + + # verify pixel values + self.assertEqual(encoding_cpu["pixel_values"].shape, encoding_gpu["pixel_values"].shape) + self.assertTrue( + torch.allclose( + encoding_cpu["pixel_values"][0, 0, 0, :3], + encoding_gpu["pixel_values"][0, 0, 0, :3].to("cpu"), + atol=1e-4, + ) ) - expected_boxes_1 = torch.tensor( - [ - [0.1576, 0.3262, 0.2814, 0.5175], - [0.4634, 0.2463, 0.2720, 0.4275], - [0.3002, 0.2956, 0.5985, 0.5913], - [0.1013, 0.1200, 0.1238, 0.0550], - [0.3297, 0.1656, 0.0347, 0.1312], - [0.2997, 0.2994, 0.5994, 0.5987], - ] + # verify area + self.assertTrue(torch.allclose(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu"))) + # verify boxes + self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape) + self.assertTrue( + torch.allclose( + encoding_cpu["labels"][0]["boxes"][0], encoding_gpu["labels"][0]["boxes"][0].to("cpu"), atol=1e-3 + ) ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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", + # verify image_id + self.assertTrue( + torch.allclose(encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu")) ) - 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + # verify is_crowd + self.assertTrue( + torch.allclose(encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu")) + ) + # verify class_labels + self.assertTrue( + torch.allclose( + encoding_cpu["labels"][0]["class_labels"], encoding_gpu["labels"][0]["class_labels"].to("cpu") + ) + ) + # verify orig_size + self.assertTrue( + torch.allclose(encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu")) + ) + # verify size + self.assertTrue(torch.allclose(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))) - def test_max_width_max_height_resizing_and_pad_strategy(self): - image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) + @slow + @require_torch_gpu + def test_fast_processor_equivalence_cpu_gpu_coco_panoptic_annotations(self): + # prepare image, target and masks_path + image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") + with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: + target = json.loads(f.read()) - # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 - image_processor = DetrImageProcessor( - size={"max_height": 100, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) + target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} - # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 - image_processor = DetrImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") + masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") - # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 - image_processor = DetrImageProcessor( - size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} + processor = self.image_processor_list[1].from_pretrained("facebook/detr-resnet-50-panoptic") + # 1. run processor on CPU + encoding_cpu = processor( + images=image, annotations=target, masks_path=masks_path, return_tensors="pt", device="cpu" ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) - - # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 - image_processor = DetrImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=True, - pad_size={"height": 301, "width": 101}, + # 2. run processor on GPU + encoding_gpu = processor( + images=image, annotations=target, masks_path=masks_path, return_tensors="pt", device="cuda" ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) - ### Check for batch - image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) - - # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 - image_processor = DetrImageProcessor( - size={"max_height": 150, "max_width": 100}, - do_pad=True, - pad_size={"height": 150, "width": 100}, + # verify pixel values + self.assertEqual(encoding_cpu["pixel_values"].shape, encoding_gpu["pixel_values"].shape) + self.assertTrue( + torch.allclose( + encoding_cpu["pixel_values"][0, 0, 0, :3], + encoding_gpu["pixel_values"][0, 0, 0, :3].to("cpu"), + atol=1e-4, + ) ) - inputs = image_processor(images=[image_1, image_2], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) - - def test_longest_edge_shortest_edge_resizing_strategy(self): - image_1 = torch.ones([958, 653, 3], dtype=torch.uint8) - - # max size is set; width < height; - # do_pad=False, longest_edge=640, shortest_edge=640, image=958x653 -> 640x436 - image_processor = DetrImageProcessor( - size={"longest_edge": 640, "shortest_edge": 640}, - do_pad=False, + # verify area + self.assertTrue(torch.allclose(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu"))) + # verify boxes + self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape) + self.assertTrue( + torch.allclose( + encoding_cpu["labels"][0]["boxes"][0], encoding_gpu["labels"][0]["boxes"][0].to("cpu"), atol=1e-3 + ) ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 640, 436])) - - image_2 = torch.ones([653, 958, 3], dtype=torch.uint8) - # max size is set; height < width; - # do_pad=False, longest_edge=640, shortest_edge=640, image=653x958 -> 436x640 - image_processor = DetrImageProcessor( - size={"longest_edge": 640, "shortest_edge": 640}, - do_pad=False, + # verify image_id + self.assertTrue( + torch.allclose(encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu")) ) - inputs = image_processor(images=[image_2], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 436, 640])) - - image_3 = torch.ones([100, 120, 3], dtype=torch.uint8) - # max size is set; width == size; height > max_size; - # do_pad=False, longest_edge=118, shortest_edge=100, image=120x100 -> 118x98 - image_processor = DetrImageProcessor( - size={"longest_edge": 118, "shortest_edge": 100}, - do_pad=False, + # verify is_crowd + self.assertTrue( + torch.allclose(encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu")) ) - inputs = image_processor(images=[image_3], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 98, 118])) - - image_4 = torch.ones([128, 50, 3], dtype=torch.uint8) - # max size is set; height == size; width < max_size; - # do_pad=False, longest_edge=256, shortest_edge=50, image=50x128 -> 50x128 - image_processor = DetrImageProcessor( - size={"longest_edge": 256, "shortest_edge": 50}, - do_pad=False, + # verify class_labels + self.assertTrue( + torch.allclose( + encoding_cpu["labels"][0]["class_labels"], encoding_gpu["labels"][0]["class_labels"].to("cpu") + ) ) - inputs = image_processor(images=[image_4], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 128, 50])) - - image_5 = torch.ones([50, 50, 3], dtype=torch.uint8) - # max size is set; height == width; width < max_size; - # do_pad=False, longest_edge=117, shortest_edge=50, image=50x50 -> 50x50 - image_processor = DetrImageProcessor( - size={"longest_edge": 117, "shortest_edge": 50}, - do_pad=False, + # verify masks + masks_sum_cpu = encoding_cpu["labels"][0]["masks"].sum() + masks_sum_gpu = encoding_gpu["labels"][0]["masks"].sum() + relative_error = torch.abs(masks_sum_cpu - masks_sum_gpu) / masks_sum_cpu + self.assertTrue(relative_error < 1e-3) + # verify orig_size + self.assertTrue( + torch.allclose(encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu")) ) - inputs = image_processor(images=[image_5], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 50, 50])) + # verify size + self.assertTrue(torch.allclose(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))) diff --git a/tests/models/grounding_dino/test_image_processing_grounding_dino.py b/tests/models/grounding_dino/test_image_processing_grounding_dino.py index 5a2839784707..fc622ead7a71 100644 --- a/tests/models/grounding_dino/test_image_processing_grounding_dino.py +++ b/tests/models/grounding_dino/test_image_processing_grounding_dino.py @@ -269,96 +269,97 @@ def test_batched_coco_detection_annotations(self): images = [image_0, image_1] annotations = [annotations_0, annotations_1] - image_processing = GroundingDinoImageProcessor() - 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, 1066 - 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( - [ - [0.4130, 0.2765, 0.0453, 0.2215], - [0.1272, 0.2016, 0.1561, 0.0940], - [0.3757, 0.4933, 0.7488, 0.9865], - [0.3759, 0.5002, 0.7492, 0.9955], - [0.1971, 0.5456, 0.3532, 0.8646], - [0.5790, 0.4115, 0.3430, 0.7161], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + 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, 1066 + 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( + [ + [0.4130, 0.2765, 0.0453, 0.2215], + [0.1272, 0.2016, 0.1561, 0.0940], + [0.3757, 0.4933, 0.7488, 0.9865], + [0.3759, 0.5002, 0.7492, 0.9955], + [0.1971, 0.5456, 0.3532, 0.8646], + [0.5790, 0.4115, 0.3430, 0.7161], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) @slow # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_panoptic_annotations with DeformableDetr->GroundingDino @@ -440,146 +441,148 @@ def test_batched_coco_panoptic_annotations(self): images = [image_0, image_1] annotations = [annotation_0, annotation_1] - # encode them - image_processing = GroundingDinoImageProcessor(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, 1066 - 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( - [ - [0.1576, 0.3262, 0.2814, 0.5175], - [0.4634, 0.2463, 0.2720, 0.4275], - [0.3002, 0.2956, 0.5985, 0.5913], - [0.1013, 0.1200, 0.1238, 0.0550], - [0.3297, 0.1656, 0.0347, 0.1312], - [0.2997, 0.2994, 0.5994, 0.5987], - ] - ) - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) - - # Check the masks have also been padded - self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) - self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) - - # 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 - self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) - self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) + for image_processing_class in self.image_processor_list: + # encode them + image_processing = image_processing_class(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, 1066 + 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( + [ + [0.1576, 0.3262, 0.2814, 0.5175], + [0.4634, 0.2463, 0.2720, 0.4275], + [0.3002, 0.2956, 0.5985, 0.5913], + [0.1013, 0.1200, 0.1238, 0.0550], + [0.3297, 0.1656, 0.0347, 0.1312], + [0.2997, 0.2994, 0.5994, 0.5987], + ] + ) + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) + + # Check the masks have also been padded + self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) + self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) + + # 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 + self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) + self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->GroundingDino def test_max_width_max_height_resizing_and_pad_strategy(self): - image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) - - # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 - image_processor = GroundingDinoImageProcessor( - size={"max_height": 100, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) - - # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 - image_processor = GroundingDinoImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - - # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 - image_processor = GroundingDinoImageProcessor( - size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) - - # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 - image_processor = GroundingDinoImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=True, - pad_size={"height": 301, "width": 101}, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) - - ### Check for batch - image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) - - # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 - image_processor = GroundingDinoImageProcessor( - size={"max_height": 150, "max_width": 100}, - do_pad=True, - pad_size={"height": 150, "width": 100}, - ) - inputs = image_processor(images=[image_1, image_2], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) + for image_processing_class in self.image_processor_list: + image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) + + # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) + + # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + + # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) + + # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=True, + pad_size={"height": 301, "width": 101}, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) + + ### Check for batch + image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) + + # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 + image_processor = image_processing_class( + size={"max_height": 150, "max_width": 100}, + do_pad=True, + pad_size={"height": 150, "width": 100}, + ) + inputs = image_processor(images=[image_1, image_2], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) def test_longest_edge_shortest_edge_resizing_strategy(self): image_1 = torch.ones([958, 653, 3], dtype=torch.uint8) diff --git a/tests/models/yolos/test_image_processing_yolos.py b/tests/models/yolos/test_image_processing_yolos.py index 1080edd2654e..67508532e9c8 100644 --- a/tests/models/yolos/test_image_processing_yolos.py +++ b/tests/models/yolos/test_image_processing_yolos.py @@ -553,47 +553,48 @@ def test_batched_coco_panoptic_annotations(self): # 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): - image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) - - # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 - image_processor = YolosImageProcessor( - size={"max_height": 100, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) - - # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 - image_processor = YolosImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=False, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - - # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 - image_processor = YolosImageProcessor( - size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) - - # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 - image_processor = YolosImageProcessor( - size={"max_height": 300, "max_width": 100}, - do_pad=True, - pad_size={"height": 301, "width": 101}, - ) - inputs = image_processor(images=[image_1], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) - - ### Check for batch - image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) - - # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 - image_processor = YolosImageProcessor( - size={"max_height": 150, "max_width": 100}, - do_pad=True, - pad_size={"height": 150, "width": 100}, - ) - inputs = image_processor(images=[image_1, image_2], return_tensors="pt") - self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) + for image_processing_class in self.image_processor_list: + image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) + + # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) + + # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=False, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + + # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 + image_processor = image_processing_class( + size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) + + # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 + image_processor = image_processing_class( + size={"max_height": 300, "max_width": 100}, + do_pad=True, + pad_size={"height": 301, "width": 101}, + ) + inputs = image_processor(images=[image_1], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) + + ### Check for batch + image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) + + # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 + image_processor = image_processing_class( + size={"max_height": 150, "max_width": 100}, + do_pad=True, + pad_size={"height": 150, "width": 100}, + ) + inputs = image_processor(images=[image_1, image_2], return_tensors="pt") + self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) diff --git a/tests/test_image_processing_common.py b/tests/test_image_processing_common.py index a3fbb6eeb781..7d89b43ce35b 100644 --- a/tests/test_image_processing_common.py +++ b/tests/test_image_processing_common.py @@ -191,7 +191,7 @@ def measure_time(image_processor, image): dummy_images = torch.randint(0, 255, (4, 3, 224, 224), dtype=torch.uint8) image_processor_slow = self.image_processing_class(**self.image_processor_dict) - image_processor_fast = self.fast_image_processing_class() + image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) fast_time = measure_time(image_processor_fast, dummy_images) slow_time = measure_time(image_processor_slow, dummy_images)