#
ViTPose

##
Overview

The ViTPose model was proposed in ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation by Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao. ViTPose employs a standard, non-hierarchical Vision Transformer as backbone for the task of keypoint estimation. A simple decoder head is added on top to predict the heatmaps from a given image. Despite its simplicity, the model gets state-of-the-art results on the challenging MS COCO Keypoint Detection benchmark. The model was further improved in ViTPose++: Vision Transformer for Generic Body Pose Estimation where the authors employ a mixture-of-experts (MoE) module in the ViT backbone along with pre-training on more data, which further enhances the performance.

The abstract from the paper is the following:

Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model called ViTPose. Specifically, ViTPose employs plain and non-hierarchical vision transformers as backbones to extract features for a given person instance and a lightweight decoder for pose estimation. It can be scaled up from 100M to 1B parameters by taking the advantages of the scalable model capacity and high parallelism of transformers, setting a new Pareto front between throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, pre-training and finetuning strategy, as well as dealing with multiple pose tasks. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our basic ViTPose model outperforms representative methods on the challenging MS COCO Keypoint Detection benchmark, while the largest model sets a new state-of-the-art.

ViTPose architecture. Taken from the original paper.

This model was contributed by nielsr and sangbumchoi. The original code can be found here.

##
Usage Tips

ViTPose is a so-called top-down keypoint detection model. This means that one first uses an object detector, like RT-DETR, to detect people (or other instances) in an image. Next, ViTPose takes the cropped images as input and predicts the keypoints for each of them.

    import torch
    import requests
    import numpy as np

    from PIL import Image

    from transformers import AutoProcessor, RTDetrForObjectDetection, VitPoseForPoseEstimation

    device = "cuda" if torch.cuda.is_available() else "cpu"

    url = "http://images.cocodataset.org/val2017/000000000139.jpg"
    image = Image.open(requests.get(url, stream=True).raw)

    # ------------------------------------------------------------------------
    # Stage 1. Detect humans on the image
    # ------------------------------------------------------------------------

    # You can choose any detector of your choice
    person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
    person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map=device)

    inputs = person_image_processor(images=image, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = person_model(**inputs)

    results = person_image_processor.post_process_object_detection(
        outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
    )
    result = results[0]  # take first image results

    # Human label refers 0 index in COCO dataset
    person_boxes = result["boxes"][result["labels"] == 0]
    person_boxes = person_boxes.cpu().numpy()

    # Convert boxes from VOC (x1, y1, x2, y2) to COCO (x1, y1, w, h) format
    person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
    person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]

    # ------------------------------------------------------------------------
    # Stage 2. Detect keypoints for each person found
    # ------------------------------------------------------------------------

    image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-base-simple")
    model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map=device)

    inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model(**inputs)

    pose_results = image_processor.post_process_pose_estimation(outputs, boxes=[person_boxes])
    image_pose_result = pose_results[0]  # results for first image

###
ViTPose++ models

The best checkpoints are those of the ViTPose++ paper. ViTPose++ models employ a so-called Mixture-of-Experts (MoE) architecture for the ViT backbone, resulting in better performance.

The ViTPose+ checkpoints use 6 experts, hence 6 different dataset indices can be passed. An overview of the various dataset indices is provided below:

- 0: COCO validation 2017 dataset, using an object detector that gets 56 AP on the “person” class
- 1: AiC dataset
- 2: MPII dataset
- 3: AP-10K dataset
- 4: APT-36K dataset
- 5: COCO-WholeBody dataset

Pass the `dataset_index` argument in the forward of the model to indicate which experts to use for each example in the batch. Example usage is shown below:

    image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-base")
    model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-base", device=device)

    inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(device)

    dataset_index = torch.tensor([0], device=device) # must be a tensor of shape (batch_size,)

    with torch.no_grad():
        outputs = model(**inputs, dataset_index=dataset_index)

The ViTPose+ checkpoints use 6 experts, hence 6 different dataset indices can be passed. An overview of the various dataset indices is provided below:

- 0: COCO validation 2017 dataset, using an object detector that gets 56 AP on the “person” class
- 1: AiC dataset
- 2: MPII dataset
- 3: AP-10K dataset
- 4: APT-36K dataset
- 5: COCO-WholeBody dataset

###
Visualization

To visualize the various keypoints, one can either leverage the `supervision` [library](https://github.com/roboflow/supervision (requires `pip install supervision`):

    import supervision as sv

    xy = torch.stack([pose_result['keypoints'] for pose_result in image_pose_result]).cpu().numpy()
    scores = torch.stack([pose_result['scores'] for pose_result in image_pose_result]).cpu().numpy()

    key_points = sv.KeyPoints(
        xy=xy, confidence=scores
    )

    edge_annotator = sv.EdgeAnnotator(
        color=sv.Color.GREEN,
        thickness=1
    )
    vertex_annotator = sv.VertexAnnotator(
        color=sv.Color.RED,
        radius=2
    )
    annotated_frame = edge_annotator.annotate(
        scene=image.copy(),
        key_points=key_points
    )
    annotated_frame = vertex_annotator.annotate(
        scene=annotated_frame,
        key_points=key_points
    )

Alternatively, one can also visualize the keypoints using OpenCV (requires `pip install opencv-python`):

    import math
    import cv2

    def draw_points(image, keypoints, scores, pose_keypoint_color, keypoint_score_threshold, radius, show_keypoint_weight):
        if pose_keypoint_color is not None:
            assert len(pose_keypoint_color) == len(keypoints)
        for kid, (kpt, kpt_score) in enumerate(zip(keypoints, scores)):
            x_coord, y_coord = int(kpt[0]), int(kpt[1])
            if kpt_score > keypoint_score_threshold:
                color = tuple(int(c) for c in pose_keypoint_color[kid])
                if show_keypoint_weight:
                    cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)
                    transparency = max(0, min(1, kpt_score))
                    cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
                else:
                    cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)

    def draw_links(image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold, thickness, show_keypoint_weight, stick_width = 2):
        height, width, _ = image.shape
        if keypoint_edges is not None and link_colors is not None:
            assert len(link_colors) == len(keypoint_edges)
            for sk_id, sk in enumerate(keypoint_edges):
                x1, y1, score1 = (int(keypoints[sk[0], 0]), int(keypoints[sk[0], 1]), scores[sk[0]])
                x2, y2, score2 = (int(keypoints[sk[1], 0]), int(keypoints[sk[1], 1]), scores[sk[1]])
                if (
                    x1 > 0
                    and x1 < width
                    and y1 > 0
                    and y1 < height
                    and x2 > 0
                    and x2 < width
                    and y2 > 0
                    and y2 < height
                    and score1 > keypoint_score_threshold
                    and score2 > keypoint_score_threshold
                ):
                    color = tuple(int(c) for c in link_colors[sk_id])
                    if show_keypoint_weight:
                        X = (x1, x2)
                        Y = (y1, y2)
                        mean_x = np.mean(X)
                        mean_y = np.mean(Y)
                        length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
                        angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
                        polygon = cv2.ellipse2Poly(
                            (int(mean_x), int(mean_y)), (int(length / 2), int(stick_width)), int(angle), 0, 360, 1
                        )
                        cv2.fillConvexPoly(image, polygon, color)
                        transparency = max(0, min(1, 0.5 * (keypoints[sk[0], 2] + keypoints[sk[1], 2])))
                        cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
                    else:
                        cv2.line(image, (x1, y1), (x2, y2), color, thickness=thickness)

    # Note: keypoint_edges and color palette are dataset-specific
    keypoint_edges = model.config.edges

    palette = np.array(
        [
            [255, 128, 0],
            [255, 153, 51],
            [255, 178, 102],
            [230, 230, 0],
            [255, 153, 255],
            [153, 204, 255],
            [255, 102, 255],
            [255, 51, 255],
            [102, 178, 255],
            [51, 153, 255],
            [255, 153, 153],
            [255, 102, 102],
            [255, 51, 51],
            [153, 255, 153],
            [102, 255, 102],
            [51, 255, 51],
            [0, 255, 0],
            [0, 0, 255],
            [255, 0, 0],
            [255, 255, 255],
        ]
    )

    link_colors = palette[[0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16]]
    keypoint_colors = palette[[16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0]]

    numpy_image = np.array(image)

    for pose_result in image_pose_result:
        scores = np.array(pose_result["scores"])
        keypoints = np.array(pose_result["keypoints"])

        # draw each point on image
        draw_points(numpy_image, keypoints, scores, keypoint_colors, keypoint_score_threshold=0.3, radius=4, show_keypoint_weight=False)

        # draw links
        draw_links(numpy_image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold=0.3, thickness=1, show_keypoint_weight=False)

    pose_image = Image.fromarray(numpy_image)
    pose_image

##
Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViTPose. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

- A demo of ViTPose on images and video can be found here.
- A notebook illustrating inference and visualization can be found here.

##
VitPoseImageProcessor

###
class transformers.VitPoseImageProcessor

< source >

( do_affine_transform: bool = True size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None **kwargs )

Parameters

- do_affine_transform (`bool`, optional, defaults to `True`) — Whether to apply an affine transformation to the input images.
- size (`Dict[str, int]` optional, defaults to `{"height" -- 256, "width": 192}`): Resolution of the image after `affine_transform` is applied. Only has an effect if `do_affine_transform` is set to `True`. Can be overridden by `size` in the `preprocess` method.
- do_rescale (`bool`, optional, defaults to `True`) — Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.).
- rescale_factor (`int` or `float`, optional, defaults to `1/255`) — Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method.
- do_normalize (`bool`, optional, defaults to `True`) — Whether or not to normalize the input with mean and standard deviation.
- image_mean (`List[int]`, defaults to `[0.485, 0.456, 0.406]`, optional) — The sequence of means for each channel, to be used when normalizing images.
- image_std (`List[int]`, defaults to `[0.229, 0.224, 0.225]`, optional) — The sequence of standard deviations for each channel, to be used when normalizing images.

Constructs a VitPose image processor.

####
preprocess

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] boxes: typing.Union[typing.List[typing.List[float]], numpy.ndarray] do_affine_transform: typing.Optional[bool] = None size: typing.Dict[str, int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None ) →
BatchFeature

Parameters

- images (`ImageInput`) — Image 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`.
- boxes (`List[List[List[float]]]` or `np.ndarray`) — List or array of bounding boxes for each image. Each box should be a list of 4 floats representing the bounding box coordinates in COCO format (top_left_x, top_left_y, width, height).
- do_affine_transform (`bool`, optional, defaults to `self.do_affine_transform`) — Whether to apply an affine transformation to the input images.
- size (`Dict[str, int]` optional, defaults to `self.size`) — Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after resizing.
- do_rescale (`bool`, optional, defaults to `self.do_rescale`) — Whether to rescale the image values between [0 - 1].
- rescale_factor (`float`, optional, defaults to `self.rescale_factor`) — Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- do_normalize (`bool`, optional, defaults to `self.do_normalize`) — Whether to normalize the image.
- image_mean (`float` or `List[float]`, optional, defaults to `self.image_mean`) — Image mean to use if `do_normalize` is set to `True`.
- image_std (`float` or `List[float]`, optional, defaults to `self.image_std`) — Image standard deviation to use if `do_normalize` is set to `True`.
- return_tensors (`str` or TensorType, optional, defaults to `'np'`) — If set, will return tensors of a particular framework. Acceptable values are:
  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return NumPy `np.ndarray` objects.
  - `'jax'`: Return JAX `jnp.ndarray` objects.

Returns

BatchFeature

A BatchFeature with the following fields:

- pixel_values — Pixel values to be fed to a model, of shape (batch_size, num_channels, height, width).

Preprocess an image or batch of images.

####
post_process_pose_estimation

< source >

( outputs: VitPoseEstimatorOutput boxes: typing.Union[typing.List[typing.List[typing.List[float]]], numpy.ndarray] kernel_size: int = 11 threshold: typing.Optional[float] = None target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None ) →
`List[List[Dict]]`

Parameters

- outputs (`VitPoseEstimatorOutput`) — VitPoseForPoseEstimation model outputs.
- boxes (`List[List[List[float]]]` or `np.ndarray`) — List or array of bounding boxes for each image. Each box should be a list of 4 floats representing the bounding box coordinates in COCO format (top_left_x, top_left_y, width, height).
- kernel_size (`int`, optional, defaults to 11) — Gaussian kernel size (K) for modulation.
- threshold (`float`, optional, defaults to None) — 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 be resize with the default value.

Returns

`List[List[Dict]]`

A list of dictionaries, each dictionary containing the keypoints and boxes for an image in the batch as predicted by the model.

Transform the heatmaps into keypoint predictions and transform them back to the image.

##
VitPoseConfig

###
class transformers.VitPoseConfig

< source >

( backbone_config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None backbone: typing.Optional[str] = None use_pretrained_backbone: bool = False use_timm_backbone: bool = False backbone_kwargs: typing.Optional[dict] = None initializer_range: float = 0.02 scale_factor: int = 4 use_simple_decoder: bool = True **kwargs )

Parameters

- backbone_config (`PretrainedConfig` or `dict`, optional, defaults to `VitPoseBackboneConfig()`) — The configuration of the backbone model. Currently, only `backbone_config` with `vitpose_backbone` as `model_type` is supported.
- backbone (`str`, optional) — Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone’s config and uses that to initialize the backbone with random weights.
- use_pretrained_backbone (`bool`, optional, defaults to `False`) — Whether to use pretrained weights for the backbone.
- use_timm_backbone (`bool`, optional, defaults to `False`) — Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library.
- backbone_kwargs (`dict`, optional) — Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- scale_factor (`int`, optional, defaults to 4) — Factor to upscale the feature maps coming from the ViT backbone.
- use_simple_decoder (`bool`, optional, defaults to `True`) — Whether to use a `VitPoseSimpleDecoder` to decode the feature maps from the backbone into heatmaps. Otherwise it uses `VitPoseClassicDecoder`.

This is the configuration class to store the configuration of a VitPoseForPoseEstimation. It is used to instantiate a VitPose model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the VitPose usyd-community/vitpose-base-simple architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import VitPoseConfig, VitPoseForPoseEstimation

    >>> # Initializing a VitPose configuration
    >>> configuration = VitPoseConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = VitPoseForPoseEstimation(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

##
VitPoseForPoseEstimation

###
class transformers.VitPoseForPoseEstimation

< source >

( config: VitPoseConfig )

Parameters

- config (VitPoseConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The VitPose model with a pose estimation head on top. This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: Tensor dataset_index: typing.Optional[torch.Tensor] = None flip_pairs: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.vitpose.modeling_vitpose.VitPoseEstimatorOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using VitPoseImageProcessor. See VitPoseImageProcessor.call() for details.
- dataset_index (`torch.Tensor` of shape `(batch_size,)`) — Index to use in the Mixture-of-Experts (MoE) blocks of the backbone.

  This corresponds to the dataset index used during training, e.g. For the single dataset index 0 refers to the corresponding dataset. For the multiple datasets index 0 refers to dataset A (e.g. MPII) and index 1 refers to dataset B (e.g. CrowdPose).

- flip_pairs (`torch.tensor`, optional) — Whether to mirror pairs of keypoints (for example, left ear — right ear).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.vitpose.modeling_vitpose.VitPoseEstimatorOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.vitpose.modeling_vitpose.VitPoseEstimatorOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (VitPoseConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Loss is not supported at this moment. See https://github.com/ViTAE-Transformer/ViTPose/tree/main/mmpose/models/losses for further detail.

- heatmaps (`torch.FloatTensor` of shape `(batch_size, num_keypoints, height, width)`) — Heatmaps as predicted by the model.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The VitPoseForPoseEstimation forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from transformers import AutoImageProcessor, VitPoseForPoseEstimation
    >>> import torch
    >>> from PIL import Image
    >>> import requests

    >>> processor = AutoImageProcessor.from_pretrained("usyd-community/vitpose-base-simple")
    >>> model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple")

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> boxes = [[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]]]
    >>> inputs = processor(image, boxes=boxes, return_tensors="pt")

    >>> with torch.no_grad():
    ...     outputs = model(**inputs)
    >>> heatmaps = outputs.heatmaps

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