mmpose.apis

mmpose.apis.collect_multi_frames(video, frame_id, indices, online=False)

Collect multi frames from the video.

Parameters

• video (mmcv.VideoReader) – A VideoReader of the input video file.

• frame_id (int) – index of the current frame

• indices (list(int)) – index offsets of the frames to collect

• online (bool) – inference mode, if set to True, can not use future frame information.

Returns

multi frames collected from the input video file.

Return type

list(ndarray)

mmpose.apis.extract_pose_sequence(pose_results, frame_idx, causal, seq_len, step=1)

Extract the target frame from 2D pose results, and pad the sequence to a fixed length.

Parameters

• pose_results (list[list[dict]]) –

  Multi-frame pose detection results stored in a nested list. Each element of the outer list is the pose detection results of a single frame, and each element of the inner list is the pose information of one person, which contains:

  • keypoints (ndarray[K, 2 or 3]): x, y, [score]

  • track_id (int): unique id of each person, required when with_track_id==True.

  • bbox ((4, ) or (5, )): left, right, top, bottom, [score]

• frame_idx (int) – The index of the frame in the original video.

• causal (bool) – If True, the target frame is the last frame in a sequence. Otherwise, the target frame is in the middle of a sequence.

• seq_len (int) – The number of frames in the input sequence.

• step (int) – Step size to extract frames from the video.

Returns

Multi-frame pose detection results stored in a nested list with a length of seq_len.

Return type

list[list[dict]]

mmpose.apis.get_track_id(results, results_last, next_id, min_keypoints=3, use_oks=False, tracking_thr=0.3, use_one_euro=False, fps=None, sigmas=None)

Get track id for each person instance on the current frame.

Parameters

• results (list[dict]) – The bbox & pose results of the current frame (bbox_result, pose_result).

• results_last (list[dict], optional) – The bbox & pose & track_id info of the last frame (bbox_result, pose_result, track_id). None is equivalent to an empty result list. Default: None

• next_id (int) – The track id for the new person instance.

• min_keypoints (int) – Minimum number of keypoints recognized as person. 0 means no minimum threshold required. Default: 3.

• use_oks (bool) – Flag to using oks tracking. default: False.

• tracking_thr (float) – The threshold for tracking.

• use_one_euro (bool) – Option to use one-euro-filter. default: False.

• fps (optional) – Parameters that d_cutoff when one-euro-filter is used as a video input

• sigmas (np.ndarray) – Standard deviation of keypoint labelling. It is necessary for oks_iou tracking (use_oks==True). It will be use sigmas of COCO as default if it is set to None. Default is None.

Returns

• results (list[dict]): The bbox & pose & track_id info of the current frame (bbox_result, pose_result, track_id).

• next_id (int): The track id for the new person instance.

Return type

tuple

mmpose.apis.inference_bottom_up_pose_model(model, img_or_path, dataset='BottomUpCocoDataset', dataset_info=None, pose_nms_thr=0.9, return_heatmap=False, outputs=None)

Inference a single image with a bottom-up pose model.

Note

• num_people: P

• num_keypoints: K

• bbox height: H

• bbox width: W

Parameters

• model (nn.Module) – The loaded pose model.

• img_or_path (str| np.ndarray) – Image filename or loaded image.

• dataset (str) – Dataset name, e.g. ‘BottomUpCocoDataset’. It is deprecated. Please use dataset_info instead.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• pose_nms_thr (float) – retain oks overlap < pose_nms_thr, default: 0.9.

• return_heatmap (bool) – Flag to return heatmap, default: False.

• outputs (list(str) | tuple(str)) – Names of layers whose outputs need to be returned, default: None.

Returns

• pose_results (list[np.ndarray]): The predicted pose info. The length of the list is the number of people (P). Each item in the list is a ndarray, containing each person’s pose (np.ndarray[Kx3]): x, y, score.

• returned_outputs (list[dict[np.ndarray[N, K, H, W] | torch.Tensor[N, K, H, W]]]): Output feature maps from layers specified in outputs. Includes ‘heatmap’ if return_heatmap is True.

Return type

tuple

mmpose.apis.inference_interhand_3d_model(model, img_or_path, det_results, bbox_thr=None, format='xywh', dataset='InterHand3DDataset')

Inference a single image with a list of hand bounding boxes.

Note

• num_bboxes: N

• num_keypoints: K

Parameters

• model (nn.Module) – The loaded pose model.

• img_or_path (str | np.ndarray) – Image filename or loaded image.

• det_results (list[dict]) – The 2D bbox sequences stored in a list. Each each element of the list is the bbox of one person, whose shape is (ndarray[4 or 5]), containing 4 box coordinates (and score).

• dataset (str) – Dataset name.

• format – bbox format (‘xyxy’ | ‘xywh’). Default: ‘xywh’. ‘xyxy’ means (left, top, right, bottom), ‘xywh’ means (left, top, width, height).

Returns

3D pose inference results. Each element is the result of an instance, which contains the predicted 3D keypoints with shape (ndarray[K,3]). If there is no valid instance, an empty list will be returned.

Return type

list[dict]

mmpose.apis.inference_mesh_model(model, img_or_path, det_results, bbox_thr=None, format='xywh', dataset='MeshH36MDataset')

Inference a single image with a list of bounding boxes.

Note

• num_bboxes: N

• num_keypoints: K

• num_vertices: V

• num_faces: F

Parameters

• model (nn.Module) – The loaded pose model.

• img_or_path (str | np.ndarray) – Image filename or loaded image.

• det_results (list[dict]) – The 2D bbox sequences stored in a list. Each element of the list is the bbox of one person. “bbox” (ndarray[4 or 5]): The person bounding box, which contains 4 box coordinates (and score).

• bbox_thr (float | None) – Threshold for bounding boxes. Only bboxes with higher scores will be fed into the pose detector. If bbox_thr is None, all boxes will be used.

• format (str) –

  bbox format (‘xyxy’ | ‘xywh’). Default: ‘xywh’.

  • ’xyxy’ means (left, top, right, bottom),

  • ’xywh’ means (left, top, width, height).

• dataset (str) – Dataset name.

Returns

3D pose inference results. Each element is the result of an instance, which contains:

• ’bbox’ (ndarray[4]): instance bounding bbox

• ’center’ (ndarray[2]): bbox center

• ’scale’ (ndarray[2]): bbox scale

• ’keypoints_3d’ (ndarray[K,3]): predicted 3D keypoints

• ’camera’ (ndarray[3]): camera parameters

• ’vertices’ (ndarray[V, 3]): predicted 3D vertices

• ’faces’ (ndarray[F, 3]): mesh faces

If there is no valid instance, an empty list will be returned.

Return type

list[dict]

mmpose.apis.inference_pose_lifter_model(model, pose_results_2d, dataset=None, dataset_info=None, with_track_id=True, image_size=None, norm_pose_2d=False)

Inference 3D pose from 2D pose sequences using a pose lifter model.

Parameters

• model (nn.Module) – The loaded pose lifter model

• pose_results_2d (list[list[dict]]) –

  The 2D pose sequences stored in a nested list. Each element of the outer list is the 2D pose results of a single frame, and each element of the inner list is the 2D pose of one person, which contains:

  • ”keypoints” (ndarray[K, 2 or 3]): x, y, [score]

  • ”track_id” (int)

• dataset (str) – Dataset name, e.g. ‘Body3DH36MDataset’

• with_track_id – If True, the element in pose_results_2d is expected to contain “track_id”, which will be used to gather the pose sequence of a person from multiple frames. Otherwise, the pose results in each frame are expected to have a consistent number and order of identities. Default is True.

• image_size (tuple|list) – image width, image height. If None, image size will not be contained in dict data.

• norm_pose_2d (bool) – If True, scale the bbox (along with the 2D pose) to the average bbox scale of the dataset, and move the bbox (along with the 2D pose) to the average bbox center of the dataset.

Returns

3D pose inference results. Each element is the result of an instance, which contains:

• ”keypoints_3d” (ndarray[K, 3]): predicted 3D keypoints

• ”keypoints” (ndarray[K, 2 or 3]): from the last frame in pose_results_2d.

• ”track_id” (int): from the last frame in pose_results_2d. If there is no valid instance, an empty list will be returned.

Return type

list[dict]

mmpose.apis.inference_top_down_pose_model(model, imgs_or_paths, person_results=None, bbox_thr=None, format='xywh', dataset='TopDownCocoDataset', dataset_info=None, return_heatmap=False, outputs=None)

Inference a single image with a list of person bounding boxes. Support single-frame and multi-frame inference setting.

Note

• num_frames: F

• num_people: P

• num_keypoints: K

• bbox height: H

• bbox width: W

Parameters

• model (nn.Module) – The loaded pose model.

• imgs_or_paths (str | np.ndarray | list(str) | list(np.ndarray)) – Image filename(s) or loaded image(s).

• person_results (list(dict), optional) –

  a list of detected persons that contains bbox and/or track_id:

  • bbox (4, ) or (5, ): The person bounding box, which contains

    4 box coordinates (and score).

  • track_id (int): The unique id for each human instance. If

    not provided, a dummy person result with a bbox covering the entire image will be used. Default: None.

• bbox_thr (float | None) – Threshold for bounding boxes. Only bboxes with higher scores will be fed into the pose detector. If bbox_thr is None, all boxes will be used.

• format (str) –

  bbox format (‘xyxy’ | ‘xywh’). Default: ‘xywh’.

  • xyxy means (left, top, right, bottom),

  • xywh means (left, top, width, height).

• dataset (str) – Dataset name, e.g. ‘TopDownCocoDataset’. It is deprecated. Please use dataset_info instead.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• return_heatmap (bool) – Flag to return heatmap, default: False

• outputs (list(str) | tuple(str)) – Names of layers whose outputs need to be returned. Default: None.

Returns

• pose_results (list[dict]): The bbox & pose info. Each item in the list is a dictionary, containing the bbox: (left, top, right, bottom, [score]) and the pose (ndarray[Kx3]): x, y, score.

• returned_outputs (list[dict[np.ndarray[N, K, H, W] | torch.Tensor[N, K, H, W]]]): Output feature maps from layers specified in outputs. Includes ‘heatmap’ if return_heatmap is True.

Return type

tuple

mmpose.apis.init_pose_model(config, checkpoint=None, device='cuda:0')

Initialize a pose model from config file.

Parameters

• config (str or mmcv.Config) – Config file path or the config object.

• checkpoint (str, optional) – Checkpoint path. If left as None, the model will not load any weights.

Returns

The constructed detector.

Return type

nn.Module

mmpose.apis.init_random_seed(seed=None, device='cuda')

Initialize random seed.

If the seed is not set, the seed will be automatically randomized, and then broadcast to all processes to prevent some potential bugs.

Parameters

• seed (int, Optional) – The seed. Default to None.

• device (str) – The device where the seed will be put on. Default to ‘cuda’.

Returns

Seed to be used.

Return type

int

mmpose.apis.multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False)

Test model with multiple gpus.

This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu modes. By setting ‘gpu_collect=True’ it encodes results to gpu tensors and use gpu communication for results collection. On cpu mode it saves the results on different gpus to ‘tmpdir’ and collects them by the rank 0 worker.

Parameters

• model (nn.Module) – Model to be tested.

• data_loader (nn.Dataloader) – Pytorch data loader.

• tmpdir (str) – Path of directory to save the temporary results from different gpus under cpu mode.

• gpu_collect (bool) – Option to use either gpu or cpu to collect results.

Returns

The prediction results.

Return type

list

mmpose.apis.process_mmdet_results(mmdet_results, cat_id=1)

Process mmdet results, and return a list of bboxes.

Parameters

• mmdet_results (list|tuple) – mmdet results.

• cat_id (int) – category id (default: 1 for human)

Returns

a list of detected bounding boxes

Return type

person_results (list)

mmpose.apis.single_gpu_test(model, data_loader)

Test model with a single gpu.

This method tests model with a single gpu and displays test progress bar.

Parameters

• model (nn.Module) – Model to be tested.

• data_loader (nn.Dataloader) – Pytorch data loader.

Returns

The prediction results.

Return type

list

mmpose.apis.train_model(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None)

Train model entry function.

Parameters

• model (nn.Module) – The model to be trained.

• dataset (Dataset) – Train dataset.

• cfg (dict) – The config dict for training.

• distributed (bool) – Whether to use distributed training. Default: False.

• validate (bool) – Whether to do evaluation. Default: False.

• timestamp (str | None) – Local time for runner. Default: None.

• meta (dict | None) – Meta dict to record some important information. Default: None

mmpose.apis.vis_3d_mesh_result(model, result, img=None, show=False, out_file=None)

Visualize the 3D mesh estimation results.

Parameters

• model (nn.Module) – The loaded model.

• result (list[dict]) – 3D mesh estimation results.

mmpose.apis.vis_3d_pose_result(model, result, img=None, dataset='Body3DH36MDataset', dataset_info=None, kpt_score_thr=0.3, radius=8, thickness=2, vis_height=400, num_instances=- 1, axis_azimuth=70, show=False, out_file=None)

Visualize the 3D pose estimation results.

Parameters

• model (nn.Module) – The loaded model.

• result (list[dict]) –

mmpose.apis.vis_pose_result(model, img, result, radius=4, thickness=1, kpt_score_thr=0.3, bbox_color='green', dataset='TopDownCocoDataset', dataset_info=None, show=False, out_file=None)

Visualize the detection results on the image.

Parameters

• model (nn.Module) – The loaded detector.

• img (str | np.ndarray) – Image filename or loaded image.

• result (list[dict]) – The results to draw over img (bbox_result, pose_result).

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• kpt_score_thr (float) – The threshold to visualize the keypoints.

• skeleton (list[tuple()]) – Default None.

• show (bool) – Whether to show the image. Default True.

• out_file (str|None) – The filename of the output visualization image.

mmpose.apis.vis_pose_tracking_result(model, img, result, radius=4, thickness=1, kpt_score_thr=0.3, dataset='TopDownCocoDataset', dataset_info=None, show=False, out_file=None)

Visualize the pose tracking results on the image.

Parameters

• model (nn.Module) – The loaded detector.

• img (str | np.ndarray) – Image filename or loaded image.

• result (list[dict]) – The results to draw over img (bbox_result, pose_result).

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• kpt_score_thr (float) – The threshold to visualize the keypoints.

• skeleton (list[tuple]) – Default None.

• show (bool) – Whether to show the image. Default True.

• out_file (str|None) – The filename of the output visualization image.

mmpose.apis.webcam

Executor

WebcamExecutor

The interface to build and execute webcam applications from configs.

Nodes

Base Nodes

Node	Base class for node, which is the interface of basic function module.
BaseVisualizerNode	Base class for nodes whose function is to create visual effects, like visualizing model predictions, showing graphics or showing text messages.

Model Nodes

DetectorNode	Detect objects from the frame image using MMDetection model.
TopDownPoseEstimatorNode	Perform top-down pose estimation using MMPose model.
PoseTrackerNode	Perform object detection and top-down pose estimation.

Visualizer Nodes

ObjectVisualizerNode	Visualize the bounding box and keypoints of objects.
NoticeBoardNode	Show text messages in the frame.
SunglassesEffectNode	Apply sunglasses effect (draw sunglasses at the facial area)to the objects with eye keypoints in the frame.
BigeyeEffectNode	Apply big-eye effect to the objects with eye keypoints in the frame.

Helper Nodes

ObjectAssignerNode	Assign the object information to the frame message.
MonitorNode	Show diagnostic information.
RecorderNode	Record the video frames into a local file.

Utils

Buffer and Message

BufferManager	A helper class to manage multiple buffers.
Message	Message base class.
FrameMessage	The message to store information of a video frame.
VideoEndingMessage	The special message to indicate the ending of the input video.

Pose

get_eye_keypoint_ids	A helper function to get the keypoint indices of left and right eyes from the model config.
get_face_keypoint_ids	A helper function to get the keypoint indices of the face from the model config.
get_hand_keypoint_ids	A helper function to get the keypoint indices of left and right hand from the model config.
get_mouth_keypoint_ids	A helper function to get the mouth keypoint index from the model config.
get_wrist_keypoint_ids	A helper function to get the keypoint indices of left and right wrists from the model config.

Event

EventManager

A helper class to manage events.

Misc

copy_and_paste	Copy the image region and paste to the background.
screen_matting	Get screen matting mask.
expand_and_clamp	Expand the bbox and clip it to fit the image shape.
limit_max_fps	A context manager to limit maximum frequence of entering the context.
is_image_file	Check if a path is an image file by its extension.
get_cached_file_path	Loads the Torch serialized object at the given URL.
load_image_from_disk_or_url	Load an image file, from disk or url.
get_config_path	Get config path from an OpenMMLab codebase.

mmpose.core

evaluation

class mmpose.core.evaluation.DistEvalHook(dataloader, start=None, interval=1, by_epoch=True, save_best=None, rule=None, test_fn=None, greater_keys=['acc', 'ap', 'ar', 'pck', 'auc', '3dpck', 'p-3dpck', '3dauc', 'p-3dauc', 'pcp'], less_keys=['loss', 'epe', 'nme', 'mpjpe', 'p-mpjpe', 'n-mpjpe'], broadcast_bn_buffer=True, tmpdir=None, gpu_collect=False, **eval_kwargs)

class mmpose.core.evaluation.EvalHook(dataloader, start=None, interval=1, by_epoch=True, save_best=None, rule=None, test_fn=None, greater_keys=['acc', 'ap', 'ar', 'pck', 'auc', '3dpck', 'p-3dpck', '3dauc', 'p-3dauc', 'pcp'], less_keys=['loss', 'epe', 'nme', 'mpjpe', 'p-mpjpe', 'n-mpjpe'], **eval_kwargs)

mmpose.core.evaluation.aggregate_scale(feature_maps_list, align_corners=False, project2image=True, size_projected=None, aggregate_scale='average')

Aggregate multi-scale outputs.

Note

batch size: N keypoints num : K heatmap width: W heatmap height: H

Parameters

• feature_maps_list (list[Tensor]) – Aggregated feature maps.

• project2image (bool) – Option to resize to base scale.

• size_projected (list[int, int]) – Base size of heatmaps [w, h].

• align_corners (bool) – Align corners when performing interpolation.

• aggregate_scale (str) –

  Methods to aggregate multi-scale feature maps. Options: ‘average’, ‘unsqueeze_concat’.

  • ’average’: Get the average of the feature maps.

  • ’unsqueeze_concat’: Concatenate the feature maps along new axis.

    Default: ‘average.

Returns

Aggregated feature maps.

Return type

Tensor

mmpose.core.evaluation.aggregate_stage_flip(feature_maps, feature_maps_flip, index=- 1, project2image=True, size_projected=None, align_corners=False, aggregate_stage='concat', aggregate_flip='average')

Inference the model to get multi-stage outputs (heatmaps & tags), and resize them to base sizes.

Parameters

• feature_maps (list[Tensor]) – feature_maps can be heatmaps, tags, and pafs.

• feature_maps_flip (list[Tensor] | None) – flipped feature_maps. feature maps can be heatmaps, tags, and pafs.

• project2image (bool) – Option to resize to base scale.

• size_projected (list[int, int]) – Base size of heatmaps [w, h].

• align_corners (bool) – Align corners when performing interpolation.

• aggregate_stage (str) –

  Methods to aggregate multi-stage feature maps. Options: ‘concat’, ‘average’. Default: ‘concat.

  • ’concat’: Concatenate the original and the flipped feature maps.

  • ’average’: Get the average of the original and the flipped

    feature maps.

• aggregate_flip (str) –

  Methods to aggregate the original and the flipped feature maps. Options: ‘concat’, ‘average’, ‘none’. Default: ‘average.

  • ’concat’: Concatenate the original and the flipped feature maps.

  • ’average’: Get the average of the original and the flipped

    feature maps..

  • ’none’: no flipped feature maps.

Returns

Aggregated feature maps with shape [NxKxWxH].

Return type

list[Tensor]

mmpose.core.evaluation.compute_similarity_transform(source_points, target_points)

Computes a similarity transform (sR, t) that takes a set of 3D points source_points (N x 3) closest to a set of 3D points target_points, where R is an 3x3 rotation matrix, t 3x1 translation, s scale. And return the transformed 3D points source_points_hat (N x 3). i.e. solves the orthogonal Procrutes problem.

Note

Points number: N

Parameters

• source_points (np.ndarray) – Source point set with shape [N, 3].

• target_points (np.ndarray) – Target point set with shape [N, 3].

Returns

Transformed source point set with shape [N, 3].

Return type

np.ndarray

mmpose.core.evaluation.flip_feature_maps(feature_maps, flip_index=None)

Flip the feature maps and swap the channels.

Parameters

• feature_maps (list[Tensor]) – Feature maps.

• flip_index (list[int] | None) – Channel-flip indexes. If None, do not flip channels.

Returns

Flipped feature_maps.

Return type

list[Tensor]

mmpose.core.evaluation.get_group_preds(grouped_joints, center, scale, heatmap_size, use_udp=False)

Transform the grouped joints back to the image.

Parameters

• grouped_joints (list) – Grouped person joints.

• center (np.ndarray[2, ]) – Center of the bounding box (x, y).

• scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].

• heatmap_size (np.ndarray[2, ]) – Size of the destination heatmaps.

• use_udp (bool) – Unbiased data processing. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR’2020).

Returns

List of the pose result for each person.

Return type

list

mmpose.core.evaluation.keypoint_3d_auc(pred, gt, mask, alignment='none')

Calculate the Area Under the Curve (3DAUC) computed for a range of 3DPCK thresholds.

Paper ref: Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision’ 3DV’2017. . This implementation is derived from mpii_compute_3d_pck.m, which is provided as part of the MPI-INF-3DHP test data release.

Note

batch_size: N num_keypoints: K keypoint_dims: C

Parameters

• pred (np.ndarray[N, K, C]) – Predicted keypoint location.

• gt (np.ndarray[N, K, C]) – Groundtruth keypoint location.

• mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

• alignment (str, optional) –

  method to align the prediction with the groundtruth. Supported options are:

  • 'none': no alignment will be applied

  • 'scale': align in the least-square sense in scale

  • 'procrustes': align in the least-square sense in scale,

    rotation and translation.

Returns

AUC computed for a range of 3DPCK thresholds.

Return type

auc

mmpose.core.evaluation.keypoint_3d_pck(pred, gt, mask, alignment='none', threshold=0.15)

Calculate the Percentage of Correct Keypoints (3DPCK) w. or w/o rigid alignment.

Paper ref: Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision’ 3DV’2017. .

Note

• batch_size: N

• num_keypoints: K

• keypoint_dims: C

Parameters

• pred (np.ndarray[N, K, C]) – Predicted keypoint location.

• gt (np.ndarray[N, K, C]) – Groundtruth keypoint location.

• mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

• alignment (str, optional) –

  method to align the prediction with the groundtruth. Supported options are:

  • 'none': no alignment will be applied

  • 'scale': align in the least-square sense in scale

  • 'procrustes': align in the least-square sense in scale,

    rotation and translation.

• threshold – If L2 distance between the prediction and the groundtruth is less then threshold, the predicted result is considered as correct. Default: 0.15 (m).

Returns

percentage of correct keypoints.

Return type

pck

mmpose.core.evaluation.keypoint_auc(pred, gt, mask, normalize, num_step=20)

Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints for coordinates.

Note

• batch_size: N

• num_keypoints: K

Parameters

• pred (np.ndarray[N, K, 2]) – Predicted keypoint location.

• gt (np.ndarray[N, K, 2]) – Groundtruth keypoint location.

• mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

• normalize (float) – Normalization factor.

Returns

Area under curve.

Return type

float

mmpose.core.evaluation.keypoint_epe(pred, gt, mask)

Calculate the end-point error.

Note

• batch_size: N

• num_keypoints: K

Parameters

• pred (np.ndarray[N, K, 2]) – Predicted keypoint location.

• gt (np.ndarray[N, K, 2]) – Groundtruth keypoint location.

• mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

Returns

Average end-point error.

Return type

float

mmpose.core.evaluation.keypoint_mpjpe(pred, gt, mask, alignment='none')

Calculate the mean per-joint position error (MPJPE) and the error after rigid alignment with the ground truth (P-MPJPE).

Note

• batch_size: N

• num_keypoints: K

• keypoint_dims: C

Parameters

• pred (np.ndarray) – Predicted keypoint location with shape [N, K, C].

• gt (np.ndarray) – Groundtruth keypoint location with shape [N, K, C].

• mask (np.ndarray) – Visibility of the target with shape [N, K]. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

• alignment (str, optional) –

  method to align the prediction with the groundtruth. Supported options are:

  • 'none': no alignment will be applied

  • 'scale': align in the least-square sense in scale

  • 'procrustes': align in the least-square sense in

    scale, rotation and translation.

Returns

A tuple containing joint position errors

• (float | np.ndarray): mean per-joint position error (mpjpe).

• (float | np.ndarray): mpjpe after rigid alignment with the

  ground truth (p-mpjpe).

Return type

tuple

mmpose.core.evaluation.keypoint_pck_accuracy(pred, gt, mask, thr, normalize)

Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints for coordinates.

Note

PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc.

• batch_size: N

• num_keypoints: K

Parameters

• pred (np.ndarray[N, K, 2]) – Predicted keypoint location.

• gt (np.ndarray[N, K, 2]) – Groundtruth keypoint location.

• mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

• thr (float) – Threshold of PCK calculation.

• normalize (np.ndarray[N, 2]) – Normalization factor for H&W.

Returns

A tuple containing keypoint accuracy.

• acc (np.ndarray[K]): Accuracy of each keypoint.

• avg_acc (float): Averaged accuracy across all keypoints.

• cnt (int): Number of valid keypoints.

Return type

tuple

mmpose.core.evaluation.keypoints_from_heatmaps(heatmaps, center, scale, unbiased=False, post_process='default', kernel=11, valid_radius_factor=0.0546875, use_udp=False, target_type='GaussianHeatmap')

Get final keypoint predictions from heatmaps and transform them back to the image.

Note

• batch size: N

• num keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• heatmaps (np.ndarray[N, K, H, W]) – model predicted heatmaps.

• center (np.ndarray[N, 2]) – Center of the bounding box (x, y).

• scale (np.ndarray[N, 2]) – Scale of the bounding box wrt height/width.

• post_process (str/None) – Choice of methods to post-process heatmaps. Currently supported: None, ‘default’, ‘unbiased’, ‘megvii’.

• unbiased (bool) – Option to use unbiased decoding. Mutually exclusive with megvii. Note: this arg is deprecated and unbiased=True can be replaced by post_process=’unbiased’ Paper ref: Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).

• kernel (int) – Gaussian kernel size (K) for modulation, which should match the heatmap gaussian sigma when training. K=17 for sigma=3 and k=11 for sigma=2.

• valid_radius_factor (float) – The radius factor of the positive area in classification heatmap for UDP.

• use_udp (bool) – Use unbiased data processing.

• target_type (str) – ‘GaussianHeatmap’ or ‘CombinedTarget’. GaussianHeatmap: Classification target with gaussian distribution. CombinedTarget: The combination of classification target (response map) and regression target (offset map). Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

Returns

A tuple containing keypoint predictions and scores.

• preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.

• maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.

Return type

tuple

mmpose.core.evaluation.keypoints_from_heatmaps3d(heatmaps, center, scale)

Get final keypoint predictions from 3d heatmaps and transform them back to the image.

Note

• batch size: N

• num keypoints: K

• heatmap depth size: D

• heatmap height: H

• heatmap width: W

Parameters

• heatmaps (np.ndarray[N, K, D, H, W]) – model predicted heatmaps.

• center (np.ndarray[N, 2]) – Center of the bounding box (x, y).

• scale (np.ndarray[N, 2]) – Scale of the bounding box wrt height/width.

Returns

A tuple containing keypoint predictions and scores.

• preds (np.ndarray[N, K, 3]): Predicted 3d keypoint location in images.

• maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.

Return type

tuple

mmpose.core.evaluation.keypoints_from_regression(regression_preds, center, scale, img_size)

Get final keypoint predictions from regression vectors and transform them back to the image.

Note

• batch_size: N

• num_keypoints: K

Parameters

• regression_preds (np.ndarray[N, K, 2]) – model prediction.

• center (np.ndarray[N, 2]) – Center of the bounding box (x, y).

• scale (np.ndarray[N, 2]) – Scale of the bounding box wrt height/width.

• img_size (list(img_width, img_height)) – model input image size.

Returns

• preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.

• maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.

Return type

tuple

mmpose.core.evaluation.multilabel_classification_accuracy(pred, gt, mask, thr=0.5)

Get multi-label classification accuracy.

Note

• batch size: N

• label number: L

Parameters

• pred (np.ndarray[N, L, 2]) – model predicted labels.

• gt (np.ndarray[N, L, 2]) – ground-truth labels.

• mask (np.ndarray[N, 1] or np.ndarray[N, L]) – reliability of

• labels. (ground-truth) –

Returns

multi-label classification accuracy.

Return type

float

mmpose.core.evaluation.pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None)

Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints from heatmaps.

Note

PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc.

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• output (np.ndarray[N, K, H, W]) – Model output heatmaps.

• target (np.ndarray[N, K, H, W]) – Groundtruth heatmaps.

• mask (np.ndarray[N, K]) – Visibility of the target. False for invisible joints, and True for visible. Invisible joints will be ignored for accuracy calculation.

• thr (float) – Threshold of PCK calculation. Default 0.05.

• normalize (np.ndarray[N, 2]) – Normalization factor for H&W.

Returns

A tuple containing keypoint accuracy.

• np.ndarray[K]: Accuracy of each keypoint.

• float: Averaged accuracy across all keypoints.

• int: Number of valid keypoints.

Return type

tuple

mmpose.core.evaluation.post_dark_udp(coords, batch_heatmaps, kernel=3)

DARK post-pocessing. Implemented by udp. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020). Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).

Note

• batch size: B

• num keypoints: K

• num persons: N

• height of heatmaps: H

• width of heatmaps: W

B=1 for bottom_up paradigm where all persons share the same heatmap. B=N for top_down paradigm where each person has its own heatmaps.

Parameters

• coords (np.ndarray[N, K, 2]) – Initial coordinates of human pose.

• batch_heatmaps (np.ndarray[B, K, H, W]) – batch_heatmaps

• kernel (int) – Gaussian kernel size (K) for modulation.

Returns

Refined coordinates.

Return type

np.ndarray([N, K, 2])

mmpose.core.evaluation.split_ae_outputs(outputs, num_joints, with_heatmaps, with_ae, select_output_index)

Split multi-stage outputs into heatmaps & tags.

Parameters

• outputs (list(Tensor)) – Outputs of network

• num_joints (int) – Number of joints

• with_heatmaps (list[bool]) – Option to output heatmaps for different stages.

• with_ae (list[bool]) – Option to output ae tags for different stages.

• select_output_index (list[int]) – Output keep the selected index

Returns

A tuple containing multi-stage outputs.

• list[Tensor]: multi-stage heatmaps.

• list[Tensor]: multi-stage tags.

Return type

tuple

fp16

class mmpose.core.fp16.Fp16OptimizerHook(grad_clip=None, coalesce=True, bucket_size_mb=- 1, loss_scale=512.0, distributed=True)

FP16 optimizer hook.

The steps of fp16 optimizer is as follows. 1. Scale the loss value. 2. BP in the fp16 model. 2. Copy gradients from fp16 model to fp32 weights. 3. Update fp32 weights. 4. Copy updated parameters from fp32 weights to fp16 model.

Refer to https://arxiv.org/abs/1710.03740 for more details.

Parameters

loss_scale (float) – Scale factor multiplied with loss.

after_train_iter(runner)

Backward optimization steps for Mixed Precision Training.

1. Scale the loss by a scale factor.

2. Backward the loss to obtain the gradients (fp16).

3. Copy gradients from the model to the fp32 weight copy.

4. Scale the gradients back and update the fp32 weight copy.

5. Copy back the params from fp32 weight copy to the fp16 model.

Parameters

runner (mmcv.Runner) – The underlines training runner.

before_run(runner)

Preparing steps before Mixed Precision Training.

1. Make a master copy of fp32 weights for optimization.

2. Convert the main model from fp32 to fp16.

Parameters

runner (mmcv.Runner) – The underlines training runner.

static copy_grads_to_fp32(fp16_net, fp32_weights)

Copy gradients from fp16 model to fp32 weight copy.

static copy_params_to_fp16(fp16_net, fp32_weights)

Copy updated params from fp32 weight copy to fp16 model.

mmpose.core.fp16.auto_fp16(apply_to=None, out_fp32=False)

Decorator to enable fp16 training automatically.

This decorator is useful when you write custom modules and want to support mixed precision training. If inputs arguments are fp32 tensors, they will be converted to fp16 automatically. Arguments other than fp32 tensors are ignored.

Parameters

• apply_to (Iterable, optional) – The argument names to be converted. None indicates all arguments.

• out_fp32 (bool) – Whether to convert the output back to fp32.

Example

>>> import torch.nn as nn
>>> class MyModule1(nn.Module):
>>>
>>>     # Convert x and y to fp16
>>>     @auto_fp16()
>>>     def forward(self, x, y):
>>>         pass

>>> import torch.nn as nn
>>> class MyModule2(nn.Module):
>>>
>>>     # convert pred to fp16
>>>     @auto_fp16(apply_to=('pred', ))
>>>     def do_something(self, pred, others):
>>>         pass

mmpose.core.fp16.cast_tensor_type(inputs, src_type, dst_type)

Recursively convert Tensor in inputs from src_type to dst_type.

Parameters

• inputs – Inputs that to be casted.

• src_type (torch.dtype) – Source type.

• dst_type (torch.dtype) – Destination type.

Returns

The same type with inputs, but all contained Tensors have been cast.

mmpose.core.fp16.force_fp32(apply_to=None, out_fp16=False)

Decorator to convert input arguments to fp32 in force.

This decorator is useful when you write custom modules and want to support mixed precision training. If there are some inputs that must be processed in fp32 mode, then this decorator can handle it. If inputs arguments are fp16 tensors, they will be converted to fp32 automatically. Arguments other than fp16 tensors are ignored.

Parameters

• apply_to (Iterable, optional) – The argument names to be converted. None indicates all arguments.

• out_fp16 (bool) – Whether to convert the output back to fp16.

Example

>>> import torch.nn as nn
>>> class MyModule1(nn.Module):
>>>
>>>     # Convert x and y to fp32
>>>     @force_fp32()
>>>     def loss(self, x, y):
>>>         pass

>>> import torch.nn as nn
>>> class MyModule2(nn.Module):
>>>
>>>     # convert pred to fp32
>>>     @force_fp32(apply_to=('pred', ))
>>>     def post_process(self, pred, others):
>>>         pass

mmpose.core.fp16.wrap_fp16_model(model)

Wrap the FP32 model to FP16.

1. Convert FP32 model to FP16.

2. Remain some necessary layers to be FP32, e.g., normalization layers.

Parameters

model (nn.Module) – Model in FP32.

utils

class mmpose.core.utils.ModelSetEpochHook

The hook that tells model the current epoch in training.

class mmpose.core.utils.WeightNormClipHook(max_norm=1.0, module_param_names='weight')

Apply weight norm clip regularization.

The module’s parameter will be clip to a given maximum norm before each forward pass.

Parameters

• max_norm (float) – The maximum norm of the parameter.

• module_param_names (str|list) – The parameter name (or name list) to apply weight norm clip.

hook(module, _input)

Hook function.

property hook_type

Hook type Subclasses should overwrite this function to return a string value in.

{forward, forward_pre, backward}

mmpose.core.utils.allreduce_grads(params, coalesce=True, bucket_size_mb=- 1)

Allreduce gradients.

Parameters

• params (list[torch.Parameters]) – List of parameters of a model

• coalesce (bool, optional) – Whether allreduce parameters as a whole. Default: True.

• bucket_size_mb (int, optional) – Size of bucket, the unit is MB. Default: -1.

mmpose.core.utils.sync_random_seed(seed=None, device='cuda')

Make sure different ranks share the same seed.

All workers must call this function, otherwise it will deadlock. This method is generally used in DistributedSampler, because the seed should be identical across all processes in the distributed group. In distributed sampling, different ranks should sample non-overlapped data in the dataset. Therefore, this function is used to make sure that each rank shuffles the data indices in the same order based on the same seed. Then different ranks could use different indices to select non-overlapped data from the same data list. :param seed: The seed. Default to None. :type seed: int, Optional :param device: The device where the seed will be put on.

Default to ‘cuda’.

Returns

Seed to be used.

Return type

int

post_processing

class mmpose.core.post_processing.Smoother(filter_cfg: Union[Dict, str], keypoint_dim: int = 2, keypoint_key: str = 'keypoints')

Smoother to apply temporal smoothing on pose estimation results with a filter.

Note

T: The temporal length of the pose sequence K: The keypoint number of each target C: The keypoint coordinate dimension

Parameters

• filter_cfg (dict | str) – The filter config. See example config files in configs/_base_/filters/ for details. Alternatively a config file path can be accepted and the config will be loaded.

• keypoint_dim (int) – The keypoint coordinate dimension, which is also indicated as C. Default: 2

• keypoint_key (str) – The dict key of the keypoints in the pose results. Default: ‘keypoints’

Example

>>> import numpy as np
>>> # Build dummy pose result
>>> results = []
>>> for t in range(10):
>>>     results_t = []
>>>     for track_id in range(2):
>>>         result = {
>>>             'track_id': track_id,
>>>             'keypoints': np.random.rand(17, 3)
>>>         }
>>>         results_t.append(result)
>>>     results.append(results_t)
>>> # Example 1: Smooth multi-frame pose results offline.
>>> filter_cfg = dict(type='GaussianFilter', window_size=3)
>>> smoother = Smoother(filter_cfg, keypoint_dim=2)
>>> smoothed_results = smoother.smooth(results)
>>> # Example 2: Smooth pose results online frame-by-frame
>>> filter_cfg = dict(type='GaussianFilter', window_size=3)
>>> smoother = Smoother(filter_cfg, keypoint_dim=2)
>>> for result_t in results:
>>>     smoothed_result_t = smoother.smooth(result_t)

smooth(results)

Apply temporal smoothing on pose estimation sequences.

Parameters

results (list[dict] | list[list[dict]]) –

The pose results of a single frame (non-nested list) or multiple frames (nested list). The result of each target is a dict, which should contains:

• track_id (optional, Any): The track ID of the target

• keypoints (np.ndarray): The keypoint coordinates in [K, C]

Returns

Temporal smoothed pose results, which has the same data structure as the input’s.

Return type

(list[dict] | list[list[dict]])

mmpose.core.post_processing.affine_transform(pt, trans_mat)

Apply an affine transformation to the points.

Parameters

• pt (np.ndarray) – a 2 dimensional point to be transformed

• trans_mat (np.ndarray) – 2x3 matrix of an affine transform

Returns

Transformed points.

Return type

np.ndarray

mmpose.core.post_processing.flip_back(output_flipped, flip_pairs, target_type='GaussianHeatmap')

Flip the flipped heatmaps back to the original form.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• output_flipped (np.ndarray[N, K, H, W]) – The output heatmaps obtained from the flipped images.

• flip_pairs (list[tuple()) – Pairs of keypoints which are mirrored (for example, left ear – right ear).

• target_type (str) – GaussianHeatmap or CombinedTarget

Returns

heatmaps that flipped back to the original image

Return type

np.ndarray

mmpose.core.post_processing.fliplr_joints(joints_3d, joints_3d_visible, img_width, flip_pairs)

Flip human joints horizontally.

Note

• num_keypoints: K

Parameters

• joints_3d (np.ndarray([K, 3])) – Coordinates of keypoints.

• joints_3d_visible (np.ndarray([K, 1])) – Visibility of keypoints.

• img_width (int) – Image width.

• flip_pairs (list[tuple]) – Pairs of keypoints which are mirrored (for example, left ear and right ear).

Returns

Flipped human joints.

• joints_3d_flipped (np.ndarray([K, 3])): Flipped joints.

• joints_3d_visible_flipped (np.ndarray([K, 1])): Joint visibility.

Return type

tuple

mmpose.core.post_processing.fliplr_regression(regression, flip_pairs, center_mode='static', center_x=0.5, center_index=0)

Flip human joints horizontally.

Note

• batch_size: N

• num_keypoint: K

Parameters

• regression (np.ndarray([..., K, C])) –

  Coordinates of keypoints, where K is the joint number and C is the dimension. Example shapes are:

  • [N, K, C]: a batch of keypoints where N is the batch size.

  • [N, T, K, C]: a batch of pose sequences, where T is the frame

    number.

• flip_pairs (list[tuple()]) – Pairs of keypoints which are mirrored (for example, left ear – right ear).

• center_mode (str) –

  The mode to set the center location on the x-axis to flip around. Options are:

  • static: use a static x value (see center_x also)

  • root: use a root joint (see center_index also)

• center_x (float) – Set the x-axis location of the flip center. Only used when center_mode=static.

• center_index (int) – Set the index of the root joint, whose x location will be used as the flip center. Only used when center_mode=root.

Returns

Flipped joints.

Return type

np.ndarray([…, K, C])

mmpose.core.post_processing.get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False)

Get the affine transform matrix, given the center/scale/rot/output_size.

Parameters

• center (np.ndarray[2, ]) – Center of the bounding box (x, y).

• scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].

• rot (float) – Rotation angle (degree).

• output_size (np.ndarray[2, ] | list(2,)) – Size of the destination heatmaps.

• shift (0-100%) – Shift translation ratio wrt the width/height. Default (0., 0.).

• inv (bool) – Option to inverse the affine transform direction. (inv=False: src->dst or inv=True: dst->src)

Returns

The transform matrix.

Return type

np.ndarray

mmpose.core.post_processing.get_warp_matrix(theta, size_input, size_dst, size_target)

Calculate the transformation matrix under the constraint of unbiased. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

Parameters

• theta (float) – Rotation angle in degrees.

• size_input (np.ndarray) – Size of input image [w, h].

• size_dst (np.ndarray) – Size of output image [w, h].

• size_target (np.ndarray) – Size of ROI in input plane [w, h].

Returns

A matrix for transformation.

Return type

np.ndarray

mmpose.core.post_processing.nearby_joints_nms(kpts_db, dist_thr, num_nearby_joints_thr=None, score_per_joint=False, max_dets=- 1)

Nearby joints NMS implementations.

Parameters

• kpts_db (list[dict]) – keypoints and scores.

• dist_thr (float) – threshold for judging whether two joints are close.

• num_nearby_joints_thr (int) – threshold for judging whether two instances are close.

• max_dets (int) – max number of detections to keep.

• score_per_joint (bool) – the input scores (in kpts_db) are per joint scores.

Returns

indexes to keep.

Return type

np.ndarray

mmpose.core.post_processing.oks_iou(g, d, a_g, a_d, sigmas=None, vis_thr=None)

Calculate oks ious.

Parameters

• g – Ground truth keypoints.

• d – Detected keypoints.

• a_g – Area of the ground truth object.

• a_d – Area of the detected object.

• sigmas – standard deviation of keypoint labelling.

• vis_thr – threshold of the keypoint visibility.

Returns

The oks ious.

Return type

list

mmpose.core.post_processing.oks_nms(kpts_db, thr, sigmas=None, vis_thr=None, score_per_joint=False)

OKS NMS implementations.

Parameters

• kpts_db – keypoints.

• thr – Retain overlap < thr.

• sigmas – standard deviation of keypoint labelling.

• vis_thr – threshold of the keypoint visibility.

• score_per_joint – the input scores (in kpts_db) are per joint scores

Returns

indexes to keep.

Return type

np.ndarray

mmpose.core.post_processing.rotate_point(pt, angle_rad)

Rotate a point by an angle.

Parameters

• pt (list[float]) – 2 dimensional point to be rotated

• angle_rad (float) – rotation angle by radian

Returns

Rotated point.

Return type

list[float]

mmpose.core.post_processing.soft_oks_nms(kpts_db, thr, max_dets=20, sigmas=None, vis_thr=None, score_per_joint=False)

Soft OKS NMS implementations.

Parameters

• kpts_db – keypoints and scores.

• thr – retain oks overlap < thr.

• max_dets – max number of detections to keep.

• sigmas – Keypoint labelling uncertainty.

• score_per_joint – the input scores (in kpts_db) are per joint scores

Returns

indexes to keep.

Return type

np.ndarray

mmpose.core.post_processing.transform_preds(coords, center, scale, output_size, use_udp=False)

Get final keypoint predictions from heatmaps and apply scaling and translation to map them back to the image.

Note

num_keypoints: K

Parameters

• coords (np.ndarray[K, ndims]) –

  • If ndims=2, corrds are predicted keypoint location.

  • If ndims=4, corrds are composed of (x, y, scores, tags)

  • If ndims=5, corrds are composed of (x, y, scores, tags, flipped_tags)

• center (np.ndarray[2, ]) – Center of the bounding box (x, y).

• scale (np.ndarray[2, ]) – Scale of the bounding box wrt [width, height].

• output_size (np.ndarray[2, ] | list(2,)) – Size of the destination heatmaps.

• use_udp (bool) – Use unbiased data processing

Returns

Predicted coordinates in the images.

Return type

np.ndarray

mmpose.core.post_processing.warp_affine_joints(joints, mat)

Apply affine transformation defined by the transform matrix on the joints.

Parameters

• joints (np.ndarray[..., 2]) – Origin coordinate of joints.

• mat (np.ndarray[3, 2]) – The affine matrix.

Returns

Result coordinate of joints.

Return type

np.ndarray[…, 2]

mmpose.models

backbones

class mmpose.models.backbones.AlexNet(num_classes=- 1)

AlexNet backbone.

The input for AlexNet is a 224x224 RGB image.

Parameters

num_classes (int) – number of classes for classification. The default value is -1, which uses the backbone as a feature extractor without the top classifier.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

class mmpose.models.backbones.CPM(in_channels, out_channels, feat_channels=128, middle_channels=32, num_stages=6, norm_cfg={'requires_grad': True, 'type': 'BN'})

CPM backbone.

Convolutional Pose Machines. More details can be found in the paper .

Parameters

• in_channels (int) – The input channels of the CPM.

• out_channels (int) – The output channels of the CPM.

• feat_channels (int) – Feature channel of each CPM stage.

• middle_channels (int) – Feature channel of conv after the middle stage.

• num_stages (int) – Number of stages.

• norm_cfg (dict) – Dictionary to construct and config norm layer.

Example

>>> from mmpose.models import CPM
>>> import torch
>>> self = CPM(3, 17)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 368, 368)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
...     print(tuple(level_output.shape))
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)

forward(x)

Model forward function.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

class mmpose.models.backbones.HRFormer(extra, in_channels=3, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, transformer_norm_cfg={'eps': 1e-06, 'type': 'LN'}, norm_eval=False, with_cp=False, zero_init_residual=False, frozen_stages=- 1)

HRFormer backbone.

This backbone is the implementation of HRFormer: High-Resolution Transformer for Dense Prediction.

Parameters

• extra (dict) –

  Detailed configuration for each stage of HRNet. There must be 4 stages, the configuration for each stage must have 5 keys:

  • num_modules (int): The number of HRModule in this stage.

  • num_branches (int): The number of branches in the HRModule.

  • block (str): The type of block.

  • num_blocks (tuple): The number of blocks in each branch.

    The length must be equal to num_branches.

  • num_channels (tuple): The number of channels in each branch.

    The length must be equal to num_branches.

• in_channels (int) – Number of input image channels. Normally 3.

• conv_cfg (dict) – Dictionary to construct and config conv layer. Default: None.

• norm_cfg (dict) – Config of norm layer. Use SyncBN by default.

• transformer_norm_cfg (dict) – Config of transformer norm layer. Use LN by default.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

Example

>>> from mmpose.models import HRFormer
>>> import torch
>>> extra = dict(
>>>     stage1=dict(
>>>         num_modules=1,
>>>         num_branches=1,
>>>         block='BOTTLENECK',
>>>         num_blocks=(2, ),
>>>         num_channels=(64, )),
>>>     stage2=dict(
>>>         num_modules=1,
>>>         num_branches=2,
>>>         block='HRFORMER',
>>>         window_sizes=(7, 7),
>>>         num_heads=(1, 2),
>>>         mlp_ratios=(4, 4),
>>>         num_blocks=(2, 2),
>>>         num_channels=(32, 64)),
>>>     stage3=dict(
>>>         num_modules=4,
>>>         num_branches=3,
>>>         block='HRFORMER',
>>>         window_sizes=(7, 7, 7),
>>>         num_heads=(1, 2, 4),
>>>         mlp_ratios=(4, 4, 4),
>>>         num_blocks=(2, 2, 2),
>>>         num_channels=(32, 64, 128)),
>>>     stage4=dict(
>>>         num_modules=2,
>>>         num_branches=4,
>>>         block='HRFORMER',
>>>         window_sizes=(7, 7, 7, 7),
>>>         num_heads=(1, 2, 4, 8),
>>>         mlp_ratios=(4, 4, 4, 4),
>>>         num_blocks=(2, 2, 2, 2),
>>>         num_channels=(32, 64, 128, 256)))
>>> self = HRFormer(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 32, 8, 8)
(1, 64, 4, 4)
(1, 128, 2, 2)
(1, 256, 1, 1)

class mmpose.models.backbones.HRNet(extra, in_channels=3, conv_cfg=None, norm_cfg={'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=False, frozen_stages=- 1)

HRNet backbone.

High-Resolution Representations for Labeling Pixels and Regions

Parameters

• extra (dict) – detailed configuration for each stage of HRNet.

• in_channels (int) – Number of input image channels. Default: 3.

• conv_cfg (dict) – dictionary to construct and config conv layer.

• norm_cfg (dict) – dictionary to construct and config norm layer.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.

• zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

Example

>>> from mmpose.models import HRNet
>>> import torch
>>> extra = dict(
>>>     stage1=dict(
>>>         num_modules=1,
>>>         num_branches=1,
>>>         block='BOTTLENECK',
>>>         num_blocks=(4, ),
>>>         num_channels=(64, )),
>>>     stage2=dict(
>>>         num_modules=1,
>>>         num_branches=2,
>>>         block='BASIC',
>>>         num_blocks=(4, 4),
>>>         num_channels=(32, 64)),
>>>     stage3=dict(
>>>         num_modules=4,
>>>         num_branches=3,
>>>         block='BASIC',
>>>         num_blocks=(4, 4, 4),
>>>         num_channels=(32, 64, 128)),
>>>     stage4=dict(
>>>         num_modules=3,
>>>         num_branches=4,
>>>         block='BASIC',
>>>         num_blocks=(4, 4, 4, 4),
>>>         num_channels=(32, 64, 128, 256)))
>>> self = HRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 32, 8, 8)

forward(x)

Forward function.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

property norm1

the normalization layer named “norm1”

Type

nn.Module

property norm2

the normalization layer named “norm2”

Type

nn.Module

train(mode=True)

Convert the model into training mode.

class mmpose.models.backbones.HourglassAENet(downsample_times=4, num_stacks=1, out_channels=34, stage_channels=(256, 384, 512, 640, 768), feat_channels=256, norm_cfg={'requires_grad': True, 'type': 'BN'})

Hourglass-AE Network proposed by Newell et al.

Associative Embedding: End-to-End Learning for Joint Detection and Grouping.

More details can be found in the paper .

Parameters

• downsample_times (int) – Downsample times in a HourglassModule.

• num_stacks (int) – Number of HourglassModule modules stacked, 1 for Hourglass-52, 2 for Hourglass-104.

• stage_channels (list[int]) – Feature channel of each sub-module in a HourglassModule.

• stage_blocks (list[int]) – Number of sub-modules stacked in a HourglassModule.

• feat_channels (int) – Feature channel of conv after a HourglassModule.

• norm_cfg (dict) – Dictionary to construct and config norm layer.

Example

>>> from mmpose.models import HourglassAENet
>>> import torch
>>> self = HourglassAENet()
>>> self.eval()
>>> inputs = torch.rand(1, 3, 512, 512)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
...     print(tuple(level_output.shape))
(1, 34, 128, 128)

forward(x)

Model forward function.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

class mmpose.models.backbones.HourglassNet(downsample_times=5, num_stacks=2, stage_channels=(256, 256, 384, 384, 384, 512), stage_blocks=(2, 2, 2, 2, 2, 4), feat_channel=256, norm_cfg={'requires_grad': True, 'type': 'BN'})

HourglassNet backbone.

Stacked Hourglass Networks for Human Pose Estimation. More details can be found in the paper .

Parameters

• downsample_times (int) – Downsample times in a HourglassModule.

• num_stacks (int) – Number of HourglassModule modules stacked, 1 for Hourglass-52, 2 for Hourglass-104.

• stage_channels (list[int]) – Feature channel of each sub-module in a HourglassModule.

• stage_blocks (list[int]) – Number of sub-modules stacked in a HourglassModule.

• feat_channel (int) – Feature channel of conv after a HourglassModule.

• norm_cfg (dict) – Dictionary to construct and config norm layer.

Example

>>> from mmpose.models import HourglassNet
>>> import torch
>>> self = HourglassNet()
>>> self.eval()
>>> inputs = torch.rand(1, 3, 511, 511)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
...     print(tuple(level_output.shape))
(1, 256, 128, 128)
(1, 256, 128, 128)

forward(x)

Model forward function.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

class mmpose.models.backbones.I3D(in_channels=3, expansion=1.0)

I3D backbone.

Please refer to the paper for details.

Args: in_channels (int): Input channels of the backbone, which is decided

on the input modality.

expansion (float): The multiplier of in_channels and out_channels.

Default: 1.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

class mmpose.models.backbones.LiteHRNet(extra, in_channels=3, conv_cfg=None, norm_cfg={'type': 'BN'}, norm_eval=False, with_cp=False)

Lite-HRNet backbone.

Lite-HRNet: A Lightweight High-Resolution Network.

Code adapted from ‘https://github.com/HRNet/Lite-HRNet’.

Parameters

• extra (dict) – detailed configuration for each stage of HRNet.

• in_channels (int) – Number of input image channels. Default: 3.

• conv_cfg (dict) – dictionary to construct and config conv layer.

• norm_cfg (dict) – dictionary to construct and config norm layer.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.

Example

>>> from mmpose.models import LiteHRNet
>>> import torch
>>> extra=dict(
>>>    stem=dict(stem_channels=32, out_channels=32, expand_ratio=1),
>>>    num_stages=3,
>>>    stages_spec=dict(
>>>        num_modules=(2, 4, 2),
>>>        num_branches=(2, 3, 4),
>>>        num_blocks=(2, 2, 2),
>>>        module_type=('LITE', 'LITE', 'LITE'),
>>>        with_fuse=(True, True, True),
>>>        reduce_ratios=(8, 8, 8),
>>>        num_channels=(
>>>            (40, 80),
>>>            (40, 80, 160),
>>>            (40, 80, 160, 320),
>>>        )),
>>>    with_head=False)
>>> self = LiteHRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 40, 8, 8)

forward(x)

Forward function.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

train(mode=True)

Convert the model into training mode.

class mmpose.models.backbones.MSPN(unit_channels=256, num_stages=4, num_units=4, num_blocks=[2, 2, 2, 2], norm_cfg={'type': 'BN'}, res_top_channels=64)

MSPN backbone. Paper ref: Li et al. “Rethinking on Multi-Stage Networks for Human Pose Estimation” (CVPR 2020).

Parameters

• unit_channels (int) – Number of Channels in an upsample unit. Default: 256

• num_stages (int) – Number of stages in a multi-stage MSPN. Default: 4

• num_units (int) – Number of downsample/upsample units in a single-stage network. Default: 4 Note: Make sure num_units == len(self.num_blocks)

• num_blocks (list) – Number of bottlenecks in each downsample unit. Default: [2, 2, 2, 2]

• norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’)

• res_top_channels (int) – Number of channels of feature from ResNetTop. Default: 64.

Example

>>> from mmpose.models import MSPN
>>> import torch
>>> self = MSPN(num_stages=2,num_units=2,num_blocks=[2,2])
>>> self.eval()
>>> inputs = torch.rand(1, 3, 511, 511)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
...     for feature in level_output:
...         print(tuple(feature.shape))
...
(1, 256, 64, 64)
(1, 256, 128, 128)
(1, 256, 64, 64)
(1, 256, 128, 128)

forward(x)

Model forward function.

init_weights(pretrained=None)

Initialize model weights.

class mmpose.models.backbones.MobileNetV2(widen_factor=1.0, out_indices=(7), frozen_stages=- 1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU6'}, norm_eval=False, with_cp=False)

MobileNetV2 backbone.

Parameters

• widen_factor (float) – Width multiplier, multiply number of channels in each layer by this amount. Default: 1.0.

• out_indices (None or Sequence[int]) – Output from which stages. Default: (7, ).

• frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.

• conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.

• norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

• act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU6’).

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Init backbone weights.

Parameters

pretrained (str | None) – If pretrained is a string, then it initializes backbone weights by loading the pretrained checkpoint. If pretrained is None, then it follows default initializer or customized initializer in subclasses.

make_layer(out_channels, num_blocks, stride, expand_ratio)

Stack InvertedResidual blocks to build a layer for MobileNetV2.

Parameters

• out_channels (int) – out_channels of block.

• num_blocks (int) – number of blocks.

• stride (int) – stride of the first block. Default: 1

• expand_ratio (int) – Expand the number of channels of the hidden layer in InvertedResidual by this ratio. Default: 6.

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

class mmpose.models.backbones.MobileNetV3(arch='small', conv_cfg=None, norm_cfg={'type': 'BN'}, out_indices=(- 1), frozen_stages=- 1, norm_eval=False, with_cp=False)

MobileNetV3 backbone.

Parameters

• arch (str) – Architecture of mobilnetv3, from {small, big}. Default: small.

• conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.

• norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

• out_indices (None or Sequence[int]) – Output from which stages. Default: (-1, ), which means output tensors from final stage.

• frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Init backbone weights.

Parameters

pretrained (str | None) – If pretrained is a string, then it initializes backbone weights by loading the pretrained checkpoint. If pretrained is None, then it follows default initializer or customized initializer in subclasses.

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

class mmpose.models.backbones.PyramidVisionTransformer(pretrain_img_size=224, in_channels=3, embed_dims=64, num_stages=4, num_layers=[3, 4, 6, 3], num_heads=[1, 2, 5, 8], patch_sizes=[4, 2, 2, 2], strides=[4, 2, 2, 2], paddings=[0, 0, 0, 0], sr_ratios=[8, 4, 2, 1], out_indices=(0, 1, 2, 3), mlp_ratios=[8, 8, 4, 4], qkv_bias=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, use_abs_pos_embed=True, norm_after_stage=False, use_conv_ffn=False, act_cfg={'type': 'GELU'}, norm_cfg={'eps': 1e-06, 'type': 'LN'}, pretrained=None, convert_weights=True, init_cfg=None)

Pyramid Vision Transformer (PVT)

Implementation of Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions.

Parameters

• pretrain_img_size (int | tuple[int]) – The size of input image when pretrain. Defaults: 224.

• in_channels (int) – Number of input channels. Default: 3.

• embed_dims (int) – Embedding dimension. Default: 64.

• num_stags (int) – The num of stages. Default: 4.

• num_layers (Sequence[int]) – The layer number of each transformer encode layer. Default: [3, 4, 6, 3].

• num_heads (Sequence[int]) – The attention heads of each transformer encode layer. Default: [1, 2, 5, 8].

• patch_sizes (Sequence[int]) – The patch_size of each patch embedding. Default: [4, 2, 2, 2].

• strides (Sequence[int]) – The stride of each patch embedding. Default: [4, 2, 2, 2].

• paddings (Sequence[int]) – The padding of each patch embedding. Default: [0, 0, 0, 0].

• sr_ratios (Sequence[int]) – The spatial reduction rate of each transformer encode layer. Default: [8, 4, 2, 1].

• out_indices (Sequence[int] | int) – Output from which stages. Default: (0, 1, 2, 3).

• mlp_ratios (Sequence[int]) – The ratio of the mlp hidden dim to the embedding dim of each transformer encode layer. Default: [8, 8, 4, 4].

• qkv_bias (bool) – Enable bias for qkv if True. Default: True.

• drop_rate (float) – Probability of an element to be zeroed. Default 0.0.

• attn_drop_rate (float) – The drop out rate for attention layer. Default 0.0.

• drop_path_rate (float) – stochastic depth rate. Default 0.1.

• use_abs_pos_embed (bool) – If True, add absolute position embedding to the patch embedding. Defaults: True.

• use_conv_ffn (bool) – If True, use Convolutional FFN to replace FFN. Default: False.

• act_cfg (dict) – The activation config for FFNs. Default: dict(type=’GELU’).

• norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’LN’).

• pretrained (str, optional) – model pretrained path. Default: None.

• convert_weights (bool) – The flag indicates whether the pre-trained model is from the original repo. We may need to convert some keys to make it compatible. Default: True.

• init_cfg (dict or list[dict], optional) – Initialization config dict. Default: None.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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 registered hooks while the latter silently ignores them.

init_weights(pretrained=None)

Initialize the weights.

class mmpose.models.backbones.PyramidVisionTransformerV2(**kwargs)

Implementation of PVTv2: Improved Baselines with Pyramid Vision Transformer.

class mmpose.models.backbones.RSN(unit_channels=256, num_stages=4, num_units=4, num_blocks=[2, 2, 2, 2], num_steps=4, norm_cfg={'type': 'BN'}, res_top_channels=64, expand_times=26)

Residual Steps Network backbone. Paper ref: Cai et al. “Learning Delicate Local Representations for Multi-Person Pose Estimation” (ECCV 2020).

Parameters

• unit_channels (int) – Number of Channels in an upsample unit. Default: 256

• num_stages (int) – Number of stages in a multi-stage RSN. Default: 4

• num_units (int) – NUmber of downsample/upsample units in a single-stage RSN. Default: 4 Note: Make sure num_units == len(self.num_blocks)

• num_blocks (list) – Number of RSBs (Residual Steps Block) in each downsample unit. Default: [2, 2, 2, 2]

• num_steps (int) – Number of steps in a RSB. Default:4

• norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’)

• res_top_channels (int) – Number of channels of feature from ResNet_top. Default: 64.

• expand_times (int) – Times by which the in_channels are expanded in RSB. Default:26.

Example

>>> from mmpose.models import RSN
>>> import torch
>>> self = RSN(num_stages=2,num_units=2,num_blocks=[2,2])
>>> self.eval()
>>> inputs = torch.rand(1, 3, 511, 511)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
...     for feature in level_output:
...         print(tuple(feature.shape))
...
(1, 256, 64, 64)
(1, 256, 128, 128)
(1, 256, 64, 64)
(1, 256, 128, 128)

forward(x)

Model forward function.

init_weights(pretrained=None)

Initialize model weights.

class mmpose.models.backbones.RegNet(arch, in_channels=3, stem_channels=32, base_channels=32, strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=True)

RegNet backbone.

More details can be found in paper .

Parameters

• arch (dict) – The parameter of RegNets. - w0 (int): initial width - wa (float): slope of width - wm (float): quantization parameter to quantize the width - depth (int): depth of the backbone - group_w (int): width of group - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck.

• strides (Sequence[int]) – Strides of the first block of each stage.

• base_channels (int) – Base channels after stem layer.

• in_channels (int) – Number of input image channels. Default: 3.

• dilations (Sequence[int]) – Dilation of each stage.

• out_indices (Sequence[int]) – Output from which stages.

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: “pytorch”.

• frozen_stages (int) – Stages to be frozen (all param fixed). -1 means not freezing any parameters. Default: -1.

• norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’, requires_grad=True).

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

Example

>>> from mmpose.models import RegNet
>>> import torch
>>> self = RegNet(
        arch=dict(
            w0=88,
            wa=26.31,
            wm=2.25,
            group_w=48,
            depth=25,
            bot_mul=1.0),
         out_indices=(0, 1, 2, 3))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 96, 8, 8)
(1, 192, 4, 4)
(1, 432, 2, 2)
(1, 1008, 1, 1)

adjust_width_group(widths, bottleneck_ratio, groups)

Adjusts the compatibility of widths and groups.

Parameters

• widths (list[int]) – Width of each stage.

• bottleneck_ratio (float) – Bottleneck ratio.

• groups (int) – number of groups in each stage

Returns

The adjusted widths and groups of each stage.

Return type

tuple(list)

forward(x)

Forward function.

static generate_regnet(initial_width, width_slope, width_parameter, depth, divisor=8)

Generates per block width from RegNet parameters.

Parameters

• initial_width ([int]) – Initial width of the backbone

• width_slope ([float]) – Slope of the quantized linear function

• width_parameter ([int]) – Parameter used to quantize the width.

• depth ([int]) – Depth of the backbone.

• divisor (int, optional) – The divisor of channels. Defaults to 8.

Returns

return a list of widths of each stage and the number of

stages

Return type

list, int

get_stages_from_blocks(widths)

Gets widths/stage_blocks of network at each stage.

Parameters

widths (list[int]) – Width in each stage.

Returns

width and depth of each stage

Return type

tuple(list)

static quantize_float(number, divisor)

Converts a float to closest non-zero int divisible by divior.

Parameters

• number (int) – Original number to be quantized.

• divisor (int) – Divisor used to quantize the number.

Returns

quantized number that is divisible by devisor.

Return type

int

class mmpose.models.backbones.ResNeSt(depth, groups=1, width_per_group=4, radix=2, reduction_factor=4, avg_down_stride=True, **kwargs)

ResNeSt backbone.

Please refer to the paper for details.

Parameters

• depth (int) – Network depth, from {50, 101, 152, 200}.

• groups (int) – Groups of conv2 in Bottleneck. Default: 32.

• width_per_group (int) – Width per group of conv2 in Bottleneck. Default: 4.

• radix (int) – Radix of SpltAtConv2d. Default: 2

• reduction_factor (int) – Reduction factor of SplitAttentionConv2d. Default: 4.

• avg_down_stride (bool) – Whether to use average pool for stride in Bottleneck. Default: True.

• in_channels (int) – Number of input image channels. Default: 3.

• stem_channels (int) – Output channels of the stem layer. Default: 64.

• num_stages (int) – Stages of the network. Default: 4.

• strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

• dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv. Default: False.

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

• conv_cfg (dict | None) – The config dict for conv layers. Default: None.

• norm_cfg (dict) – The config dict for norm layers.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

make_res_layer(**kwargs)

Make a ResLayer.

class mmpose.models.backbones.ResNeXt(depth, groups=32, width_per_group=4, **kwargs)

ResNeXt backbone.

Please refer to the paper for details.

Parameters

• depth (int) – Network depth, from {50, 101, 152}.

• groups (int) – Groups of conv2 in Bottleneck. Default: 32.

• width_per_group (int) – Width per group of conv2 in Bottleneck. Default: 4.

• in_channels (int) – Number of input image channels. Default: 3.

• stem_channels (int) – Output channels of the stem layer. Default: 64.

• num_stages (int) – Stages of the network. Default: 4.

• strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

• dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv. Default: False.

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

• conv_cfg (dict | None) – The config dict for conv layers. Default: None.

• norm_cfg (dict) – The config dict for norm layers.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

make_res_layer(**kwargs)

Make a ResLayer.

class mmpose.models.backbones.ResNet(depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=True)

ResNet backbone.

Please refer to the paper for details.

Parameters

• depth (int) – Network depth, from {18, 34, 50, 101, 152}.

• in_channels (int) – Number of input image channels. Default: 3.

• stem_channels (int) – Output channels of the stem layer. Default: 64.

• base_channels (int) – Middle channels of the first stage. Default: 64.

• num_stages (int) – Stages of the network. Default: 4.

• strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

• dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv. Default: False.

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

• conv_cfg (dict | None) – The config dict for conv layers. Default: None.

• norm_cfg (dict) – The config dict for norm layers.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

Example

>>> from mmpose.models import ResNet
>>> import torch
>>> self = ResNet(depth=18, out_indices=(0, 1, 2, 3))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 64, 8, 8)
(1, 128, 4, 4)
(1, 256, 2, 2)
(1, 512, 1, 1)

forward(x)

Forward function.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

make_res_layer(**kwargs)

Make a ResLayer.

property norm1

the normalization layer named “norm1”

Type

nn.Module

train(mode=True)

Convert the model into training mode.

class mmpose.models.backbones.ResNetV1d(**kwargs)

ResNetV1d variant described in Bag of Tricks.

Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1.

class mmpose.models.backbones.SCNet(depth, **kwargs)

SCNet backbone.

Improving Convolutional Networks with Self-Calibrated Convolutions, Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Changhu Wang, Jiashi Feng, IEEE CVPR, 2020. http://mftp.mmcheng.net/Papers/20cvprSCNet.pdf

Parameters

• depth (int) – Depth of scnet, from {50, 101}.

• in_channels (int) – Number of input image channels. Normally 3.

• base_channels (int) – Number of base channels of hidden layer.

• num_stages (int) – SCNet stages, normally 4.

• strides (Sequence[int]) – Strides of the first block of each stage.

• dilations (Sequence[int]) – Dilation of each stage.

• out_indices (Sequence[int]) – Output from which stages.

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters.

• norm_cfg (dict) – Dictionary to construct and config norm layer.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity.

Example

>>> from mmpose.models import SCNet
>>> import torch
>>> self = SCNet(depth=50, out_indices=(0, 1, 2, 3))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 224, 224)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 256, 56, 56)
(1, 512, 28, 28)
(1, 1024, 14, 14)
(1, 2048, 7, 7)

class mmpose.models.backbones.SEResNeXt(depth, groups=32, width_per_group=4, **kwargs)

SEResNeXt backbone.

Please refer to the paper for details.

Parameters

• depth (int) – Network depth, from {50, 101, 152}.

• groups (int) – Groups of conv2 in Bottleneck. Default: 32.

• width_per_group (int) – Width per group of conv2 in Bottleneck. Default: 4.

• se_ratio (int) – Squeeze ratio in SELayer. Default: 16.

• in_channels (int) – Number of input image channels. Default: 3.

• stem_channels (int) – Output channels of the stem layer. Default: 64.

• num_stages (int) – Stages of the network. Default: 4.

• strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

• dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv. Default: False.

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

• conv_cfg (dict | None) – The config dict for conv layers. Default: None.

• norm_cfg (dict) – The config dict for norm layers.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

Example

>>> from mmpose.models import SEResNeXt
>>> import torch
>>> self = SEResNet(depth=50, out_indices=(0, 1, 2, 3))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 224, 224)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 256, 56, 56)
(1, 512, 28, 28)
(1, 1024, 14, 14)
(1, 2048, 7, 7)

make_res_layer(**kwargs)

Make a ResLayer.

class mmpose.models.backbones.SEResNet(depth, se_ratio=16, **kwargs)

SEResNet backbone.

Please refer to the paper for details.

Parameters

• depth (int) – Network depth, from {50, 101, 152}.

• se_ratio (int) – Squeeze ratio in SELayer. Default: 16.

• in_channels (int) – Number of input image channels. Default: 3.

• stem_channels (int) – Output channels of the stem layer. Default: 64.

• num_stages (int) – Stages of the network. Default: 4.

• strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

• dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv. Default: False.

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

• conv_cfg (dict | None) – The config dict for conv layers. Default: None.

• norm_cfg (dict) – The config dict for norm layers.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

Example

>>> from mmpose.models import SEResNet
>>> import torch
>>> self = SEResNet(depth=50, out_indices=(0, 1, 2, 3))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 224, 224)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 256, 56, 56)
(1, 512, 28, 28)
(1, 1024, 14, 14)
(1, 2048, 7, 7)

make_res_layer(**kwargs)

Make a ResLayer.

class mmpose.models.backbones.ShuffleNetV1(groups=3, widen_factor=1.0, out_indices=(2), frozen_stages=- 1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, norm_eval=False, with_cp=False)

ShuffleNetV1 backbone.

Parameters

• groups (int, optional) – The number of groups to be used in grouped 1x1 convolutions in each ShuffleUnit. Default: 3.

• widen_factor (float, optional) – Width multiplier - adjusts the number of channels in each layer by this amount. Default: 1.0.

• out_indices (Sequence[int]) – Output from which stages. Default: (2, )

• frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.

• conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.

• norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

• act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU’).

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Init backbone weights.

Parameters

pretrained (str | None) – If pretrained is a string, then it initializes backbone weights by loading the pretrained checkpoint. If pretrained is None, then it follows default initializer or customized initializer in subclasses.

make_layer(out_channels, num_blocks, first_block=False)

Stack ShuffleUnit blocks to make a layer.

Parameters

• out_channels (int) – out_channels of the block.

• num_blocks (int) – Number of blocks.

• first_block (bool, optional) – Whether is the first ShuffleUnit of a sequential ShuffleUnits. Default: False, which means using the grouped 1x1 convolution.

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

class mmpose.models.backbones.ShuffleNetV2(widen_factor=1.0, out_indices=(3), frozen_stages=- 1, conv_cfg=None, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, norm_eval=False, with_cp=False)

ShuffleNetV2 backbone.

Parameters

• widen_factor (float) – Width multiplier - adjusts the number of channels in each layer by this amount. Default: 1.0.

• out_indices (Sequence[int]) – Output from which stages. Default: (0, 1, 2, 3).

• frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.

• conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.

• norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

• act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU’).

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Init backbone weights.

Parameters

pretrained (str | None) – If pretrained is a string, then it initializes backbone weights by loading the pretrained checkpoint. If pretrained is None, then it follows default initializer or customized initializer in subclasses.

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

class mmpose.models.backbones.SwinTransformer(pretrain_img_size=224, in_channels=3, embed_dims=96, patch_size=4, window_size=7, mlp_ratio=4, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), strides=(4, 2, 2, 2), out_indices=(0, 1, 2, 3), qkv_bias=True, qk_scale=None, patch_norm=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, use_abs_pos_embed=False, act_cfg={'type': 'GELU'}, norm_cfg={'type': 'LN'}, with_cp=False, convert_weights=False, frozen_stages=- 1)

Swin Transformer A PyTorch implement of : Swin Transformer: Hierarchical Vision Transformer using Shifted Windows -

https://arxiv.org/abs/2103.14030

Inspiration from https://github.com/microsoft/Swin-Transformer

Parameters

• pretrain_img_size (int | tuple[int]) – The size of input image when pretrain. Defaults: 224.

• in_channels (int) – The num of input channels. Defaults: 3.

• embed_dims (int) – The feature dimension. Default: 96.

• patch_size (int | tuple[int]) – Patch size. Default: 4.

• window_size (int) – Window size. Default: 7.

• mlp_ratio (int) – Ratio of mlp hidden dim to embedding dim. Default: 4.

• depths (tuple[int]) – Depths of each Swin Transformer stage. Default: (2, 2, 6, 2).

• num_heads (tuple[int]) – Parallel attention heads of each Swin Transformer stage. Default: (3, 6, 12, 24).

• strides (tuple[int]) – The patch merging or patch embedding stride of each Swin Transformer stage. (In swin, we set kernel size equal to stride.) Default: (4, 2, 2, 2).

• out_indices (tuple[int]) – Output from which stages. Default: (0, 1, 2, 3).

• qkv_bias (bool, optional) – If True, add a learnable bias to query, key, value. Default: True

• qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set. Default: None.

• patch_norm (bool) – If add a norm layer for patch embed and patch merging. Default: True.

• drop_rate (float) – Dropout rate. Defaults: 0.

• attn_drop_rate (float) – Attention dropout rate. Default: 0.

• drop_path_rate (float) – Stochastic depth rate. Defaults: 0.1.

• use_abs_pos_embed (bool) – If True, add absolute position embedding to the patch embedding. Defaults: False.

• act_cfg (dict) – Config dict for activation layer. Default: dict(type=’LN’).

• norm_cfg (dict) – Config dict for normalization layer at output of backone. Defaults: dict(type=’LN’).

• with_cp (bool, optional) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• pretrained (str, optional) – model pretrained path. Default: None.

• convert_weights (bool) – The flag indicates whether the pre-trained model is from the original repo. We may need to convert some keys to make it compatible. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). Default: -1 (-1 means not freezing any parameters).

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Initialize the weights in backbone.

Parameters

pretrained (str, optional) – Path to pre-trained weights. Defaults to None.

train(mode=True)

Convert the model into training mode while keep layers freezed.

class mmpose.models.backbones.TCFormer(in_channels=3, embed_dims=[64, 128, 256, 512], num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_cfg={'eps': 1e-06, 'type': 'LN'}, num_layers=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], num_stages=4, pretrained=None, k=5, sample_ratios=[0.25, 0.25, 0.25], return_map=False, convert_weights=True)

Token Clustering Transformer (TCFormer)

Implementation of Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer <https://arxiv.org/abs/2204.08680>

Args: in_channels (int): Number of input channels. Default: 3. embed_dims (list[int]): Embedding dimension. Default:

[64, 128, 256, 512].

num_heads (Sequence[int]): The attention heads of each transformer

encode layer. Default: [1, 2, 5, 8].

mlp_ratios (Sequence[int]): The ratio of the mlp hidden dim to the

embedding dim of each transformer block.

qkv_bias (bool): Enable bias for qkv if True. Default: True. qk_scale (float | None, optional): Override default qk scale of

head_dim ** -0.5 if set. Default: None.

drop_rate (float): Probability of an element to be zeroed.

Default 0.0.

attn_drop_rate (float): The drop out rate for attention layer.

Default 0.0.

drop_path_rate (float): stochastic depth rate. Default 0. norm_cfg (dict): Config dict for normalization layer.

Default: dict(type=’LN’, eps=1e-6).

num_layers (Sequence[int]): The layer number of each transformer encode

layer. Default: [3, 4, 6, 3].

sr_ratios (Sequence[int]): The spatial reduction rate of each

transformer block. Default: [8, 4, 2, 1].

num_stages (int): The num of stages. Default: 4. pretrained (str, optional): model pretrained path. Default: None. k (int): number of the nearest neighbor used for local density. sample_ratios (list[float]): The sample ratios of CTM modules.

Default: [0.25, 0.25, 0.25]

return_map (bool): If True, transfer dynamic tokens to feature map at

last. Default: False

convert_weights (bool): The flag indicates whether the

pre-trained model is from the original repo. We may need to convert some keys to make it compatible. Default: True.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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 registered hooks while the latter silently ignores them.

class mmpose.models.backbones.TCN(in_channels, stem_channels=1024, num_blocks=2, kernel_sizes=(3, 3, 3), dropout=0.25, causal=False, residual=True, use_stride_conv=False, conv_cfg={'type': 'Conv1d'}, norm_cfg={'type': 'BN1d'}, max_norm=None)

TCN backbone.

Temporal Convolutional Networks. More details can be found in the paper .

Parameters

• in_channels (int) – Number of input channels, which equals to num_keypoints * num_features.

• stem_channels (int) – Number of feature channels. Default: 1024.

• num_blocks (int) – NUmber of basic temporal convolutional blocks. Default: 2.

• kernel_sizes (Sequence[int]) – Sizes of the convolving kernel of each basic block. Default: (3, 3, 3).

• dropout (float) – Dropout rate. Default: 0.25.

• causal (bool) – Use causal convolutions instead of symmetric convolutions (for real-time applications). Default: False.

• residual (bool) – Use residual connection. Default: True.

• use_stride_conv (bool) – Use TCN backbone optimized for single-frame batching, i.e. where batches have input length = receptive field, and output length = 1. This implementation replaces dilated convolutions with strided convolutions to avoid generating unused intermediate results. The weights are interchangeable with the reference implementation. Default: False

• conv_cfg (dict) – dictionary to construct and config conv layer. Default: dict(type=’Conv1d’).

• norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN1d’).

• max_norm (float|None) – if not None, the weight of convolution layers will be clipped to have a maximum norm of max_norm.

Example

>>> from mmpose.models import TCN
>>> import torch
>>> self = TCN(in_channels=34)
>>> self.eval()
>>> inputs = torch.rand(1, 34, 243)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
(1, 1024, 235)
(1, 1024, 217)

forward(x)

Forward function.

init_weights(pretrained=None)

Initialize the weights.

class mmpose.models.backbones.V2VNet(input_channels, output_channels, mid_channels=32)

V2VNet.

Please refer to the paper <https://arxiv.org/abs/1711.07399>

for details.

Parameters

• input_channels (int) – Number of channels of the input feature volume.

• output_channels (int) – Number of channels of the output volume.

• mid_channels (int) – Input and output channels of the encoder-decoder block.

forward(x)

Forward function.

class mmpose.models.backbones.VGG(depth, num_classes=- 1, num_stages=5, dilations=(1, 1, 1, 1, 1), out_indices=None, frozen_stages=- 1, conv_cfg=None, norm_cfg=None, act_cfg={'type': 'ReLU'}, norm_eval=False, ceil_mode=False, with_last_pool=True)

VGG backbone.

Parameters

• depth (int) – Depth of vgg, from {11, 13, 16, 19}.

• with_norm (bool) – Use BatchNorm or not.

• num_classes (int) – number of classes for classification.

• num_stages (int) – VGG stages, normally 5.

• dilations (Sequence[int]) – Dilation of each stage.

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. When it is None, the default behavior depends on whether num_classes is specified. If num_classes <= 0, the default value is (4, ), outputting the last feature map before classifier. If num_classes > 0, the default value is (5, ), outputting the classification score. Default: None.

• frozen_stages (int) – Stages to be frozen (all param fixed). -1 means not freezing any parameters.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• ceil_mode (bool) – Whether to use ceil_mode of MaxPool. Default: False.

• with_last_pool (bool) – Whether to keep the last pooling before classifier. Default: True.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Init backbone weights.

Parameters

pretrained (str | None) – If pretrained is a string, then it initializes backbone weights by loading the pretrained checkpoint. If pretrained is None, then it follows default initializer or customized initializer in subclasses.

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

class mmpose.models.backbones.ViPNAS_MobileNetV3(wid=[16, 16, 24, 40, 80, 112, 160], expan=[None, 1, 5, 4, 5, 5, 6], dep=[None, 1, 4, 4, 4, 4, 4], ks=[3, 3, 7, 7, 5, 7, 5], group=[None, 8, 120, 20, 100, 280, 240], att=[None, True, True, False, True, True, True], stride=[2, 1, 2, 2, 2, 1, 2], act=['HSwish', 'ReLU', 'ReLU', 'ReLU', 'HSwish', 'HSwish', 'HSwish'], conv_cfg=None, norm_cfg={'type': 'BN'}, frozen_stages=- 1, norm_eval=False, with_cp=False)

ViPNAS_MobileNetV3 backbone.

“ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search” More details can be found in the paper .

Parameters

• wid (list(int)) – Searched width config for each stage.

• expan (list(int)) – Searched expansion ratio config for each stage.

• dep (list(int)) – Searched depth config for each stage.

• ks (list(int)) – Searched kernel size config for each stage.

• group (list(int)) – Searched group number config for each stage.

• att (list(bool)) – Searched attention config for each stage.

• stride (list(int)) – Stride config for each stage.

• act (list(dict)) – Activation config for each stage.

• conv_cfg (dict) – Config dict for convolution layer. Default: None, which means using conv2d.

• norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).

• frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

forward(x)

Forward function.

Parameters

x (Tensor | tuple[Tensor]) – x could be a torch.Tensor or a tuple of torch.Tensor, containing input data for forward computation.

init_weights(pretrained=None)

Init backbone weights.

Parameters

pretrained (str | None) – If pretrained is a string, then it initializes backbone weights by loading the pretrained checkpoint. If pretrained is None, then it follows default initializer or customized initializer in subclasses.

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

class mmpose.models.backbones.ViPNAS_ResNet(depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=True, wid=[48, 80, 160, 304, 608], expan=[None, 1, 1, 1, 1], dep=[None, 4, 6, 7, 3], ks=[7, 3, 5, 5, 5], group=[None, 16, 16, 16, 16], att=[None, True, False, True, True])

ViPNAS_ResNet backbone.

“ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search” More details can be found in the paper .

Parameters

• depth (int) – Network depth, from {18, 34, 50, 101, 152}.

• in_channels (int) – Number of input image channels. Default: 3.

• num_stages (int) – Stages of the network. Default: 4.

• strides (Sequence[int]) – Strides of the first block of each stage. Default: (1, 2, 2, 2).

• dilations (Sequence[int]) – Dilation of each stage. Default: (1, 1, 1, 1).

• out_indices (Sequence[int]) – Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: (3, ).

• style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer.

• deep_stem (bool) – Replace 7x7 conv in input stem with 3 3x3 conv. Default: False.

• avg_down (bool) – Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False.

• frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1.

• conv_cfg (dict | None) – The config dict for conv layers. Default: None.

• norm_cfg (dict) – The config dict for norm layers.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.

• zero_init_residual (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

• wid (list(int)) – Searched width config for each stage.

• expan (list(int)) – Searched expansion ratio config for each stage.

• dep (list(int)) – Searched depth config for each stage.

• ks (list(int)) – Searched kernel size config for each stage.

• group (list(int)) – Searched group number config for each stage.

• att (list(bool)) – Searched attention config for each stage.

forward(x)

Forward function.

init_weights(pretrained=None)

Initialize model weights.

make_res_layer(**kwargs)

Make a ViPNAS ResLayer.

property norm1

the normalization layer named “norm1”

Type

nn.Module

train(mode=True)

Convert the model into training mode.

necks

class mmpose.models.necks.FPN(in_channels, out_channels, num_outs, start_level=0, end_level=- 1, add_extra_convs=False, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, act_cfg=None, upsample_cfg={'mode': 'nearest'})

Feature Pyramid Network.

This is an implementation of paper Feature Pyramid Networks for Object Detection.

Parameters

• in_channels (list[int]) – Number of input channels per scale.

• out_channels (int) – Number of output channels (used at each scale).

• num_outs (int) – Number of output scales.

• start_level (int) – Index of the start input backbone level used to build the feature pyramid. Default: 0.

• end_level (int) – Index of the end input backbone level (exclusive) to build the feature pyramid. Default: -1, which means the last level.

• add_extra_convs (bool | str) –

  If bool, it decides whether to add conv layers on top of the original feature maps. Default to False. If True, it is equivalent to add_extra_convs=’on_input’. If str, it specifies the source feature map of the extra convs. Only the following options are allowed

  • ’on_input’: Last feat map of neck inputs (i.e. backbone feature).

  • ’on_lateral’: Last feature map after lateral convs.

  • ’on_output’: The last output feature map after fpn convs.

• relu_before_extra_convs (bool) – Whether to apply relu before the extra conv. Default: False.

• no_norm_on_lateral (bool) – Whether to apply norm on lateral. Default: False.

• conv_cfg (dict) – Config dict for convolution layer. Default: None.

• norm_cfg (dict) – Config dict for normalization layer. Default: None.

• act_cfg (dict) – Config dict for activation layer in ConvModule. Default: None.

• upsample_cfg (dict) – Config dict for interpolate layer. Default: dict(mode=’nearest’).

Example

>>> import torch
>>> in_channels = [2, 3, 5, 7]
>>> scales = [340, 170, 84, 43]
>>> inputs = [torch.rand(1, c, s, s)
...           for c, s in zip(in_channels, scales)]
>>> self = FPN(in_channels, 11, len(in_channels)).eval()
>>> outputs = self.forward(inputs)
>>> for i in range(len(outputs)):
...     print(f'outputs[{i}].shape = {outputs[i].shape}')
outputs[0].shape = torch.Size([1, 11, 340, 340])
outputs[1].shape = torch.Size([1, 11, 170, 170])
outputs[2].shape = torch.Size([1, 11, 84, 84])
outputs[3].shape = torch.Size([1, 11, 43, 43])

forward(inputs)

Forward function.

init_weights()

Initialize model weights.

class mmpose.models.necks.GlobalAveragePooling

Global Average Pooling neck.

Note that we use view to remove extra channel after pooling. We do not use squeeze as it will also remove the batch dimension when the tensor has a batch dimension of size 1, which can lead to unexpected errors.

forward(inputs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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 registered hooks while the latter silently ignores them.

class mmpose.models.necks.MTA(in_channels=[64, 128, 256, 512], out_channels=128, num_outs=4, start_level=0, end_level=- 1, add_extra_convs=False, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, act_cfg=None, num_heads=[2, 2, 2, 2], mlp_ratios=[4, 4, 4, 4], sr_ratios=[8, 4, 2, 1], qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, transformer_norm_cfg={'type': 'LN'}, use_sr_conv=False)

Multi-stage Token feature Aggregation (MTA) module in TCFormer.

Parameters

• in_channels (list[int]) – Number of input channels per stage. Default: [64, 128, 256, 512].

• out_channels (int) – Number of output channels (used at each scale).

• num_outs (int) – Number of output scales. Default: 4.

• start_level (int) – Index of the start input backbone level used to build the feature pyramid. Default: 0.

• end_level (int) – Index of the end input backbone level (exclusive) to build the feature pyramid. Default: -1, which means the last level.

• add_extra_convs (bool | str) – If bool, it decides whether to add conv layers on top of the original feature maps. Default to False. If True, it is equivalent to add_extra_convs=’on_input’. If str, it specifies the source feature map of the extra convs. Only the following options are allowed - ‘on_input’: Last feat map of neck inputs (i.e. backbone feature). - ‘on_output’: The last output feature map after fpn convs.

• relu_before_extra_convs (bool) – Whether to apply relu before the extra conv. Default: False.

• no_norm_on_lateral (bool) – Whether to apply norm on lateral. Default: False.

• conv_cfg (dict) – Config dict for convolution layer. Default: None.

• norm_cfg (dict) – Config dict for normalization layer. Default: None.

• act_cfg (dict) – Config dict for activation layer in ConvModule.

• num_heads (Sequence[int]) – The attention heads of each transformer block. Default: [2, 2, 2, 2].

• mlp_ratios (Sequence[int]) – The ratio of the mlp hidden dim to the embedding dim of each transformer block.

• sr_ratios (Sequence[int]) – The spatial reduction rate of each transformer block. Default: [8, 4, 2, 1].

• qkv_bias (bool) – Enable bias for qkv if True. Default: True.

• qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set. Default: None.

• drop_rate (float) – Probability of an element to be zeroed. Default 0.0.

• attn_drop_rate (float) – The drop out rate for attention layer. Default 0.0.

• drop_path_rate (float) – stochastic depth rate. Default 0.

• transformer_norm_cfg (dict) – Config dict for normalization layer in transformer blocks. Default: dict(type=’LN’).

• use_sr_conv (bool) – If True, use a conv layer for spatial reduction. If False, use a pooling process for spatial reduction. Defaults: False.

forward(inputs)

Forward function.

init_weights()

Initialize the weights.

class mmpose.models.necks.PoseWarperNeck(in_channels, out_channels, inner_channels, deform_groups=17, dilations=(3, 6, 12, 18, 24), trans_conv_kernel=1, res_blocks_cfg=None, offsets_kernel=3, deform_conv_kernel=3, in_index=0, input_transform=None, freeze_trans_layer=True, norm_eval=False, im2col_step=80)

PoseWarper neck.

“Learning temporal pose estimation from sparsely-labeled videos”.

Parameters

• in_channels (int) – Number of input channels from backbone

• out_channels (int) – Number of output channels

• inner_channels (int) – Number of intermediate channels of the res block

• deform_groups (int) – Number of groups in the deformable conv

• dilations (list|tuple) – different dilations of the offset conv layers

• trans_conv_kernel (int) – the kernel of the trans conv layer, which is used to get heatmap from the output of backbone. Default: 1

• res_blocks_cfg (dict|None) –

  config of residual blocks. If None, use the default values. If not None, it should contain the following keys:

  • block (str): the type of residual block, Default: ‘BASIC’.

  • num_blocks (int): the number of blocks, Default: 20.

• offsets_kernel (int) – the kernel of offset conv layer.

• deform_conv_kernel (int) – the kernel of defomrable conv layer.

• in_index (int|Sequence[int]) – Input feature index. Default: 0

• input_transform (str|None) –

  Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, None. Default: None.

  • ’resize_concat’: Multiple feature maps will be resize to the same size as first one and than concat together. Usually used in FCN head of HRNet.

  • ’multiple_select’: Multiple feature maps will be bundle into a list and passed into decode head.

  • None: Only one select feature map is allowed.

• freeze_trans_layer (bool) – Whether to freeze the transition layer (stop grad and set eval mode). Default: True.

• norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.

• im2col_step (int) – the argument im2col_step in deformable conv, Default: 80.

forward(inputs, frame_weight)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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 registered hooks while the latter silently ignores them.

train(mode=True)

Convert the model into training mode.

detectors

class mmpose.models.detectors.AssociativeEmbedding(backbone, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)

Associative embedding pose detectors.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• keypoint_head (dict) – Keypoint head to process feature.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained models.

• loss_pose (None) – Deprecated arguments. Please use loss_keypoint for heads instead.

forward(img=None, targets=None, masks=None, joints=None, img_metas=None, return_loss=True, return_heatmap=False, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss is True.

Note

• batch_size: N

• num_keypoints: K

• num_img_channel: C

• img_width: imgW

• img_height: imgH

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – Input image.

• targets (list(torch.Tensor[N,K,H,W])) – Multi-scale target heatmaps.

• masks (list(torch.Tensor[N,H,W])) – Masks of multi-scale target heatmaps

• joints (list(torch.Tensor[N,M,K,2])) – Joints of multi-scale target heatmaps for ae loss

• img_metas (dict) –

  Information about val & test. By default it includes:

  • ”image_file”: image path

  • ”aug_data”: input

  • ”test_scale_factor”: test scale factor

  • ”base_size”: base size of input

  • ”center”: center of image

  • ”scale”: scale of image

  • ”flip_index”: flip index of keypoints

• loss (return) – return_loss=True for training, return_loss=False for validation & test.

• return_heatmap (bool) – Option to return heatmap.

Returns

if ‘return_loss’ is true, then return losses. Otherwise, return predicted poses, scores, image paths and heatmaps.

Return type

dict|tuple

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor) – Input image.

Returns

Outputs.

Return type

Tensor

forward_test(img, img_metas, return_heatmap=False, **kwargs)

Inference the bottom-up model.

Note

• Batchsize: N (currently support batchsize = 1)

• num_img_channel: C

• img_width: imgW

• img_height: imgH

Parameters

• flip_index (List(int)) –

• aug_data (List(Tensor[NxCximgHximgW])) – Multi-scale image

• test_scale_factor (List(float)) – Multi-scale factor

• base_size (Tuple(int)) – Base size of image when scale is 1

• center (np.ndarray) – center of image

• scale (np.ndarray) – the scale of image

forward_train(img, targets, masks, joints, img_metas, **kwargs)

Forward the bottom-up model and calculate the loss.

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH heatmaps weight: W heatmaps height: H max_num_people: M

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – Input image.

• targets (List(torch.Tensor[N,K,H,W])) – Multi-scale target heatmaps.

• masks (List(torch.Tensor[N,H,W])) – Masks of multi-scale target heatmaps

• joints (List(torch.Tensor[N,M,K,2])) – Joints of multi-scale target heatmaps for ae loss

• img_metas (dict) – Information about val&test By default this includes: - “image_file”: image path - “aug_data”: input - “test_scale_factor”: test scale factor - “base_size”: base size of input - “center”: center of image - “scale”: scale of image - “flip_index”: flip index of keypoints

Returns

The total loss for bottom-up

Return type

dict

init_weights(pretrained=None)

Weight initialization for model.

show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color=None, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, font_scale=0.5, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)

Draw result over img.

Parameters

• img (str or Tensor) – The image to be displayed.

• result (list[dict]) – The results to draw over img (bbox_result, pose_result).

• skeleton (list[list]) – The connection of keypoints. skeleton is 0-based indexing.

• kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.

• pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.

• pose_link_color (np.array[Mx3]) – Color of M links. If None, do not draw links.

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• font_scale (float) – Font scales of texts.

• win_name (str) – The window name.

• show (bool) – Whether to show the image. Default: False.

• show_keypoint_weight (bool) – Whether to change the transparency using the predicted confidence scores of keypoints.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized image only if not show or out_file

Return type

Tensor

property with_keypoint

Check if has keypoint_head.

class mmpose.models.detectors.CID(backbone, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)

Contextual Instance Decouple for Multi-Person Pose Estimation.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• keypoint_head (dict) – Keypoint head to process feature.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained models.

• loss_pose (None) – Deprecated arguments. Please use loss_keypoint for heads instead.

forward(img=None, multi_heatmap=None, multi_mask=None, instance_coord=None, instance_heatmap=None, instance_mask=None, instance_valid=None, img_metas=None, return_loss=True, return_heatmap=False, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss is True.

Note

• batch_size: N

• num_keypoints: K

• num_img_channel: C

• img_width: imgW

• img_height: imgH

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – Input image.

• multi_heatmap (torch.Tensor[N,C,H,W]) – Multi-person heatmaps

• multi_mask (torch.Tensor[N,1,H,W]) – Multi-person heatmap mask

• instance_coord (torch.Tensor[N,M,2]) – Instance center coord

• instance_heatmap (torch.Tensor[N,M,C,H,W]) – Single person heatmap for each instance

• instance_mask (torch.Tensor[N,M,C,1,1]) – Single person heatmap mask

• instance_valid (torch.Tensor[N,M]) – Bool mask to indicate the existence of each person

• img_metas (dict) –

  Information about val & test. By default it includes:

  • ”image_file”: image path

  • ”aug_data”: input

  • ”test_scale_factor”: test scale factor

  • ”base_size”: base size of input

  • ”center”: center of image

  • ”scale”: scale of image

  • ”flip_index”: flip index of keypoints

• loss (return) – return_loss=True for training, return_loss=False for validation & test.

• return_heatmap (bool) – Option to return heatmap.

Returns

if ‘return_loss’ is true, then return losses. Otherwise, return predicted poses, scores, image paths and heatmaps.

Return type

dict|tuple

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor) – Input image.

Returns

Outputs.

Return type

Tensor

forward_test(img, img_metas, return_heatmap=False, **kwargs)

Inference the bottom-up model.

Note

• Batchsize: N (currently support batchsize = 1)

• num_img_channel: C

• img_width: imgW

• img_height: imgH

Parameters

• flip_index (List(int)) –

• aug_data (List(Tensor[NxCximgHximgW])) – Multi-scale image

• test_scale_factor (List(float)) – Multi-scale factor

• base_size (Tuple(int)) – Base size of image when scale is 1

• center (np.ndarray) – center of image

• scale (np.ndarray) – the scale of image

forward_train(img, multi_heatmap, multi_mask, instance_coord, instance_heatmap, instance_mask, instance_valid, img_metas, **kwargs)

Forward CID model and calculate the loss.

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH heatmaps weight: W heatmaps height: H max_num_people: M

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – Input image.

• multi_heatmap (torch.Tensor[N,C,H,W]) – Multi-person heatmaps

• multi_mask (torch.Tensor[N,1,H,W]) – Multi-person heatmap mask

• instance_coord (torch.Tensor[N,M,2]) – Instance center coord

• instance_heatmap (torch.Tensor[N,M,C,H,W]) – Single person heatmap for each instance

• instance_mask (torch.Tensor[N,M,C,1,1]) – Single person heatmap mask

• instance_valid (torch.Tensor[N,M]) – Bool mask to indicate the existence of each person

• img_metas (dict) – Information about val&test By default this includes: - “image_file”: image path - “aug_data”: input - “test_scale_factor”: test scale factor - “base_size”: base size of input - “center”: center of image - “scale”: scale of image - “flip_index”: flip index of keypoints

Returns

The total loss for bottom-up

Return type

dict

init_weights(pretrained=None)

Weight initialization for model.

show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color=None, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, font_scale=0.5, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)

Draw result over img.

Parameters

• img (str or Tensor) – The image to be displayed.

• result (list[dict]) – The results to draw over img (bbox_result, pose_result).

• skeleton (list[list]) – The connection of keypoints. skeleton is 0-based indexing.

• kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.

• pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.

• pose_link_color (np.array[Mx3]) – Color of M links. If None, do not draw links.

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• font_scale (float) – Font scales of texts.

• win_name (str) – The window name.

• show (bool) – Whether to show the image. Default: False.

• show_keypoint_weight (bool) – Whether to change the transparency using the predicted confidence scores of keypoints.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized image only if not show or out_file

Return type

Tensor

property with_keypoint

Check if has keypoint_head.

class mmpose.models.detectors.DetectAndRegress(backbone, human_detector, pose_regressor, train_cfg=None, test_cfg=None, pretrained=None, freeze_2d=True)

DetectAndRegress approach for multiview human pose detection.

Parameters

• backbone (ConfigDict) – Dictionary to construct the 2D pose detector

• human_detector (ConfigDict) – dictionary to construct human detector

• pose_regressor (ConfigDict) – dictionary to construct pose regressor

• train_cfg (ConfigDict) – Config for training. Default: None.

• test_cfg (ConfigDict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained 2D model. Default: None.

• freeze_2d (bool) – Whether to freeze the 2D model in training. Default: True.

forward(img=None, img_metas=None, return_loss=True, targets=None, masks=None, targets_3d=None, input_heatmaps=None, **kwargs)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH feature_maps width: W feature_maps height: H volume_length: cubeL volume_width: cubeW volume_height: cubeH

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• return_loss – Option to return loss. return loss=True for training, return loss=False for validation & test.

• targets (list(torch.Tensor[NxKxHxW])) – Multi-camera target feature_maps of the 2D model.

• masks (list(torch.Tensor[NxHxW])) – Multi-camera masks of the input to the 2D model.

• targets_3d (torch.Tensor[NxcubeLxcubeWxcubeH]) – Ground-truth 3D heatmap of human centers.

• input_heatmaps (list(torch.Tensor[NxKxHxW])) –

  Multi-camera feature_maps when the 2D model is not available.

  Default: None.

• **kwargs –

Returns

if ‘return_loss’ is true, then return losses.

Otherwise, return predicted poses, human centers and sample_id

Return type

dict

forward_dummy(img, input_heatmaps=None, num_candidates=5)

Used for computing network FLOPs.

forward_test(img, img_metas, input_heatmaps=None)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH feature_maps width: W feature_maps height: H volume_length: cubeL volume_width: cubeW volume_height: cubeH

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• input_heatmaps (list(torch.Tensor[NxKxHxW])) –

  Multi-camera feature_maps when the 2D model is not available.

  Default: None.

Returns

predicted poses, human centers and sample_id

Return type

dict

forward_train(img, img_metas, targets=None, masks=None, targets_3d=None, input_heatmaps=None)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH feature_maps width: W feature_maps height: H volume_length: cubeL volume_width: cubeW volume_height: cubeH

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• targets (list(torch.Tensor[NxKxHxW])) – Multi-camera target feature_maps of the 2D model.

• masks (list(torch.Tensor[NxHxW])) – Multi-camera masks of the input to the 2D model.

• targets_3d (torch.Tensor[NxcubeLxcubeWxcubeH]) – Ground-truth 3D heatmap of human centers.

• input_heatmaps (list(torch.Tensor[NxKxHxW])) –

  Multi-camera feature_maps when the 2D model is not available.

  Default: None.

Returns

losses.

Return type

dict

show_result(img, img_metas, visualize_2d=False, input_heatmaps=None, dataset_info=None, radius=4, thickness=2, out_dir=None, show=False)

Visualize the results.

train(mode=True)

Sets the module in training mode. :param mode: whether to set training mode (True)

or evaluation mode (False). Default: True.

Returns

self

Return type

Module

train_step(data_batch, optimizer, **kwargs)

The iteration step during training.

This method defines an iteration step during training, except for the back propagation and optimizer updating, which are done in an optimizer hook. Note that in some complicated cases or models, the whole process including back propagation and optimizer updating is also defined in this method, such as GAN.

Parameters

• data_batch (dict) – The output of dataloader.

• optimizer (torch.optim.Optimizer | dict) – The optimizer of runner is passed to train_step(). This argument is unused and reserved.

Returns

It should contain at least 3 keys: loss, log_vars,

num_samples. loss is a tensor for back propagation, which can be a weighted sum of multiple losses. log_vars contains all the variables to be sent to the logger. num_samples indicates the batch size (when the model is DDP, it means the batch size on each GPU), which is used for averaging the logs.

Return type

dict

class mmpose.models.detectors.DisentangledKeypointRegressor(backbone, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None)

Disentangled keypoint regression pose detector.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• keypoint_head (dict) – Keypoint head to process feature.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained models.

forward(img=None, heatmaps=None, masks=None, offsets=None, offset_weights=None, img_metas=None, return_loss=True, return_heatmap=False, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss is True.

Note

• batch_size: N

• num_keypoints: K

• num_img_channel: C

• img_width: imgW

• img_height: imgH

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – # input image.

• targets (list(torch.Tensor[N,K,H,W])) – Multi-scale target heatmaps.

• masks (list(torch.Tensor[N,H,W])) – Masks of multi-scale target heatmaps

• joints (list(torch.Tensor[N,M,K,2])) – Joints of multi-scale target heatmaps for ae loss

• img_metas (dict) –

  Information about val & test. By default it includes:

  • ”image_file”: image path

  • ”aug_data”: # input

  • ”test_scale_factor”: test scale factor

  • ”base_size”: base size of # input

  • ”center”: center of image

  • ”scale”: scale of image

  • ”flip_index”: flip index of keypoints

• loss (return) – return_loss=True for training, return_loss=False for validation & test.

• return_heatmap (bool) – Option to return heatmap.

Returns

if ‘return_loss’ is true, then return losses. Otherwise, return predicted poses, scores, image paths and heatmaps.

Return type

dict|tuple

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor) – # input image.

Returns

Outputs.

Return type

Tensor

forward_test(img, img_metas, return_heatmap=False, **kwargs)

Inference the one-stage model.

Note

• Batchsize: N (currently support batchsize = 1)

• num_img_channel: C

• img_width: imgW

• img_height: imgH

Parameters

• flip_index (List(int)) –

• aug_data (List(Tensor[NxCximgHximgW])) – Multi-scale image

• num_joints (int) – Number of joints of an instsance. test_scale_factor (List(float)): Multi-scale factor

• base_size (Tuple(int)) – Base size of image when scale is 1

• image_size (int) – Short edge of images when scale is 1

• heatmap_size (int) – Short edge of outputs when scale is 1

• center (np.ndarray) – center of image

• scale (np.ndarray) – the scale of image

• skeleton (List(List(int))) – Links of joints

forward_train(img, heatmaps, masks, offsets, offset_weights, img_metas, **kwargs)

Forward the bottom-up model and calculate the loss.

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH heatmaps weight: W heatmaps height: H max_num_people: M

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – # input image.

• targets (List(torch.Tensor[N,K,H,W])) – Multi-scale target heatmaps.

• masks (List(torch.Tensor[N,H,W])) – Masks of multi-scale target heatmaps

• joints (List(torch.Tensor[N,M,K,2])) – Joints of multi-scale target heatmaps for ae loss

• img_metas (dict) – Information about val&test By default this includes: - “image_file”: image path - “aug_data”: # input - “test_scale_factor”: test scale factor - “base_size”: base size of # input - “center”: center of image - “scale”: scale of image - “flip_index”: flip index of keypoints

Returns

The total loss for bottom-up

Return type

dict

init_weights(pretrained=None)

Weight initialization for model.

show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color=None, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, font_scale=0.5, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)

Draw result over img.

Parameters

• img (str or Tensor) – The image to be displayed.

• result (list[dict]) – The results to draw over img (bbox_result, pose_result).

• skeleton (list[list]) – The connection of keypoints. skeleton is 0-based indexing.

• kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.

• pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.

• pose_link_color (np.array[Mx3]) – Color of M links. If None, do not draw links.

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• font_scale (float) – Font scales of texts.

• win_name (str) – The window name.

• show (bool) – Whether to show the image. Default: False.

• show_keypoint_weight (bool) – Whether to change the transparency using the predicted confidence scores of keypoints.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized image only if not show or out_file

Return type

Tensor

property with_keypoint

Check if has keypoint_head.

class mmpose.models.detectors.GestureRecognizer(backbone, neck=None, cls_head=None, train_cfg=None, test_cfg=None, modality='rgb', pretrained=None)

Hand gesture recognizer.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• neck (dict) – Neck Modules to process feature.

• cls_head (dict) – Classification head to process feature.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• modality (str or list or tuple) – Data modality. Default: None.

• pretrained (str) – Path to the pretrained models.

forward(video, label=None, img_metas=None, return_loss=True, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs.

Note:

• batch_size: N

• num_vid_channel: C (Default: 3)

• video height: vidH

• video width: vidW

• video length: vidL

Args:

video (list[torch.Tensor[NxCxvidLxvidHxvidW]]): Input videos. label (torch.Tensor[N]): Category label of videos. img_metas (list(dict)): Information about data.

By default this includes: - “fps: video frame rate - “modality”: modality of input videos

return_loss (bool): Option to return loss. return loss=True

for training, return loss=False for validation & test.

Returns:

dict|tuple: if return loss is true, then return losses. Otherwise, return predicted gestures for clips with a certain length. .

forward_test(video, label, img_metas, **kwargs)

Defines the computation performed at every call when testing.

forward_train(video, label, img_metas, **kwargs)

Defines the computation performed at every call when training.

init_weights(pretrained=None)

Weight initialization for model.

set_train_epoch(epoch: int)

set the training epoch of heads to support customized behaviour.

show_result(video, result, **kwargs)

Visualize the results.

class mmpose.models.detectors.Interhand3D(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)

Top-down interhand 3D pose detector of paper ref: Gyeongsik Moon.

“InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image”. A child class of TopDown detector.

forward(img, target=None, target_weight=None, img_metas=None, return_loss=True, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. When return_loss=True, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when resturn_loss=False, img and img_meta should be double nested (i.e. list[Tensor], list[list[dict]]), with the outer list indicating test time augmentations.

Note

• batch_size: N

• num_keypoints: K

• num_img_channel: C (Default: 3)

• img height: imgH

• img width: imgW

• heatmaps height: H

• heatmaps weight: W

Parameters

• img (torch.Tensor[NxCximgHximgW]) – Input images.

• target (list[torch.Tensor]) – Target heatmaps, relative hand

• depth and hand type. (root) –

• target_weight (list[torch.Tensor]) – Weights for target

• heatmaps –

• hand root depth and hand type. (relative) –

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

  • ”heatmap3d_depth_bound”: depth bound of hand keypoint 3D

    heatmap

  • ”root_depth_bound”: depth bound of relative root depth 1D

    heatmap

• return_loss (bool) – Option to return loss. return loss=True for training, return loss=False for validation & test.

Returns

if return loss is true, then return losses. Otherwise, return predicted poses, boxes, image paths, heatmaps, relative hand root depth and hand type.

Return type

dict|tuple

forward_test(img, img_metas, **kwargs)

Defines the computation performed at every call when testing.

show_result(result, img=None, skeleton=None, kpt_score_thr=0.3, radius=8, bbox_color='green', thickness=2, pose_kpt_color=None, pose_link_color=None, vis_height=400, num_instances=- 1, axis_azimuth=- 115, win_name='', show=False, wait_time=0, out_file=None)

Visualize 3D pose estimation results.

Parameters

• result (list[dict]) –

  The pose estimation results containing:

  • ”keypoints_3d” ([K,4]): 3D keypoints

  • ”keypoints” ([K,3] or [T,K,3]): Optional for visualizing

    2D inputs. If a sequence is given, only the last frame will be used for visualization

  • ”bbox” ([4,] or [T,4]): Optional for visualizing 2D inputs

  • ”title” (str): title for the subplot

• img (str or Tensor) – Optional. The image to visualize 2D inputs on.

• skeleton (list of [idx_i,idx_j]) – Skeleton described by a list of links, each is a pair of joint indices.

• kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.

• radius (int) – Radius of circles.

• bbox_color (str or tuple or Color) – Color of bbox lines.

• thickness (int) – Thickness of lines.

• pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.

• pose_link_color (np.array[Mx3]) – Color of M limbs. If None, do not draw limbs.

• vis_height (int) – The image height of the visualization. The width will be N*vis_height depending on the number of visualized items.

• num_instances (int) – Number of instances to be shown in 3D. If smaller than 0, all the instances in the pose_result will be shown. Otherwise, pad or truncate the pose_result to a length of num_instances.

• axis_azimuth (float) – axis azimuth angle for 3D visualizations.

• win_name (str) – The window name.

• show (bool) – Whether to show the image. Default: False.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized img, only if not show or out_file.

Return type

Tensor

class mmpose.models.detectors.MultiTask(backbone, heads, necks=None, head2neck=None, pretrained=None)

Multi-task detectors.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• heads (list[dict]) – heads to output predictions.

• necks (list[dict] | None) – necks to process feature.

• (dict{int (head2neck) – int}): head index to neck index.

• pretrained (str) – Path to the pretrained models.

forward(img, target=None, target_weight=None, img_metas=None, return_loss=True, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. When return_loss=True, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when resturn_loss=False, img and img_meta should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations.

Note

• batch_size: N

• num_keypoints: K

• num_img_channel: C (Default: 3)

• img height: imgH

• img weight: imgW

• heatmaps height: H

• heatmaps weight: W

Parameters

• img (torch.Tensor[N,C,imgH,imgW]) – Input images.

• target (list[torch.Tensor]) – Targets.

• target_weight (List[torch.Tensor]) – Weights.

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

• return_loss (bool) – Option to return loss. return loss=True for training, return loss=False for validation & test.

Returns

if return loss is true, then return losses. Otherwise, return predicted poses, boxes, image paths and heatmaps.

Return type

dict|tuple

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor) – Input image.

Returns

Outputs.

Return type

list[Tensor]

forward_test(img, img_metas, **kwargs)

Defines the computation performed at every call when testing.

forward_train(img, target, target_weight, img_metas, **kwargs)

Defines the computation performed at every call when training.

init_weights(pretrained=None)

Weight initialization for model.

property with_necks

Check if has keypoint_head.

class mmpose.models.detectors.ParametricMesh(backbone, mesh_head, smpl, disc=None, loss_gan=None, loss_mesh=None, train_cfg=None, test_cfg=None, pretrained=None)

Model-based 3D human mesh detector. Take a single color image as input and output 3D joints, SMPL parameters and camera parameters.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• mesh_head (dict) – Mesh head to process feature.

• smpl (dict) – Config for SMPL model.

• disc (dict) – Discriminator for SMPL parameters. Default: None.

• loss_gan (dict) – Config for adversarial loss. Default: None.

• loss_mesh (dict) – Config for mesh loss. Default: None.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained models.

forward(img, img_metas=None, return_loss=False, **kwargs)

Forward function.

Calls either forward_train or forward_test depending on whether return_loss=True.

Note

• batch_size: N

• num_img_channel: C (Default: 3)

• img height: imgH

• img width: imgW

Parameters

• img (torch.Tensor[N x C x imgH x imgW]) – Input images.

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

• return_loss (bool) – Option to return loss. return loss=True for training, return loss=False for validation & test.

Returns

Return predicted 3D joints, SMPL parameters, boxes and image paths.

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor) – Input image.

Returns

Outputs.

Return type

Tensor

forward_test(img, img_metas, return_vertices=False, return_faces=False, **kwargs)

Defines the computation performed at every call when testing.

forward_train(*args, **kwargs)

Forward function for training.

For ParametricMesh, we do not use this interface.

get_3d_joints_from_mesh(vertices)

Get 3D joints from 3D mesh using predefined joints regressor.

init_weights(pretrained=None)

Weight initialization for model.

show_result(result, img, show=False, out_file=None, win_name='', wait_time=0, bbox_color='green', mesh_color=(76, 76, 204), **kwargs)

Visualize 3D mesh estimation results.

Parameters

• result (list[dict]) –

  The mesh estimation results containing:

  • ”bbox” (ndarray[4]): instance bounding bbox

  • ”center” (ndarray[2]): bbox center

  • ”scale” (ndarray[2]): bbox scale

  • ”keypoints_3d” (ndarray[K,3]): predicted 3D keypoints

  • ”camera” (ndarray[3]): camera parameters

  • ”vertices” (ndarray[V, 3]): predicted 3D vertices

  • ”faces” (ndarray[F, 3]): mesh faces

• img (str or Tensor) – Optional. The image to visualize 2D inputs on.

• win_name (str) – The window name.

• show (bool) – Whether to show the image. Default: False.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

• bbox_color (str or tuple or Color) – Color of bbox lines.

• mesh_color (str or tuple or Color) – Color of mesh surface.

Returns

Visualized img, only if not show or out_file.

Return type

ndarray

train_step(data_batch, optimizer, **kwargs)

Train step function.

In this function, the detector will finish the train step following the pipeline:

1. get fake and real SMPL parameters

2. optimize discriminator (if have)

3. optimize generator

If self.train_cfg.disc_step > 1, the train step will contain multiple iterations for optimizing discriminator with different input data and only one iteration for optimizing generator after disc_step iterations for discriminator.

Parameters

• data_batch (torch.Tensor) – Batch of data as input.

• optimizer (dict[torch.optim.Optimizer]) – Dict with optimizers for generator and discriminator (if have).

Returns

Dict with loss, information for logger, the number of samples.

Return type

outputs (dict)

val_step(data_batch, **kwargs)

Forward function for evaluation.

Parameters

data_batch (dict) – Contain data for forward.

Returns

Contain the results from model.

Return type

dict

class mmpose.models.detectors.PoseLifter(backbone, neck=None, keypoint_head=None, traj_backbone=None, traj_neck=None, traj_head=None, loss_semi=None, train_cfg=None, test_cfg=None, pretrained=None)

Pose lifter that lifts 2D pose to 3D pose.

The basic model is a pose model that predicts root-relative pose. If traj_head is not None, a trajectory model that predicts absolute root joint position is also built.

Parameters

• backbone (dict) – Config for the backbone of pose model.

• neck (dict|None) – Config for the neck of pose model.

• keypoint_head (dict|None) – Config for the head of pose model.

• traj_backbone (dict|None) – Config for the backbone of trajectory model. If traj_backbone is None and traj_head is not None, trajectory model will share backbone with pose model.

• traj_neck (dict|None) – Config for the neck of trajectory model.

• traj_head (dict|None) – Config for the head of trajectory model.

• loss_semi (dict|None) – Config for semi-supervision loss.

• train_cfg (dict|None) – Config for keypoint head during training.

• test_cfg (dict|None) – Config for keypoint head during testing.

• pretrained (str|None) – Path to pretrained weights.

forward(input, target=None, target_weight=None, metas=None, return_loss=True, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss=True.

Note

• batch_size: N

• num_input_keypoints: Ki

• input_keypoint_dim: Ci

• input_sequence_len: Ti

• num_output_keypoints: Ko

• output_keypoint_dim: Co

• input_sequence_len: To

Parameters

• input (torch.Tensor[NxKixCixTi]) – Input keypoint coordinates.

• target (torch.Tensor[NxKoxCoxTo]) – Output keypoint coordinates. Defaults to None.

• target_weight (torch.Tensor[NxKox1]) – Weights across different joint types. Defaults to None.

• metas (list(dict)) – Information about data augmentation

• return_loss (bool) – Option to return loss. return loss=True for training, return loss=False for validation & test.

Returns

If reutrn_loss is true, return losses. Otherwise return predicted poses.

Return type

dict|Tensor

forward_dummy(input)

Used for computing network FLOPs. See tools/get_flops.py.

Parameters

input (torch.Tensor) – Input pose

Returns

Model output

Return type

Tensor

forward_test(input, metas, **kwargs)

Defines the computation performed at every call when training.

forward_train(input, target, target_weight, metas, **kwargs)

Defines the computation performed at every call when training.

init_weights(pretrained=None)

Weight initialization for model.

show_result(result, img=None, skeleton=None, pose_kpt_color=None, pose_link_color=None, radius=8, thickness=2, vis_height=400, num_instances=- 1, axis_azimuth=70, win_name='', show=False, wait_time=0, out_file=None)

Visualize 3D pose estimation results.

Parameters

• result (list[dict]) –

  The pose estimation results containing:

  • ”keypoints_3d” ([K,4]): 3D keypoints

  • ”keypoints” ([K,3] or [T,K,3]): Optional for visualizing

    2D inputs. If a sequence is given, only the last frame will be used for visualization

  • ”bbox” ([4,] or [T,4]): Optional for visualizing 2D inputs

  • ”title” (str): title for the subplot

• img (str or Tensor) – Optional. The image to visualize 2D inputs on.

• skeleton (list of [idx_i,idx_j]) – Skeleton described by a list of links, each is a pair of joint indices.

• pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.

• pose_link_color (np.array[Mx3]) – Color of M links. If None, do not draw links.

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• vis_height (int) – The image height of the visualization. The width will be N*vis_height depending on the number of visualized items.

• num_instances (int) – Number of instances to be shown in 3D. If smaller than 0, all the instances in the result will be shown. Otherwise, pad or truncate the result to a length of num_instances.

• axis_azimuth (float) – axis azimuth angle for 3D visualizations.

• win_name (str) – The window name.

• show (bool) – Whether to directly show the visualization.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized img, only if not show or out_file.

Return type

Tensor

property with_keypoint

Check if has keypoint_head.

property with_neck

Check if has keypoint_neck.

property with_traj

Check if has trajectory_head.

property with_traj_backbone

Check if has trajectory_backbone.

property with_traj_neck

Check if has trajectory_neck.

class mmpose.models.detectors.PoseWarper(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None, concat_tensors=True)

Top-down pose detectors for multi-frame settings for video inputs.

“Learning temporal pose estimation from sparsely-labeled videos”.

A child class of TopDown detector. The main difference between PoseWarper and TopDown lies in that the former takes a list of tensors as input image while the latter takes a single tensor as input image in forward method.

Parameters

• backbone (dict) – Backbone modules to extract features.

• neck (dict) – intermediate modules to transform features.

• keypoint_head (dict) – Keypoint head to process feature.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained models.

• loss_pose (None) – Deprecated arguments. Please use loss_keypoint for heads instead.

• concat_tensors (bool) – Whether to concat the tensors on the batch dim, which can speed up, Default: True

forward(img, target=None, target_weight=None, img_metas=None, return_loss=True, return_heatmap=False, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. When return_loss=True, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when resturn_loss=False, img and img_meta should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations.

Note

• number of frames: F

• batch_size: N

• num_keypoints: K

• num_img_channel: C (Default: 3)

• img height: imgH

• img width: imgW

• heatmaps height: H

• heatmaps weight: W

Parameters

• imgs (list[F,torch.Tensor[N,C,imgH,imgW]]) – multiple input frames

• target (torch.Tensor[N,K,H,W]) – Target heatmaps for one frame.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: paths to multiple video frames

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

• return_loss (bool) – Option to return loss. return loss=True for training, return loss=False for validation & test.

• return_heatmap (bool) – Option to return heatmap.

Returns

if return loss is true, then return losses. Otherwise, return predicted poses, boxes, image paths and heatmaps.

Return type

dict|tuple

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor[N,C,imgH,imgW], or list|tuple of tensors) – multiple input frames, N >= 2.

Returns

Output heatmaps.

Return type

Tensor

forward_test(imgs, img_metas, return_heatmap=False, **kwargs)

Defines the computation performed at every call when testing.

forward_train(imgs, target, target_weight, img_metas, **kwargs)

Defines the computation performed at every call when training.

class mmpose.models.detectors.TopDown(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)

Top-down pose detectors.

Parameters

• backbone (dict) – Backbone modules to extract feature.

• keypoint_head (dict) – Keypoint head to process feature.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• pretrained (str) – Path to the pretrained models.

• loss_pose (None) – Deprecated arguments. Please use loss_keypoint for heads instead.

forward(img, target=None, target_weight=None, img_metas=None, return_loss=True, return_heatmap=False, **kwargs)

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. When return_loss=True, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when resturn_loss=False, img and img_meta should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations.

Note

• batch_size: N

• num_keypoints: K

• num_img_channel: C (Default: 3)

• img height: imgH

• img width: imgW

• heatmaps height: H

• heatmaps weight: W

Parameters

• img (torch.Tensor[NxCximgHximgW]) – Input images.

• target (torch.Tensor[NxKxHxW]) – Target heatmaps.

• target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

• return_loss (bool) – Option to return loss. return loss=True for training, return loss=False for validation & test.

• return_heatmap (bool) – Option to return heatmap.

Returns

if return loss is true, then return losses. Otherwise, return predicted poses, boxes, image paths and heatmaps.

Return type

dict|tuple

forward_dummy(img)

Used for computing network FLOPs.

See tools/get_flops.py.

Parameters

img (torch.Tensor) – Input image.

Returns

Output heatmaps.

Return type

Tensor

forward_test(img, img_metas, return_heatmap=False, **kwargs)

Defines the computation performed at every call when testing.

forward_train(img, target, target_weight, img_metas, **kwargs)

Defines the computation performed at every call when training.

init_weights(pretrained=None)

Weight initialization for model.

show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color='green', pose_kpt_color=None, pose_link_color=None, text_color='white', radius=4, thickness=1, font_scale=0.5, bbox_thickness=1, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)

Draw result over img.

Parameters

• img (str or Tensor) – The image to be displayed.

• result (list[dict]) – The results to draw over img (bbox_result, pose_result).

• skeleton (list[list]) – The connection of keypoints. skeleton is 0-based indexing.

• kpt_score_thr (float, optional) – Minimum score of keypoints to be shown. Default: 0.3.

• bbox_color (str or tuple or Color) – Color of bbox lines.

• pose_kpt_color (np.array[Nx3]`) – Color of N keypoints. If None, do not draw keypoints.

• pose_link_color (np.array[Mx3]) – Color of M links. If None, do not draw links.

• text_color (str or tuple or Color) – Color of texts.

• radius (int) – Radius of circles.

• thickness (int) – Thickness of lines.

• font_scale (float) – Font scales of texts.

• win_name (str) – The window name.

• show (bool) – Whether to show the image. Default: False.

• show_keypoint_weight (bool) – Whether to change the transparency using the predicted confidence scores of keypoints.

• wait_time (int) – Value of waitKey param. Default: 0.

• out_file (str or None) – The filename to write the image. Default: None.

Returns

Visualized img, only if not show or out_file.

Return type

Tensor

property with_keypoint

Check if has keypoint_head.

property with_neck

Check if has neck.

class mmpose.models.detectors.VoxelCenterDetector(image_size, heatmap_size, space_size, cube_size, space_center, center_net, center_head, train_cfg=None, test_cfg=None)

Detect human center by 3D CNN on voxels.

Please refer to the paper <https://arxiv.org/abs/2004.06239> for details. :param image_size: input size of the 2D model. :type image_size: list :param heatmap_size: output size of the 2D model. :type heatmap_size: list :param space_size: Size of the 3D space. :type space_size: list :param cube_size: Size of the input volume to the 3D CNN. :type cube_size: list :param space_center: Coordinate of the center of the 3D space. :type space_center: list :param center_net: Dictionary to construct the center net. :type center_net: ConfigDict :param center_head: Dictionary to construct the center head. :type center_head: ConfigDict :param train_cfg: Config for training. Default: None. :type train_cfg: ConfigDict :param test_cfg: Config for testing. Default: None. :type test_cfg: ConfigDict

assign2gt(center_candidates, gt_centers, gt_num_persons)

“Assign gt id to each valid human center candidate.

forward(img, img_metas, return_loss=True, feature_maps=None, targets_3d=None)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH heatmaps width: W heatmaps height: H

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• return_loss – Option to return loss. return loss=True for training, return loss=False for validation & test.

• targets_3d (torch.Tensor[NxcubeLxcubeWxcubeH]) – Ground-truth 3D heatmap of human centers.

• feature_maps (list(torch.Tensor[NxKxHxW])) – Multi-camera feature_maps.

Returns

if ‘return_loss’ is true, then return losses.

Otherwise, return predicted poses

Return type

dict

forward_dummy(feature_maps)

Used for computing network FLOPs.

forward_test(img, img_metas, feature_maps=None)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH heatmaps width: W heatmaps height: H

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• feature_maps (list(torch.Tensor[NxKxHxW])) – Multi-camera feature_maps.

Returns

human centers

forward_train(img, img_metas, feature_maps=None, targets_3d=None, return_preds=False)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH heatmaps width: W heatmaps height: H

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• targets_3d (torch.Tensor[NxcubeLxcubeWxcubeH]) – Ground-truth 3D heatmap of human centers.

• feature_maps (list(torch.Tensor[NxKxHxW])) – Multi-camera feature_maps.

• return_preds (bool) – Whether to return prediction results

Returns

if ‘return_pred’ is true, then return losses

and human centers. Otherwise, return losses only

Return type

dict

show_result(**kwargs)

Visualize the results.

class mmpose.models.detectors.VoxelSinglePose(image_size, heatmap_size, sub_space_size, sub_cube_size, num_joints, pose_net, pose_head, train_cfg=None, test_cfg=None)

VoxelPose Please refer to the paper <https://arxiv.org/abs/2004.06239> for details.

Parameters

• image_size (list) – input size of the 2D model.

• heatmap_size (list) – output size of the 2D model.

• sub_space_size (list) – Size of the cuboid human proposal.

• sub_cube_size (list) – Size of the input volume to the pose net.

• pose_net (ConfigDict) – Dictionary to construct the pose net.

• pose_head (ConfigDict) – Dictionary to construct the pose head.

• train_cfg (ConfigDict) – Config for training. Default: None.

• test_cfg (ConfigDict) – Config for testing. Default: None.

forward(img, img_metas, return_loss=True, feature_maps=None, human_candidates=None, **kwargs)

Note

batch_size: N num_keypoints: K num_img_channel: C img_width: imgW img_height: imgH feature_maps width: W feature_maps height: H volume_length: cubeL volume_width: cubeW volume_height: cubeH

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• feature_maps (list(torch.Tensor[NxCxHxW])) – Multi-camera input feature_maps.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• human_candidates (torch.Tensor[NxPx5]) – Human candidates.

• return_loss – Option to return loss. return loss=True for training, return loss=False for validation & test.

forward_dummy(feature_maps, num_candidates=5)

Used for computing network FLOPs.

forward_test(img, img_metas, feature_maps=None, human_candidates=None, **kwargs)

Defines the computation performed at training. .. note:

batch_size: N
num_keypoints: K
num_img_channel: C
img_width: imgW
img_height: imgH
feature_maps width: W
feature_maps height: H
volume_length: cubeL
volume_width: cubeW
volume_height: cubeH

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• feature_maps (list(torch.Tensor[NxCxHxW])) – Multi-camera input feature_maps.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• human_candidates (torch.Tensor[NxPx5]) – Human candidates.

Returns

predicted poses, human centers and sample_id

Return type

dict

forward_train(img, img_metas, feature_maps=None, human_candidates=None, return_preds=False, **kwargs)

Defines the computation performed at training. .. note:

batch_size: N
num_keypoints: K
num_img_channel: C
img_width: imgW
img_height: imgH
feature_maps width: W
feature_maps height: H
volume_length: cubeL
volume_width: cubeW
volume_height: cubeH

Parameters

• img (list(torch.Tensor[NxCximgHximgW])) – Multi-camera input images to the 2D model.

• feature_maps (list(torch.Tensor[NxCxHxW])) – Multi-camera input feature_maps.

• img_metas (list(dict)) – Information about image, 3D groundtruth and camera parameters.

• human_candidates (torch.Tensor[NxPx5]) – Human candidates.

• return_preds (bool) – Whether to return prediction results

Returns

losses.

Return type

dict

show_result(**kwargs)

Visualize the results.

heads

class mmpose.models.heads.AEHigherResolutionHead(in_channels, num_joints, tag_per_joint=True, extra=None, num_deconv_layers=1, num_deconv_filters=(32), num_deconv_kernels=(4), num_basic_blocks=4, cat_output=None, with_ae_loss=None, loss_keypoint=None)

Associative embedding with higher resolution head. paper ref: Bowen Cheng et al. “HigherHRNet: Scale-Aware Representation Learning for Bottom- Up Human Pose Estimation”.

Parameters

• in_channels (int) – Number of input channels.

• num_joints (int) – Number of joints

• tag_per_joint (bool) – If tag_per_joint is True, the dimension of tags equals to num_joints, else the dimension of tags is 1. Default: True

• extra (dict) – Configs for extra conv layers. Default: None

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• cat_output (list[bool]) – Option to concat outputs.

• with_ae_loss (list[bool]) – Option to use ae loss.

• loss_keypoint (dict) – Config for loss. Default: None.

forward(x)

Forward function.

get_loss(outputs, targets, masks, joints)

Calculate bottom-up keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• num_outputs: O

• heatmaps height: H

• heatmaps weight: W

Parameters

• outputs (list(torch.Tensor[N,K,H,W])) – Multi-scale output heatmaps.

• targets (List(torch.Tensor[N,K,H,W])) – Multi-scale target heatmaps.

• masks (List(torch.Tensor[N,H,W])) – Masks of multi-scale target heatmaps

• joints (List(torch.Tensor[N,M,K,2])) – Joints of multi-scale target heatmaps for ae loss

init_weights()

Initialize model weights.

class mmpose.models.heads.AEMultiStageHead(in_channels, out_channels, num_stages=1, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), extra=None, loss_keypoint=None)

Associative embedding multi-stage head. paper ref: Alejandro Newell et al. “Associative Embedding: End-to-end Learning for Joint Detection and Grouping”

Parameters

• in_channels (int) – Number of input channels.

• out_channels (int) – Number of output channels.

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• loss_keypoint (dict) – Config for loss. Default: None.

forward(x)

Forward function.

Returns

a list of heatmaps from multiple stages.

Return type

out (list[Tensor])

get_loss(output, targets, masks, joints)

Calculate bottom-up keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (List(torch.Tensor[NxKxHxW])) – Output heatmaps.

• targets (List(List(torch.Tensor[NxKxHxW]))) – Multi-stage and multi-scale target heatmaps.

• masks (List(List(torch.Tensor[NxHxW]))) – Masks of multi-stage and multi-scale target heatmaps

• joints (List(List(torch.Tensor[NxMxKx2]))) – Joints of multi-stage multi-scale target heatmaps for ae loss

init_weights()

Initialize model weights.

class mmpose.models.heads.AESimpleHead(in_channels, num_joints, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), tag_per_joint=True, with_ae_loss=None, extra=None, loss_keypoint=None)

Associative embedding simple head. paper ref: Alejandro Newell et al. “Associative Embedding: End-to-end Learning for Joint Detection and Grouping”

Parameters

• in_channels (int) – Number of input channels.

• num_joints (int) – Number of joints.

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• tag_per_joint (bool) – If tag_per_joint is True, the dimension of tags equals to num_joints, else the dimension of tags is 1. Default: True

• with_ae_loss (list[bool]) – Option to use ae loss or not.

• loss_keypoint (dict) – Config for loss. Default: None.

get_loss(outputs, targets, masks, joints)

Calculate bottom-up keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• num_outputs: O

• heatmaps height: H

• heatmaps weight: W

Parameters

• outputs (list(torch.Tensor[N,K,H,W])) – Multi-scale output heatmaps.

• targets (List(torch.Tensor[N,K,H,W])) – Multi-scale target heatmaps.

• masks (List(torch.Tensor[N,H,W])) – Masks of multi-scale target heatmaps

• joints (List(torch.Tensor[N,M,K,2])) – Joints of multi-scale target heatmaps for ae loss

class mmpose.models.heads.CIDHead(in_channels, gfd_channels, num_joints, multi_hm_loss_factor=1.0, single_hm_loss_factor=4.0, contrastive_loss_factor=1.0, max_train_instances=200, prior_prob=0.01)

CID head. paper ref: Dongkai Wang et al. “Contextual Instance Decouple for Robust Multi-Person Pose Estimation”.

Parameters

• in_channels (int) – Number of input channels.

• gfd_channels (int) – Number of instance feature map channels

• num_joints (int) – Number of joints

• multi_hm_loss_factor (float) – loss weight for multi-person heatmap

• single_hm_loss_factor (float) – loss weight for single person heatmap

• contrastive_loss_factor (float) – loss weight for contrastive loss

• max_train_instances (int) – limit the number of instances

• training to avoid (during) –

• prior_prob (float) – focal loss bias initialization

forward(features, forward_info=None)

Forward function.

init_weights()

Initialize model weights.

class mmpose.models.heads.CuboidCenterHead(space_size, space_center, cube_size, max_num=10, max_pool_kernel=3)

Get results from the 3D human center heatmap. In this module, human 3D centers are local maximums obtained from the 3D heatmap via NMS (max- pooling).

Parameters

• space_size (list[3]) – The size of the 3D space.

• cube_size (list[3]) – The size of the heatmap volume.

• space_center (list[3]) – The coordinate of space center.

• max_num (int) – Maximum of human center detections.

• max_pool_kernel (int) – Kernel size of the max-pool kernel in nms.

forward(heatmap_volumes)

Parameters

heatmap_volumes (torch.Tensor(NXLXWXH)) – 3D human center heatmaps predicted by the network.

Returns

Coordinates of human centers.

Return type

human_centers (torch.Tensor(NXPX5))

class mmpose.models.heads.CuboidPoseHead(beta)

forward(heatmap_volumes, grid_coordinates)

Parameters

• heatmap_volumes (torch.Tensor(NxKxLxWxH)) – 3D human pose heatmaps predicted by the network.

• grid_coordinates (torch.Tensor(Nx(LxWxH)x3)) – Coordinates of the grids in the heatmap volumes.

Returns

Coordinates of human poses.

Return type

human_poses (torch.Tensor(NxKx3))

class mmpose.models.heads.DEKRHead(in_channels, num_joints, num_heatmap_filters=32, num_offset_filters_per_joint=15, in_index=0, input_transform=None, num_deconv_layers=0, num_deconv_filters=None, num_deconv_kernels=None, extra={'final_conv_kernel': 0}, align_corners=False, heatmap_loss=None, offset_loss=None)

DisEntangled Keypoint Regression head. “Bottom-up human pose estimation via disentangled keypoint regression”, CVPR’2021.

Parameters

• in_channels (int) – Number of input channels.

• num_joints (int) – Number of joints.

• num_heatmap_filters (int) – Number of filters for heatmap branch.

• num_offset_filters_per_joint (int) – Number of filters for each joint.

• in_index (int|Sequence[int]) – Input feature index. Default: 0

• input_transform (str|None) –

  Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, None. Default: None.

  • ’resize_concat’: Multiple feature maps will be resized to the

    same size as the first one and then concat together. Usually used in FCN head of HRNet.

  • ’multiple_select’: Multiple feature maps will be bundle into

    a list and passed into decode head.

  • None: Only one select feature map is allowed.

• align_corners (bool) – align_corners argument of F.interpolate. Default: False.

• heatmap_loss (dict) – Config for heatmap loss. Default: None.

• offset_loss (dict) – Config for offset loss. Default: None.

forward(x)

Forward function.

get_loss(outputs, heatmaps, masks, offsets, offset_weights)

Calculate the dekr loss.

Note

• batch_size: N

• num_channels: C

• num_joints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• outputs (List(torch.Tensor[N,C,H,W])) – Multi-scale outputs.

• heatmaps (List(torch.Tensor[N,K+1,H,W])) – Multi-scale heatmap targets.

• masks (List(torch.Tensor[N,K+1,H,W])) – Weights of multi-scale heatmap targets.

• offsets (List(torch.Tensor[N,K*2,H,W])) – Multi-scale offset targets.

• offset_weights (List(torch.Tensor[N,K*2,H,W])) – Weights of multi-scale offset targets.

init_weights()

Initialize model weights.

class mmpose.models.heads.DeconvHead(in_channels=3, out_channels=17, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), extra=None, in_index=0, input_transform=None, align_corners=False, loss_keypoint=None)

Simple deconv head.

Parameters

• in_channels (int) – Number of input channels.

• out_channels (int) – Number of output channels.

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• in_index (int|Sequence[int]) – Input feature index. Default: 0

• input_transform (str|None) –

  Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, None. Default: None.

  • ’resize_concat’: Multiple feature maps will be resized to the

    same size as the first one and then concat together. Usually used in FCN head of HRNet.

  • ’multiple_select’: Multiple feature maps will be bundle into

    a list and passed into decode head.

  • None: Only one select feature map is allowed.

• align_corners (bool) – align_corners argument of F.interpolate. Default: False.

• loss_keypoint (dict) – Config for loss. Default: None.

forward(x)

Forward function.

get_loss(outputs, targets, masks)

Calculate bottom-up masked mse loss.

Note

• batch_size: N

• num_channels: C

• heatmaps height: H

• heatmaps weight: W

Parameters

• outputs (List(torch.Tensor[N,C,H,W])) – Multi-scale outputs.

• targets (List(torch.Tensor[N,C,H,W])) – Multi-scale targets.

• masks (List(torch.Tensor[N,H,W])) – Masks of multi-scale targets.

init_weights()

Initialize model weights.

class mmpose.models.heads.DeepposeRegressionHead(in_channels, num_joints, loss_keypoint=None, out_sigma=False, train_cfg=None, test_cfg=None)

Deeppose regression head with fully connected layers.

“DeepPose: Human Pose Estimation via Deep Neural Networks”.

Parameters

• in_channels (int) – Number of input channels

• num_joints (int) – Number of joints

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

• out_sigma (bool) – Predict the sigma (the viriance of the joint location) together with the joint location. Default: False

decode(img_metas, output, **kwargs)

Decode the keypoints from output regression.

Parameters

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

• output (np.ndarray[N, K, >=2]) – predicted regression vector.

• kwargs – dict contains ‘img_size’. img_size (tuple(img_width, img_height)): input image size.

forward(x)

Forward function.

get_accuracy(output, target, target_weight)

Calculate accuracy for top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

Parameters

• output (torch.Tensor[N, K, 2 or 4]) – Output keypoints.

• target (torch.Tensor[N, K, 2]) – Target keypoints.

• target_weight (torch.Tensor[N, K, 2]) – Weights across different joint types.

get_loss(output, target, target_weight)

Calculate top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

Parameters

• output (torch.Tensor[N, K, 2 or 4]) – Output keypoints.

• target (torch.Tensor[N, K, 2]) – Target keypoints.

• target_weight (torch.Tensor[N, K, 2]) – Weights across different joint types.

inference_model(x, flip_pairs=None)

Inference function.

Returns

Output regression.

Return type

output_regression (np.ndarray)

Parameters

• x (torch.Tensor[N, K, 2]) – Input features.

• flip_pairs (None | list[tuple()) – Pairs of keypoints which are mirrored.

class mmpose.models.heads.HMRMeshHead(in_channels, smpl_mean_params=None, n_iter=3)

SMPL parameters regressor head of simple baseline. “End-to-end Recovery of Human Shape and Pose”, CVPR’2018.

Parameters

• in_channels (int) – Number of input channels

• smpl_mean_params (str) – The file name of the mean SMPL parameters

• n_iter (int) – The iterations of estimating delta parameters

forward(x)

Forward function.

x is the image feature map and is expected to be in shape (batch size x channel number x height x width)

init_weights()

Initialize model weights.

class mmpose.models.heads.Interhand3DHead(keypoint_head_cfg, root_head_cfg, hand_type_head_cfg, loss_keypoint=None, loss_root_depth=None, loss_hand_type=None, train_cfg=None, test_cfg=None)

Interhand 3D head of paper ref: Gyeongsik Moon. “InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image”.

Parameters

• keypoint_head_cfg (dict) – Configs of Heatmap3DHead for hand keypoint estimation.

• root_head_cfg (dict) – Configs of Heatmap1DHead for relative hand root depth estimation.

• hand_type_head_cfg (dict) – Configs of MultilabelClassificationHead for hand type classification.

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

• loss_root_depth (dict) – Config for relative root depth loss. Default: None.

• loss_hand_type (dict) – Config for hand type classification loss. Default: None.

decode(img_metas, output, **kwargs)

Decode hand keypoint, relative root depth and hand type.

Parameters

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

  • ”heatmap3d_depth_bound”: depth bound of hand keypoint

    3D heatmap

  • ”root_depth_bound”: depth bound of relative root depth

    1D heatmap

• output (list[np.ndarray]) – model predicted 3D heatmaps, relative root depth and hand type.

forward(x)

Forward function.

get_accuracy(output, target, target_weight)

Calculate accuracy for hand type.

Parameters

• output (list[Tensor]) – a list of outputs from multiple heads.

• target (list[Tensor]) – a list of targets for multiple heads.

• target_weight (list[Tensor]) – a list of targets weight for multiple heads.

get_loss(output, target, target_weight)

Calculate loss for hand keypoint heatmaps, relative root depth and hand type.

Parameters

• output (list[Tensor]) – a list of outputs from multiple heads.

• target (list[Tensor]) – a list of targets for multiple heads.

• target_weight (list[Tensor]) – a list of targets weight for multiple heads.

inference_model(x, flip_pairs=None)

Inference function.

Returns

list of output hand keypoint heatmaps, relative root depth and hand type.

Return type

output (list[np.ndarray])

Parameters

• x (torch.Tensor[N,K,H,W]) – Input features.

• flip_pairs (None | list[tuple()) – Pairs of keypoints which are mirrored.

class mmpose.models.heads.MultiModalSSAHead(num_classes, modality, in_channels=1024, avg_pool_kernel=(1, 7, 7), dropout_prob=0.0, train_cfg=None, test_cfg=None, **kwargs)

Sparial-temporal Semantic Alignment Head proposed in “Improving the performance of unimodal dynamic hand-gesture recognition with multimodal training”,

Please refer to the paper for details.

Parameters

• num_classes (int) – number of classes.

• modality (list[str]) – modalities of input videos for backbone.

• in_channels (int) – number of channels of feature maps. Default: 1024

• avg_pool_kernel (tuple[int]) – kernel size of pooling layer. Default: (1, 7, 7)

• dropout_prob (float) – probablity to use dropout on input feature map. Default: 0

• train_cfg (dict) – training config.

• test_cfg (dict) – testing config.

forward(x, img_metas)

Forward function.

get_accuracy(logits, label, img_metas)

Compute the accuracy of predicted gesture.

Note

• batch_size: N

• number of classes: nC

• logit length: L

Parameters

• logits (list[NxnCxL]) – predicted logits for each modality.

• label (list(dict)) – Category label.

• img_metas (list(dict)) – Information about data. By default this includes: - “fps: video frame rate - “modality”: modality of input videos

Returns

computed accuracy for each modality.

Return type

dict[str, torch.tensor]

get_loss(logits, label, fmaps=None)

Compute the Cross Entropy loss and SSA loss.

Note

• batch_size: N

• number of classes: nC

• feature map channel: C

• feature map height: H

• feature map width: W

• feature map length: L

• logit length: Lg

Parameters

• logits (list[NxnCxLg]) – predicted logits for each modality.

• label (list(dict)) – Category label.

• fmaps (list[torch.Tensor[NxCxLxHxW]]) – feature maps for each modality.

Returns

computed losses.

Return type

dict[str, torch.tensor]

init_weights()

Initialize model weights.

set_train_epoch(epoch: int)

set the epoch to control the activation of SSA loss.

class mmpose.models.heads.TemporalRegressionHead(in_channels, num_joints, max_norm=None, loss_keypoint=None, is_trajectory=False, train_cfg=None, test_cfg=None)

Regression head of VideoPose3D.

“3D human pose estimation in video with temporal convolutions and semi-supervised training”, CVPR’2019.

Parameters

• in_channels (int) – Number of input channels

• num_joints (int) – Number of joints

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

• max_norm (float|None) – if not None, the weight of convolution layers will be clipped to have a maximum norm of max_norm.

• is_trajectory (bool) – If the model only predicts root joint position, then this arg should be set to True. In this case, traj_loss will be calculated. Otherwise, it should be set to False. Default: False.

decode(metas, output)

Decode the keypoints from output regression.

Parameters

• metas (list(dict)) –

  Information about data augmentation. By default this includes:

  • ”target_image_path”: path to the image file

• output (np.ndarray[N, K, 3]) – predicted regression vector.

• metas –

  Information about data augmentation including:

  • target_image_path (str): Optional, path to the image file

  • target_mean (float): Optional, normalization parameter of

    the target pose.

  • target_std (float): Optional, normalization parameter of the

    target pose.

  • root_position (np.ndarray[3,1]): Optional, global

    position of the root joint.

  • root_index (torch.ndarray[1,]): Optional, original index of

    the root joint before root-centering.

forward(x)

Forward function.

get_accuracy(output, target, target_weight, metas)

Calculate accuracy for keypoint loss.

Note

• batch_size: N

• num_keypoints: K

Parameters

• output (torch.Tensor[N, K, 3]) – Output keypoints.

• target (torch.Tensor[N, K, 3]) – Target keypoints.

• target_weight (torch.Tensor[N, K, 3]) – Weights across different joint types.

• metas (list(dict)) –

  Information about data augmentation including:

  • target_image_path (str): Optional, path to the image file

  • target_mean (float): Optional, normalization parameter of

    the target pose.

  • target_std (float): Optional, normalization parameter of the

    target pose.

  • root_position (np.ndarray[3,1]): Optional, global

    position of the root joint.

  • root_index (torch.ndarray[1,]): Optional, original index of

    the root joint before root-centering.

get_loss(output, target, target_weight)

Calculate keypoint loss.

Note

• batch_size: N

• num_keypoints: K

Parameters

• output (torch.Tensor[N, K, 3]) – Output keypoints.

• target (torch.Tensor[N, K, 3]) – Target keypoints.

• target_weight (torch.Tensor[N, K, 3]) – Weights across different joint types. If self.is_trajectory is True and target_weight is None, target_weight will be set inversely proportional to joint depth.

inference_model(x, flip_pairs=None)

Inference function.

Returns

Output regression.

Return type

output_regression (np.ndarray)

Parameters

• x (torch.Tensor[N, K, 2]) – Input features.

• flip_pairs (None | list[tuple()) – Pairs of keypoints which are mirrored.

init_weights()

Initialize the weights.

class mmpose.models.heads.TopdownHeatmapBaseHead

Base class for top-down heatmap heads.

All top-down heatmap heads should subclass it. All subclass should overwrite:

Methods:get_loss, supporting to calculate loss. Methods:get_accuracy, supporting to calculate accuracy. Methods:forward, supporting to forward model. Methods:inference_model, supporting to inference model.

decode(img_metas, output, **kwargs)

Decode keypoints from heatmaps.

Parameters

• img_metas (list(dict)) –

  Information about data augmentation By default this includes:

  • ”image_file: path to the image file

  • ”center”: center of the bbox

  • ”scale”: scale of the bbox

  • ”rotation”: rotation of the bbox

  • ”bbox_score”: score of bbox

• output (np.ndarray[N, K, H, W]) – model predicted heatmaps.

abstract forward(**kwargs)

Forward function.

abstract get_accuracy(**kwargs)

Gets the accuracy.

abstract get_loss(**kwargs)

Gets the loss.

abstract inference_model(**kwargs)

Inference function.

class mmpose.models.heads.TopdownHeatmapMSMUHead(out_shape, unit_channels=256, out_channels=17, num_stages=4, num_units=4, use_prm=False, norm_cfg={'type': 'BN'}, loss_keypoint=None, train_cfg=None, test_cfg=None)

Heads for multi-stage multi-unit heads used in Multi-Stage Pose estimation Network (MSPN), and Residual Steps Networks (RSN).

Parameters

• unit_channels (int) – Number of input channels.

• out_channels (int) – Number of output channels.

• out_shape (tuple) – Shape of the output heatmap.

• num_stages (int) – Number of stages.

• num_units (int) – Number of units in each stage.

• use_prm (bool) – Whether to use pose refine machine (PRM). Default: False.

• norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’)

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

forward(x)

Forward function.

Returns

a list of heatmaps from multiple stages

and units.

Return type

out (list[Tensor])

get_accuracy(output, target, target_weight)

Calculate accuracy for top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

get_loss(output, target, target_weight)

Calculate top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• num_outputs: O

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,O,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,O,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,O,K,1]) – Weights across different joint types.

inference_model(x, flip_pairs=None)

Inference function.

Returns

Output heatmaps.

Return type

output_heatmap (np.ndarray)

Parameters

• x (list[torch.Tensor[N,K,H,W]]) – Input features.

• flip_pairs (None | list[tuple]) – Pairs of keypoints which are mirrored.

init_weights()

Initialize model weights.

class mmpose.models.heads.TopdownHeatmapMultiStageHead(in_channels=512, out_channels=17, num_stages=1, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), extra=None, loss_keypoint=None, train_cfg=None, test_cfg=None)

Top-down heatmap multi-stage head.

TopdownHeatmapMultiStageHead is consisted of multiple branches, each of which has num_deconv_layers(>=0) number of deconv layers and a simple conv2d layer.

Parameters

• in_channels (int) – Number of input channels.

• out_channels (int) – Number of output channels.

• num_stages (int) – Number of stages.

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

forward(x)

Forward function.

Returns

a list of heatmaps from multiple stages.

Return type

out (list[Tensor])

get_accuracy(output, target, target_weight)

Calculate accuracy for top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

get_loss(output, target, target_weight)

Calculate top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• num_outputs: O

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

inference_model(x, flip_pairs=None)

Inference function.

Returns

Output heatmaps.

Return type

output_heatmap (np.ndarray)

Parameters

• x (List[torch.Tensor[NxKxHxW]]) – Input features.

• flip_pairs (None | list[tuple()) – Pairs of keypoints which are mirrored.

init_weights()

Initialize model weights.

class mmpose.models.heads.TopdownHeatmapSimpleHead(in_channels, out_channels, num_deconv_layers=3, num_deconv_filters=(256, 256, 256), num_deconv_kernels=(4, 4, 4), extra=None, in_index=0, input_transform=None, align_corners=False, loss_keypoint=None, train_cfg=None, test_cfg=None)

Top-down heatmap simple head. paper ref: Bin Xiao et al. Simple Baselines for Human Pose Estimation and Tracking.

TopdownHeatmapSimpleHead is consisted of (>=0) number of deconv layers and a simple conv2d layer.

Parameters

• in_channels (int) – Number of input channels

• out_channels (int) – Number of output channels

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• in_index (int|Sequence[int]) – Input feature index. Default: 0

• input_transform (str|None) –

  Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, None. Default: None.

  • ’resize_concat’: Multiple feature maps will be resized to the

    same size as the first one and then concat together. Usually used in FCN head of HRNet.

  • ’multiple_select’: Multiple feature maps will be bundle into

    a list and passed into decode head.

  • None: Only one select feature map is allowed.

• align_corners (bool) – align_corners argument of F.interpolate. Default: False.

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

forward(x)

Forward function.

get_accuracy(output, target, target_weight)

Calculate accuracy for top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

get_loss(output, target, target_weight)

Calculate top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

inference_model(x, flip_pairs=None)

Inference function.

Returns

Output heatmaps.

Return type

output_heatmap (np.ndarray)

Parameters

• x (torch.Tensor[N,K,H,W]) – Input features.

• flip_pairs (None | list[tuple]) – Pairs of keypoints which are mirrored.

init_weights()

Initialize model weights.

class mmpose.models.heads.ViPNASHeatmapSimpleHead(in_channels, out_channels, num_deconv_layers=3, num_deconv_filters=(144, 144, 144), num_deconv_kernels=(4, 4, 4), num_deconv_groups=(16, 16, 16), extra=None, in_index=0, input_transform=None, align_corners=False, loss_keypoint=None, train_cfg=None, test_cfg=None)

ViPNAS heatmap simple head.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search. More details can be found in the paper .

TopdownHeatmapSimpleHead is consisted of (>=0) number of deconv layers and a simple conv2d layer.

Parameters

• in_channels (int) – Number of input channels

• out_channels (int) – Number of output channels

• num_deconv_layers (int) – Number of deconv layers. num_deconv_layers should >= 0. Note that 0 means no deconv layers.

• num_deconv_filters (list|tuple) – Number of filters. If num_deconv_layers > 0, the length of

• num_deconv_kernels (list|tuple) – Kernel sizes.

• num_deconv_groups (list|tuple) – Group number.

• in_index (int|Sequence[int]) – Input feature index. Default: -1

• input_transform (str|None) –

  Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, None. Default: None.

  • ’resize_concat’: Multiple feature maps will be resize to the

    same size as first one and than concat together. Usually used in FCN head of HRNet.

  • ’multiple_select’: Multiple feature maps will be bundle into

    a list and passed into decode head.

  • None: Only one select feature map is allowed.

• align_corners (bool) – align_corners argument of F.interpolate. Default: False.

• loss_keypoint (dict) – Config for keypoint loss. Default: None.

forward(x)

Forward function.

get_accuracy(output, target, target_weight)

Calculate accuracy for top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

get_loss(output, target, target_weight)

Calculate top-down keypoint loss.

Note

• batch_size: N

• num_keypoints: K

• heatmaps height: H

• heatmaps weight: W

Parameters

• output (torch.Tensor[N,K,H,W]) – Output heatmaps.

• target (torch.Tensor[N,K,H,W]) – Target heatmaps.

• target_weight (torch.Tensor[N,K,1]) – Weights across different joint types.

inference_model(x, flip_pairs=None)

Inference function.

Returns

Output heatmaps.

Return type

output_heatmap (np.ndarray)

Parameters

• x (torch.Tensor[N,K,H,W]) – Input features.

• flip_pairs (None | list[tuple]) – Pairs of keypoints which are mirrored.

init_weights()

Initialize model weights.

losses

class mmpose.models.losses.AELoss(loss_type)

Associative Embedding loss.

Associative Embedding: End-to-End Learning for Joint Detection and Grouping.

forward(tags, joints)

Accumulate the tag loss for each image in the batch.

Note

• batch_size: N

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

• num_keypoints: K

Parameters

• tags (torch.Tensor[N,KxHxW,1]) – tag channels of output.

• joints (torch.Tensor[N,M,K,2]) – joints information.

singleTagLoss(pred_tag, joints)

Associative embedding loss for one image.

Note

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

• num_keypoints: K

Parameters

• pred_tag (torch.Tensor[KxHxW,1]) – tag of output for one image.

• joints (torch.Tensor[M,K,2]) – joints information for one image.

class mmpose.models.losses.AdaptiveWingLoss(alpha=2.1, omega=14, epsilon=1, theta=0.5, use_target_weight=False, loss_weight=1.0)

Adaptive wing loss. paper ref: ‘Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression’ Wang et al. ICCV’2019.

Parameters

• alpha (float), omega (float), epsilon (float), theta (float) – are hyper-parameters.

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

criterion(pred, target)

Criterion of wingloss.

Note

batch_size: N num_keypoints: K

Parameters

• pred (torch.Tensor[NxKxHxW]) – Predicted heatmaps.

• target (torch.Tensor[NxKxHxW]) – Target heatmaps.

forward(output, target, target_weight)

Forward function.

Note

batch_size: N num_keypoints: K

Parameters

• output (torch.Tensor[NxKxHxW]) – Output heatmaps.

• target (torch.Tensor[NxKxHxW]) – Target heatmaps.

• target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.

class mmpose.models.losses.BCELoss(use_target_weight=False, loss_weight=1.0)

Binary Cross Entropy loss.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_labels: K

Parameters

• output (torch.Tensor[N, K]) – Output classification.

• target (torch.Tensor[N, K]) – Target classification.

• target_weight (torch.Tensor[N, K] or torch.Tensor[N]) – Weights across different labels.

class mmpose.models.losses.BoneLoss(joint_parents, use_target_weight=False, loss_weight=1.0)

Bone length loss.

Parameters

• joint_parents (list) – Indices of each joint’s parent joint.

• use_target_weight (bool) – Option to use weighted bone loss. Different bone types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N, K-1]) – Weights across different bone types.

class mmpose.models.losses.FocalHeatmapLoss(alpha=2, beta=4)

forward(pred, gt, mask=None)

Modified focal loss.

Exactly the same as CornerNet. Runs faster and costs a little bit more memory :param pred: :type pred: batch x c x h x w :param gt_regr: :type gt_regr: batch x c x h x w

class mmpose.models.losses.GANLoss(gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0)

Define GAN loss.

Parameters

• gan_type (str) – Support ‘vanilla’, ‘lsgan’, ‘wgan’, ‘hinge’.

• real_label_val (float) – The value for real label. Default: 1.0.

• fake_label_val (float) – The value for fake label. Default: 0.0.

• loss_weight (float) – Loss weight. Default: 1.0. Note that loss_weight is only for generators; and it is always 1.0 for discriminators.

forward(input, target_is_real, is_disc=False)

Parameters

• input (Tensor) – The input for the loss module, i.e., the network prediction.

• target_is_real (bool) – Whether the targe is real or fake.

• is_disc (bool) – Whether the loss for discriminators or not. Default: False.

Returns

GAN loss value.

Return type

Tensor

get_target_label(input, target_is_real)

Get target label.

Parameters

• input (Tensor) – Input tensor.

• target_is_real (bool) – Whether the target is real or fake.

Returns

Target tensor. Return bool for wgan, otherwise, return Tensor.

Return type

(bool | Tensor)

class mmpose.models.losses.HeatmapLoss(supervise_empty=True)

Accumulate the heatmap loss for each image in the batch.

Parameters

supervise_empty (bool) – Whether to supervise empty channels.

forward(pred, gt, mask)

Forward function.

Note

• batch_size: N

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

• num_keypoints: K

Parameters

• pred (torch.Tensor[N,K,H,W]) – heatmap of output.

• gt (torch.Tensor[N,K,H,W]) – target heatmap.

• mask (torch.Tensor[N,H,W]) – mask of target.

class mmpose.models.losses.JointsMSELoss(use_target_weight=False, loss_weight=1.0)

MSE loss for heatmaps.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

forward(output, target, target_weight)

Forward function.

class mmpose.models.losses.JointsOHKMMSELoss(use_target_weight=False, topk=8, loss_weight=1.0)

MSE loss with online hard keypoint mining.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• topk (int) – Only top k joint losses are kept.

• loss_weight (float) – Weight of the loss. Default: 1.0.

forward(output, target, target_weight)

Forward function.

class mmpose.models.losses.L1Loss(use_target_weight=False, loss_weight=1.0)

L1Loss loss .

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

Parameters

• output (torch.Tensor[N, K, 2]) – Output regression.

• target (torch.Tensor[N, K, 2]) – Target regression.

• target_weight (torch.Tensor[N, K, 2]) – Weights across different joint types.

class mmpose.models.losses.MPJPELoss(use_target_weight=False, loss_weight=1.0)

MPJPE (Mean Per Joint Position Error) loss.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N,K,D]) – Weights across different joint types.

class mmpose.models.losses.MSELoss(use_target_weight=False, loss_weight=1.0)

MSE loss for coordinate regression.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

Parameters

• output (torch.Tensor[N, K, 2]) – Output regression.

• target (torch.Tensor[N, K, 2]) – Target regression.

• target_weight (torch.Tensor[N, K, 2]) – Weights across different joint types.

class mmpose.models.losses.MeshLoss(joints_2d_loss_weight, joints_3d_loss_weight, vertex_loss_weight, smpl_pose_loss_weight, smpl_beta_loss_weight, img_res, focal_length=5000)

Mix loss for 3D human mesh. It is composed of loss on 2D joints, 3D joints, mesh vertices and smpl parameters (if any).

Parameters

• joints_2d_loss_weight (float) – Weight for loss on 2D joints.

• joints_3d_loss_weight (float) – Weight for loss on 3D joints.

• vertex_loss_weight (float) – Weight for loss on 3D verteices.

• smpl_pose_loss_weight (float) – Weight for loss on SMPL pose parameters.

• smpl_beta_loss_weight (float) – Weight for loss on SMPL shape parameters.

• img_res (int) – Input image resolution.

• focal_length (float) – Focal length of camera model. Default=5000.

forward(output, target)

Forward function.

Parameters

• output (dict) – dict of network predicted results. Keys: ‘vertices’, ‘joints_3d’, ‘camera’, ‘pose’(optional), ‘beta’(optional)

• target (dict) – dict of ground-truth labels. Keys: ‘vertices’, ‘joints_3d’, ‘joints_3d_visible’, ‘joints_2d’, ‘joints_2d_visible’, ‘pose’, ‘beta’, ‘has_smpl’

Returns

dict of losses.

Return type

dict

joints_2d_loss(pred_joints_2d, gt_joints_2d, joints_2d_visible)

Compute 2D reprojection loss on the joints.

The loss is weighted by joints_2d_visible.

joints_3d_loss(pred_joints_3d, gt_joints_3d, joints_3d_visible)

Compute 3D joints loss for the examples that 3D joint annotations are available.

The loss is weighted by joints_3d_visible.

project_points(points_3d, camera)

Perform orthographic projection of 3D points using the camera parameters, return projected 2D points in image plane.

Note

• batch size: B

• point number: N

Parameters

• points_3d (Tensor([B, N, 3])) – 3D points.

• camera (Tensor([B, 3])) – camera parameters with the 3 channel as (scale, translation_x, translation_y)

Returns

projected 2D points in image space.

Return type

Tensor([B, N, 2])

smpl_losses(pred_rotmat, pred_betas, gt_pose, gt_betas, has_smpl)

Compute SMPL parameters loss for the examples that SMPL parameter annotations are available.

The loss is weighted by has_smpl.

vertex_loss(pred_vertices, gt_vertices, has_smpl)

Compute 3D vertex loss for the examples that 3D human mesh annotations are available.

The loss is weighted by the has_smpl.

class mmpose.models.losses.MultiLossFactory(num_joints, num_stages, ae_loss_type, with_ae_loss, push_loss_factor, pull_loss_factor, with_heatmaps_loss, heatmaps_loss_factor, supervise_empty=True)

Loss for bottom-up models.

Parameters

• num_joints (int) – Number of keypoints.

• num_stages (int) – Number of stages.

• ae_loss_type (str) – Type of ae loss.

• with_ae_loss (list[bool]) – Use ae loss or not in multi-heatmap.

• push_loss_factor (list[float]) – Parameter of push loss in multi-heatmap.

• pull_loss_factor (list[float]) – Parameter of pull loss in multi-heatmap.

• with_heatmap_loss (list[bool]) – Use heatmap loss or not in multi-heatmap.

• heatmaps_loss_factor (list[float]) – Parameter of heatmap loss in multi-heatmap.

• supervise_empty (bool) – Whether to supervise empty channels.

forward(outputs, heatmaps, masks, joints)

Forward function to calculate losses.

Note

• batch_size: N

• heatmaps weight: W

• heatmaps height: H

• max_num_people: M

• num_keypoints: K

• output_channel: C C=2K if use ae loss else K

Parameters

• outputs (list(torch.Tensor[N,C,H,W])) – outputs of stages.

• heatmaps (list(torch.Tensor[N,K,H,W])) – target of heatmaps.

• masks (list(torch.Tensor[N,H,W])) – masks of heatmaps.

• joints (list(torch.Tensor[N,M,K,2])) – joints of ae loss.

class mmpose.models.losses.RLELoss(use_target_weight=False, size_average=True, residual=True, q_dis='laplace')

RLE Loss.

Human Pose Regression With Residual Log-Likelihood Estimation arXiv:.

Code is modified from the official implementation.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• size_average (bool) – Option to average the loss by the batch_size.

• residual (bool) – Option to add L1 loss and let the flow learn the residual error distribution.

• q_dis (string) – Option for the identity Q(error) distribution, Options: “laplace” or “gaussian”

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D*2]) – Output regression, including coords and sigmas.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N, K, D]) – Weights across different joint types.

class mmpose.models.losses.SemiSupervisionLoss(joint_parents, projection_loss_weight=1.0, bone_loss_weight=1.0, warmup_iterations=0)

Semi-supervision loss for unlabeled data. It is composed of projection loss and bone loss.

Paper ref: 3D human pose estimation in video with temporal convolutions and semi-supervised training Dario Pavllo et al. CVPR’2019.

Parameters

• joint_parents (list) – Indices of each joint’s parent joint.

• projection_loss_weight (float) – Weight for projection loss.

• bone_loss_weight (float) – Weight for bone loss.

• warmup_iterations (int) – Number of warmup iterations. In the first warmup_iterations iterations, the model is trained only on labeled data, and semi-supervision loss will be 0. This is a workaround since currently we cannot access epoch number in loss functions. Note that the iteration number in an epoch can be changed due to different GPU numbers in multi-GPU settings. So please set this parameter carefully. warmup_iterations = dataset_size // samples_per_gpu // gpu_num * warmup_epochs

forward(output, target)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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 registered hooks while the latter silently ignores them.

static project_joints(x, intrinsics)

Project 3D joint coordinates to 2D image plane using camera intrinsic parameters.

Parameters

• x (torch.Tensor[N, K, 3]) – 3D joint coordinates.

• intrinsics (torch.Tensor[N, 4] | torch.Tensor[N, 9]) – Camera intrinsics: f (2), c (2), k (3), p (2).

class mmpose.models.losses.SmoothL1Loss(use_target_weight=False, loss_weight=1.0)

SmoothL1Loss loss.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N, K, D]) – Weights across different joint types.

class mmpose.models.losses.SoftWeightSmoothL1Loss(use_target_weight=False, supervise_empty=True, beta=1.0, loss_weight=1.0)

Smooth L1 loss with soft weight for regression.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• supervise_empty (bool) – Whether to supervise the output with zero weight.

• beta (float) – Specifies the threshold at which to change between L1 and L2 loss.

• loss_weight (float) – Weight of the loss. Default: 1.0.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N, K, D]) – Weights across different joint types.

static smooth_l1_loss(input, target, reduction='none', beta=1.0)

Re-implement torch.nn.functional.smooth_l1_loss with beta to support pytorch <= 1.6.

class mmpose.models.losses.SoftWingLoss(omega1=2.0, omega2=20.0, epsilon=0.5, use_target_weight=False, loss_weight=1.0)

Soft Wing Loss ‘Structure-Coherent Deep Feature Learning for Robust Face Alignment’ Lin et al. TIP’2021.

loss =

1. |x| , if |x| < omega1

2. omega2*ln(1+|x|/epsilon) + B, if |x| >= omega1

Parameters

• omega1 (float) – The first threshold.

• omega2 (float) – The second threshold.

• epsilon (float) – Also referred to as curvature.

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

criterion(pred, target)

Criterion of wingloss.

Note

batch_size: N num_keypoints: K dimension of keypoints: D (D=2 or D=3)

Parameters

• pred (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

forward(output, target, target_weight=None)

Forward function.

Note

batch_size: N num_keypoints: K dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N, K, D]) – Weights across different joint types.

class mmpose.models.losses.WingLoss(omega=10.0, epsilon=2.0, use_target_weight=False, loss_weight=1.0)

Wing Loss. paper ref: ‘Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks’ Feng et al. CVPR’2018.

Parameters

• omega (float) – Also referred to as width.

• epsilon (float) – Also referred to as curvature.

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

criterion(pred, target)

Criterion of wingloss.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• pred (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

forward(output, target, target_weight=None)

Forward function.

Note

• batch_size: N

• num_keypoints: K

• dimension of keypoints: D (D=2 or D=3)

Parameters

• output (torch.Tensor[N, K, D]) – Output regression.

• target (torch.Tensor[N, K, D]) – Target regression.

• target_weight (torch.Tensor[N,K,D]) – Weights across different joint types.

misc

mmpose.datasets

class mmpose.datasets.AnimalATRWDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

ATRW dataset for animal pose estimation.

“ATRW: A Benchmark for Amur Tiger Re-identification in the Wild” ACM MM’2020. More details can be found in the paper .

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

ATRW keypoint indexes:

0: "left_ear",
1: "right_ear",
2: "nose",
3: "right_shoulder",
4: "right_front_paw",
5: "left_shoulder",
6: "left_front_paw",
7: "right_hip",
8: "right_knee",
9: "right_back_paw",
10: "left_hip",
11: "left_knee",
12: "left_back_paw",
13: "tail",
14: "center"

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='mAP', **kwargs)

Evaluate coco keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘data/coco/val2017 /000000393226.jpg’]

  • heatmap (np.ndarray[N, K, H, W]): model output heatmap

  • bbox_id (list(int)).

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.AnimalFlyDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

AnimalFlyDataset for animal pose estimation.

“Fast animal pose estimation using deep neural networks” Nature methods’2019. More details can be found in the paper .

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Vinegar Fly keypoint indexes:

0: "head",
1: "eyeL",
2: "eyeR",
3: "neck",
4: "thorax",
5: "abdomen",
6: "forelegR1",
7: "forelegR2",
8: "forelegR3",
9: "forelegR4",
10: "midlegR1",
11: "midlegR2",
12: "midlegR3",
13: "midlegR4",
14: "hindlegR1",
15: "hindlegR2",
16: "hindlegR3",
17: "hindlegR4",
18: "forelegL1",
19: "forelegL2",
20: "forelegL3",
21: "forelegL4",
22: "midlegL1",
23: "midlegL2",
24: "midlegL3",
25: "midlegL4",
26: "hindlegL1",
27: "hindlegL2",
28: "hindlegL3",
29: "hindlegL4",
30: "wingL",
31: "wingR"

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='PCK', **kwargs)

Evaluate Fly keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘Test/source/0.jpg’]

  • output_heatmap (np.ndarray[N, K, H, W]): model outputs.

• res_folder (str) – Path of directory to save the results.

• metric (str | list[str]) – Metric to be performed. Options: ‘PCK’, ‘AUC’, ‘EPE’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.AnimalHorse10Dataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

AnimalHorse10Dataset for animal pose estimation.

“Pretraining boosts out-of-domain robustness for pose estimation” WACV’2021. More details can be found in the paper .

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Horse-10 keypoint indexes:

0: 'Nose',
1: 'Eye',
2: 'Nearknee',
3: 'Nearfrontfetlock',
4: 'Nearfrontfoot',
5: 'Offknee',
6: 'Offfrontfetlock',
7: 'Offfrontfoot',
8: 'Shoulder',
9: 'Midshoulder',
10: 'Elbow',
11: 'Girth',
12: 'Wither',
13: 'Nearhindhock',
14: 'Nearhindfetlock',
15: 'Nearhindfoot',
16: 'Hip',
17: 'Stifle',
18: 'Offhindhock',
19: 'Offhindfetlock',
20: 'Offhindfoot',
21: 'Ischium'

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='PCK', **kwargs)

Evaluate horse-10 keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘Test/source/0.jpg’]

  • output_heatmap (np.ndarray[N, K, H, W]): model outputs.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Options: ‘PCK’, ‘NME’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.AnimalLocustDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

AnimalLocustDataset for animal pose estimation.

“DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning” Elife’2019. More details can be found in the paper.

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Desert Locust keypoint indexes:

0: "head",
1: "neck",
2: "thorax",
3: "abdomen1",
4: "abdomen2",
5: "anttipL",
6: "antbaseL",
7: "eyeL",
8: "forelegL1",
9: "forelegL2",
10: "forelegL3",
11: "forelegL4",
12: "midlegL1",
13: "midlegL2",
14: "midlegL3",
15: "midlegL4",
16: "hindlegL1",
17: "hindlegL2",
18: "hindlegL3",
19: "hindlegL4",
20: "anttipR",
21: "antbaseR",
22: "eyeR",
23: "forelegR1",
24: "forelegR2",
25: "forelegR3",
26: "forelegR4",
27: "midlegR1",
28: "midlegR2",
29: "midlegR3",
30: "midlegR4",
31: "hindlegR1",
32: "hindlegR2",
33: "hindlegR3",
34: "hindlegR4"

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='PCK', **kwargs)

Evaluate Fly keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘Test/source/0.jpg’]

  • output_heatmap (np.ndarray[N, K, H, W]): model outputs.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Options: ‘PCK’, ‘AUC’, ‘EPE’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.AnimalMacaqueDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

MacaquePose dataset for animal pose estimation.

“MacaquePose: A novel ‘in the wild’ macaque monkey pose dataset for markerless motion capture” bioRxiv’2020. More details can be found in the paper .

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Macaque keypoint indexes:

0: 'nose',
1: 'left_eye',
2: 'right_eye',
3: 'left_ear',
4: 'right_ear',
5: 'left_shoulder',
6: 'right_shoulder',
7: 'left_elbow',
8: 'right_elbow',
9: 'left_wrist',
10: 'right_wrist',
11: 'left_hip',
12: 'right_hip',
13: 'left_knee',
14: 'right_knee',
15: 'left_ankle',
16: 'right_ankle'

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='mAP', **kwargs)

Evaluate coco keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

batch_size: N num_keypoints: K heatmap height: H heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘data/coco/val2017 /000000393226.jpg’]

  • heatmap (np.ndarray[N, K, H, W]): model output heatmap

  • bbox_id (list(int)).

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.AnimalPoseDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

Animal-Pose dataset for animal pose estimation.

“Cross-domain Adaptation For Animal Pose Estimation” ICCV’2019 More details can be found in the paper .

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Animal-Pose keypoint indexes:

0: 'L_Eye',
1: 'R_Eye',
2: 'L_EarBase',
3: 'R_EarBase',
4: 'Nose',
5: 'Throat',
6: 'TailBase',
7: 'Withers',
8: 'L_F_Elbow',
9: 'R_F_Elbow',
10: 'L_B_Elbow',
11: 'R_B_Elbow',
12: 'L_F_Knee',
13: 'R_F_Knee',
14: 'L_B_Knee',
15: 'R_B_Knee',
16: 'L_F_Paw',
17: 'R_F_Paw',
18: 'L_B_Paw',
19: 'R_B_Paw'

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='mAP', **kwargs)

Evaluate coco keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘data/coco/val2017 /000000393226.jpg’]

  • heatmap (np.ndarray[N, K, H, W]): model output heatmap

  • bbox_id (list(int)).

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.AnimalZebraDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

AnimalZebraDataset for animal pose estimation.

“DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning” Elife’2019. More details can be found in the paper.

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

Desert Locust keypoint indexes:

0: "snout",
1: "head",
2: "neck",
3: "forelegL1",
4: "forelegR1",
5: "hindlegL1",
6: "hindlegR1",
7: "tailbase",
8: "tailtip"

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='PCK', **kwargs)

Evaluate Fly keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• batch_size: N

• num_keypoints: K

• heatmap height: H

• heatmap width: W

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (np.ndarray[N,K,3]): The first two dimensions are coordinates, score is the third dimension of the array.

  • boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], scale[1],area, score]

  • image_paths (list[str]): For example, [‘Test/source/0.jpg’]

  • output_heatmap (np.ndarray[N, K, H, W]): model outputs.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Options: ‘PCK’, ‘AUC’, ‘EPE’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.Body3DH36MDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

Human3.6M dataset for 3D human pose estimation.

“Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments”, TPAMI`2014. More details can be found in the paper.

Human3.6M keypoint indexes:

0: 'root (pelvis)',
1: 'right_hip',
2: 'right_knee',
3: 'right_foot',
4: 'left_hip',
5: 'left_knee',
6: 'left_foot',
7: 'spine',
8: 'thorax',
9: 'neck_base',
10: 'head',
11: 'left_shoulder',
12: 'left_elbow',
13: 'left_wrist',
14: 'right_shoulder',
15: 'right_elbow',
16: 'right_wrist'

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

build_sample_indices()

Split original videos into sequences and build frame indices.

This method overrides the default one in the base class.

evaluate(results, res_folder=None, metric='mpjpe', **kwargs)

Evaluate keypoint results.

get_camera_param(imgname)

Get camera parameters of a frame by its image name.

load_annotations()

Load data annotation.

load_config(data_cfg)

Initialize dataset attributes according to the config.

Override this method to set dataset specific attributes.

class mmpose.datasets.Body3DMviewDirectCampusDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

Campus dataset for direct multi-view human pose estimation.

3D Pictorial Structures for Multiple Human Pose Estimation’ CVPR’2014 More details can be found in the paper <http://campar.in.tum.de/pub/belagiannis2014cvpr/belagiannis2014cvpr.pdf>

The dataset loads both 2D and 3D annotations as well as camera parameters. It is worth mentioning that when training multi-view 3D pose models, due to the limited and incomplete annotations of this dataset, we may not use this dataset to train the model. Instead, we use the 2D pose estimator trained on COCO, and use independent 3D human poses from the CMU Panoptic dataset to train the 3D model. For testing, we first estimate 2D poses and generate 2D heatmaps for this dataset as the input to 3D model.

Campus keypoint indices:

'Right-Ankle': 0,
'Right-Knee': 1,
'Right-Hip': 2,
'Left-Hip': 3,
'Left-Knee': 4,
'Left-Ankle': 5,
'Right-Wrist': 6,
'Right-Elbow': 7,
'Right-Shoulder': 8,
'Left-Shoulder': 9,
'Left-Elbow': 10,
'Left-Wrist': 11,
'Bottom-Head': 12,
'Top-Head': 13,

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

static coco2campus3D(coco_pose)

transform coco order(our method output) 3d pose to campus dataset order with interpolation.

Parameters

coco_pose – np.array with shape 17x3

Returns: 3D pose in campus order with shape 14x3

evaluate(results, res_folder=None, metric='pcp', recall_threshold=500, alpha_error=0.5, **kwargs)

Parameters

• results (list[dict]) – Testing results containing the following items: - pose_3d (np.ndarray): predicted 3D human pose - sample_id (np.ndarray): sample id of a frame.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘pcp’.

• recall_threshold – threshold for calculating recall.

• alpha_error – coefficient when calculating error for correct parts.

• **kwargs –

Returns:

static get_new_center(center_list)

Generate new center or select from the center list randomly.

The proability and the parameters related to cooridinates can also be tuned, just make sure that the center is within the given 3D space.

isvalid(new_center, bbox, bbox_list)

Check if the new person bbox are valid, which need to satisfies:

1. the center is visible in at least 2 views, and

2. have a sufficiently small iou with all other person bboxes.

load_config(data_cfg)

Initialize dataset attributes according to the config.

Override this method to set dataset specific attributes.

class mmpose.datasets.Body3DMviewDirectPanopticDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

Panoptic dataset for direct multi-view human pose estimation.

Panoptic Studio: A Massively Multiview System for Social Motion Capture’ ICCV’2015 More details can be found in the `paper .

The dataset loads both 2D and 3D annotations as well as camera parameters.

Panoptic keypoint indexes:

'neck': 0,
'nose': 1,
'mid-hip': 2,
'l-shoulder': 3,
'l-elbow': 4,
'l-wrist': 5,
'l-hip': 6,
'l-knee': 7,
'l-ankle': 8,
'r-shoulder': 9,
'r-elbow': 10,
'r-wrist': 11,
'r-hip': 12,
'r-knee': 13,
'r-ankle': 14,
'l-eye': 15,
'l-ear': 16,
'r-eye': 17,
'r-ear': 18,

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='mpjpe', **kwargs)

Parameters

• results (list[dict]) – Testing results containing the following items: - pose_3d (np.ndarray): predicted 3D human pose - sample_id (np.ndarray): sample id of a frame.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘mpjpe’.

• **kwargs –

Returns:

load_config(data_cfg)

Initialize dataset attributes according to the config.

Override this method to set dataset specific attributes.

class mmpose.datasets.Body3DMviewDirectShelfDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

Shelf dataset for direct multi-view human pose estimation.

3D Pictorial Structures for Multiple Human Pose Estimation’ CVPR’2014 More details can be found in the paper <http://campar.in.tum.de/pub/belagiannis2014cvpr/belagiannis2014cvpr.pdf>

The dataset loads both 2D and 3D annotations as well as camera parameters. It is worth mentioning that when training multi-view 3D pose models, due to the limited and incomplete annotations of this dataset, we may not use this dataset to train the model. Instead, we use the 2D pose estimator trained on COCO, and use independent 3D human poses from the CMU Panoptic dataset to train the 3D model. For testing, we first estimate 2D poses and generate 2D heatmaps for this dataset as the input to 3D model.

Shelf keypoint indices:

'Right-Ankle': 0,
'Right-Knee': 1,
'Right-Hip': 2,
'Left-Hip': 3,
'Left-Knee': 4,
'Left-Ankle': 5,
'Right-Wrist': 6,
'Right-Elbow': 7,
'Right-Shoulder': 8,
'Left-Shoulder': 9,
'Left-Elbow': 10,
'Left-Wrist': 11,
'Bottom-Head': 12,
'Top-Head': 13,

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

static coco2shelf3D(coco_pose, alpha=0.75)

transform coco order(our method output) 3d pose to shelf dataset order with interpolation.

Parameters

coco_pose – np.array with shape 17x3

Returns: 3D pose in shelf order with shape 14x3

evaluate(results, res_folder=None, metric='pcp', recall_threshold=500, alpha_error=0.5, alpha_head=0.75, **kwargs)

Parameters

• results (list[dict]) – Testing results containing the following items: - pose_3d (np.ndarray): predicted 3D human pose - sample_id (np.ndarray): sample id of a frame.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘pcp’.

• recall_threshold – threshold for calculating recall.

• alpha_error – coefficient when calculating correct parts.

• alpha_head – coefficient for conputing head keypoints position when converting coco poses to shelf poses

• **kwargs –

Returns:

static get_new_center(center_list)

Generate new center or select from the center list randomly.

The proability and the parameters related to cooridinates can also be tuned, just make sure that the center is within the given 3D space.

static isvalid(bbox, bbox_list)

Check if the new person bbox are valid, which need to satisfies:

have a sufficiently small iou with all other person bboxes.

load_config(data_cfg)

Initialize dataset attributes according to the config.

Override this method to set dataset specific attributes.

class mmpose.datasets.BottomUpAicDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

Aic dataset for bottom-up pose estimation.

“AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding”, arXiv’2017. More details can be found in the paper

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

AIC keypoint indexes:

0: "right_shoulder",
1: "right_elbow",
2: "right_wrist",
3: "left_shoulder",
4: "left_elbow",
5: "left_wrist",
6: "right_hip",
7: "right_knee",
8: "right_ankle",
9: "left_hip",
10: "left_knee",
11: "left_ankle",
12: "head_top",
13: "neck"

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

class mmpose.datasets.BottomUpCocoDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

COCO dataset for bottom-up pose estimation.

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

COCO keypoint indexes:

0: 'nose',
1: 'left_eye',
2: 'right_eye',
3: 'left_ear',
4: 'right_ear',
5: 'left_shoulder',
6: 'right_shoulder',
7: 'left_elbow',
8: 'right_elbow',
9: 'left_wrist',
10: 'right_wrist',
11: 'left_hip',
12: 'right_hip',
13: 'left_knee',
14: 'right_knee',
15: 'left_ankle',
16: 'right_ankle'

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

evaluate(results, res_folder=None, metric='mAP', **kwargs)

Evaluate coco keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.

Note

• num_people: P

• num_keypoints: K

Parameters

• results (list[dict]) –

  Testing results containing the following items:

  • preds (list[np.ndarray(P, K, 3+tag_num)]): Pose predictions for all people in images.

  • scores (list[P]): List of person scores.

  • image_path (list[str]): For example, [‘coco/images/ val2017/000000397133.jpg’]

  • heatmap (np.ndarray[N, K, H, W]): model outputs.

• res_folder (str, optional) – The folder to save the testing results. If not specified, a temp folder will be created. Default: None.

• metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.

Returns

Evaluation results for evaluation metric.

Return type

dict

class mmpose.datasets.BottomUpCocoWholeBodyDataset(ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False)

CocoWholeBodyDataset dataset for bottom-up pose estimation.

Whole-Body Human Pose Estimation in the Wild’, ECCV’2020. More details can be found in the `paper .

The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.

In total, we have 133 keypoints for wholebody pose estimation.

COCO-WholeBody keypoint indexes:

0-16: 17 body keypoints,
17-22: 6 foot keypoints,
23-90: 68 face keypoints,
91-132: 42 hand keypoints

Parameters

• ann_file (str) – Path to the annotation file.

• img_prefix (str) – Path to a directory where images are held. Default: None.

• data_cfg (dict) – config

• pipeline (list[dict | callable]) – A sequence of data transforms.

• dataset_info (DatasetInfo) – A class containing all dataset info.

• test_mode (bool) – Store True when building test or validation dataset. Default: False.

class mmpose.datasets.BottomUpCrowdPoseDataset(ann_file,