API Documentation¶
mmpose.apis¶
- 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)[source]¶
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]) – The bbox & pose & track_id info of the last frame (bbox_result, pose_result, track_id).
next_id (int) – The track id for the new person instance.
min_keypoints (int) – Minimum number of keypoints recognized as person. 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
- Returns
- The bbox & pose & track_id info of the
current frame (bbox_result, pose_result, track_id).
int: The track id for the new person instance.
- Return type
list[dict]
- mmpose.apis.inference_bottom_up_pose_model(model, img_or_path, pose_nms_thr=0.9, return_heatmap=False, outputs=None)[source]¶
Inference a single image.
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.
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
- 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 (ndarray[Kx3]): x, y, score.
- 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
list[ndarray]
- mmpose.apis.inference_top_down_pose_model(model, img_or_path, person_results, bbox_thr=None, format='xywh', dataset='TopDownCocoDataset', return_heatmap=False, outputs=None)[source]¶
Inference a single image with a list of person bounding boxes.
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.
person_results (List(dict)) – the item in the dict may contain ‘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.
bbox_thr – Threshold for bounding boxes. Only bboxes with higher scores will be fed into the pose detector. If bbox_thr is None, ignore it.
format – 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’.
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
- 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
- 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
list[dict]
- mmpose.apis.init_pose_model(config, checkpoint=None, device='cuda:0')[source]¶
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.multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False)[source]¶
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.single_gpu_test(model, data_loader)[source]¶
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)[source]¶
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_pose_result(model, img, result, kpt_score_thr=0.3, dataset='TopDownCocoDataset', show=False, out_file=None)[source]¶
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).
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, kpt_score_thr=0.3, dataset='TopDownCocoDataset', show=False, out_file=None)[source]¶
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).
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.core¶
evaluation¶
- class mmpose.core.evaluation.DistEvalHook(dataloader, interval=1, gpu_collect=False, save_best=True, key_indicator='AP', rule=None, **eval_kwargs)[source]¶
Distributed evaluation hook.
This hook will regularly perform evaluation in a given interval when performing in distributed environment.
- Parameters
dataloader (DataLoader) – A PyTorch dataloader.
interval (int) – Evaluation interval (by epochs). Default: 1.
gpu_collect (bool) – Whether to use gpu or cpu to collect results. Default: False.
save_best (bool) – Whether to save best checkpoint during evaluation. Default: True.
key_indicator (str | None) – Key indicator to measure the best checkpoint during evaluation when
save_best
is set to True. Options are the evaluation metrics to the test dataset. e.g.,top1_acc
,top5_acc
,mean_class_accuracy
,mean_average_precision
for action recognition dataset (RawframeDataset and VideoDataset).AR@AN
,auc
for action localization dataset (ActivityNetDataset). Default: top1_acc.rule (str | None) – Comparison rule for best score. If set to None, it will infer a reasonable rule. Default: ‘None’.
eval_kwargs (dict, optional) – Arguments for evaluation.
- class mmpose.core.evaluation.EvalHook(dataloader, interval=1, gpu_collect=False, save_best=True, key_indicator='AP', rule=None, **eval_kwargs)[source]¶
Non-Distributed evaluation hook.
This hook will regularly perform evaluation in a given interval when performing in non-distributed environment.
- Parameters
dataloader (DataLoader) – A PyTorch dataloader.
interval (int) – Evaluation interval (by epochs). Default: 1.
gpu_collect (bool) – Whether to use gpu or cpu to collect results. Default: False.
save_best (bool) – Whether to save best checkpoint during evaluation. Default: True.
key_indicator (str | None) – Key indicator to measure the best checkpoint during evaluation when
save_best
is set to True. Options are the evaluation metrics to the test dataset. e.g.,acc
,AP
,PCK
. Default: AP.rule (str | None) – Comparison rule for best score. If set to None, it will infer a reasonable rule. Default: ‘None’.
eval_kwargs (dict, optional) – Arguments for evaluation.
- mmpose.core.evaluation.aggregate_results(scale, aggregated_heatmaps, tags_list, heatmaps, tags, test_scale_factor, project2image, flip_test, align_corners=False)[source]¶
Aggregate multi-scale outputs.
Note
batch size: N keypoints num : K heatmap width: W heatmap height: H
- Parameters
scale (int) – current scale
aggregated_heatmaps (torch.Tensor | None) – Aggregated heatmaps.
tags_list (list(torch.Tensor)) – Tags list of previous scale.
heatmaps (List(torch.Tensor[NxKxWxH])) – A batch of heatmaps.
tags (List(torch.Tensor[NxKxWxH])) – A batch of tag maps.
test_scale_factor (List(int)) – Multi-scale factor for testing.
project2image (bool) – Option to resize to base scale.
flip_test (bool) – Option to use flip test.
align_corners (bool) – Align corners when performing interpolation.
- Returns
a tuple containing aggregated results.
aggregated_heatmaps (torch.Tensor): Heatmaps with multi scale.
tags_list (list(torch.Tensor)): Tag list of multi scale.
- Return type
tuple
- mmpose.core.evaluation.compute_similarity_transform(source_points, target_points)[source]¶
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.
Notes
Points number: N
- Parameters
source_points (np.ndarray([N, 3])) – Source point set.
target_points (np.ndarray([N, 3])) – Target point set.
- Returns
Transformed source point set.
- Return type
source_points_hat (np.ndarray([N, 3]))
- mmpose.core.evaluation.get_group_preds(grouped_joints, center, scale, heatmap_size, use_udp=False)[source]¶
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.get_multi_stage_outputs(outputs, outputs_flip, num_joints, with_heatmaps, with_ae, tag_per_joint=True, flip_index=None, project2image=True, size_projected=None, align_corners=False)[source]¶
Inference the model to get multi-stage outputs (heatmaps & tags), and resize them to base sizes.
- Parameters
outputs (list(torch.Tensor)) – Outputs of network
outputs_flip (list(torch.Tensor)) – Flip 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.
tag_per_joint (bool) – Option to use one tag map per joint.
flip_index (list[int]) – Keypoint flip index.
project2image (bool) – Option to resize to base scale.
size_projected ([w, h]) – Base size of heatmaps.
align_corners (bool) – Align corners when performing interpolation.
- Returns
A tuple containing multi-stage outputs.
outputs (list(torch.Tensor)): List of simple outputs and flip outputs.
heatmaps (torch.Tensor): Multi-stage heatmaps that are resized to the base size.
tags (torch.Tensor): Multi-stage tags that are resized to the base size.
- Return type
tuple
- mmpose.core.evaluation.keypoint_auc(pred, gt, mask, normalize, num_step=20)[source]¶
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)[source]¶
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')[source]¶
Calculate the mean per-joint position error (MPJPE) and the error after rigid alignment with the ground truth (P-MPJPE).
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
A tuple containing joint position errors
mpjpe (float|np.ndarray[N]): mean per-joint position error.
- p-mpjpe (float|np.ndarray[N]): mpjpe after rigid alignment with the
ground truth
- Return type
tuple
- mmpose.core.evaluation.keypoint_pck_accuracy(pred, gt, mask, thr, normalize)[source]¶
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')[source]¶
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_regression(regression_preds, center, scale, img_size)[source]¶
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
Predicted keypoint location in images. maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
- Return type
preds (np.ndarray[N, K, 2])
- mmpose.core.evaluation.pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None)[source]¶
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)[source]¶
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 hight 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
res (np.ndarray[N, K, 2])
fp16¶
- class mmpose.core.fp16.Fp16OptimizerHook(grad_clip=None, coalesce=True, bucket_size_mb=- 1, loss_scale=512.0, distributed=True)[source]¶
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)[source]¶
Backward optimization steps for Mixed Precision Training.
Scale the loss by a scale factor.
Backward the loss to obtain the gradients (fp16).
Copy gradients from the model to the fp32 weight copy.
Scale the gradients back and update the fp32 weight copy.
Copy back the params from fp32 weight copy to the fp16 model.
- Parameters
runner (
mmcv.Runner
) – The underlines training runner.
- before_run(runner)[source]¶
Preparing steps before Mixed Precision Training.
Make a master copy of fp32 weights for optimization.
Convert the main model from fp32 to fp16.
- Parameters
runner (
mmcv.Runner
) – The underlines training runner.
- mmpose.core.fp16.auto_fp16(apply_to=None, out_fp32=False)[source]¶
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)[source]¶
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)[source]¶
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
utils¶
- class mmpose.core.utils.WeightNormClipHook(max_norm=1.0, module_param_names='weight')[source]¶
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.
- 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)[source]¶
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.
post_processing¶
- mmpose.core.post_processing.affine_transform(pt, trans_mat)[source]¶
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')[source]¶
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)[source]¶
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 – 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)[source]¶
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 human joints.
regression_flipped (np.ndarray([…, K, C])): Flipped joints.
- Return type
tuple
- mmpose.core.post_processing.get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False)[source]¶
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)[source]¶
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
matrix (np.ndarray)
- mmpose.core.post_processing.oks_iou(g, d, a_g, a_d, sigmas=None, vis_thr=None)[source]¶
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)[source]¶
OKS NMS implementations.
- Parameters
kpts_db – keypoints.
thr – Retain overlap < thr.
sigmas – standard deviation of keypoint labelling.
vis_thr – threshold of the keypoint visibility.
- Returns
indexes to keep.
- Return type
np.ndarray
- mmpose.core.post_processing.rotate_point(pt, angle_rad)[source]¶
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)[source]¶
Soft OKS NMS implementations.
- Parameters
kpts_db –
thr – retain oks overlap < thr.
max_dets – max number of detections to keep.
sigmas – Keypoint labelling uncertainty.
- Returns
indexes to keep.
- Return type
np.ndarray
- mmpose.core.post_processing.transform_preds(coords, center, scale, output_size, use_udp=False)[source]¶
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)[source]¶
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
matrix (np.ndarray[…, 2])
mmpose.models¶
backbones¶
- class mmpose.models.backbones.AlexNet(num_classes=- 1)[source]¶
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.
- 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'})[source]¶
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)
- 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)[source]¶
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.
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) (1, 64, 4, 4) (1, 128, 2, 2) (1, 256, 1, 1)
- init_weights(pretrained=None)[source]¶
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
- 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'})[source]¶
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)
- 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)[source]¶
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)
- 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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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=(10), frozen_stages=- 1, norm_eval=False, with_cp=False)[source]¶
MobileNetV3 backbone.
- Parameters
arch (str) – Architechture 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: (10, ), which means output tensors from final stage.
frozen_stages (int) – Stages to be frozen (all param fixed). Defualt: -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. Defualt: False.
- forward(x)[source]¶
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)[source]¶
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)[source]¶
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.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)[source]¶
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)
- 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)[source]¶
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 bottlneck.
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)) >>> 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)[source]¶
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)
- static generate_regnet(initial_width, width_slope, width_parameter, depth, divisor=8)[source]¶
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
- class mmpose.models.backbones.ResNeSt(depth, groups=1, width_per_group=4, radix=2, reduction_factor=4, avg_down_stride=True, **kwargs)[source]¶
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.
- class mmpose.models.backbones.ResNeXt(depth, groups=32, width_per_group=4, **kwargs)[source]¶
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 (bool) – Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.
- 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)[source]¶
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) >>> 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)
- init_weights(pretrained=None)[source]¶
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
- class mmpose.models.backbones.ResNetV1d(**kwargs)[source]¶
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)[source]¶
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) >>> 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, 64, 56, 56) (1, 128, 28, 28) (1, 256, 14, 14) (1, 512, 7, 7)
- class mmpose.models.backbones.SEResNeXt(depth, groups=32, width_per_group=4, **kwargs)[source]¶
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.
- class mmpose.models.backbones.SEResNet(depth, se_ratio=16, **kwargs)[source]¶
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) >>> 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, 64, 56, 56) (1, 128, 28, 28) (1, 256, 14, 14) (1, 512, 7, 7)
- 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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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 not using the grouped 1x1 convolution.
- train(mode=True)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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.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)[source]¶
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)
- 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)[source]¶
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, ), outputing the last feature map before classifier. If num_classes > 0, the default value is (5, ), outputing 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)[source]¶
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)[source]¶
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)[source]¶
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
detectors¶
- class mmpose.models.detectors.BottomUp(backbone, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)[source]¶
Bottom-up 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)[source]¶
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[NxCximgHximgW]) – Input image.
targets (List(torch.Tensor[NxKxHxW])) – Multi-scale target heatmaps.
masks (List(torch.Tensor[NxHxW])) – Masks of multi-scale target heatmaps
joints (List(torch.Tensor[NxMxKx2])) – 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
loss (return) – 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, scores, image paths and heatmaps.
- Return type
dict|tuple
- forward_dummy(img)[source]¶
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)[source]¶
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)[source]¶
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[NxCximgHximgW]) – Input image.
targets (List(torch.Tensor[NxKxHxW])) – Multi-scale target heatmaps.
masks (List(torch.Tensor[NxHxW])) – Masks of multi-scale target heatmaps
joints (List(torch.Tensor[NxMxKx2])) – 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
- show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color=None, pose_kpt_color=None, pose_limb_color=None, radius=4, thickness=1, font_scale=0.5, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)[source]¶
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.
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_limb_color (np.array[Mx3]) – Color of M limbs. If None, do not draw limbs.
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.MultiTask(backbone, heads, necks=None, head2neck=None, pretrained=None)[source]¶
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)[source]¶
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[NxCximgHximgW]) – 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)[source]¶
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)[source]¶
Defines the computation performed at every call when testing.
- forward_train(img, target, target_weight, img_metas, **kwargs)[source]¶
Defines the computation performed at every call when training.
- 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)[source]¶
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)[source]¶
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)[source]¶
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, **kwargs)[source]¶
Defines the computation performed at every call when testing.
- forward_train(*args, **kwargs)[source]¶
Forward function for training.
For ParametricMesh, we do not use this interface.
- get_3d_joints_from_mesh(vertices)[source]¶
Get 3D joints from 3D mesh using predefined joints regressor.
- train_step(data_batch, optimizer, **kwargs)[source]¶
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)
- class mmpose.models.detectors.PoseLifter(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None)[source]¶
Pose lifter that lifts 2D pose to 3D pose.
- forward(input, target=None, target_weight=None, metas=None, return_loss=True, **kwargs)[source]¶
Calls either forward_train or forward_test depending on whether return_loss=True.
Note
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)[source]¶
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)[source]¶
Defines the computation performed at every call when training.
- forward_train(input, target, target_weight, metas, **kwargs)[source]¶
Defines the computation performed at every call when training.
- property with_keypoint¶
Check if has keypoint_head.
- property with_neck¶
Check if has keypoint_head.
- class mmpose.models.detectors.TopDown(backbone, neck=None, keypoint_head=None, train_cfg=None, test_cfg=None, pretrained=None, loss_pose=None)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
Defines the computation performed at every call when testing.
- forward_train(img, target, target_weight, img_metas, **kwargs)[source]¶
Defines the computation performed at every call when training.
- show_result(img, result, skeleton=None, kpt_score_thr=0.3, bbox_color='green', pose_kpt_color=None, pose_limb_color=None, text_color=(255, 0, 0), radius=4, thickness=1, font_scale=0.5, win_name='', show=False, show_keypoint_weight=False, wait_time=0, out_file=None)[source]¶
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.
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_limb_color (np.array[Mx3]) – Color of M limbs. If None, do not draw limbs.
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 keypoint_head.
keypoint_heads¶
- class mmpose.models.keypoint_heads.BottomUpHigherResolutionHead(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)[source]¶
Bottom-up head for Higher Resolution.
- 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 –
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.
- get_loss(output, targets, masks, joints)[source]¶
Calculate bottom-up keypoint loss.
Note
batch_size: N num_keypoints: K num_outputs: O heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
targets (List(torch.Tensor[NxKxHxW])) – Multi-scale target heatmaps.
masks (List(torch.Tensor[NxHxW])) – Masks of multi-scale target heatmaps
joints (List(torch.Tensor[NxMxKx2])) – Joints of multi-scale target heatmaps for ae loss
- class mmpose.models.keypoint_heads.BottomUpSimpleHead(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)[source]¶
Bottom-up simple head.
- 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(output, targets, masks, joints)[source]¶
Calculate bottom-up keypoint loss.
Note
batch_size: N num_keypoints: K num_outputs: O heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
targets (List(torch.Tensor[NxKxHxW])) – Multi-scale target heatmaps.
masks (List(torch.Tensor[NxHxW])) – Masks of multi-scale target heatmaps
joints (List(torch.Tensor[NxMxKx2])) – Joints of multi-scale target heatmaps for ae loss
- class mmpose.models.keypoint_heads.FcHead(in_channels, num_joints, loss_keypoint=None, train_cfg=None, test_cfg=None)[source]¶
regression head with fully connected layers.
paper ref: Alexander Toshev and Christian Szegedy, ``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.
- decode(img_metas, output, **kwargs)[source]¶
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.
- get_accuracy(output, target, target_weight)[source]¶
Calculate accuracy for top-down keypoint loss.
Note
batch_size: N num_keypoints: K
- Parameters
output (torch.Tensor[N, K, 2]) – 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)[source]¶
Calculate top-down keypoint loss.
Note
batch_size: N num_keypoints: K
- Parameters
output (torch.Tensor[N, K, 2]) – Output keypoints.
target (torch.Tensor[N, K, 2]) – Target keypoints.
target_weight (torch.Tensor[N, K, 2]) – Weights across different joint types.
- class mmpose.models.keypoint_heads.HeatMap3DHead(in_channels, out_channels, depth_size=64, 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)[source]¶
3D heatmap head of paper ref: Gyeongsik Moon. “InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image” HeatMap3DHead is a variant of TopDownSimpleHead, and is composed 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
depth_size (int) – Number of depth discretization size
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: -1
input_transform (str|None) –
Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, 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. Default: None.
align_corners (bool) – align_corners argument of F.interpolate. Default: False.
loss_keypoint (dict) – Config for keypoint loss. Default: None.
- decode(img_metas, output, **kwargs)[source]¶
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, D, H, W]) – model predicted 3D heatmaps.
- get_accuracy(output, target, target_weight)[source]¶
Calculate accuracy for top-down keypoint loss.
Note
batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
- get_loss(output, target, target_weight)[source]¶
Calculate 3D heatmap loss.
Note
batch size: N num keypoints: K heatmaps depth size: D heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxDxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxDxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
- class mmpose.models.keypoint_heads.Heatmap1DHead(in_channels=2048, heatmap_size=64, hidden_dims=(512), loss_value=None, train_cfg=None, test_cfg=None)[source]¶
Root depth head of paper ref: Gyeongsik Moon. “InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image”.
- Parameters
in_channels (int) – Number of input channels
heatmap_size (int) – Heatmap size
hidden_dims (list|tuple) – Number of feature dimension of FC layers.
loss_value (dict) – Config for heatmap 1d loss. Default: None.
- decode(img_metas, output, **kwargs)[source]¶
Decode heatmap 1d values.
- Parameters
img_metas (list(dict)) – Information about data augmentation By default this includes: - “image_file: path to the image file
output (np.ndarray[N, 1]) – model predicted values.
- class mmpose.models.keypoint_heads.MultilabelClassificationHead(in_channels=2048, num_labels=2, hidden_dims=(512), loss_classification=None, train_cfg=None, test_cfg=None)[source]¶
Multi-label classification head. Paper ref: Gyeongsik Moon. “InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image”.
- Parameters
in_channels (int) – Number of input channels
num_labels (int) – Number of labels
hidden_dims (list|tuple) – Number of hidden dimension of FC layers.
loss_classification (dict) – Config for classification loss. Default: None.
- decode(img_metas, output, **kwargs)[source]¶
Decode keypoints from heatmaps.
- Parameters
img_metas (list(dict)) – Information about data augmentation By default this includes: - “image_file”: path to the image file
output (np.ndarray[N, L]) – model predicted labels.
- get_accuracy(output, target, target_weight)[source]¶
Calculate accuracy for classification.
Note
batch size: N number labels: L
- Parameters
output (torch.Tensor[N, L]) – Output hand visibility.
target (torch.Tensor[N, L]) – Target hand visibility.
target_weight (torch.Tensor[N, L]) – Weights across different labels.
- class mmpose.models.keypoint_heads.TemporalRegressionHead(in_channels, num_joints, max_norm=None, loss_keypoint=None, train_cfg=None, test_cfg=None)[source]¶
Regression head of VideoPose3D.
Paper ref: Dario Pavllo. ``3D human pose estimation in video with temporal convolutions and
semi-supervised training``
- Args:
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.
- decode(metas, output)[source]¶
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.
- get_accuracy(output, target, target_weight, metas)[source]¶
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)[source]¶
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.
- class mmpose.models.keypoint_heads.TopDownMSMUHead(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)[source]¶
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)[source]¶
Forward function.
- Returns
- a list of heatmaps from multiple stages
and units.
- Return type
out (list[Tensor])
- get_accuracy(output, target, target_weight)[source]¶
Calculate accuracy for top-down keypoint loss.
Note
batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
- get_loss(output, target, target_weight)[source]¶
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[NxOxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxOxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxOxKx1]) – Weights across different joint types.
- class mmpose.models.keypoint_heads.TopDownMultiStageHead(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)[source]¶
Heads for multi-stage pose models.
TopDownMultiStageHead 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)[source]¶
Forward function.
- Returns
a list of heatmaps from multiple stages.
- Return type
out (list[Tensor])
- get_accuracy(output, target, target_weight)[source]¶
Calculate accuracy for top-down keypoint loss.
Note
batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
- get_loss(output, target, target_weight)[source]¶
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[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
- class mmpose.models.keypoint_heads.TopDownSimpleHead(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)[source]¶
Top-down model head of simple baseline paper ref: Bin Xiao.
Simple Baselines for Human Pose Estimation and Tracking
.TopDownSimpleHead 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: -1
input_transform (str|None) –
Transformation type of input features. Options: ‘resize_concat’, ‘multiple_select’, 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. Default: None.
align_corners (bool) – align_corners argument of F.interpolate. Default: False.
loss_keypoint (dict) – Config for keypoint loss. Default: None.
- get_accuracy(output, target, target_weight)[source]¶
Calculate accuracy for top-down keypoint loss.
Note
batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
- get_loss(output, target, target_weight)[source]¶
Calculate top-down keypoint loss.
Note
batch_size: N num_keypoints: K heatmaps height: H heatmaps weight: W
- Parameters
output (torch.Tensor[NxKxHxW]) – Output heatmaps.
target (torch.Tensor[NxKxHxW]) – Target heatmaps.
target_weight (torch.Tensor[NxKx1]) – Weights across different joint types.
losses¶
- class mmpose.models.losses.AELoss(loss_type)[source]¶
Associative Embedding loss.
Associative Embedding: End-to-End Learning for Joint Detection and Grouping <https://arxiv.org/abs/1611.05424v2>
- forward(tags, joints)[source]¶
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[Nx(KxHxW)x1]) – tag channels of output.
joints (torch.Tensor[NxMxKx2]) – joints information.
- singleTagLoss(pred_tag, joints)[source]¶
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)x1]) – tag of output for one image.
joints (torch.Tensor[MxKx2]) – joints information for one image.
- class mmpose.models.losses.BCELoss(use_target_weight=False, loss_weight=1.0)[source]¶
Binary Cross Entropy loss.
- forward(output, target, target_weight)[source]¶
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.GANLoss(gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0)[source]¶
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)[source]¶
- 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
- class mmpose.models.losses.HeatmapLoss[source]¶
Accumulate the heatmap loss for each image in the batch.
- class mmpose.models.losses.JointsMSELoss(use_target_weight=False, loss_weight=1.0)[source]¶
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.
- class mmpose.models.losses.JointsOHKMMSELoss(use_target_weight=False, topk=8, loss_weight=1.0)[source]¶
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.
- class mmpose.models.losses.MPJPELoss(use_target_weight=False, loss_weight=1.0)[source]¶
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)[source]¶
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)[source]¶
MSE loss for coordinate regression.
- 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)[source]¶
Mix loss for 3D human mesh. It is composed of loss on 2D joints, 3D joints, mesh vertices and smpl paramters (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)[source]¶
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
losses (dict)
- joints_2d_loss(pred_joints_2d, gt_joints_2d, joints_2d_visible)[source]¶
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)[source]¶
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)[source]¶
Perform orthographic projection of 3D points using the camera parameters, return projected 2D points in image plane.
Notes
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
points_2d (Tensor([B, N, 2]))
- 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)[source]¶
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.
- forward(outputs, heatmaps, masks, joints)[source]¶
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[NxCxHxW])) – outputs of stages.
heatmaps (List(torch.Tensor[NxKxHxW])) – target of heatmaps.
masks (List(torch.Tensor[NxHxW])) – masks of heatmaps.
joints (List(torch.Tensor[NxMxKx2])) – joints of ae loss.
- class mmpose.models.losses.SmoothL1Loss(use_target_weight=False, loss_weight=1.0)[source]¶
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)[source]¶
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)[source]¶
Wing Loss ‘Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks’ Feng et al. CVPR’2018.
- Parameters
omega (float), epsilon (float) –
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)[source]¶
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)[source]¶
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.
mmpose.datasets¶
- class mmpose.datasets.AnimalATRWDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
ATRW dataset for animal pose estimation.
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(dict)) –
- 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) – Path of directory to save the results.
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, test_mode=False)[source]¶
AnimalFlyDataset for animal pose estimation.
`Fast animal pose estimation using deep neural networks’ Nature methods’2019. More details can be found in the `paper <https://www.biorxiv.org/content/
biorxiv/early/2018/05/25/331181.full.pdf>`__ .
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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 outpus.
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, test_mode=False)[source]¶
AnimalHorse10Dataset for animal pose estimation.
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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 outpus.
res_folder (str) – Path of directory to save the results.
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, test_mode=False)[source]¶
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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 outpus.
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.AnimalMacaqueDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MacaquePose dataset for animal pose estimation.
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(dict)) –
- 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) – Path of directory to save the results.
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, test_mode=False)[source]¶
Animal-Pose dataset for animal pose estimation.
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(dict)) –
- 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) – Path of directory to save the results.
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, test_mode=False)[source]¶
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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 outpus.
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.BottomUpCocoDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(preds, scores, image_path, heatmap)) –
preds (list[np.ndarray(P, K, 3+tag_num)]): Pose predictions for all people in images.
scores (list[P]):
image_path (list[str]): For example, [‘coco/images/
val2017/000000397133.jpg’] * 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. Defaults: ‘mAP’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.BottomUpCrowdPoseDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CrowdPose 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.
CrowdPose keypoint indexes:
0: 'left_shoulder', 1: 'right_shoulder', 2: 'left_elbow', 3: 'right_elbow', 4: 'left_wrist', 5: 'right_wrist', 6: 'left_hip', 7: 'right_hip', 8: 'left_knee', 9: 'right_knee', 10: 'left_ankle', 11: 'right_ankle', 12: 'top_head', 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.BottomUpMhpDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MHPv2.0 dataset for top-down pose estimation.
The Multi-Human Parsing project of Learning and Vision (LV) Group, National University of Singapore (NUS) is proposed to push the frontiers of fine-grained visual understanding of humans in crowd scene. <https://lv-mhp.github.io/>
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
MHP keypoint indexes:
0: "right ankle", 1: "right knee", 2: "right hip", 3: "left hip", 4: "left knee", 5: "left ankle", 6: "pelvis", 7: "thorax", 8: "upper neck", 9: "head top", 10: "right wrist", 11: "right elbow", 12: "right shoulder", 13: "left shoulder", 14: "left elbow", 15: "left 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.Compose(transforms)[source]¶
Compose a data pipeline with a sequence of transforms.
- Parameters
transforms (list[dict | callable]) – Either config dicts of transforms or transform objects.
- class mmpose.datasets.DeepFashionDataset(ann_file, img_prefix, subset, data_cfg, pipeline, test_mode=False)[source]¶
DeepFashion dataset (full-body clothes) for fashion landmark detection.
`DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations’ CVPR’2016 and `Fashion Landmark Detection in the Wild’ ECCV’2016
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
The dataset contains 3 categories for full-body, upper-body and lower-body.
Fashion landmark indexes for upper-body clothes:
0: 'left collar', 1: 'right collar', 2: 'left sleeve', 3: 'right sleeve', 4: 'left hem', 5: 'right hem'
Fashion landmark indexes for lower-body clothes:
0: 'left waistline', 1: 'right waistline', 2: 'left hem', 3: 'right hem'
Fashion landmark indexes for full-body clothes:
0: 'left collar', 1: 'right collar', 2: 'left sleeve', 3: 'right sleeve', 4: 'left waistline', 5: 'right waistline', 6: 'left hem', 7: 'right hem'
- Parameters
ann_file (str) – Path to the annotation file.
img_prefix (str) – Path to a directory where images are held. Default: None.
subset (str) – The FLD dataset has 3 subsets, ‘upper’, ‘lower’, and ‘full’, denoting different types of clothes.
data_cfg (dict) – config
pipeline (list[dict | callable]) – A sequence of data transforms.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
Evaluate freihand 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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, [ ‘img_00000001.jpg’]
- output_heatmap (np.ndarray[N, K, H, W])
model outpus.
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.DistributedSampler(dataset, num_replicas=None, rank=None, shuffle=True, seed=0)[source]¶
DistributedSampler inheriting from torch.utils.data.DistributedSampler.
In pytorch of lower versions, there is no shuffle argument. This child class will port one to DistributedSampler.
- class mmpose.datasets.Face300WDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
Face300W dataset for top-down face keypoint localization.
300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS) 2019.
The dataset loads raw images and apply specified transforms to return a dict containing the image tensors and other information.
The landmark annotations follow the 68 points mark-up. The definition can be found in https://ibug.doc.ic.ac.uk/resources/300-W/.
- 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='NME', **kwargs)[source]¶
Evaluate freihand 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- preds (np.ndarray[1,K,3])
The first two dimensions are coordinates, score is the third dimension of the array.
- boxes (np.ndarray[1,6])
[center[0], center[1], scale[0] , scale[1],area, score]
- image_path (list[str])
For example, [‘3’, ‘0’, ‘0’, ‘W’, ‘/’, ‘i’, ‘b’, ‘u’, ‘g’, ‘/’, ‘i’, ‘m’, ‘a’, ‘g’, ‘e’, ‘_’, ‘0’, ‘1’, ‘8’, ‘.’, ‘j’, ‘p’, ‘g’]
- output_heatmap (np.ndarray[N, K, H, W])
model outpus.
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Options: ‘NME’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.FreiHandDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
FreiHand dataset for top-down hand pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
FreiHand keypoint indexes:
0: 'wrist', 1: 'thumb1', 2: 'thumb2', 3: 'thumb3', 4: 'thumb4', 5: 'forefinger1', 6: 'forefinger2', 7: 'forefinger3', 8: 'forefinger4', 9: 'middle_finger1', 10: 'middle_finger2', 11: 'middle_finger3', 12: 'middle_finger4', 13: 'ring_finger1', 14: 'ring_finger2', 15: 'ring_finger3', 16: 'ring_finger4', 17: 'pinky_finger1', 18: 'pinky_finger2', 19: 'pinky_finger3', 20: 'pinky_finger4'
- 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
Evaluate freihand 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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, [‘training/rgb/ 00031426.jpg’]
- output_heatmap (np.ndarray[N, K, H, W])
model outpus.
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.InterHand2DDataset(ann_file, camera_file, joint_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
InterHand2.6M 2D dataset for top-down hand pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
InterHand2.6M keypoint indexes:
0: 'thumb4', 1: 'thumb3', 2: 'thumb2', 3: 'thumb1', 4: 'forefinger4', 5: 'forefinger3', 6: 'forefinger2', 7: 'forefinger1', 8: 'middle_finger4', 9: 'middle_finger3', 10: 'middle_finger2', 11: 'middle_finger1', 12: 'ring_finger4', 13: 'ring_finger3', 14: 'ring_finger2', 15: 'ring_finger1', 16: 'pinky_finger4', 17: 'pinky_finger3', 18: 'pinky_finger2', 19: 'pinky_finger1', 20: '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.
test_mode (str) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
Evaluate interhand2d 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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, [‘C’, ‘a’, ‘p’, ‘t’, ‘u’, ‘r’, ‘e’, ‘1’, ‘2’, ‘/’, ‘0’, ‘3’, ‘9’, ‘0’, ‘_’, ‘d’, ‘h’, ‘_’, ‘t’, ‘o’, ‘u’, ‘c’, ‘h’, ‘R’, ‘O’, ‘M’, ‘/’, ‘c’, ‘a’, ‘m’, ‘4’, ‘1’, ‘0’, ‘2’, ‘0’, ‘9’, ‘/’, ‘i’, ‘m’, ‘a’, ‘g’, ‘e’, ‘6’, ‘2’, ‘4’, ‘3’, ‘4’, ‘.’, ‘j’, ‘p’, ‘g’]
- output_heatmap (np.ndarray[N, K, H, W])
model outpus.
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.MeshAdversarialDataset(train_dataset, adversarial_dataset)[source]¶
Mix Dataset for the adversarial training in 3D human mesh estimation task.
The dataset combines data from two datasets and return a dict containing data from two datasets.
- Parameters
train_dataset (Dataset) – Dataset for 3D human mesh estimation.
adversarial_dataset (Dataset) – Dataset for adversarial learning, provides real SMPL parameters.
- class mmpose.datasets.MeshH36MDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
Human3.6M Dataset for 3D human mesh estimation. It inherits all function from MeshBaseDataset and has its own evaluate fuction.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
- 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.MeshMixDataset(configs, partition)[source]¶
Mix Dataset for 3D human mesh estimation.
The dataset combines data from multiple datasets (MeshBaseDataset) and sample the data from different datasets with the provided proportions. The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
- Parameters
configs (list) – List of configs for multiple datasets.
partition (list) – Sample proportion of multiple datasets. The the elements of it should be non-negative and the sum of it should be 1.
- class mmpose.datasets.MoshDataset(ann_file, pipeline, test_mode=False)[source]¶
Mosh Dataset for the adversarial training in 3D human mesh estimation task.
The dataset return a dict containing real-world SMPL parameters.
- Parameters
ann_file (str) – Path to the annotation file.
pipeline (list[dict | callable]) – A sequence of data transforms.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.OneHand10KDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
OneHand10K dataset for top-down hand pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
OneHand10K keypoint indexes:
0: 'wrist', 1: 'thumb1', 2: 'thumb2', 3: 'thumb3', 4: 'thumb4', 5: 'forefinger1', 6: 'forefinger2', 7: 'forefinger3', 8: 'forefinger4', 9: 'middle_finger1', 10: 'middle_finger2', 11: 'middle_finger3', 12: 'middle_finger4', 13: 'ring_finger1', 14: 'ring_finger2', 15: 'ring_finger3', 16: 'ring_finger4', 17: 'pinky_finger1', 18: 'pinky_finger2', 19: 'pinky_finger3', 20: 'pinky_finger4'
- 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
Evaluate onehand10k 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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 outpus.
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.PanopticDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
Panoptic dataset for top-down hand pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
Panoptic keypoint indexes:
0: 'wrist', 1: 'thumb1', 2: 'thumb2', 3: 'thumb3', 4: 'thumb4', 5: 'forefinger1', 6: 'forefinger2', 7: 'forefinger3', 8: 'forefinger4', 9: 'middle_finger1', 10: 'middle_finger2', 11: 'middle_finger3', 12: 'middle_finger4', 13: 'ring_finger1', 14: 'ring_finger2', 15: 'ring_finger3', 16: 'ring_finger4', 17: 'pinky_finger1', 18: 'pinky_finger2', 19: 'pinky_finger3', 20: 'pinky_finger4'
- 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCKh', **kwargs)[source]¶
Evaluate panoptic 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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, [‘hand_labels/’ ‘manual_test/000648952_02_l.jpg’]
- output_heatmap (np.ndarray[N, K, H, W])
model outpus.
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Options: ‘PCKh’, ‘AUC’, ‘EPE’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.TopDownAicDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
AicDataset dataset for top-down pose estimation.
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.TopDownCocoDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CocoDataset dataset for top-down pose estimation.
Microsoft COCO: Common Objects in Context’ ECCV’2014 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.
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(dict)) –
- 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) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.TopDownCocoWholeBodyDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CocoWholeBodyDataset dataset for top-down 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.TopDownCrowdPoseDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CrowdPoseDataset dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
CrowdPose keypoint indexes:
0: 'left_shoulder', 1: 'right_shoulder', 2: 'left_elbow', 3: 'right_elbow', 4: 'left_wrist', 5: 'right_wrist', 6: 'left_hip', 7: 'right_hip', 8: 'left_knee', 9: 'right_knee', 10: 'left_ankle', 11: 'right_ankle', 12: 'top_head', 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.TopDownFreiHandDataset(*args, **kwargs)[source]¶
Deprecated TopDownFreiHandDataset.
- class mmpose.datasets.TopDownJhmdbDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
JhmdbDataset dataset for top-down pose estimation.
- `Towards understanding action recognition
<https://openaccess.thecvf.com/content_iccv_2013/papers/ Jhuang_Towards_Understanding_Action_2013_ICCV_paper.pdf>`__
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
- sub-JHMDB keypoint indexes::
0: “neck”, 1: “belly”, 2: “head”, 3: “right_shoulder”, 4: “left_shoulder”, 5: “right_hip”, 6: “left_hip”, 7: “right_elbow”, 8: “left_elbow”, 9: “right_knee”, 10: “left_knee”, 11: “right_wrist”, 12: “left_wrist”, 13: “right_ankle”, 14: “left_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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
Evaluate onehand10k 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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_path (list[str]) :output_heatmap (np.ndarray[N, K, H, W]): model outpus.
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Options: ‘PCK’, ‘tPCK’. PCK means normalized by the bounding boxes, while tPCK means normalized by the torso size.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.TopDownMhpDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MHPv2.0 dataset for top-down pose estimation.
The Multi-Human Parsing project of Learning and Vision (LV) Group, National University of Singapore (NUS) is proposed to push the frontiers of fine-grained visual understanding of humans in crowd scene. <https://lv-mhp.github.io/>
Note that, the evaluation metric used here is mAP (adapted from COCO), which may be different from the official evaluation codes. ‘https://github.com/ZhaoJ9014/Multi-Human-Parsing/tree/master/’ ‘Evaluation/Multi-Human-Pose’ Please be cautious if you use the results in papers.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
MHP keypoint indexes:
0: "right ankle", 1: "right knee", 2: "right hip", 3: "left hip", 4: "left knee", 5: "left ankle", 6: "pelvis", 7: "thorax", 8: "upper neck", 9: "head top", 10: "right wrist", 11: "right elbow", 12: "right shoulder", 13: "left shoulder", 14: "left elbow", 15: "left 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.TopDownMpiiDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MPII Dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
MPII keypoint indexes:
0: 'right_ankle' 1: 'right_knee', 2: 'right_hip', 3: 'left_hip', 4: 'left_knee', 5: 'left_ankle', 6: 'pelvis', 7: 'thorax', 8: 'upper_neck', 9: 'head_top', 10: 'right_wrist', 11: 'right_elbow', 12: 'right_shoulder', 13: 'left_shoulder', 14: 'left_elbow', 15: 'left_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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCKh', **kwargs)[source]¶
Evaluate PCKh for MPII dataset. Adapted from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch Copyright (c) Microsoft, under the MIT License.
Note
batch_size: N num_keypoints: K heatmap height: H heatmap width: W
- Parameters
outputs (list(preds, boxes, image_path, heatmap)) –
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, [‘/val2017/000000 397133.jpg’]
heatmap (np.ndarray[N, K, H, W]): model output heatmap.
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metrics to be performed. Defaults: ‘PCKh’.
- Returns
PCKh for each joint
- Return type
dict
- class mmpose.datasets.TopDownMpiiTrbDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MPII-TRB Dataset dataset for top-down pose estimation.
TRB: A Novel Triplet Representation for Understanding 2D Human Body 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.
MPII-TRB keypoint indexes:
0: 'left_shoulder' 1: 'right_shoulder' 2: 'left_elbow' 3: 'right_elbow' 4: 'left_wrist' 5: 'right_wrist' 6: 'left_hip' 7: 'right_hip' 8: 'left_knee' 9: 'right_knee' 10: 'left_ankle' 11: 'right_ankle' 12: 'head' 13: 'neck' 14: 'right_neck' 15: 'left_neck' 16: 'medial_right_shoulder' 17: 'lateral_right_shoulder' 18: 'medial_right_bow' 19: 'lateral_right_bow' 20: 'medial_right_wrist' 21: 'lateral_right_wrist' 22: 'medial_left_shoulder' 23: 'lateral_left_shoulder' 24: 'medial_left_bow' 25: 'lateral_left_bow' 26: 'medial_left_wrist' 27: 'lateral_left_wrist' 28: 'medial_right_hip' 29: 'lateral_right_hip' 30: 'medial_right_knee' 31: 'lateral_right_knee' 32: 'medial_right_ankle' 33: 'lateral_right_ankle' 34: 'medial_left_hip' 35: 'lateral_left_hip' 36: 'medial_left_knee' 37: 'lateral_left_knee' 38: 'medial_left_ankle' 39: 'lateral_left_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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCKh', **kwargs)[source]¶
Evaluate PCKh for MPII-TRB dataset.
Note
batch_size: N num_keypoints: K heatmap height: H heatmap width: W
- Parameters
outputs (list(preds, boxes, image_paths, heatmap)) –
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, [‘/val2017/000000 397133.jpg’]
heatmap (np.ndarray[N, K, H, W]): model output heatmap.
bbox_ids (list[str]): For example, [‘27407’]
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metrics to be performed. Defaults: ‘PCKh’.
- Returns
PCKh for each joint
- Return type
dict
- class mmpose.datasets.TopDownOCHumanDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
OChuman dataset for top-down pose estimation.
“Occluded Human (OCHuman)” dataset contains 8110 heavily occluded human instances within 4731 images. OCHuman dataset is designed for validation and testing. To evaluate on OCHuman, the model should be trained on COCO training set, and then test the robustness of the model to occlusion using OCHuman.
OCHuman keypoint indexes (same as COCO):
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.TopDownOneHand10KDataset(*args, **kwargs)[source]¶
Deprecated TopDownOneHand10KDataset.
- class mmpose.datasets.TopDownPanopticDataset(*args, **kwargs)[source]¶
Deprecated TopDownPanopticDataset.
- class mmpose.datasets.TopDownPoseTrack18Dataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
PoseTrack18 dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
- PoseTrack2018 keypoint indexes::
0: ‘nose’, 1: ‘head_bottom’, 2: ‘head_top’, 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
Evaluate coco keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.
Note
num_keypoints: K
- Parameters
outputs (list(preds, boxes, image_paths)) –
- 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, [‘val/010016_mpii_test /000024.jpg’]
- heatmap (np.ndarray[N, K, H, W])
model output heatmap.
:bbox_id (list(int))
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- mmpose.datasets.build_dataloader(dataset, samples_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, seed=None, drop_last=True, pin_memory=True, **kwargs)[source]¶
Build PyTorch DataLoader.
In distributed training, each GPU/process has a dataloader. In non-distributed training, there is only one dataloader for all GPUs.
- Parameters
dataset (Dataset) – A PyTorch dataset.
samples_per_gpu (int) – Number of training samples on each GPU, i.e., batch size of each GPU.
workers_per_gpu (int) – How many subprocesses to use for data loading for each GPU.
num_gpus (int) – Number of GPUs. Only used in non-distributed training.
dist (bool) – Distributed training/test or not. Default: True.
shuffle (bool) – Whether to shuffle the data at every epoch. Default: True.
drop_last (bool) – Whether to drop the last incomplete batch in epoch. Default: True
pin_memory (bool) – Whether to use pin_memory in DataLoader. Default: True
kwargs – any keyword argument to be used to initialize DataLoader
- Returns
A PyTorch dataloader.
- Return type
DataLoader
- mmpose.datasets.build_dataset(cfg, default_args=None)[source]¶
Build a dataset from config dict.
- Parameters
cfg (dict) – Config dict. It should at least contain the key “type”.
default_args (dict, optional) – Default initialization arguments. Default: None.
- Returns
The constructed dataset.
- Return type
Dataset
datasets¶
- class mmpose.datasets.datasets.top_down.TopDownAicDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
AicDataset dataset for top-down pose estimation.
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.top_down.TopDownCocoDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CocoDataset dataset for top-down pose estimation.
Microsoft COCO: Common Objects in Context’ ECCV’2014 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.
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(dict)) –
- 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) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.datasets.top_down.TopDownCocoWholeBodyDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CocoWholeBodyDataset dataset for top-down 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.top_down.TopDownCrowdPoseDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CrowdPoseDataset dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
CrowdPose keypoint indexes:
0: 'left_shoulder', 1: 'right_shoulder', 2: 'left_elbow', 3: 'right_elbow', 4: 'left_wrist', 5: 'right_wrist', 6: 'left_hip', 7: 'right_hip', 8: 'left_knee', 9: 'right_knee', 10: 'left_ankle', 11: 'right_ankle', 12: 'top_head', 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.top_down.TopDownJhmdbDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
JhmdbDataset dataset for top-down pose estimation.
- `Towards understanding action recognition
<https://openaccess.thecvf.com/content_iccv_2013/papers/ Jhuang_Towards_Understanding_Action_2013_ICCV_paper.pdf>`__
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
- sub-JHMDB keypoint indexes::
0: “neck”, 1: “belly”, 2: “head”, 3: “right_shoulder”, 4: “left_shoulder”, 5: “right_hip”, 6: “left_hip”, 7: “right_elbow”, 8: “left_elbow”, 9: “right_knee”, 10: “left_knee”, 11: “right_wrist”, 12: “left_wrist”, 13: “right_ankle”, 14: “left_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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCK', **kwargs)[source]¶
Evaluate onehand10k 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
outputs (list(preds, boxes, image_path, output_heatmap)) –
- 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_path (list[str]) :output_heatmap (np.ndarray[N, K, H, W]): model outpus.
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Options: ‘PCK’, ‘tPCK’. PCK means normalized by the bounding boxes, while tPCK means normalized by the torso size.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.datasets.top_down.TopDownMhpDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MHPv2.0 dataset for top-down pose estimation.
The Multi-Human Parsing project of Learning and Vision (LV) Group, National University of Singapore (NUS) is proposed to push the frontiers of fine-grained visual understanding of humans in crowd scene. <https://lv-mhp.github.io/>
Note that, the evaluation metric used here is mAP (adapted from COCO), which may be different from the official evaluation codes. ‘https://github.com/ZhaoJ9014/Multi-Human-Parsing/tree/master/’ ‘Evaluation/Multi-Human-Pose’ Please be cautious if you use the results in papers.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
MHP keypoint indexes:
0: "right ankle", 1: "right knee", 2: "right hip", 3: "left hip", 4: "left knee", 5: "left ankle", 6: "pelvis", 7: "thorax", 8: "upper neck", 9: "head top", 10: "right wrist", 11: "right elbow", 12: "right shoulder", 13: "left shoulder", 14: "left elbow", 15: "left 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.top_down.TopDownMpiiDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MPII Dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
MPII keypoint indexes:
0: 'right_ankle' 1: 'right_knee', 2: 'right_hip', 3: 'left_hip', 4: 'left_knee', 5: 'left_ankle', 6: 'pelvis', 7: 'thorax', 8: 'upper_neck', 9: 'head_top', 10: 'right_wrist', 11: 'right_elbow', 12: 'right_shoulder', 13: 'left_shoulder', 14: 'left_elbow', 15: 'left_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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCKh', **kwargs)[source]¶
Evaluate PCKh for MPII dataset. Adapted from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch Copyright (c) Microsoft, under the MIT License.
Note
batch_size: N num_keypoints: K heatmap height: H heatmap width: W
- Parameters
outputs (list(preds, boxes, image_path, heatmap)) –
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, [‘/val2017/000000 397133.jpg’]
heatmap (np.ndarray[N, K, H, W]): model output heatmap.
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metrics to be performed. Defaults: ‘PCKh’.
- Returns
PCKh for each joint
- Return type
dict
- class mmpose.datasets.datasets.top_down.TopDownMpiiTrbDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MPII-TRB Dataset dataset for top-down pose estimation.
TRB: A Novel Triplet Representation for Understanding 2D Human Body 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.
MPII-TRB keypoint indexes:
0: 'left_shoulder' 1: 'right_shoulder' 2: 'left_elbow' 3: 'right_elbow' 4: 'left_wrist' 5: 'right_wrist' 6: 'left_hip' 7: 'right_hip' 8: 'left_knee' 9: 'right_knee' 10: 'left_ankle' 11: 'right_ankle' 12: 'head' 13: 'neck' 14: 'right_neck' 15: 'left_neck' 16: 'medial_right_shoulder' 17: 'lateral_right_shoulder' 18: 'medial_right_bow' 19: 'lateral_right_bow' 20: 'medial_right_wrist' 21: 'lateral_right_wrist' 22: 'medial_left_shoulder' 23: 'lateral_left_shoulder' 24: 'medial_left_bow' 25: 'lateral_left_bow' 26: 'medial_left_wrist' 27: 'lateral_left_wrist' 28: 'medial_right_hip' 29: 'lateral_right_hip' 30: 'medial_right_knee' 31: 'lateral_right_knee' 32: 'medial_right_ankle' 33: 'lateral_right_ankle' 34: 'medial_left_hip' 35: 'lateral_left_hip' 36: 'medial_left_knee' 37: 'lateral_left_knee' 38: 'medial_left_ankle' 39: 'lateral_left_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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='PCKh', **kwargs)[source]¶
Evaluate PCKh for MPII-TRB dataset.
Note
batch_size: N num_keypoints: K heatmap height: H heatmap width: W
- Parameters
outputs (list(preds, boxes, image_paths, heatmap)) –
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, [‘/val2017/000000 397133.jpg’]
heatmap (np.ndarray[N, K, H, W]): model output heatmap.
bbox_ids (list[str]): For example, [‘27407’]
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metrics to be performed. Defaults: ‘PCKh’.
- Returns
PCKh for each joint
- Return type
dict
- class mmpose.datasets.datasets.top_down.TopDownOCHumanDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
OChuman dataset for top-down pose estimation.
“Occluded Human (OCHuman)” dataset contains 8110 heavily occluded human instances within 4731 images. OCHuman dataset is designed for validation and testing. To evaluate on OCHuman, the model should be trained on COCO training set, and then test the robustness of the model to occlusion using OCHuman.
OCHuman keypoint indexes (same as COCO):
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.top_down.TopDownPoseTrack18Dataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
PoseTrack18 dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
- PoseTrack2018 keypoint indexes::
0: ‘nose’, 1: ‘head_bottom’, 2: ‘head_top’, 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
Evaluate coco keypoint results. The pose prediction results will be saved in ${res_folder}/result_keypoints.json.
Note
num_keypoints: K
- Parameters
outputs (list(preds, boxes, image_paths)) –
- 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, [‘val/010016_mpii_test /000024.jpg’]
- heatmap (np.ndarray[N, K, H, W])
model output heatmap.
:bbox_id (list(int))
res_folder (str) – Path of directory to save the results.
metric (str | list[str]) – Metric to be performed. Defaults: ‘mAP’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.datasets.bottom_up.BottomUpAicDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
Aic dataset for bottom-up pose estimation.
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.bottom_up.BottomUpCocoDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- evaluate(outputs, res_folder, metric='mAP', **kwargs)[source]¶
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
outputs (list(preds, scores, image_path, heatmap)) –
preds (list[np.ndarray(P, K, 3+tag_num)]): Pose predictions for all people in images.
scores (list[P]):
image_path (list[str]): For example, [‘coco/images/
val2017/000000397133.jpg’] * 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. Defaults: ‘mAP’.
- Returns
Evaluation results for evaluation metric.
- Return type
dict
- class mmpose.datasets.datasets.bottom_up.BottomUpCrowdPoseDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
CrowdPose 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.
CrowdPose keypoint indexes:
0: 'left_shoulder', 1: 'right_shoulder', 2: 'left_elbow', 3: 'right_elbow', 4: 'left_wrist', 5: 'right_wrist', 6: 'left_hip', 7: 'right_hip', 8: 'left_knee', 9: 'right_knee', 10: 'left_ankle', 11: 'right_ankle', 12: 'top_head', 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
- class mmpose.datasets.datasets.bottom_up.BottomUpMhpDataset(ann_file, img_prefix, data_cfg, pipeline, test_mode=False)[source]¶
MHPv2.0 dataset for top-down pose estimation.
The Multi-Human Parsing project of Learning and Vision (LV) Group, National University of Singapore (NUS) is proposed to push the frontiers of fine-grained visual understanding of humans in crowd scene. <https://lv-mhp.github.io/>
The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information.
MHP keypoint indexes:
0: "right ankle", 1: "right knee", 2: "right hip", 3: "left hip", 4: "left knee", 5: "left ankle", 6: "pelvis", 7: "thorax", 8: "upper neck", 9: "head top", 10: "right wrist", 11: "right elbow", 12: "right shoulder", 13: "left shoulder", 14: "left elbow", 15: "left 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.
test_mode (bool) – Store True when building test or validation dataset. Default: False.
pipelines¶
- class mmpose.datasets.pipelines.loading.LoadImageFromFile(to_float32=False, color_type='color', channel_order='rgb')[source]¶
Loading image from file.
- Parameters
color_type (str) – Flags specifying the color type of a loaded image, candidates are ‘color’, ‘grayscale’ and ‘unchanged’.
channel_order (str) – Order of channel, candidates are ‘bgr’ and ‘rgb’.
Albumentation augmentation (pixel-level transforms only). Adds custom pixel-level transformations from Albumentations library. Please visit https://albumentations.readthedocs.io to get more information.
Note: we only support pixel-level transforms. Please visit https://github.com/albumentations-team/ albumentations#pixel-level-transforms to get more information about pixel-level transforms.
An example of
transforms
is as followed: .. code-block:[ dict( type='RandomBrightnessContrast', brightness_limit=[0.1, 0.3], contrast_limit=[0.1, 0.3], p=0.2), dict(type='ChannelShuffle', p=0.1), dict( type='OneOf', transforms=[ dict(type='Blur', blur_limit=3, p=1.0), dict(type='MedianBlur', blur_limit=3, p=1.0) ], p=0.1), ]
- Parameters
transforms (list[dict]) – A list of Albumentation transformations
keymap (dict) – Contains {‘input key’:’albumentation-style key’}, e.g., {‘img’: ‘image’}.
Import a module from albumentations.
It resembles some of
build_from_cfg()
logic. :param cfg: Config dict. It should at least contain the key “type”. :type cfg: dict- Returns
The constructed object.
- Return type
obj
Dictionary mapper.
Renames keys according to keymap provided. :param d: old dict :type d: dict :param keymap: {‘old_key’:’new_key’} :type keymap: dict
- Returns
new dict.
- Return type
dict
Collect data from the loader relevant to the specific task.
This keeps the items in keys as it is, and collect items in meta_keys into a meta item called meta_name.This is usually the last stage of the data loader pipeline. For example, when keys=’imgs’, meta_keys=(‘filename’, ‘label’, ‘original_shape’), meta_name=’img_metas’, the results will be a dict with keys ‘imgs’ and ‘img_metas’, where ‘img_metas’ is a DataContainer of another dict with keys ‘filename’, ‘label’, ‘original_shape’.
- Parameters
keys (Sequence[str|tuple]) – Required keys to be collected. If a tuple (key, key_new) is given as an element, the item retrived by key will be renamed as key_new in collected data.
meta_name (str) – The name of the key that contains meta infomation. This key is always populated. Default: “img_metas”.
meta_keys (Sequence[str|tuple]) – Keys that are collected under meta_name. The contents of the meta_name dictionary depends on meta_keys.
Compose a data pipeline with a sequence of transforms.
- Parameters
transforms (list[dict | callable]) – Either config dicts of transforms or transform objects.
Gather the targets for multitask heads.
- Parameters
pipeline_list (list[list]) – List of pipelines for all heads.
pipeline_indices (list[int]) – Pipeline index of each head.
Normalize the Tensor image (CxHxW), with mean and std.
Required key: ‘img’. Modifies key: ‘img’.
- Parameters
mean (list[float]) – Mean values of 3 channels.
std (list[float]) – Std values of 3 channels.
Apply photometric distortion to image sequentially, every transformation is applied with a probability of 0.5. The position of random contrast is in second or second to last.
random brightness
random contrast (mode 0)
convert color from BGR to HSV
random saturation
random hue
convert color from HSV to BGR
random contrast (mode 1)
randomly swap channels
- Parameters
brightness_delta (int) – delta of brightness.
contrast_range (tuple) – range of contrast.
saturation_range (tuple) – range of saturation.
hue_delta (int) – delta of hue.
Brightness distortion.
Contrast distortion.
Multiple with alpha and add beta with clip.
Rename the keys.
Args: key_pairs (Sequence[tuple]): Required keys to be renamed. If a tuple (key_src, key_tgt) is given as an element, the item retrived by key_src will be renamed as key_tgt.
Transform image to Tensor.
Required key: ‘img’. Modifies key: ‘img’.
- Parameters
results (dict) – contain all information about training.
- class mmpose.datasets.pipelines.top_down_transform.TopDownAffine(use_udp=False)[source]¶
Affine transform the image to make input.
Required keys:’img’, ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’,’scale’, ‘rotation’ and ‘center’. Modified keys:’img’, ‘joints_3d’, and ‘joints_3d_visible’.
- Parameters
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.top_down_transform.TopDownGenerateTarget(sigma=2, kernel=(11, 11), valid_radius_factor=0.0546875, target_type='GaussianHeatMap', encoding='MSRA', unbiased_encoding=False)[source]¶
Generate the target heatmap.
Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.
- Parameters
sigma – Sigma of heatmap gaussian for ‘MSRA’ approach.
kernel – Kernel of heatmap gaussian for ‘Megvii’ approach.
encoding (str) – Approach to generate target heatmaps. Currently supported approaches: ‘MSRA’, ‘Megvii’, ‘UDP’. Default:’MSRA’
unbiased_encoding (bool) – Option to use unbiased encoding methods. Paper ref: Zhang et al. Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR 2020).
keypoint_pose_distance – Keypoint pose distance for UDP. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
target_type (str) – supported targets: ‘GaussianHeatMap’, ‘CombinedTarget’. Default:’GaussianHeatMap’ 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).
- class mmpose.datasets.pipelines.top_down_transform.TopDownGenerateTargetRegression[source]¶
Generate the target regression vector (coordinates).
Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.
- class mmpose.datasets.pipelines.top_down_transform.TopDownGetRandomScaleRotation(rot_factor=40, scale_factor=0.5, rot_prob=0.6)[source]¶
Data augmentation with random scaling & rotating.
Required key: ‘scale’. Modifies key: ‘scale’ and ‘rotation’.
- Parameters
rot_factor (int) – Rotating to
[-2*rot_factor, 2*rot_factor]
.scale_factor (float) – Scaling to
[1-scale_factor, 1+scale_factor]
.rot_prob (float) – Probability of random rotation.
- class mmpose.datasets.pipelines.top_down_transform.TopDownHalfBodyTransform(num_joints_half_body=8, prob_half_body=0.3)[source]¶
Data augmentation with half-body transform. Keep only the upper body or the lower body at random.
Required keys: ‘joints_3d’, ‘joints_3d_visible’, and ‘ann_info’. Modifies key: ‘scale’ and ‘center’.
- Parameters
num_joints_half_body (int) – Threshold of performing half-body transform. If the body has fewer number of joints (< num_joints_half_body), ignore this step.
prob_half_body (float) – Probability of half-body transform.
- class mmpose.datasets.pipelines.top_down_transform.TopDownRandomFlip(flip_prob=0.5)[source]¶
Data augmentation with random image flip.
Required keys: ‘img’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’ and ‘ann_info’. Modifies key: ‘img’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’ and ‘flipped’.
- Parameters
flip (bool) – Option to perform random flip.
flip_prob (float) – Probability of flip.
- class mmpose.datasets.pipelines.top_down_transform.TopDownRandomTranslation(trans_factor=0.15)[source]¶
Data augmentation with random translation.
Required key: ‘scale’ and ‘center’. Modifies key: ‘center’.
Notes
bbox height: H bbox width: W
- Parameters
trans_factor (float) – Translating center to
``[-trans_factor –
* [W (trans_factor]) –
+ center``. (H]) –
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpGenerateHeatmapTarget(sigma, use_udp=False)[source]¶
Generate multi-scale heatmap target for bottom-up.
- Parameters
sigma (int) – Sigma of heatmap Gaussian
max_num_people (int) – Maximum number of people in an image
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpGeneratePAFTarget(limb_width, skeleton=None)[source]¶
Generate multi-scale heatmaps and part affinity fields (PAF) target for bottom-up. Paper ref: Cao et al. Realtime Multi-Person 2D Human Pose Estimation using Part Affinity Fields (CVPR 2017).
- Parameters
limb_width (int) – Limb width of part affinity fields
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpGenerateTarget(sigma, max_num_people, use_udp=False)[source]¶
Generate multi-scale heatmap target for bottom-up.
- Parameters
sigma (int) – Sigma of heatmap Gaussian
max_num_people (int) – Maximum number of people in an image
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpGetImgSize(test_scale_factor, current_scale=1, use_udp=False)[source]¶
Get multi-scale image sizes for bottom-up, including base_size and test_scale_factor. Keep the ratio and the image is resized to results[‘ann_info’][‘image_size’]×current_scale.
- Parameters
test_scale_factor (List[float]) – Multi scale
current_scale (int) – default 1
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpRandomAffine(rot_factor, scale_factor, scale_type, trans_factor, use_udp=False)[source]¶
Data augmentation with random scaling & rotating.
- Parameters
rot_factor (int) – Rotating to [-rotation_factor, rotation_factor]
scale_factor (float) – Scaling to [1-scale_factor, 1+scale_factor]
scale_type – wrt
long
orshort
length of the image.trans_factor – Translation factor.
scale_aware_sigma – Option to use scale-aware sigma
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpRandomFlip(flip_prob=0.5)[source]¶
Data augmentation with random image flip for bottom-up.
- Parameters
flip_prob (float) – Probability of flip.
- class mmpose.datasets.pipelines.bottom_up_transform.BottomUpResizeAlign(transforms, use_udp=False)[source]¶
Resize multi-scale size and align transform for bottom-up.
- Parameters
transforms (List) – ToTensor & Normalize
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.bottom_up_transform.HeatmapGenerator(output_size, num_joints, sigma=- 1, use_udp=False)[source]¶
Generate heatmaps for bottom-up models.
- Parameters
num_joints (int) – Number of keypoints
output_size (int) – Size of feature map
sigma (int) – Sigma of the heatmaps.
use_udp (bool) – To use 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).
- class mmpose.datasets.pipelines.bottom_up_transform.JointsEncoder(max_num_people, num_joints, output_size, tag_per_joint)[source]¶
Encodes the visible joints into (coordinates, score); The coordinate of one joint and its score are of int type.
(idx * output_size**2 + y * output_size + x, 1) or (0, 0).
- Parameters
max_num_people (int) – Max number of people in an image
num_joints (int) – Number of keypoints
output_size (int) – Size of feature map
tag_per_joint (bool) – Option to use one tag map per joint.
- class mmpose.datasets.pipelines.bottom_up_transform.PAFGenerator(output_size, limb_width, skeleton)[source]¶
Generate part affinity fields.
- Parameters
output_size (int) – Size of feature map.
limb_width (int) – Limb width of part affinity fields.
skeleton (list[list]) – connections of joints.
- class mmpose.datasets.pipelines.mesh_transform.IUVToTensor[source]¶
Transform IUV image to part index mask and uv coordinates image. The 3 channels of IUV image means: part index, u coordinates, v coordinates.
Required key: ‘iuv’, ‘ann_info’. Modifies key: ‘part_index’, ‘uv_coordinates’.
- Parameters
results (dict) – contain all information about training.
- class mmpose.datasets.pipelines.mesh_transform.LoadIUVFromFile(to_float32=False)[source]¶
Loading IUV image from file.
- class mmpose.datasets.pipelines.mesh_transform.MeshAffine[source]¶
Affine transform the image to get input image. Affine transform the 2D keypoints, 3D kepoints and IUV image too.
Required keys: ‘img’, ‘joints_2d’,’joints_2d_visible’, ‘joints_3d’, ‘joints_3d_visible’, ‘pose’, ‘iuv’, ‘ann_info’,’scale’, ‘rotation’ and ‘center’. Modifies key: ‘img’, ‘joints_2d’,’joints_2d_visible’, ‘joints_3d’, ‘pose’, ‘iuv’.
- class mmpose.datasets.pipelines.mesh_transform.MeshGetRandomScaleRotation(rot_factor=30, scale_factor=0.25, rot_prob=0.6)[source]¶
Data augmentation with random scaling & rotating.
Required key: ‘scale’. Modifies key: ‘scale’ and ‘rotation’.
- Parameters
rot_factor (int) – Rotating to
[-2*rot_factor, 2*rot_factor]
.scale_factor (float) – Scaling to
[1-scale_factor, 1+scale_factor]
.rot_prob (float) – Probability of random rotation.
- class mmpose.datasets.pipelines.mesh_transform.MeshRandomChannelNoise(noise_factor=0.4)[source]¶
Data augmentation with random channel noise.
Required keys: ‘img’ Modifies key: ‘img’
- Parameters
noise_factor (float) – Multiply each channel with a factor between``[1-scale_factor, 1+scale_factor]``
- class mmpose.datasets.pipelines.mesh_transform.MeshRandomFlip(flip_prob=0.5)[source]¶
Data augmentation with random image flip.
Required keys: ‘img’, ‘joints_2d’,’joints_2d_visible’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’, ‘pose’, ‘iuv’ and ‘ann_info’. Modifies key: ‘img’, ‘joints_2d’,’joints_2d_visible’, ‘joints_3d’, ‘joints_3d_visible’, ‘center’, ‘pose’, ‘iuv’.
- Parameters
flip_prob (float) – Probability of flip.
- class mmpose.datasets.pipelines.pose3d_transform.CameraProjection(item, mode, output_name=None, camera_type='SimpleCamera', camera_param=None)[source]¶
Apply camera projection to joint coordinates.
- Parameters
item (str) – The name of the pose to apply camera projection.
mode (str) – The type of camera projection, supported options are - world_to_camera - world_to_pixel - camera_to_world - camera_to_pixel
output_name (str|None) – The name of the projected pose. If None (default) is given, the projected pose will be stored in place.
camera_type (str) – The camera class name (should be registered in CAMERA).
camera_param (dict|None) – The camera parameter dict. See the camera class definition for more details. If None is given, the camera parameter will be obtained during processing of each data sample with the key “camera_param”.
- Required keys:
item camera_param (if camera parameters are not given in initialization)
- Modified keys:
output_name
- class mmpose.datasets.pipelines.pose3d_transform.Generate3DHeatmapTarget(sigma=2, joint_indices=None)[source]¶
Generate the target 3d heatmap.
Required keys: ‘joints_3d’, ‘joints_3d_visible’, ‘ann_info’. Modified keys: ‘target’, and ‘target_weight’.
- Parameters
sigma – Sigma of heatmap gaussian.
joint_indices (list) – Indices of joints used for heatmap generation.
None (If) –
- class mmpose.datasets.pipelines.pose3d_transform.GetRootCenteredPose(item, root_index, visible_item=None, remove_root=False, root_name=None)[source]¶
Zero-center the pose around a given root joint. Optionally, the root joint can be removed from the origianl pose and stored as a separate item.
Note that the root-centered joints may no longer align with some annotation information (e.g. flip_pairs, num_joints, inference_channel, etc.) due to the removal of the root joint.
- Parameters
item (str) – The name of the pose to apply root-centering.
root_index (int) – Root joint index in the pose.
visible_item (str) – The name of the visibility item.
remove_root (bool) – If true, remove the root joint from the pose
root_name (str) – Optional. If not none, it will be used as the key to store the root position separated from the original pose.
- Required keys:
item
- Modified keys:
item, visible_item, root_name
- class mmpose.datasets.pipelines.pose3d_transform.NormalizeJointCoordinate(item, mean=None, std=None, norm_param_file=None)[source]¶
Normalize the joint coordinate with given mean and std.
- Parameters
item (str) – The name of the pose to normalize.
mean (array) – Mean values of joint coordiantes in shape [K, C].
std (array) – Std values of joint coordinates in shape [K, C].
norm_param_file (str) – Optionally load a dict containing mean and std from a file using mmcv.load.
- Required keys:
item
- Modified keys:
item
- class mmpose.datasets.pipelines.pose3d_transform.PoseSequenceToTensor(item)[source]¶
Convert pose sequence from numpy array to Tensor.
The original pose sequence should have a shape of [T,K,C] or [K,C], where T is the sequence length, K and C are keypoint number and dimension. The converted pose sequence will have a shape of [K*C, T].
- Parameters
item (str) – The name of the pose sequence
- Requred keys:
item
- Modified keys:
item
- class mmpose.datasets.pipelines.pose3d_transform.RelativeJointRandomFlip(item, root_index, visible_item=None, flip_prob=0.5)[source]¶
Data augmentation with random horizontal joint flip around a root joint.
- Parameters
item (str) – The name of the pose to flip.
root_index (int) – Root joint index in the pose.
visible_item (str) – The name of the visibility item which will be flipped accordingly along with the pose.
flip_prob (float) – Probability of flip.
- Required keys:
item
- Modified keys:
item
samplers¶
- class mmpose.datasets.samplers.DistributedSampler(dataset, num_replicas=None, rank=None, shuffle=True, seed=0)[source]¶
DistributedSampler inheriting from torch.utils.data.DistributedSampler.
In pytorch of lower versions, there is no shuffle argument. This child class will port one to DistributedSampler.
mmpose.utils¶
- mmpose.utils.get_root_logger(log_file=None, log_level=20)[source]¶
Use get_logger method in mmcv to get the root logger.
The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If log_file is specified, a FileHandler will also be added. The name of the root logger is the top-level package name, e.g., “mmpose”.
- Parameters
log_file (str | None) – The log filename. If specified, a FileHandler will be added to the root logger.
log_level (int) – The root logger level. Note that only the process of rank 0 is affected, while other processes will set the level to “Error” and be silent most of the time.
- Returns
The root logger.
- Return type
logging.Logger