Codecs

In the keypoint detection task, depending on the algorithm, it is often necessary to generate targets in different formats, such as normalized coordinates, vectors and heatmaps, etc. Similarly, for the model outputs, a decoding process is required to transform them into coordinates.

Encoding and decoding are closely related and inverse each other. In earlier versions of MMPose, encoding and decoding are implemented at different modules, making it less intuitive and unified.

MMPose 1.0 introduced a new module Codec to integrate the encoding and decoding together in a modular and user-friendly form.

Here is a diagram to show where the Codec is:

A typical codec consists of two parts:

• Encoder

• Decoder

Encoder

The encoder transforms the coordinates in the input image space into the needed target format:

• Normalized Coordinates

• One-dimensional Vectors

• Gaussian Heatmaps

For example, in the Regression-based method, the encoder will be:

def encode(self,
           keypoints: np.ndarray,
           keypoints_visible: Optional[np.ndarray] = None) -> dict:
    """Encoding keypoints from input image space to normalized space.

    Args:
        keypoints (np.ndarray): Keypoint coordinates in shape (N, K, D)
        keypoints_visible (np.ndarray): Keypoint visibilities in shape
            (N, K)

    Returns:
        dict:
        - keypoint_labels (np.ndarray): The normalized regression labels in
            shape (N, K, D) where D is 2 for 2d coordinates
        - keypoint_weights (np.ndarray): The target weights in shape
            (N, K)
    """
    if keypoints_visible is None:
        keypoints_visible = np.ones(keypoints.shape[:2], dtype=np.float32)

    w, h = self.input_size
    valid = ((keypoints >= 0) &
             (keypoints <= [w - 1, h - 1])).all(axis=-1) & (
                 keypoints_visible > 0.5)

    keypoint_labels = (keypoints / np.array([w, h])).astype(np.float32)
    keypoint_weights = np.where(valid, 1., 0.).astype(np.float32)

    encoded = dict(
        keypoint_labels=keypoint_labels, keypoint_weights=keypoint_weights)

    return encoded

The encoded data is converted to Tensor format in PackPoseInputs and packed in data_sample.gt_instance_labels for model calls, which is generally used for loss calculation, as demonstrated by loss() in RegressionHead.

def loss(self,
         inputs: Tuple[Tensor],
         batch_data_samples: OptSampleList,
         train_cfg: ConfigType = {}) -> dict:
    """Calculate losses from a batch of inputs and data samples."""

    pred_outputs = self.forward(inputs)

    keypoint_labels = torch.cat(
        [d.gt_instance_labels.keypoint_labels for d in batch_data_samples])
    keypoint_weights = torch.cat([
        d.gt_instance_labels.keypoint_weights for d in batch_data_samples
    ])

    # calculate losses
    losses = dict()
    loss = self.loss_module(pred_outputs, keypoint_labels,
                            keypoint_weights.unsqueeze(-1))

    losses.update(loss_kpt=loss)
    ### Omitted ###

Decoder

The decoder transforms the model outputs into coordinates in the input image space, which is the opposite processing of the encoder.

For example, in the Regression-based method, the decoder will be:

def decode(self, encoded: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    """Decode keypoint coordinates from normalized space to input image
    space.

    Args:
        encoded (np.ndarray): Coordinates in shape (N, K, D)

    Returns:
        tuple:
        - keypoints (np.ndarray): Decoded coordinates in shape (N, K, D)
        - scores (np.ndarray): The keypoint scores in shape (N, K).
            It usually represents the confidence of the keypoint prediction

    """

    if encoded.shape[-1] == 2:
        N, K, _ = encoded.shape
        normalized_coords = encoded.copy()
        scores = np.ones((N, K), dtype=np.float32)
    elif encoded.shape[-1] == 4:
        # split coords and sigma if outputs contain output_sigma
        normalized_coords = encoded[..., :2].copy()
        output_sigma = encoded[..., 2:4].copy()
        scores = (1 - output_sigma).mean(axis=-1)
    else:
        raise ValueError(
            'Keypoint dimension should be 2 or 4 (with sigma), '
            f'but got {encoded.shape[-1]}')

    w, h = self.input_size
    keypoints = normalized_coords * np.array([w, h])

    return keypoints, scores

By default, the decode() method only performs decoding on a single instance. You can also implement the batch_decode() method to boost the decoding process.

Common Usage

The example below shows how to use a codec in your config:

• Define the Codec

• Generate Targets

• Head

Define the Codec

Take the Regression-based method to generate normalized coordinates as an example, you can define a codec in your config as follows:

codec = dict(type='RegressionLabel', input_size=(192, 256))

Generate Targets

In pipelines, A codec should be passed into GenerateTarget to work as the encoder:

dict(type='GenerateTarget', encoder=codec)

Head

In MMPose workflows, we decode the model outputs in Head, which requires a codec to work as the decoder:

head=dict(
    type='RLEHead',
    in_channels=2048,
    num_joints=17,
    loss=dict(type='RLELoss', use_target_weight=True),
    decoder=codec
)

Here is the phase of a config file:

# codec settings
codec = dict(type='RegressionLabel', input_size=(192, 256))                     ## definition ##

# model settings
model = dict(
    type='TopdownPoseEstimator',
    data_preprocessor=dict(
        type='PoseDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True),
    backbone=dict(
        type='ResNet',
        depth=50,
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
    ),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='RLEHead',
        in_channels=2048,
        num_joints=17,
        loss=dict(type='RLELoss', use_target_weight=True),
        decoder=codec),                                                         ## Head ##
    test_cfg=dict(
        flip_test=True,
        shift_coords=True,
    ))

# base dataset settings
dataset_type = 'CocoDataset'
data_mode = 'topdown'
data_root = 'data/coco/'

file_client_args = dict(backend='disk')

# pipelines
train_pipeline = [
    dict(type='LoadImage', file_client_args=file_client_args),
    dict(type='GetBBoxCenterScale'),
    dict(type='RandomFlip', direction='horizontal'),
    dict(type='RandomHalfBody'),
    dict(type='RandomBBoxTransform'),
    dict(type='TopdownAffine', input_size=codec['input_size']),
    dict(type='GenerateTarget', encoder=codec),   ## Generate Target ##
    dict(type='PackPoseInputs')
]
test_pipeline = [
    dict(type='LoadImage', file_client_args=file_client_args),
    dict(type='GetBBoxCenterScale'),
    dict(type='TopdownAffine', input_size=codec['input_size']),
    dict(type='PackPoseInputs')
]