r"""Returns Hanning (also known as Hann) kernel, used in signal processing and KCF tracker. .. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}

Get features from i3d model and flatten them to 1d feature, valid target endpoints are defined in InceptionI3d.VALID_ENDPOINTS VALID_ENDP

Calculate keypoint loss. Note: - batch_size: N - num_keypoints: K Args: output (torch.Tensor[N,

intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]] Return batched fx, fy, cx, cy

Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).

There is the initial point and 3 points per cubic bezier curve. Thus, the curve will only pass though n points, which will be the sharp e

r"""Function that returns kernel for 1st or 2nd order scale pyramid gradients, using one of the following operators: sobel, diff.

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. :param timesteps: a 1-D

Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the co

Render 3D meshes on background image. Args: img(np.ndarray): Background image. vertices (list of np.ndarray): Vetrex coordinates

Inference function. Returns: output_regression (np.ndarray): Output regression. Args: x (torch.Tensor[N, K,

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Weight initialization for model.

Initialize model weights.

Initialize model weights.

Initialize model weights.

Initialize model weights.

Check if is one of the norms.

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

Get final keypoint predictions from 3d heatmaps and transform them back to the image. Note: - batch size: N - num keypoints:

Create a linear module.

Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Accept local filepath, URL,

Open a checkpoint, build Multi-HMR using saved arguments, load the model weigths.

load an ``obj`` mesh. Args: path (str): path to mesh. albedo_path (str, optional): path to the albedo texture image,

load a mesh using ``trimesh.load()``. Can load various formats like ``glb`` and serves as a fallback. Note: We will try

Make a ResLayer.

Dictionary mapper. Renames keys according to keymap provided. Args: d (dict): old dict keymap (dict): {'old_

Convert rotations given as rotation matrices to axis/angle. Args: matrix: Rotation matrices as tensor of shape (..., 3, 3). Ret

nn.Module: the normalization layer named "norm1"

nn.Module: the normalization layer named "norm1"

nn.Module: the normalization layer named "norm2"

This method will be invoked on event `_exit_`. Subclasses should override this method to specifying the exiting behavior.

DARK post-pocessing. Implemented by udp. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human

Pre-process, such as pre-pad and mod pad, so that the images can be divisible