Transfer gray map to matplotlib colormap

Check if params contain overflow.

Inference image(s) with the segmentor. Args: model (nn.Module): The loaded segmentor. imgs (str/ndarray or list[str/ndarray]): Ei

Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network)

Check whether it is a list of some type. A partial method of :func:`is_seq_of`.

Check if a method of base class is overridden in derived class. Args: method (str): the method name to check. base_class (type):

Cast elements of an iterable object into some type. Args: inputs (Iterable): The input object. dst_type (type): Destination type.

Concatenate all file paths. Args: filepath (str or Path): Path to be concatenated. Returns: str: The result

Compute 2D reprojection loss if 2D keypoint annotations are available. The confidence is binary and indicates whether the keypoints exist or

Compute 2D reprojection loss if 2D keypoint annotations are available. The confidence (conf) is binary and indicates whether the keypoints ex

Compute 3D keypoint loss if 3D keypoint annotations are available.

Compute 3D keypoint loss if 3D keypoint annotations are available.

Load the check point for resuming training or finetuning.

Load all the networks from the disk. Parameters: epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, na

Source: https://stackoverflow.com/a/10773699

Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``F

Computes gradient of the Lovasz extension w.r.t sorted errors. See Alg. 1 in paper.

Binary Lovasz hinge loss. Args: logits (torch.Tensor): [P], logits at each prediction (between -infty and +infty). la

Multi-class Lovasz-Softmax loss. Args: probs (torch.Tensor): [P, C], class probabilities at each prediction (between 0 and 1)

Encodes masks to an uncompressed RLE, in the format expected by pycoco tools.

create a single empty directory if it didn't exist Parameters: path (str) -- a single directory path

Test model with multiple gpus. This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu mod

nn.Module: normalization layer after the first convolution layer

[H, W, C] => [B=1, H, W, C]

Convert parameter number into a string. Args: num_params (float): Parameter number to be converted. units (str | None): Converted

Remove padding and upscale masks to the original image size. Arguments: masks (torch.Tensor): Batched masks from the mask_

Prepare results dict for pipeline.

Predicts masks. See 'forward' for more details.

Predict masks for the given input prompts, using the currently set image. Input prompts are batched torch tensors and are expected to

Normalize pixel values and pad to a square input.

Process an image and return face landmarks.

Write data to a given ``filepath`` with 'wb' mode. Note: ``put`` should create a directory if the directory of ``filepath``

Write data to a given ``filepath`` with 'w' mode. Note: ``put_text`` should create a directory if the directory of ``

Randomly sample an img_scale when ``ratio_range`` is specified. A ratio will be randomly sampled from the range specified by ``ratio_

Convert roi based relative point coordinates to image based absolute point coordinates. Args: rois (Tensor): RoIs or BBoxes, shape (N

Removes small disconnected regions and holes in a mask. Returns the mask and an indicator of if the mask has been modified.

Calculate the new size to be rescaled to. Args: old_size (tuple[int]): The old size (w, h) of image. scale (float | tuple[int]):

Resize pos_embed weights. Resize pos_embed using bicubic interpolate method. Args: pos_embed (torch.Tensor): pos_embed we

ResNet-101 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__. .. note:: The bottleneck of Torch

Compute a binary mask from an uncompressed RLE.