Build loss.

A decorator factory to check if prerequisites are satisfied. Args: prerequisites (str of list[str]): Prerequisites to be checked.

Contrast distortion.

3x3 convolution with padding.

Copy gradients from fp16 model to fp32 weight copy.

Copy updated params from fp32 weight copy to fp16 model.

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

Get current learning rates. Returns: list[float] | dict[str, list[float]]: Current learning rates of all param gr

Return the data prediction model (with thresholding).

tpMap: center: tpMap[1, 0, :, :] displacement: tpMap[1, 1:5, :, :]

:param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and

Denormalize input grid from range [0, 1] to [-1, 1] Args: grid (Tensor): The grid to be denormalize, range [0, 1]. Returns: Te

Evaluate the dataset. Args: results (list): Testing results of the dataset. metric (str | list[str]): Metrics to be e

Extract features from images.

Flattens predictions in the batch (binary case) Remove labels equal to 'ignore_index'.

Flattens predictions in the batch.

Convert FLOPs number into a string. Note that Here we take a multiply-add counts as one FLOP. Args: flops (float): FLOPs number to b

Placeholder of forward function.

Forward function.

Placeholder of forward function.

Scatters points into voxels. Args: points (torch.Tensor): Points to be reduced into voxels. coors (torch.Tensor): Cor

Read data from a given ``filepath`` with 'rb' mode. Args: filepath (str or Path): Path to read data. Returns:

Randomly get a crop bounding box.

Get frame by index. Args: frame_id (int): Index of the expected frame, 0-based. Returns: ndarray or None: Re

Get hostname and username. Return empty string if exception raised, e.g. ``getpass.getuser()`` will lead to error in docker container

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Calculate parameter number of a model. Args: model (nn.module): The model for parameter number calculation. Returns: float:

Check if params contain overflow.

A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd).

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

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)

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

Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initi

Return the noise prediction model.

nn.Module: normalization layer after the first convolution layer

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

Prepare results dict for pipeline.

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 ``multiscale_mode=='range'``. Args: img_scales (list[tuple]): Images scale range for sampling.

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

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