Scale the data in the corresponding channel.

Calculate accuracy according to the prediction and target. Args: pred (torch.Tensor): The model prediction, shape (N, num_class, ...)

Allreduce parameters. Args: params (list[torch.Parameters]): List of parameters or buffers of a model. coalesce (bool

Convert various input to color tuples. Args: color (:obj:`Color`/str/tuple/int/ndarray): Color inputs Returns: tuple[int]: A

Convert a video with ffmpeg. This provides a general api to ffmpeg, the executed command is:: `ffmpeg -y <pre_options> -i <in_file> <opt

Crop from ``img``

DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Encode images with backbone and decode into a semantic segmentation map of the same size as input.

Placeholder for Forward function for training.

Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict wher

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

Get class weight for loss function. Args: class_weight (list[float] | str | None): If class_weight is a str, take it as a fil

Read data from a given ``filepath`` with 'r' mode. Args: filepath (str or Path): Path to read data. encoding (str): T

Resize image to a given size. Args: img (ndarray): The input image. size (tuple[int]): Target size (w, h). return_scale (

Show an image. Args: img (str or ndarray): The image to be displayed. win_name (str): The window name. wait_time (int): V

Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or li

Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image i

load checkpoint through HTTP or HTTPS scheme path. In distributed setting, this function only download checkpoint at local rank 0. Args:

make plugins for block. Args: in_channels (int): Input channels of plugin. plugins (list[dict]): List of plugins cfg

Compute alpha_t of a given continuous-time label t in [0, T].

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be

Since built-in ``any`` works only when the element of iterable is not iterable, implement the function.

perform evaluation and save ckpt.

Freeze stages param and norm stats.

Sample from fine grained features. Args: prev_output (list[Tensor]): Prediction of previous decode head. points (Tens

Sample from fine grained features. Args: x (list[Tensor]): Feature pyramid from by neck or backbone. points (Tensor):

Initialize rule, key_indicator, comparison_func, and best score. Here is the rule to determine which rule is used for key indicator w

Override default load function. AWS overrides the function _load_from_state_dict to recover weight_gamma and weight_beta if they are

merge dict ``a`` into dict ``b`` (non-inplace). Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid in-place modific

Convert a pillow image to numpy array. Args: img (:obj:`PIL.Image.Image`): The image loaded using PIL flag (str): Flags specifyin

Save the current checkpoint and delete unwanted checkpoint.

Save the best checkpoint. It will compare the score according to the compare function, write related information (best score, best ch

Rescale a size by a ratio. Args: size (tuple[int]): (w, h). scale (float | tuple(float)): Scaling factor. Returns: t

Judge whether to perform evaluation. Here is the rule to judge whether to perform evaluation: 1. It will not perform evaluation durin

Swap the parameter of model with parameter in ema_buffer.

Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allr

Clip bboxes to fit the image shape. Args: bboxes (ndarray): Shape (..., 4*k) img_shape (tuple[int]): (height, width) of the image

Convert a BGR image to grayscale image. Args: img (ndarray): The input image. keepdim (bool): If False (by default), then return

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Build PyTorch DataLoader. In distributed training, each GPU/process has a dataloader. In non-distributed training, there is only one dataload

Build datasets.

Builder for drop out layers.