Module to calculate the accuracy. Args: topk (tuple, optional): The criterion used to calculate the accuracy. Def

CrossEntropyLoss. Args: use_sigmoid (bool, optional): Whether the prediction uses sigmoid of softmax. Defaults to

Generate argparser from config file automatically (experimental)

Calculate the binary CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, 1) or (N, ). When the shape

Calculate the CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, C), C is the number of classes.

Dumps config into a file or returns a string representation of the config. If a file argument is given, saves the config to that file

Extra repr.

Find the position of next comma in the string. If no ',' is found in the string, return the string length. All chars inside '

Forward function to calculate accuracy. Args: pred (torch.Tensor): Prediction of models. target (torch.Tensor): Targe

Forward function. Args: cls_score (torch.Tensor): The prediction. label (torch.Tensor): The learning label of the pre

Generate config from config str. Args: cfg_str (str): Config str. file_format (str): Config file format corresponding

Calculate the CrossEntropy loss for masks. Args: pred (torch.Tensor): The prediction with shape (N, C, *), C is the number of

Merge list into cfg_dict. Merge the dict parsed by MultipleKVAction into this cfg. Examples: >>> options = {'model.backb

Replace the string "${key}" with the corresponding value. Replace the "${key}" with the value of ori_cfg.key in the config. And support repla

r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (

r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (

r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args

r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args:

Create a weighted version of a given loss function. To use this decorator, the loss function must have the signature like `loss_func(pred, ta

r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except fo

r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except for