| Initializer | +Registered name | +Function | +
|---|---|---|
| ConstantInit | +Constant | +Initialize the weight and bias with a constant, commonly used for Convolution | +
| XavierInit | +Xavier | +Initialize the weight by Xavier initialization, and initialize the bias with a constant | +
| NormalInit | +Normal | +Initialize the weight by normal distribution, and initialize the bias with a constant | +
| TruncNormalInit | +TruncNormal | +Initialize the weight by truncated normal distribution, and initialize the bias with a constant，commonly used for Transformer | +
| UniformInit | +Uniform | +Initialize the weight by uniform distribution, and initialize the bias with a constant，commonly used for convolution | +
| KaimingInit | +Kaiming | +Initialize the weight by Kaiming initialization, and initialize the bias with a constant. Commonly used for convolution | +
| Caffe2XavierInit | +Caffe2Xavier | +Xavier initialization in Caffe2, and Kaiming initialization in PyTorh with "fan_in" and "normal" mode. Commonly used for convolution | +
| PretrainedInit | +Pretrained | +Initialize the model with the pretrained model | +
| Initialization function | +Function | +
|---|---|
| constant_init | +Initialize the weight and bias with a constant, commonly used for Convolution | +
| xavier_init | +Initialize the weight by Xavier initialization, and initialize the bias with a constant | +
| normal_init | +Initialize the weight by normal distribution, and initialize the bias with a constant | +
| trunc_normal_init | +Initialize the weight by truncated normal distribution, and initialize the bias with a constant，commonly used for Transformer | +
| uniform_init | +Initialize the weight by uniform distribution, and initialize the bias with a constant，commonly used for convolution | +
| kaiming_init | +Initialize the weight by Kaiming initialization, and initialize the bias with a constant. Commonly used for convolution | +
| caffe2_xavier_init | +Xavier initialization in Caffe2, and Kaiming initialization in PyTorh with "fan_in" and "normal" mode. Commonly used for convolution | +
| bias_init_with_prob | +Initialize the bias with the probability | +
| Initializer | +Registered name | +Function | +
|---|---|---|
| ConstantInit | +Constant | +将 weight 和 bias 初始化为指定常量，通常用于初始化卷积 | +
| XavierInit | +Xavier | +将 weight Xavier 方式初始化，将 bias 初始化成指定常量，通常用于初始化卷积 | +
| NormalInit | +Normal | +将 weight 以正态分布的方式初始化，将 bias 初始化成指定常量，通常用于初始化卷积 | +
| TruncNormalInit | +TruncNormal | +将 weight 以被截断的正态分布的方式初始化，参数 a 和 b 为正态分布的有效区域；将 bias 初始化成指定常量，通常用于初始化 Transformer | +
| UniformInit | +Uniform | +将 weight 以均匀分布的方式初始化，参数 a 和 b 为均匀分布的范围；将 bias 初始化为指定常量，通常用于初始化卷积 | +
| KaimingInit | +Kaiming | +将 weight 以 Kaiming 的方式初始化，将 bias 初始化成指定常量，通常用于初始化卷积 | +
| Caffe2XavierInit | +Caffe2Xavier | +Caffe2 中 Xavier 初始化方式，在 Pytorch 中对应 "fan_in", "normal" 模式的 Kaiming 初始化，，通常用于初始化卷 | +
| Pretrained | +PretrainedInit | +加载预训练权重 | +
| 初始化函数 | +功能 | +
|---|---|
| constant_init | +将 weight 和 bias 初始化为指定常量，通常用于初始化卷积 | +
| xavier_init | +将 weight 以 Xavier 方式初始化，将 bias 初始化成指定常量，通常用于初始化卷积 | +
| normal_init | +将 weight 以正态分布的方式初始化，将 bias 初始化成指定常量，通常用于初始化卷积 | +
| trunc_normal_init | +将 weight 以被截断的正态分布的方式初始化，参数 a 和 b 为正态分布的有效区域；将 bias 初始化成指定常量，通常用于初始化 Transformer | +
| uniform_init | +将 weight 以均匀分布的方式初始化，参数 a 和 b 为均匀分布的范围；将 bias 初始化为指定常量，通常用于初始化卷积 | +
| kaiming_init | +将 weight 以 Kaiming 方式初始化，将 bias 初始化成指定常量，通常用于初始化卷积 | +
| caffe2_xavier_init | +Caffe2 中 Xavier 初始化方式，在 Pytorch 中对应 "fan_in", "normal" 模式的 Kaiming 初始化，通常用于初始化卷积 | +
| bias_init_with_prob | +以概率值的形式初始化 bias | +