"""
Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

from torch import nn


class InitWeights_He(object):
    def __init__(self,
                 neg_slope: float = 1e-2,
                 mode: str = "fan_in",
                 nonlinearity="leaky_relu",
                 ):
        """
        Init weights according to https://arxiv.org/abs/1502.01852
        
        Args:
            neg_slope (float, optional): the negative slope of the rectifier
                used after this layer (only with 'leaky_relu').
                Defaults to 1e-2.
            mode: mode of `kaiming_normal_` mode
            nonlinearity: name of non linear function. Recommended only with
                relu and leaky relu
        """
        self.neg_slope = neg_slope

    def __call__(self, module: nn.Module):
        """
        Apply weight init
        
        Args:
            module: module to initialize weights of (only inits wights of convs)
        """
        if isinstance(module, (nn.Conv3d, nn.Conv2d, nn.ConvTranspose2d, nn.ConvTranspose3d)):
            module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope)
            if module.bias is not None:
                module.bias = nn.init.constant_(module.bias, 0)