#    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.
import torch
from nnunet.network_architecture.generic_UNet import Generic_UNet
from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2
from nnunet.utilities.nd_softmax import softmax_helper
from torch import nn


class nnUNetTrainerV2_allConv3x3(nnUNetTrainerV2):
    def initialize_network(self):
        """
        - momentum 0.99
        - SGD instead of Adam
        - self.lr_scheduler = None because we do poly_lr
        - deep supervision = True
        - i am sure I forgot something here

        Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
        :return:
        """
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        for s in range(len(self.net_conv_kernel_sizes)):
            for i in range(len(self.net_conv_kernel_sizes[s])):
                self.net_conv_kernel_sizes[s][i] = 3

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.LeakyReLU
        net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}

        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper