def __init__(self, input_channels, base_num_features, num_classes, num_pool, num_conv_per_stage=2,
                 feat_map_mul_on_downscale=2, conv_op=nn.Conv2d,
                 norm_op=nn.BatchNorm2d, norm_op_kwargs=None,
                 dropout_op=nn.Dropout2d, dropout_op_kwargs=None,
                 nonlin=nn.LeakyReLU, nonlin_kwargs=None, deep_supervision=True, dropout_in_localization=False,
                 final_nonlin=softmax_helper, weightInitializer=InitWeights_He(1e-2), pool_op_kernel_sizes=None,
                 conv_kernel_sizes=None,
                 upscale_logits=False, convolutional_pooling=False, convolutional_upsampling=False,
                 max_num_features=None, basic_block=ConvDropoutNormNonlin,
                 seg_output_use_bias=False):
        """
        basically more flexible than v1, architecture is the same

        Does this look complicated? Nah bro. Functionality > usability

        This does everything you need, including world peace.

        Questions? -> f.isensee@dkfz.de
        """
        super(Generic_UNet, self).__init__()
        self.convolutional_upsampling = convolutional_upsampling
        self.convolutional_pooling = convolutional_pooling
        self.upscale_logits = upscale_logits
        if nonlin_kwargs is None:
            nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
        if dropout_op_kwargs is None:
            dropout_op_kwargs = {'p': 0.5, 'inplace': True}
        if norm_op_kwargs is None:
            norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}

        self.conv_kwargs = {'stride': 1, 'dilation': 1, 'bias': True}

        self.nonlin = nonlin
        self.nonlin_kwargs = nonlin_kwargs
        self.dropout_op_kwargs = dropout_op_kwargs
        self.norm_op_kwargs = norm_op_kwargs
        self.weightInitializer = weightInitializer
        self.conv_op = conv_op
        self.norm_op = norm_op
        self.dropout_op = dropout_op
        self.num_classes = num_classes
        self.final_nonlin = final_nonlin
        self._deep_supervision = deep_supervision
        self.do_ds = deep_supervision

        if conv_op == nn.Conv2d:
            upsample_mode = 'bilinear'
            pool_op = nn.MaxPool2d
            transpconv = nn.ConvTranspose2d
            if pool_op_kernel_sizes is None:
                pool_op_kernel_sizes = [(2, 2)] * num_pool
            if conv_kernel_sizes is None:
                conv_kernel_sizes = [(3, 3)] * (num_pool + 1)
        elif conv_op == nn.Conv3d:
            upsample_mode = 'trilinear'
            pool_op = nn.MaxPool3d
            transpconv = nn.ConvTranspose3d
            if pool_op_kernel_sizes is None:
                pool_op_kernel_sizes = [(2, 2, 2)] * num_pool
            if conv_kernel_sizes is None:
                conv_kernel_sizes = [(3, 3, 3)] * (num_pool + 1)
        else:
            raise ValueError(
                "unknown convolution dimensionality, conv op: %s" % str(conv_op))

        self.input_shape_must_be_divisible_by = np.prod(
            pool_op_kernel_sizes, 0, dtype=np.int64)
        self.pool_op_kernel_sizes = pool_op_kernel_sizes
        self.conv_kernel_sizes = conv_kernel_sizes

        self.conv_pad_sizes = []
        for krnl in self.conv_kernel_sizes:
            self.conv_pad_sizes.append([1 if i == 3 else 0 for i in krnl])

        if max_num_features is None:
            if self.conv_op == nn.Conv3d:
                self.max_num_features = self.MAX_NUM_FILTERS_3D
            else:
                self.max_num_features = self.MAX_FILTERS_2D
        else:
            self.max_num_features = max_num_features

        self.conv_blocks_context = []
        self.conv_blocks_localization = []
        self.td = []
        self.tu = []
        self.seg_outputs = []

        output_features = base_num_features
        input_features = input_channels

        for d in range(num_pool):
            # determine the first stride
            if d != 0 and self.convolutional_pooling:
                first_stride = pool_op_kernel_sizes[d - 1]
            else:
                first_stride = None

            self.conv_kwargs['kernel_size'] = self.conv_kernel_sizes[d]
            self.conv_kwargs['padding'] = self.conv_pad_sizes[d]
            # add convolutions
            self.conv_blocks_context.append(StackedConvLayers(input_features, output_features, num_conv_per_stage,
                                                              self.conv_op, self.conv_kwargs, self.norm_op,
                                                              self.norm_op_kwargs, self.dropout_op,
                                                              self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs,
                                                              first_stride, basic_block=basic_block))
            if not self.convolutional_pooling:
                self.td.append(pool_op(pool_op_kernel_sizes[d]))
            input_features = output_features
            output_features = int(
                np.round(output_features * feat_map_mul_on_downscale))

            output_features = min(output_features, self.max_num_features)

        # now the bottleneck.
        # determine the first stride
        if self.convolutional_pooling:
            first_stride = pool_op_kernel_sizes[-1]
        else:
            first_stride = None

        # the output of the last conv must match the number of features from the skip connection if we are not using
        # convolutional upsampling. If we use convolutional upsampling then the reduction in feature maps will be
        # done by the transposed conv
        if self.convolutional_upsampling:
            final_num_features = output_features
        else:
            final_num_features = self.conv_blocks_context[-1].output_channels

        self.conv_kwargs['kernel_size'] = self.conv_kernel_sizes[num_pool]
        self.conv_kwargs['padding'] = self.conv_pad_sizes[num_pool]
        self.conv_blocks_context.append(nn.Sequential(
            StackedConvLayers(input_features, output_features, num_conv_per_stage - 1, self.conv_op, self.conv_kwargs,
                              self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin,
                              self.nonlin_kwargs, first_stride, basic_block=basic_block),
            StackedConvLayers(output_features, final_num_features, 1, self.conv_op, self.conv_kwargs,
                              self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin,
                              self.nonlin_kwargs, basic_block=basic_block)))

        # if we don't want to do dropout in the localization pathway then we set the dropout prob to zero here
        if not dropout_in_localization:
            old_dropout_p = self.dropout_op_kwargs['p']
            self.dropout_op_kwargs['p'] = 0.0

        # now lets build the localization pathway
        for u in range(num_pool):
            nfeatures_from_down = final_num_features
            nfeatures_from_skip = self.conv_blocks_context[
                -(2 + u)].output_channels  # self.conv_blocks_context[-1] is bottleneck, so start with -2
            n_features_after_tu_and_concat = nfeatures_from_skip * 2

            # the first conv reduces the number of features to match those of skip
            # the following convs work on that number of features
            # if not convolutional upsampling then the final conv reduces the num of features again
            if u != num_pool - 1 and not self.convolutional_upsampling:
                final_num_features = self.conv_blocks_context[-(
                    3 + u)].output_channels
            else:
                final_num_features = nfeatures_from_skip

            if not self.convolutional_upsampling:
                self.tu.append(
                    Upsample(scale_factor=pool_op_kernel_sizes[-(u + 1)], mode=upsample_mode))
            else:
                self.tu.append(transpconv(nfeatures_from_down, nfeatures_from_skip, pool_op_kernel_sizes[-(u + 1)],
                                          pool_op_kernel_sizes[-(u + 1)], bias=False))

            self.conv_kwargs['kernel_size'] = self.conv_kernel_sizes[- (u + 1)]
            self.conv_kwargs['padding'] = self.conv_pad_sizes[- (u + 1)]
            self.conv_blocks_localization.append(nn.Sequential(
                StackedConvLayers(n_features_after_tu_and_concat, nfeatures_from_skip, num_conv_per_stage - 1,
                                  self.conv_op, self.conv_kwargs, self.norm_op, self.norm_op_kwargs, self.dropout_op,
                                  self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs, basic_block=basic_block),
                StackedConvLayers(nfeatures_from_skip, final_num_features, 1, self.conv_op, self.conv_kwargs,
                                  self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs,
                                  self.nonlin, self.nonlin_kwargs, basic_block=basic_block)
            ))

        for ds in range(len(self.conv_blocks_localization)):
            self.seg_outputs.append(conv_op(self.conv_blocks_localization[ds][-1].output_channels, num_classes,
                                            1, 1, 0, 1, 1, seg_output_use_bias))

        self.upscale_logits_ops = []
        cum_upsample = np.cumprod(
            np.vstack(pool_op_kernel_sizes), axis=0)[::-1]
        for usl in range(num_pool - 1):
            if self.upscale_logits:
                self.upscale_logits_ops.append(Upsample(scale_factor=tuple([int(i) for i in cum_upsample[usl + 1]]),
                                                        mode=upsample_mode))
            else:
                self.upscale_logits_ops.append(lambda x: x)

        if not dropout_in_localization:
            self.dropout_op_kwargs['p'] = old_dropout_p

        # register all modules properly
        self.conv_blocks_localization = nn.ModuleList(
            self.conv_blocks_localization)
        self.conv_blocks_context = nn.ModuleList(self.conv_blocks_context)
        self.td = nn.ModuleList(self.td)
        self.tu = nn.ModuleList(self.tu)
        self.seg_outputs = nn.ModuleList(self.seg_outputs)
        if self.upscale_logits:
            self.upscale_logits_ops = nn.ModuleList(
                self.upscale_logits_ops)  # lambda x:x is not a Module so we need to distinguish here

        if self.weightInitializer is not None:
            self.apply(self.weightInitializer)
            # self.apply(print_module_training_status)

        basically more flexible than v1, architecture is the same

        Does this look complicated? Nah bro. Functionality > usability

        This does everything you need, including world peace.

        Questions? -> f.isensee@dkfz.de
        
code/networks/nnunet.py