Method forward_attention

model.py:254–258

(self, h)

                module.bias.data.zero_()

    def forward_attention(self, h):
        A_ = self.attention_survival_net(h)  # h shape is N_tilesxdim
        A_raw = torch.transpose(A_, 1, 0)  # K_attention_classesxN_tiles
        A = F.softmax(A_raw, dim=-1)  # #normalize attentions scores over tiles
        return A_raw, A

    def forward_fusion(self, h1, h2, h3):