model.py 文件源码

def forward(self, prev_samples, upper_tier_conditioning):
        (batch_size, _, _) = upper_tier_conditioning.size()

        prev_samples = self.embedding(
            prev_samples.contiguous().view(-1)
        ).view(
            batch_size, -1, self.q_levels
        )

        prev_samples = prev_samples.permute(0, 2, 1)
        upper_tier_conditioning = upper_tier_conditioning.permute(0, 2, 1)

        x = F.relu(self.input(prev_samples) + upper_tier_conditioning)
        x = F.relu(self.hidden(x))
        x = self.output(x).permute(0, 2, 1).contiguous()

        return F.log_softmax(x.view(-1, self.q_levels)) \
                .view(batch_size, -1, self.q_levels)