models.py 文件源码

def forward(self, embeddings_supervised, speeds, is_reverse, steering_wheel, steering_wheel_raw, multiactions_vecs):
        def act(x):
            return F.leaky_relu(x, negative_slope=0.2, inplace=True)

        x_emb_sup = embeddings_supervised # 512x3x5
        x_emb_sup = act(self.emb_sup_c1_sd(self.emb_sup_c1_bn(self.emb_sup_c1(x_emb_sup)))) # 1024x1x3
        x_emb_sup = x_emb_sup.view(-1, 1024*1*3)
        x_emb_sup = add_white_noise(x_emb_sup, 0.005, self.training)

        x_emb_add = torch.cat([speeds, is_reverse, steering_wheel, steering_wheel_raw, multiactions_vecs], 1)
        x_emb_add = act(self.emb_add_fc1_bn(self.emb_add_fc1(x_emb_add)))
        x_emb_add = add_white_noise(x_emb_add, 0.005, self.training)

        x_emb = torch.cat([x_emb_sup, x_emb_add], 1)
        x_emb = F.dropout(x_emb, p=0.05, training=self.training)

        embs = F.relu(self.emb_fc1_bn(self.emb_fc1(x_emb)))
        # this is currently always on, to decrease the likelihood of systematic
        # errors that are repeated over many frames
        embs = add_white_noise(embs, 0.005, True)

        return embs