def forward()

in model/reader.py [0:0]

• 21 lines of code
• 2 McCabe index (conditional complexity)

    def forward(self, inputs):
        name = inputs['name'].long()  # (T, B, H, W, placement, name_len)
        T, B, height, width, n_placement, n_text = name.size()

        # encode everything
        cell = self.encode_cell(inputs)
        inv = self.encode_inv(inputs)
        wiki = self.encode_wiki(inputs)
        task = self.encode_task(inputs)

        rep = self.fuse(inputs, cell, inv, wiki, task)

        policy_logits = self.policy(rep)
        baseline = self.baseline(rep)

        # mask out invalid actions
        action_mask = inputs['valid'].float().view(T*B, -1)
        policy_logits -= (1-action_mask) * 1e20
        if self.training:
            action = torch.multinomial(F.softmax(policy_logits, dim=1), num_samples=1)
        else:
            # Don't sample when testing.
            action = torch.argmax(policy_logits, dim=1)

        policy_logits = policy_logits.view(T, B, self.num_actions)
        baseline = baseline.view(T, B)
        action = action.view(T, B)

        aux_loss = self.compute_aux_loss(inputs, cell, inv, wiki, task)
        return dict(policy_logits=policy_logits, baseline=baseline, action=action, aux_loss=aux_loss)