def forward()

in torchbeast/monobeast.py [0:0]

• 41 lines of code
• 6 McCabe index (conditional complexity)

    def forward(self, inputs, core_state=()):
        x = inputs["frame"]  # [T, B, C, H, W].
        T, B, *_ = x.shape
        x = torch.flatten(x, 0, 1)  # Merge time and batch.
        x = x.float() / 255.0
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = x.view(T * B, -1)
        x = F.relu(self.fc(x))

        one_hot_last_action = F.one_hot(
            inputs["last_action"].view(T * B), self.num_actions
        ).float()
        clipped_reward = torch.clamp(inputs["reward"], -1, 1).view(T * B, 1)
        core_input = torch.cat([x, clipped_reward, one_hot_last_action], dim=-1)

        if self.use_lstm:
            core_input = core_input.view(T, B, -1)
            core_output_list = []
            notdone = (~inputs["done"]).float()
            for input, nd in zip(core_input.unbind(), notdone.unbind()):
                # Reset core state to zero whenever an episode ended.
                # Make `done` broadcastable with (num_layers, B, hidden_size)
                # states:
                nd = nd.view(1, -1, 1)
                core_state = tuple(nd * s for s in core_state)
                output, core_state = self.core(input.unsqueeze(0), core_state)
                core_output_list.append(output)
            core_output = torch.flatten(torch.cat(core_output_list), 0, 1)
        else:
            core_output = core_input
            core_state = tuple()

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

        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)

        return (
            dict(policy_logits=policy_logits, baseline=baseline, action=action),
            core_state,
        )