Variable deprecated- how to change the code

username: granth_jain
cooked: <p>Hi,</p>
<p>I am trying to implement a pytorch 0.2 code in pytorch 1.6</p>
<p>In 0.2 code it contains Variable wrapper in the code as below.</p>
<blockquote>
<p>while(True):<br>
values = <span class="chcklst-box fa fa-square-o fa-fw"></span><br>
log_probs = <span class="chcklst-box fa fa-square-o fa-fw"></span><br>
rewards = <span class="chcklst-box fa fa-square-o fa-fw"></span><br>
entropies = <span class="chcklst-box fa fa-square-o fa-fw"></span></p>
<pre><code>                for step in range(params.num_steps):
                    
                    if(done):
                        h_out = (Variable(torch.zeros([1, params.lstm_size])), Variable(torch.zeros([1, params.lstm_size])))
                        state = torch.DoubleTensor(env.reset())
                    else:
                        h_out = (Variable(h_out[0].data), Variable(h_out[1].data))

                    h_in = h_out
                    state = state 
                    value, action_values, h_out = model((Variable(state.reshape(1,-1)), h_in))
                    action_values = action_values.reshape(-1,)
                    prob = F.softmax(action_values - max(action_values), dim = 0)
                    log_prob = F.log_softmax(action_values - max(action_values), dim = 0)
                    entropy = -(log_prob * prob).sum()
                    entropies.append(entropy)
                    # action = epsilon_greedy(prob, epsilon)
                    action = Categorical(prob).sample().reshape(-1,)
                    log_prob_a = log_prob.gather(0, Variable(action))
                    values.append(value)
                    log_probs.append(log_prob_a)
                    # print("action_values:",action_values)
                    # print("prob:",prob)
                    # print("log_prob:",log_prob)
                    # print("action:",action, "log_prob_a:",log_prob_a)
                    state, reward, done, info, _ = env.step(action)
                    # reward = max(min(reward, 1), -1)
                    count +=1
                    
                    if done:
                        state = env.reset()

                    rewards.append(reward)
    
                    if done:
                        break
                    
                R = torch.zeros(1, 1)
                if not done:
                    value, _, _ = model((Variable(state.reshape(1,-1)), h_out))
                    R = value.data
                values.append(Variable(R))
                policy_loss = 0
                value_loss = 0
                R = Variable(R)
                gae = torch.zeros(1, 1)
                for i in reversed(range(len(rewards))):
                    R = params.gamma * R + rewards[i]
                    advantage = R - values[i]
                    value_loss = value_loss + 0.5 * advantage.pow(2)
                    TD = rewards[i] + params.gamma * values[i+1].data - values[i].data
                    gae = gae * params.gamma * params.tau + TD
                    policy_loss = policy_loss - log_probs[i] * Variable(gae) - 0.01 * entropies[i]

                optimizer.zero_grad()
                (policy_loss + 0.5 * value_loss).backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), 40)
                ensure_shared_grads(model, shared_model)
                optimizer.step()
</code></pre>
</blockquote>
<p>However variable is deprecated from pytorch 0.4, as I understand from the documentation-<br>
Variable and Tensors are merged , tensors start to record gradients when requires_grad attribute is set True.</p>
<p>So, Can I just change the whole code from Variable(x) to torch.DoubleTensor(x,requires_grad = True), or do I need to change anything else as well?</p>
<p>From documentation it also says to use detach().Can I just replace Variable(x) by x.detach() when reusing x??</p>
<p>Thanks</p>

username: Shima_Shahfar
cooked: <p>Yes, you can just remove the Variable outside and set requires_grad = True when you are creating your tensors. If you don’t need gradients for some part of your calculations you can just use variable_name.detach() and it will detach the output from the computational graph. Which means no gradient will be back-propagated along this variable.</p>