Pytorch - AttributeError: 'tuple' object has no attribute 'dim'

Question

I am trying to use this architecture:

class Net(BaseFeaturesExtractor):

    def __init__(self, observation_space: gym.spaces.Box, features_dim: int = 512):
        super(Net, self).__init__(observation_space, features_dim)

        self.conv1 = nn.Conv2d(1, 64, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
        self.conv4 = nn.Conv2d(64, 64, 3, stride=1)

        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(64)
        self.bn3 = nn.BatchNorm2d(64)
        self.bn4 = nn.BatchNorm2d(64)

        self.fc1 = nn.Linear(64 * (7 - 4) * (6 - 4), 128)
        self.fc_bn1 = nn.BatchNorm1d(128)

        self.fc2 = nn.Linear(128, 64)
        self.fc_bn2 = nn.BatchNorm1d(64)

        self.fc3 = nn.Linear(64, 7)

        self.fc4 = nn.Linear(64, 1)


    def forward(self, s):
        #                                                            s: batch_size x board_x x board_y
        s = s.view(-1, 1, 7, 6)  # batch_size x 1 x board_x x board_y
        s = F.relu(self.bn1(self.conv1(s)))  # batch_size x num_channels x board_x x board_y
        s = F.relu(self.bn2(self.conv2(s)))  # batch_size x num_channels x board_x x board_y
        s = F.relu(self.bn3(self.conv3(s)))  # batch_size x num_channels x (board_x-2) x (board_y-2)
        s = F.relu(self.bn4(self.conv4(s)))  # batch_size x num_channels x (board_x-4) x (board_y-4)
        s = s.view(-1,64 * (7 - 4) * (6 - 4))

        s = F.dropout(
            F.relu(self.fc_bn1(self.fc1(s))),
            p=0.3,
            training=self.training)  # batch_size x 128
        s = F.dropout(
            F.relu(self.fc_bn2(self.fc2(s))),
            p=0.3,
            training=self.training)  # batch_size x 64

        pi = self.fc3(s)  # batch_size x action_size
        v = self.fc4(s)  # batch_size x 1

        return F.log_softmax(pi, dim=1), th.tanh(v)

When I am trying to use this architecture, I am getting following error:

Traceback (most recent call last):
  File "/Users/joe/Documents/JUPYTER/ConnectX/training3.py", line 130, in <module>
    learner.learn(total_timesteps=iterations, callback=eval_callback)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/ppo/ppo.py", line 264, in learn
    reset_num_timesteps=reset_num_timesteps,
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/on_policy_algorithm.py", line 222, in learn
    continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/on_policy_algorithm.py", line 154, in collect_rollouts
    actions, values, log_probs = self.policy.forward(obs_tensor)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/policies.py", line 545, in forward
    latent_pi, latent_vf, latent_sde = self._get_latent(obs)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/policies.py", line 564, in _get_latent
    latent_pi, latent_vf = self.mlp_extractor(features)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/torch_layers.py", line 220, in forward
    shared_latent = self.shared_net(features)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/container.py", line 117, in forward
    input = module(input)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 93, in forward
    return F.linear(input, self.weight, self.bias)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/functional.py", line 1688, in linear
    if input.dim() == 2 and bias is not None:
AttributeError: 'tuple' object has no attribute 'dim'

How this problem can be fixed?

I'm not sure, but it looks like there's some issue with your BaseFeaturesExtractor class. Could you edit the question by adding the code for the class? — planet_pluto
– planet_pluto, Commented Nov 22, 2020 at 11:51
@planet_pluto It is part of Stable Baselines 3 library. github.com/DLR-RM/stable-baselines3/blob/… — Joe Rakhimov
– Joe Rakhimov, Commented Nov 22, 2020 at 13:20

planet_pluto · Accepted Answer · 2020-11-22 14:16:26Z

I tried to reproduce a small working code based on the class definitions given by you and I was able to get the outputs from the model. Here is the following code:

# BaseFeaturesExtractor class
import gym
import torch as th
from torch import nn

class BaseFeaturesExtractor(nn.Module):
    """
    Base class that represents a features extractor.
    :param observation_space:
    :param features_dim: Number of features extracted.
    """

    def __init__(self, observation_space: gym.Space, features_dim: int = 0):
        super(BaseFeaturesExtractor, self).__init__()
        assert features_dim > 0
        self._observation_space = observation_space
        self._features_dim = features_dim

    @property
    def features_dim(self) -> int:
        return self._features_dim

    def forward(self, observations: th.Tensor) -> th.Tensor:
        raise NotImplementedError()

# Net class
class Net(BaseFeaturesExtractor):

    def __init__(self, observation_space: gym.spaces.Box, features_dim: int = 512):
        super(Net, self).__init__(observation_space, features_dim)

        self.conv1 = nn.Conv2d(1, 64, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
        self.conv4 = nn.Conv2d(64, 64, 3, stride=1)

        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(64)
        self.bn3 = nn.BatchNorm2d(64)
        self.bn4 = nn.BatchNorm2d(64)

        self.fc1 = nn.Linear(64 * (7 - 4) * (6 - 4), 128)
        self.fc_bn1 = nn.BatchNorm1d(128)

        self.fc2 = nn.Linear(128, 64)
        self.fc_bn2 = nn.BatchNorm1d(64)

        self.fc3 = nn.Linear(64, 7)

        self.fc4 = nn.Linear(64, 1)


    def forward(self, s):
        #                                                            s: batch_size x board_x x board_y
        s = s.view(-1, 1, 7, 6)  # batch_size x 1 x board_x x board_y
        s = F.relu(self.bn1(self.conv1(s)))  # batch_size x num_channels x board_x x board_y
        s = F.relu(self.bn2(self.conv2(s)))  # batch_size x num_channels x board_x x board_y
        s = F.relu(self.bn3(self.conv3(s)))  # batch_size x num_channels x (board_x-2) x (board_y-2)
        s = F.relu(self.bn4(self.conv4(s)))  # batch_size x num_channels x (board_x-4) x (board_y-4)
        s = s.view(-1,64 * (7 - 4) * (6 - 4))

        s = F.dropout(
            F.relu(self.fc_bn1(self.fc1(s))),
            p=0.3,
            training=self.training)  # batch_size x 128
        s = F.dropout(
            F.relu(self.fc_bn2(self.fc2(s))),
            p=0.3,
            training=self.training)  # batch_size x 64

        pi = self.fc3(s)  # batch_size x action_size
        v = self.fc4(s)  # batch_size x 1

        return F.log_softmax(pi, dim=1), th.tanh(v)

# Minimal code to reproduce a forward pass

import numpy as np
import torch
import torch.nn.functional as F

params = gym.spaces.Box(np.array([-1,0,0]), np.array([+1,+1,+1]))
model = Net(params)
inputs = torch.randn(2, 1, 7, 6)
outputs = model(inputs)
print(outputs[0].shape, outputs[1].shape) # prints (torch.Size([2, 7]), torch.Size([2, 1]))

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