Trying to implement Autoencoder in Pytorch

[dhirajsuvarna]

Hi,

I am trying to implement autendoer in pytorch and I did write the model which I suppose is excatly what is present in this repo.

Model in pytorch

class PCAutoEncoder(nn.Module):
    def __init__(self, point_dim, num_points):
        super(PCAutoEncoder, self).__init__()

        self.conv1 = nn.Conv1d(in_channels=point_dim, out_channels=64, kernel_size=1)
        self.conv2 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=1)
        self.conv3 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=1)
        self.conv4 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=1)
        self.conv5 = nn.Conv1d(in_channels=128, out_channels=1024, kernel_size=1)
        self.fc1 = nn.Linear(in_features=1024, out_features=1024)
        self.fc2 = nn.Linear(in_features=1024, out_features=1024)
        self.fc3 = nn.Linear(in_features=1024, out_features=num_points*3)
        #batch norm
        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(128)
        self.bn3 = nn.BatchNorm1d(1024)
    
    def forward(self, x):
        batch_size = x.shape[0]
        point_dim = x.shape[1]
        num_points = x.shape[2]
        #encoder
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn1(self.conv2(x)))
        x = F.relu(self.bn1(self.conv3(x)))
        x = F.relu(self.bn2(self.conv4(x)))
        x = F.relu(self.bn3(self.conv5(x)))
        # do max pooling 
        x = torch.max(x, 2, keepdim=True)[0]
        x = x.view(-1, 1024)
        # get the global embedding
        global_feat = x
        #decoder
        x = F.relu(self.bn3(self.fc1(x)))
        x = F.relu(self.bn3(self.fc2(x)))
        reconstructed_points = self.fc3(x)
        #do reshaping
        reconstructed_points = reconstructed_points.reshape(batch_size, point_dim, num_points)
        return reconstructed_points, global_feat

However, after training this model for 200 ephocs, when I try to generate the output point cloud all i can generate if scatterd points as shown below -

Any direction to figure out the problem would be helpful.

[dhirajsuvarna]

+1 @charlesq34

[skyir0n]

It is helpful to use only one category in datatset (suppose ShapeNet) so as to train this autoencoder model.

[dhirajsuvarna]

@skyir0n - the above result is from training only one category from the dataset.

[skyir0n]

In your code, batch normalization is performed by using the same BN module. Due to learnable paramerters in a BN module (alpha and gamma for scaling and shifting), they should be defined, respectively, at each layer.

[siddharthKatageri]

@dhirajsuvarna hi, did you solve the problem? what was going wrong?

[dvirginz]

+1 @dhirajsuvarna @siddharthKatageri

[dhirajsuvarna]

@dvirginz , @siddharthKatageri,
I do have some idea of whats going wrong, but haven't got the time to try it out. I will check this again and update you guys withing a week (fingers crossed)

[siddharthKatageri]

@dhirajsuvarna @dvirginz
here's a simple implementation of pointcloud autoencoder in pytorch

  def __init__(self, num_points):
    super().__init__()
    self.conv1 = nn.Conv1d(3,64,1)
    self.conv2 = nn.Conv1d(64,128,1)
    self.conv3 = nn.Conv1d(128,256,1)
    self.conv4 = nn.Conv1d(256,1024,1)
    
    self.bn1 = nn.BatchNorm1d(64)   
    self.bn2 = nn.BatchNorm1d(128)
    self.bn3 = nn.BatchNorm1d(256)
    self.bn4 = nn.BatchNorm1d(1024)

    ###############

    self.fc1 = nn.Linear(1024, 1024) 
    self.fc2 = nn.Linear(1024, 1024)   
    self.fc3 = nn.Linear(1024, num_points*3)

    self.bn5 = nn.BatchNorm1d(1024)
    self.bn6 = nn.BatchNorm1d(1024)
    


  def forward(self, input):
    batchsize, dim, npoints = input.shape
    xb = F.relu(self.bn1(self.conv1(input)))
    xb = F.relu(self.bn2(self.conv2(xb)))
    xb = F.relu(self.bn3(self.conv3(xb)))
    xb = self.bn4(self.conv4(xb))
    xb = nn.MaxPool1d(xb.size(-1))(xb)  

    ######################
    embedding = nn.Flatten(1)(xb)  
    #can also be written as (xb.view(-1, 1024))
    ######################
      
    xb = F.relu(self.bn5(self.fc1(embedding)))
    xb = F.relu(self.bn6(self.fc2(xb)))
    output = self.fc3(xb)
    output = output.view(batchsize, dim, npoints)
    return  output, embedding

I have used chamfer distance as a loss function provided by pytorch3d.
Tried on bed class in ModelNet dataset and the model is able to reconstruct from the embedding computed by the encoder.

[saltoricristiano]

Hi @siddharthKatageri,
thanks for sharing your autoencoder! I'm trying to run your model on ModelNet dataset with pytorch3d chamfer loss, but actually I'm having some problem to get reasonable results.
ORIGINAL

RECONSTRUCTED

I'm using all a smaller version of ModelNet with 10 classes. Did you use only one of them? Any additional hint?
If you can help, thank you very much in advance!

[Xin546946]

I have the same issue as well. Is there some tricks on training the pointnet ae? I think pointnet ae is not the best choice for reconstructing the point cloud since MLP is not able to control the permutation invariance but it should work rather than a cluster cloud.

[L1nn97]

same issue with u guys, i tried to use emd loss, the reconstructed point cloud always looks like a strange cube.

[saltoricristiano]

Hi all, are you training the auto encoder on all the classes? I was able to obtain good reconstructions only with per-class trainings...

[L1nn97]

Hi all, are you training the auto encoder on all the classes? I was able to obtain good reconstructions only with per-class trainings...

thank！wondering how many points u sampled during training for each batch？

[piseabhijeet]

Hi all, are you training the auto encoder on all the classes? I was able to obtain good reconstructions only with per-class trainings...

Hi @saltoricristiano

Thank you for reporting the work. But an AE should scale up for multiple classes too. Isn't it?

[piseabhijeet]

Hi,

I am trying to implement autendoer in pytorch and I did write the model which I suppose is excatly what is present in this repo.

Model in pytorch

class PCAutoEncoder(nn.Module):
    def __init__(self, point_dim, num_points):
        super(PCAutoEncoder, self).__init__()

        self.conv1 = nn.Conv1d(in_channels=point_dim, out_channels=64, kernel_size=1)
        self.conv2 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=1)
        self.conv3 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=1)
        self.conv4 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=1)
        self.conv5 = nn.Conv1d(in_channels=128, out_channels=1024, kernel_size=1)
        self.fc1 = nn.Linear(in_features=1024, out_features=1024)
        self.fc2 = nn.Linear(in_features=1024, out_features=1024)
        self.fc3 = nn.Linear(in_features=1024, out_features=num_points*3)
        #batch norm
        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(128)
        self.bn3 = nn.BatchNorm1d(1024)
    
    def forward(self, x):
        batch_size = x.shape[0]
        point_dim = x.shape[1]
        num_points = x.shape[2]
        #encoder
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn1(self.conv2(x)))
        x = F.relu(self.bn1(self.conv3(x)))
        x = F.relu(self.bn2(self.conv4(x)))
        x = F.relu(self.bn3(self.conv5(x)))
        # do max pooling 
        x = torch.max(x, 2, keepdim=True)[0]
        x = x.view(-1, 1024)
        # get the global embedding
        global_feat = x
        #decoder
        x = F.relu(self.bn3(self.fc1(x)))
        x = F.relu(self.bn3(self.fc2(x)))
        reconstructed_points = self.fc3(x)
        #do reshaping
        reconstructed_points = reconstructed_points.reshape(batch_size, point_dim, num_points)
        return reconstructed_points, global_feat

However, after training this model for 200 ephocs, when I try to generate the output point cloud all i can generate if scatterd points as shown below -

Any direction to figure out the problem would be helpful.

Hi @dhirajsuvarna

Did you get it to work finally?

[piseabhijeet]

Also @saltoricristiano

check this out:
#1 (comment)

I hope you are doing the same

[CUN-bjy]

Hi,

I am trying to implement autendoer in pytorch and I did write the model which I suppose is excatly what is present in this repo.

Model in pytorch

after training this model for 200 ephocs, when I try to generate the output point cloud all i can generate if scatterd points as shown below -

Any direction to figure out the problem would be helpful.

I tested with @dhirajsuvarna 's code(https://github.com/dhirajsuvarna/pointnet-autoencoder-pytorch).
And I also have the same problem.

In my case, the shape of tensor inputs was wrong(specifically for the chamfer loss module)(https://github.com/dhirajsuvarna/pointnet-autoencoder-pytorch/blob/3bb4a90a8bc016c1d3ab3ab7433f039fb3759196/train_shapenet.py#L64-L83)

for chamfer loss, the tensor shape has to be (batch_size, num_points, num_dim).
but the above code's shape was (batch_size, num_dim, num_points).

            points = data
            points = points.transpose(2, 1)
            points = points.to(device)

            optimizer.zero_grad()
            reconstructed_points, latent_vector = autoencoder(points)

            points = points.transpose(1, 2)
            reconstructed_points = reconstructed_points.transpose(1, 2)
            dist1, dist2 = chamfer_dist(points, reconstructed_points)
            train_loss = (torch.mean(dist1)) + (torch.mean(dist2))

plus, there was just 3 batch norm layer while applied layers are 7.
https://github.com/dhirajsuvarna/pointnet-autoencoder-pytorch/blob/3bb4a90a8bc016c1d3ab3ab7433f039fb3759196/model/model.py#L31-L33

With 3 batchnorm layer, it works well in train mode but it doesn't work well in eval mode of autoencoder.
By this, pytorch/pytorch#5406

after changing this, work well.

[PoopBear1]

Hi,
I am trying to implement autendoer in pytorch and I did write the model which I suppose is excatly what is present in this repo.
Model in pytorch
after training this model for 200 ephocs, when I try to generate the output point cloud all i can generate if scatterd points as shown below -
Any direction to figure out the problem would be helpful.

I tested with @dhirajsuvarna 's code(https://github.com/dhirajsuvarna/pointnet-autoencoder-pytorch). And I also have the same problem.

In my case, the shape of tensor inputs was wrong(specifically for the chamfer loss module)(https://github.com/dhirajsuvarna/pointnet-autoencoder-pytorch/blob/3bb4a90a8bc016c1d3ab3ab7433f039fb3759196/train_shapenet.py#L64-L83)

for chamfer loss, the tensor shape has to be (batch_size, num_points, num_dim). but the above code's shape was (batch_size, num_dim, num_points).

            points = data
            points = points.transpose(2, 1)
            points = points.to(device)

            optimizer.zero_grad()
            reconstructed_points, latent_vector = autoencoder(points)

            points = points.transpose(1, 2)
            reconstructed_points = reconstructed_points.transpose(1, 2)
            dist1, dist2 = chamfer_dist(points, reconstructed_points)
            train_loss = (torch.mean(dist1)) + (torch.mean(dist2))

plus, there was just 3 batch norm layer while applied layers are 7. https://github.com/dhirajsuvarna/pointnet-autoencoder-pytorch/blob/3bb4a90a8bc016c1d3ab3ab7433f039fb3759196/model/model.py#L31-L33

With 3 batchnorm layer, it works well in train mode but it doesn't work well in eval mode of autoencoder. By this, pytorch/pytorch#5406

after changing this, work well.

Thanks for sharing this correct form that Chamfer Distance requires.
Do you happen to know what is going on after changing the code to (batch_size, num_points, num_dim)?
My predicted output becomes a weird 3D cube from the sparse points (as the output 2).

Appreciated your help in advance.