Generated point clouds look different when using batches

[ArmanMaesumi]

Hi, thank you for releasing the code base!

I encountered a strange issue. When I generate point clouds, my results are different depending on if I do it sequentially, or as a batch. Here is a small example (running inside model_test.py):

with torch.no_grad():
    batch_size = 10

    # generate random latent matrices for 10 shapes
    noise = self.noise_generator(bs=batch_size)

    # First try passing noise through model one at a time (sequentially)
    x = self.sphere_generator(bs=1)
    pcs_sequential= np.zeros((batch_size, 2048, 3))
    for i, latent_matrix in enumerate(noise):
        out_pc = self.G(x, latent_matrix[None])
        out_pc = out_pc.transpose(2, 1)
        out_pc = out_pc.cpu().detach().numpy()
        out_pc = normalize_point_cloud(out_pc)
        pcs_sequential[i] = out_pc 
    
    for i in range(batch_size): # Render results
        pyplot_draw_point_cloud(pcs_sequential[i], output_filename=f'nobatch_{i}.png')

    # Now try running all noise through model (full batch) 
    x = self.sphere_generator(bs=batch_size)
    
    out_pc = self.G(x, noise)
    out_pc = out_pc.transpose(2,1)

    pcs_batching = out_pc.cpu().detach().numpy()
    pcs_batching = normalize_point_cloud(pcs_batching)
    print (pcs_sequential - pcs_batching) # <--- We should expect this to be all zeros
    
    for i in range(batch_size): # Render results
        pyplot_draw_point_cloud(pcs_batching[i], output_filename=f'batch_{i}.png')

Here the point clouds generated sequentially and the point clouds generated in a batch are different. When I compute pcs_sequential - pcs_batching the values are non-zero, and the rendered point clouds are visibly different.

Any help would be appreciated!