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IntelLabs / bayesian-torch

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

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Kernel_size

flydephone opened this issue · comments

commented

The Conv2dReparameterization only allows kernels with same dim (e.g., 2×2)
However, some CNN model has different kernels (e.g., in inceptionresnetv2, the kernel size in block17 is 1×7）

So, I modified the code in conv_variational.py from:

        self.mu_kernel = Parameter(
            torch.Tensor(out_channels, in_channels // groups, kernel_size,
                         kernel_size))
        self.rho_kernel = Parameter(
            torch.Tensor(out_channels, in_channels // groups, kernel_size,
                         kernel_size))
        self.register_buffer(
            'eps_kernel',
            torch.Tensor(out_channels, in_channels // groups, kernel_size,
                         kernel_size),
            persistent=False)
        self.register_buffer(
            'prior_weight_mu',
            torch.Tensor(out_channels, in_channels // groups, kernel_size,
                         kernel_size),
            persistent=False)
        self.register_buffer(
            'prior_weight_sigma',
            torch.Tensor(out_channels, in_channels // groups, kernel_size,
                         kernel_size),
            persistent=False)

to

        self.mu_kernel = Parameter(
            torch.Tensor(out_channels, in_channels // groups, kernel_size[0],
                         kernel_size[1]))
        self.rho_kernel = Parameter(
            torch.Tensor(out_channels, in_channels // groups, kernel_size[0],
                         kernel_size[1]))
        self.register_buffer(
            'eps_kernel',
            torch.Tensor(out_channels, in_channels // groups, kernel_size[0],
                         kernel_size[1]),
            persistent=False)
        self.register_buffer(
            'prior_weight_mu',
            torch.Tensor(out_channels, in_channels // groups, kernel_size[0],
                         kernel_size[1]),
            persistent=False)
        self.register_buffer(
            'prior_weight_sigma',
            torch.Tensor(out_channels, in_channels // groups, kernel_size[0],
                         kernel_size[1]),
            persistent=False)

also, the kernel_size=d.kernel_size[0] was changes to kernel_size=d.kernel_size in dnn_to_cnn.py

commented

@flydephone Thank you for creating the issue and your contribution to Bayesian-Torch. All the convolutional layers (Conv2dReparameterization, Conv3dReparameterization, ConvTranspose2dReparameterization, ConvTranspose3dReparameterization, Conv2dFlipout, Conv3dFlipout, ConvTranspose2dFlipout, ConvTranspose3dFlipout) are now enabled to support arbitrary kernel sizes.
Commit a8543ad (@msubedar ) fixes this issue.