How can I convert this tensoflow code to pytorch?

#tensoflow
Conv2D(self.filter_1, (1, 64), activation='elu', padding="same",kernel_constraint=max_norm(2., axis=(0, 1, 2)))

nn.Sequential(
        nn.Conv2D(16, (1, 64), padding="same",kernel_constraint=max_norm(2., axis=(0, 1, 2)),
        nn.ELU()
        )

CodePudding user response：

You need two things:

1. You need to know what the input channel size is. In your example, you've only given the number of output channels, 16. Keras calculates this on its own during runtime, but you have to specify input channels when making torch nn.Conv2d.
2. You need to implement the max_norm constraint on the conv kernel yourself.

With this in mind, let's write a simple wrapper around the nn.Conv2d, that just enforces the constraint on the weights each time forward is called:

import torch
from torch import nn
import torch.nn.functional as F

class Conv2D_Norm_Constrained(nn.Conv2d):
    def __init__(self, max_norm_val, norm_dim, **kwargs):
        super().__init__(**kwargs)
        self.max_norm_val = max_norm_val
        self.norm_dim = norm_dim

    def get_constrained_weights(self, epsilon=1e-8):
        norm = self.weight.norm(2, dim=self.norm_dim, keepdim=True)
        return self.weight * (torch.clamp(norm, 0, self.max_norm_val) / (norm   epsilon))

    def forward(self, input):
        return F.conv2d(input, self.get_constrained_weights(), self.bias, self.stride, self.padding, self.dilation, self.groups)

Assuming your input channels are something like 8, we can write:

nn.Sequential(
    Conv2D_Norm_Constrained(in_channels=8, out_channels=16, kernel_size=(1, 64), padding="same", max_norm_val=2.0, norm_dim=(0, 1, 2)),
    nn.ELU()
)