hazdzz

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

class CausalConv1d(nn.Conv1d):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, enable_padding=False, dilation=1, groups=1, bias=True):
        if enable_padding == True:
            self.__padding = (kernel_size - 1) * dilation
        else:
            self.__padding = 0

yusuke0519

# # -*- coding: utf-8 -*-
import itertools
from torch.utils import data


def l2diff(x1, x2):
    """
    standard euclidean norm
    """
    return ((x1-x2)**2).sum().sqrt()

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''