Spaces:
Sleeping
Sleeping
| import torch | |
| from torch import nn | |
| from typing import Any | |
| class BatchNormConv1d(nn.Module): | |
| """ | |
| A nn.Conv1d followed by an optional activation function, and nn.BatchNorm1d | |
| """ | |
| def __init__( | |
| self, | |
| in_dim: int, | |
| out_dim: int, | |
| kernel_size: int, | |
| stride: int, | |
| padding: int, | |
| activation: Any = None, | |
| ): | |
| super().__init__() | |
| self.conv1d = nn.Conv1d( | |
| in_dim, | |
| out_dim, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| bias=False, | |
| ) | |
| self.bn = nn.BatchNorm1d(out_dim) | |
| self.activation = activation | |
| def forward(self, x: Any): | |
| x = self.conv1d(x) | |
| if self.activation is not None: | |
| x = self.activation(x) | |
| return self.bn(x) | |
| class LinearNorm(torch.nn.Module): | |
| def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): | |
| super().__init__() | |
| self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) | |
| torch.nn.init.xavier_uniform_( | |
| self.linear_layer.weight, | |
| gain=torch.nn.init.calculate_gain(w_init_gain)) | |
| def forward(self, x): | |
| return self.linear_layer(x) | |
| class ConvNorm(torch.nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, | |
| padding=None, dilation=1, bias=True, w_init_gain='linear'): | |
| super().__init__() | |
| if padding is None: | |
| assert(kernel_size % 2 == 1) | |
| padding = int(dilation * (kernel_size - 1) / 2) | |
| self.conv = torch.nn.Conv1d(in_channels, out_channels, | |
| kernel_size=kernel_size, stride=stride, | |
| padding=padding, dilation=dilation, | |
| bias=bias) | |
| torch.nn.init.xavier_uniform_( | |
| self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) | |
| def forward(self, signal): | |
| conv_signal = self.conv(signal) | |
| return conv_signal | |