Spaces:
Runtime error
Runtime error
File size: 5,280 Bytes
f7400bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
import torch.nn as nn
import torch
class nonlinearity(nn.Module):
def __init(self):
super().__init__()
def forward(self, x):
return x * torch.sigmoid(x)
class ResConv1DBlock(nn.Module):
def __init__(self, n_in, n_state, dilation=1, activation='silu', norm=None, dropout=0.2):
super(ResConv1DBlock, self).__init__()
padding = dilation
self.norm = norm
if norm == "LN":
self.norm1 = nn.LayerNorm(n_in)
self.norm2 = nn.LayerNorm(n_in)
elif norm == "GN":
self.norm1 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
self.norm2 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
elif norm == "BN":
self.norm1 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
self.norm2 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
else:
self.norm1 = nn.Identity()
self.norm2 = nn.Identity()
if activation == "relu":
self.activation1 = nn.ReLU()
self.activation2 = nn.ReLU()
elif activation == "silu":
self.activation1 = nonlinearity()
self.activation2 = nonlinearity()
elif activation == "gelu":
self.activation1 = nn.GELU()
self.activation2 = nn.GELU()
self.conv1 = nn.Conv1d(n_in, n_state, 3, 1, padding, dilation)
self.conv2 = nn.Conv1d(n_state, n_in, 1, 1, 0, )
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x_orig = x
if self.norm == "LN":
x = self.norm1(x.transpose(-2, -1))
x = self.activation1(x.transpose(-2, -1))
else:
x = self.norm1(x)
x = self.activation1(x)
x = self.conv1(x)
if self.norm == "LN":
x = self.norm2(x.transpose(-2, -1))
x = self.activation2(x.transpose(-2, -1))
else:
x = self.norm2(x)
x = self.activation2(x)
x = self.conv2(x)
x = self.dropout(x)
x = x + x_orig
return x
class Resnet1D(nn.Module):
def __init__(self, n_in, n_depth, dilation_growth_rate=1, reverse_dilation=True, activation='relu', norm=None):
super().__init__()
blocks = [ResConv1DBlock(n_in, n_in, dilation=dilation_growth_rate ** depth, activation=activation, norm=norm)
for depth in range(n_depth)]
if reverse_dilation:
blocks = blocks[::-1]
self.model = nn.Sequential(*blocks)
def forward(self, x):
return self.model(x)
class CausalResConv1DBlock(nn.Module):
def __init__(self, n_in, n_state, dilation=1, activation='silu', norm=None, dropout=None):
super().__init__()
self.norm = norm
if norm == "LN":
self.norm1 = nn.LayerNorm(n_in)
self.norm2 = nn.LayerNorm(n_in)
elif norm == "GN":
self.norm1 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
self.norm2 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
elif norm == "BN":
self.norm1 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
self.norm2 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
else:
self.norm1 = nn.Identity()
self.norm2 = nn.Identity()
if activation == "relu":
self.activation1 = nn.ReLU()
self.activation2 = nn.ReLU()
elif activation == "silu":
self.activation1 = nonlinearity()
self.activation2 = nonlinearity()
elif activation == "gelu":
self.activation1 = nn.GELU()
self.activation2 = nn.GELU()
self.left_padding = (3 - 1) * dilation
self.conv1 = nn.Conv1d(n_in, n_state, kernel_size=3, stride=1, padding=0, dilation=dilation)
self.conv2 = nn.Conv1d(n_state, n_in, kernel_size=1, stride=1, padding=0)
def forward(self, x):
x_orig = x
if self.norm == "LN":
x = self.norm1(x.transpose(-2, -1)).transpose(-2, -1)
x = self.activation1(x)
else:
x = self.norm1(x)
x = self.activation1(x)
x = nn.functional.pad(x, (self.left_padding, 0))
x = self.conv1(x)
if self.norm == "LN":
x = self.norm2(x.transpose(-2, -1)).transpose(-2, -1)
x = self.activation2(x)
else:
x = self.norm2(x)
x = self.activation2(x)
x = self.conv2(x)
x = x + x_orig
return x
class CausalResnet1D(nn.Module):
def __init__(self, n_in, n_depth, dilation_growth_rate=1, reverse_dilation=True, activation='relu', norm=None):
super().__init__()
blocks = [
CausalResConv1DBlock(
n_in,
n_in,
dilation=dilation_growth_rate ** depth,
activation=activation,
norm=norm
) for depth in range(n_depth)
]
if reverse_dilation:
blocks = blocks[::-1]
self.model = nn.Sequential(*blocks)
def forward(self, x):
return self.model(x) |