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from typing import Optional
import torch
import torch.nn as nn
from .utils import get_activation_fn
class ResConv1DBlock(nn.Module):
def __init__(self, n_in: int, n_state: int, dilation: int = 1, activation: str = 'silu', dropout: float = 0.1,
norm: Optional[str] = None, norm_groups: int = 32, norm_eps: float = 1e-5) -> None:
super(ResConv1DBlock, self).__init__()
self.norm = norm
if norm == "LN":
self.norm1 = nn.LayerNorm(n_in, eps=norm_eps)
self.norm2 = nn.LayerNorm(n_in, eps=norm_eps)
elif norm == "GN":
self.norm1 = nn.GroupNorm(num_groups=norm_groups, num_channels=n_in, eps=norm_eps)
self.norm2 = nn.GroupNorm(num_groups=norm_groups, num_channels=n_in, eps=norm_eps)
elif norm == "BN":
self.norm1 = nn.BatchNorm1d(num_features=n_in, eps=norm_eps)
self.norm2 = nn.BatchNorm1d(num_features=n_in, eps=norm_eps)
else:
self.norm1 = nn.Identity()
self.norm2 = nn.Identity()
self.activation = get_activation_fn(activation)
self.conv1 = nn.Conv1d(n_in, n_state, 3, 1, padding=dilation, dilation=dilation)
self.conv2 = nn.Conv1d(n_state, n_in, 1, 1, 0)
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x_orig = x
if self.norm == "LN":
x = self.norm1(x.transpose(-2, -1))
x = self.activation(x.transpose(-2, -1))
else:
x = self.norm1(x)
x = self.activation(x)
x = self.conv1(x)
if self.norm == "LN":
x = self.norm2(x.transpose(-2, -1))
x = self.activation(x.transpose(-2, -1))
else:
x = self.norm2(x)
x = self.activation(x)
x = self.conv2(x)
x = self.dropout(x)
x = x + x_orig
return x
class Resnet1D(nn.Module):
def __init__(self, n_in: int, n_state: int, n_depth: int, reverse_dilation: bool = True,
dilation_growth_rate: int = 3, activation: str = 'relu', dropout: float = 0.1,
norm: Optional[str] = None, norm_groups: int = 32, norm_eps: float = 1e-5) -> None:
super(Resnet1D, self).__init__()
blocks = [ResConv1DBlock(n_in, n_state, dilation=dilation_growth_rate ** depth, activation=activation,
dropout=dropout, norm=norm, norm_groups=norm_groups, norm_eps=norm_eps)
for depth in range(n_depth)]
if reverse_dilation:
blocks = blocks[::-1]
self.model = nn.Sequential(*blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.model(x)
class ResEncoder(nn.Module):
def __init__(self,
in_width: int = 263,
mid_width: int = 512,
out_width: int = 512,
down_t: int = 2,
stride_t: int = 2,
n_depth: int = 3,
dilation_growth_rate: int = 3,
activation: str = 'relu',
dropout: float = 0.1,
norm: Optional[str] = None,
norm_groups: int = 32,
norm_eps: float = 1e-5,
double_z: bool = False) -> None:
super(ResEncoder, self).__init__()
blocks = []
filter_t, pad_t = stride_t * 2, stride_t // 2
blocks.append(nn.Conv1d(in_width, mid_width, 3, 1, 1))
blocks.append(get_activation_fn(activation))
for i in range(down_t):
block = nn.Sequential(
nn.Conv1d(mid_width, mid_width, filter_t, stride_t, pad_t),
Resnet1D(mid_width, mid_width, n_depth, reverse_dilation=True, dilation_growth_rate=dilation_growth_rate,
activation=activation, dropout=dropout, norm=norm, norm_groups=norm_groups, norm_eps=norm_eps))
blocks.append(block)
blocks.append(nn.Conv1d(mid_width, out_width * 2 if double_z else out_width, 3, 1, 1))
self.model = nn.Sequential(*blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.model(x.permute(0, 2, 1)) # B x C x T
class ResDecoder(nn.Module):
def __init__(self,
in_width: int = 263,
mid_width: int = 512,
out_width: int = 512,
down_t: int = 2,
stride_t: int = 2,
n_depth: int = 3,
dilation_growth_rate: int = 3,
activation: str = 'relu',
dropout: float = 0.1,
norm: Optional[str] = None,
norm_groups: int = 32,
norm_eps: float = 1e-5) -> None:
super(ResDecoder, self).__init__()
blocks = [nn.Conv1d(out_width, mid_width, 3, 1, 1), get_activation_fn(activation)]
for i in range(down_t):
block = nn.Sequential(
Resnet1D(mid_width, mid_width, n_depth, reverse_dilation=True, dilation_growth_rate=dilation_growth_rate,
activation=activation, dropout=dropout, norm=norm, norm_groups=norm_groups, norm_eps=norm_eps),
nn.Upsample(scale_factor=stride_t, mode='nearest'),
nn.Conv1d(mid_width, mid_width, 3, 1, 1))
blocks.append(block)
blocks.append(nn.Conv1d(mid_width, mid_width, 3, 1, 1))
blocks.append(get_activation_fn(activation))
blocks.append(nn.Conv1d(mid_width, in_width, 3, 1, 1))
self.model = nn.Sequential(*blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.model(x).permute(0, 2, 1) # B x T x C
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