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import math
from typing import Tuple
import torch
import torch.nn.functional as F
from einops import rearrange
def pad1d(
x: torch.Tensor,
paddings: Tuple[int, int],
mode: str = "constant",
value: float = 0.0,
):
# Copied from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/conv.py
"""Tiny wrapper around F.pad, just to allow for reflect padding on small input.
If this is the case, we insert extra 0 padding to the right before the reflection happen.
"""
length = x.shape[-1]
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
if mode == "reflect":
max_pad = max(padding_left, padding_right)
extra_pad = 0
if length <= max_pad:
extra_pad = max_pad - length + 1
x = F.pad(x, (0, extra_pad))
padded = F.pad(x, paddings, mode, value)
end = padded.shape[-1] - extra_pad
return padded[..., :end]
else:
return F.pad(x, paddings, mode, value)
def get_extra_padding_for_conv1d(
x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0
) -> int:
# Copied from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/conv.py
"""See `pad_for_conv1d`."""
length = x.shape[-1]
n_frames = (length - kernel_size + padding_total) / stride + 1
ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
return ideal_length - length
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, x: torch.Tensor) -> torch.Tensor:
kernel_size = self.kernel_size[0]
stride = self.stride[0]
dilation = self.dilation[0]
kernel_size = (
kernel_size - 1
) * dilation + 1 # effective kernel size with dilations
padding_total = kernel_size - stride
extra_padding = get_extra_padding_for_conv1d(
x, kernel_size, stride, padding_total
)
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
x = pad1d(x, (padding_left, padding_right + extra_padding))
return super().forward(x)
class ConvBlock1d(torch.nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
*,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
num_groups: int = 8,
) -> None:
super().__init__()
self.groupnorm = torch.nn.GroupNorm(
num_groups=num_groups, num_channels=in_channels
)
self.activation = torch.nn.SiLU()
self.project = Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
)
def forward(
self,
x: torch.Tensor,
) -> torch.Tensor:
x = self.groupnorm(x)
x = self.activation(x)
return self.project(x)
class ResnetBlock1d(torch.nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
*,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
num_groups: int = 8,
) -> None:
super().__init__()
self.block1 = ConvBlock1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
num_groups=num_groups,
)
self.block2 = ConvBlock1d(
in_channels=out_channels,
out_channels=out_channels,
num_groups=num_groups,
)
self.to_out = (
Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=1)
if in_channels != out_channels
else torch.nn.Identity()
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.block1(x)
h = self.block2(h)
return h + self.to_out(x)
class Patcher(torch.nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
patch_size: int,
):
super().__init__()
assert_message = f"out_channels must be divisible by patch_size ({patch_size})"
assert out_channels % patch_size == 0, assert_message
self.patch_size = patch_size
self.block = ResnetBlock1d(
in_channels=in_channels,
out_channels=out_channels // patch_size,
num_groups=1,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.block(x)
x = rearrange(x, "b c (l p) -> b (c p) l", p=self.patch_size)
return x
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