159 lines
5.2 KiB
Python
159 lines
5.2 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.utils.checkpoint import checkpoint
|
|
|
|
from models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
channels,
|
|
dropout,
|
|
out_channels=None,
|
|
use_conv=False,
|
|
use_scale_shift_norm=False,
|
|
dims=2,
|
|
up=False,
|
|
down=False,
|
|
kernel_size=3,
|
|
do_checkpoint=True,
|
|
):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.dropout = dropout
|
|
self.out_channels = out_channels or channels
|
|
self.use_conv = use_conv
|
|
self.use_scale_shift_norm = use_scale_shift_norm
|
|
self.do_checkpoint = do_checkpoint
|
|
padding = 1 if kernel_size == 3 else 2
|
|
|
|
self.in_layers = nn.Sequential(
|
|
normalization(channels),
|
|
nn.SiLU(),
|
|
nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
|
|
)
|
|
|
|
self.updown = up or down
|
|
|
|
if up:
|
|
self.h_upd = Upsample(channels, False, dims)
|
|
self.x_upd = Upsample(channels, False, dims)
|
|
elif down:
|
|
self.h_upd = Downsample(channels, False, dims)
|
|
self.x_upd = Downsample(channels, False, dims)
|
|
else:
|
|
self.h_upd = self.x_upd = nn.Identity()
|
|
|
|
self.out_layers = nn.Sequential(
|
|
normalization(self.out_channels),
|
|
nn.SiLU(),
|
|
nn.Dropout(p=dropout),
|
|
zero_module(
|
|
nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
|
|
),
|
|
)
|
|
|
|
if self.out_channels == channels:
|
|
self.skip_connection = nn.Identity()
|
|
elif use_conv:
|
|
self.skip_connection = nn.Conv1d(
|
|
dims, channels, self.out_channels, kernel_size, padding=padding
|
|
)
|
|
else:
|
|
self.skip_connection = nn.Conv1d(dims, channels, self.out_channels, 1)
|
|
|
|
def forward(self, x):
|
|
if self.do_checkpoint:
|
|
return checkpoint(
|
|
self._forward, x
|
|
)
|
|
else:
|
|
return self._forward(x)
|
|
|
|
def _forward(self, x):
|
|
if self.updown:
|
|
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
|
h = in_rest(x)
|
|
h = self.h_upd(h)
|
|
x = self.x_upd(x)
|
|
h = in_conv(h)
|
|
else:
|
|
h = self.in_layers(x)
|
|
h = self.out_layers(h)
|
|
return self.skip_connection(x) + h
|
|
|
|
|
|
class AudioMiniEncoder(nn.Module):
|
|
def __init__(self,
|
|
spec_dim,
|
|
embedding_dim,
|
|
base_channels=128,
|
|
depth=2,
|
|
resnet_blocks=2,
|
|
attn_blocks=4,
|
|
num_attn_heads=4,
|
|
dropout=0,
|
|
downsample_factor=2,
|
|
kernel_size=3):
|
|
super().__init__()
|
|
self.init = nn.Sequential(
|
|
nn.Conv1d(spec_dim, base_channels, 3, padding=1)
|
|
)
|
|
ch = base_channels
|
|
res = []
|
|
self.layers = depth
|
|
for l in range(depth):
|
|
for r in range(resnet_blocks):
|
|
res.append(ResBlock(ch, dropout, do_checkpoint=False, kernel_size=kernel_size))
|
|
res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
|
|
ch *= 2
|
|
self.res = nn.Sequential(*res)
|
|
self.final = nn.Sequential(
|
|
normalization(ch),
|
|
nn.SiLU(),
|
|
nn.Conv1d(ch, embedding_dim, 1)
|
|
)
|
|
attn = []
|
|
for a in range(attn_blocks):
|
|
attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=False))
|
|
self.attn = nn.Sequential(*attn)
|
|
self.dim = embedding_dim
|
|
|
|
def forward(self, x):
|
|
h = self.init(x)
|
|
h = self.res(h)
|
|
h = self.final(h)
|
|
for blk in self.attn:
|
|
h = checkpoint(blk, h)
|
|
return h[:, :, 0]
|
|
|
|
|
|
class AudioMiniEncoderWithClassifierHead(nn.Module):
|
|
def __init__(self, classes, distribute_zero_label=True, **kwargs):
|
|
super().__init__()
|
|
self.enc = AudioMiniEncoder(**kwargs)
|
|
self.head = nn.Linear(self.enc.dim, classes)
|
|
self.num_classes = classes
|
|
self.distribute_zero_label = distribute_zero_label
|
|
|
|
def forward(self, x, labels=None):
|
|
h = self.enc(x)
|
|
logits = self.head(h)
|
|
if labels is None:
|
|
return logits
|
|
else:
|
|
if self.distribute_zero_label:
|
|
oh_labels = nn.functional.one_hot(labels, num_classes=self.num_classes)
|
|
zeros_indices = (labels == 0).unsqueeze(-1)
|
|
# Distribute 20% of the probability mass on all classes when zero is specified, to compensate for dataset noise.
|
|
zero_extra_mass = torch.full_like(oh_labels, dtype=torch.float, fill_value=.2/(self.num_classes-1))
|
|
zero_extra_mass[:, 0] = -.2
|
|
zero_extra_mass = zero_extra_mass * zeros_indices
|
|
oh_labels = oh_labels + zero_extra_mass
|
|
else:
|
|
oh_labels = labels
|
|
loss = nn.functional.cross_entropy(logits, oh_labels)
|
|
return loss
|