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Running
on
T4
File size: 5,030 Bytes
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import torch
import torch.nn as nn
from tortoise.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.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 = 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
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