| | 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) |
| | |
| | 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 |
| |
|
