| 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 |
|
|
