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A10G
Running
on
A10G
| import torch | |
| from audioldm.latent_diffusion.ema import * | |
| from audioldm.variational_autoencoder.modules import Encoder, Decoder | |
| from audioldm.variational_autoencoder.distributions import DiagonalGaussianDistribution | |
| from audioldm.hifigan.utilities import get_vocoder, vocoder_infer | |
| class AutoencoderKL(nn.Module): | |
| def __init__( | |
| self, | |
| ddconfig=None, | |
| lossconfig=None, | |
| image_key="fbank", | |
| embed_dim=None, | |
| time_shuffle=1, | |
| subband=1, | |
| ckpt_path=None, | |
| reload_from_ckpt=None, | |
| ignore_keys=[], | |
| colorize_nlabels=None, | |
| monitor=None, | |
| base_learning_rate=1e-5, | |
| scale_factor=1 | |
| ): | |
| super().__init__() | |
| self.encoder = Encoder(**ddconfig) | |
| self.decoder = Decoder(**ddconfig) | |
| self.subband = int(subband) | |
| if self.subband > 1: | |
| print("Use subband decomposition %s" % self.subband) | |
| self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1) | |
| self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) | |
| self.vocoder = get_vocoder(None, "cpu") | |
| self.embed_dim = embed_dim | |
| if monitor is not None: | |
| self.monitor = monitor | |
| self.time_shuffle = time_shuffle | |
| self.reload_from_ckpt = reload_from_ckpt | |
| self.reloaded = False | |
| self.mean, self.std = None, None | |
| self.scale_factor = scale_factor | |
| def encode(self, x): | |
| # x = self.time_shuffle_operation(x) | |
| x = self.freq_split_subband(x) | |
| h = self.encoder(x) | |
| moments = self.quant_conv(h) | |
| posterior = DiagonalGaussianDistribution(moments) | |
| return posterior | |
| def decode(self, z): | |
| z = self.post_quant_conv(z) | |
| dec = self.decoder(z) | |
| dec = self.freq_merge_subband(dec) | |
| return dec | |
| def decode_to_waveform(self, dec): | |
| dec = dec.squeeze(1).permute(0, 2, 1) | |
| wav_reconstruction = vocoder_infer(dec, self.vocoder) | |
| return wav_reconstruction | |
| def forward(self, input, sample_posterior=True): | |
| posterior = self.encode(input) | |
| if sample_posterior: | |
| z = posterior.sample() | |
| else: | |
| z = posterior.mode() | |
| if self.flag_first_run: | |
| print("Latent size: ", z.size()) | |
| self.flag_first_run = False | |
| dec = self.decode(z) | |
| return dec, posterior | |
| def freq_split_subband(self, fbank): | |
| if self.subband == 1 or self.image_key != "stft": | |
| return fbank | |
| bs, ch, tstep, fbins = fbank.size() | |
| assert fbank.size(-1) % self.subband == 0 | |
| assert ch == 1 | |
| return ( | |
| fbank.squeeze(1) | |
| .reshape(bs, tstep, self.subband, fbins // self.subband) | |
| .permute(0, 2, 1, 3) | |
| ) | |
| def freq_merge_subband(self, subband_fbank): | |
| if self.subband == 1 or self.image_key != "stft": | |
| return subband_fbank | |
| assert subband_fbank.size(1) == self.subband # Channel dimension | |
| bs, sub_ch, tstep, fbins = subband_fbank.size() | |
| return subband_fbank.permute(0, 2, 1, 3).reshape(bs, tstep, -1).unsqueeze(1) | |
| def device(self): | |
| return next(self.parameters()).device | |
| def encode_first_stage(self, x): | |
| return self.encode(x) | |
| def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): | |
| if predict_cids: | |
| if z.dim() == 4: | |
| z = torch.argmax(z.exp(), dim=1).long() | |
| z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) | |
| z = rearrange(z, "b h w c -> b c h w").contiguous() | |
| z = 1.0 / self.scale_factor * z | |
| return self.decode(z) | |
| def get_first_stage_encoding(self, encoder_posterior): | |
| if isinstance(encoder_posterior, DiagonalGaussianDistribution): | |
| z = encoder_posterior.sample() | |
| elif isinstance(encoder_posterior, torch.Tensor): | |
| z = encoder_posterior | |
| else: | |
| raise NotImplementedError( | |
| f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented" | |
| ) | |
| return self.scale_factor * z |