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import math
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import torch
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from torch import nn
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from .factorized_vector_quantize import FactorizedVectorQuantize
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class ResidualVQ(nn.Module):
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def __init__(
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self,
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*,
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num_quantizers,
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codebook_size,
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**kwargs
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):
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super().__init__()
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VQ = FactorizedVectorQuantize
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if type(codebook_size) == int:
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codebook_size = [codebook_size] * num_quantizers
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self.layers = nn.ModuleList([VQ(codebook_size=size, **kwargs) for size in codebook_size])
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self.num_quantizers = num_quantizers
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def forward(self, x):
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quantized_out = 0.
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residual = x
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all_losses = []
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all_indices = []
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for idx, layer in enumerate(self.layers):
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quantized, indices, loss = layer(residual)
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residual = residual - quantized
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quantized_out = quantized_out + quantized
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loss = loss.mean()
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all_indices.append(indices)
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all_losses.append(loss)
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all_losses, all_indices = map(torch.stack, (all_losses, all_indices))
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return quantized_out, all_indices, all_losses
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def vq2emb(self, vq, proj=True):
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quantized_out = 0.
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for idx, layer in enumerate(self.layers):
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quantized = layer.vq2emb(vq[:, :, idx], proj=proj)
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quantized_out = quantized_out + quantized
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return quantized_out
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def get_emb(self):
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embs = []
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for idx, layer in enumerate(self.layers):
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embs.append(layer.get_emb())
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return embs
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