ibq.py

# -*- coding:utf-8 -*-

from torch import nn

from .modules.diffusionmodules.model import Encoder, Decoder
from .modules.vqvae.quantize import IndexPropagationQuantize

class IBQ(nn.Module):

    def __init__(
        self,
        ddconfig,
        n_embed,
        embed_dim,
        beta=0.25,
        use_entropy_loss=False,
        cosine_similarity=False,
        entropy_temperature=0.01,
        sample_minimization_weight=1.0,
        batch_maximization_weight=1.0,
        **kwargs,
    ):
        super().__init__()

        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)

        self.quantize = IndexPropagationQuantize(
            n_embed,
            embed_dim,
            beta,
            use_entropy_loss,
            cosine_similarity=cosine_similarity,
            entropy_temperature=entropy_temperature,
            sample_minimization_weight=sample_minimization_weight,
            batch_maximization_weight=batch_maximization_weight,
        )

        self.quant_conv = nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
        self.post_quant_conv = nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        quant, emb_loss, info = self.quantize(h)
        return quant, emb_loss, info

    def decode(self, quant, return_intermediate_feature=False):
        quant = self.post_quant_conv(quant)
        dec = self.decoder(quant, return_intermediate_feature=return_intermediate_feature)
        return dec

    def decode_code(self, code_b, shape=None):
        # shape specifying (batch, height, width, channel)
        quant_b = self.quantize.get_codebook_entry(code_b, shape=shape)
        dec = self.decode(quant_b)
        return dec

    def forward(self, input, return_intermediate_feature=False):
        quant, diff, _ = self.encode(input)
        dec = self.decode(quant, return_intermediate_feature=return_intermediate_feature)
        return dec, diff