[Module] CodeFormer

codeformer_arch.py

# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py

import math
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from typing import Optional

from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
from basicsr.utils.registry import ARCH_REGISTRY

def calc_mean_std(feat, eps=1e-5):
    """Calculate mean and std for adaptive_instance_normalization.

    Args:
        feat (Tensor): 4D tensor.
        eps (float): A small value added to the variance to avoid
            divide-by-zero. Default: 1e-5.
    """
    size = feat.size()
    assert len(size) == 4, 'The input feature should be 4D tensor.'
    b, c = size[:2]
    feat_var = feat.view(b, c, -1).var(dim=2) + eps
    feat_std = feat_var.sqrt().view(b, c, 1, 1)
    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
    return feat_mean, feat_std


def adaptive_instance_normalization(content_feat, style_feat):
    """Adaptive instance normalization.

    Adjust the reference features to have the similar color and illuminations
    as those in the degradate features.

    Args:
        content_feat (Tensor): The reference feature.
        style_feat (Tensor): The degradate features.
    """
    size = content_feat.size()
    style_mean, style_std = calc_mean_std(style_feat)
    content_mean, content_std = calc_mean_std(content_feat)
    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
    return normalized_feat * style_std.expand(size) + style_mean.expand(size)


class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """

    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, x, mask=None):
        if mask is None:
            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
        not_mask = ~mask
        y_embed = not_mask.cumsum(1, dtype=torch.float32)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos

def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")


class TransformerSALayer(nn.Module):
    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
        # Implementation of Feedforward model - MLP
        self.linear1 = nn.Linear(embed_dim, dim_mlp)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_mlp, embed_dim)

        self.norm1 = nn.LayerNorm(embed_dim)
        self.norm2 = nn.LayerNorm(embed_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

        self.activation = _get_activation_fn(activation)

    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos

    def forward(self, tgt,
                tgt_mask: Optional[Tensor] = None,
                tgt_key_padding_mask: Optional[Tensor] = None,
                query_pos: Optional[Tensor] = None):

        # self attention
        tgt2 = self.norm1(tgt)
        q = k = self.with_pos_embed(tgt2, query_pos)
        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
                              key_padding_mask=tgt_key_padding_mask)[0]
        tgt = tgt + self.dropout1(tgt2)

        # ffn
        tgt2 = self.norm2(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout2(tgt2)
        return tgt

class Fuse_sft_block(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.encode_enc = ResBlock(2*in_ch, out_ch)

        self.scale = nn.Sequential(
                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
                    nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))

        self.shift = nn.Sequential(
                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
                    nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))

    def forward(self, enc_feat, dec_feat, w=1):
        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
        scale = self.scale(enc_feat)
        shift = self.shift(enc_feat)
        residual = w * (dec_feat * scale + shift)
        out = dec_feat + residual
        return out


@ARCH_REGISTRY.register()
class CodeFormer(VQAutoEncoder):
    def __init__(self, dim_embd=512, n_head=8, n_layers=9,
                codebook_size=1024, latent_size=256,
                connect_list=('32', '64', '128', '256'),
                fix_modules=('quantize', 'generator')):
        super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)

        if fix_modules is not None:
            for module in fix_modules:
                for param in getattr(self, module).parameters():
                    param.requires_grad = False

        self.connect_list = connect_list
        self.n_layers = n_layers
        self.dim_embd = dim_embd
        self.dim_mlp = dim_embd*2

        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
        self.feat_emb = nn.Linear(256, self.dim_embd)

        # transformer
        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
                                    for _ in range(self.n_layers)])

        # logits_predict head
        self.idx_pred_layer = nn.Sequential(
            nn.LayerNorm(dim_embd),
            nn.Linear(dim_embd, codebook_size, bias=False))

        self.channels = {
            '16': 512,
            '32': 256,
            '64': 256,
            '128': 128,
            '256': 128,
            '512': 64,
        }

        # after second residual block for > 16, before attn layer for ==16
        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
        # after first residual block for > 16, before attn layer for ==16
        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}

        # fuse_convs_dict
        self.fuse_convs_dict = nn.ModuleDict()
        for f_size in self.connect_list:
            in_ch = self.channels[f_size]
            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)

    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)):
            module.weight.data.normal_(mean=0.0, std=0.02)
            if isinstance(module, nn.Linear) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
        # ################### Encoder #####################
        enc_feat_dict = {}
        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
        for i, block in enumerate(self.encoder.blocks):
            x = block(x)
            if i in out_list:
                enc_feat_dict[str(x.shape[-1])] = x.clone()

        lq_feat = x
        # ################# Transformer ###################
        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
        # BCHW -> BC(HW) -> (HW)BC
        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
        query_emb = feat_emb
        # Transformer encoder
        for layer in self.ft_layers:
            query_emb = layer(query_emb, query_pos=pos_emb)

        # output logits
        logits = self.idx_pred_layer(query_emb) # (hw)bn
        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n

        if code_only: # for training stage II
          # logits doesn't need softmax before cross_entropy loss
            return logits, lq_feat

        # ################# Quantization ###################
        # if self.training:
        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
        #     # b(hw)c -> bc(hw) -> bchw
        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
        # ------------
        soft_one_hot = F.softmax(logits, dim=2)
        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
        # preserve gradients
        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()

        if detach_16:
            quant_feat = quant_feat.detach() # for training stage III
        if adain:
            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)

        # ################## Generator ####################
        x = quant_feat
        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]

        for i, block in enumerate(self.generator.blocks):
            x = block(x)
            if i in fuse_list: # fuse after i-th block
                f_size = str(x.shape[-1])
                if w>0:
                    x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
        out = x
        # logits doesn't need softmax before cross_entropy loss
        return out, logits, lq_feat

VQ-AutoEncoder 구조를 기반으로 하며, 다양한 모듈과 함수가 포함되어 있습니다. 주요 구성 요소는 다음과 같습니다:

calc_mean_std: Adaptive Instance Normalization을 위한 평균과 표준편차를 계산하는 함수입니다.

adaptive_instance_normalization: 참조 피쳐와 손상된 피쳐의 색상과 조명을 조정하는 Adaptive Instance Normalization 함수입니다.

PositionEmbeddingSine: 이미지에 대해 작동하도록 일반화된 위치 임베딩을 생성하는 클래스입니다.

TransformerSALayer: Self-Attention 레이어와 Feedforward MLP를 구현한 클래스입니다.

Fuse_sft_block: 인코더와 디코더 간의 정보 융합을 위한 블록을 구현한 클래스입니다.

CodeFormer: VQ-AutoEncoder를 확장한 CodeFormer 모델 클래스입니다. 여러 레이어와 모듈을 포함하고 있으며, 입력 이미지를 임베딩하고 변환하는 과정을 수행합니다.

vqgan_arch.py

# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py

'''
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py

'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from basicsr.utils import get_root_logger
from basicsr.utils.registry import ARCH_REGISTRY

def normalize(in_channels):
    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)


@torch.jit.script
def swish(x):
    return x*torch.sigmoid(x)


#  Define VQVAE classes
class VectorQuantizer(nn.Module):
    def __init__(self, codebook_size, emb_dim, beta):
        super(VectorQuantizer, self).__init__()
        self.codebook_size = codebook_size  # number of embeddings
        self.emb_dim = emb_dim  # dimension of embedding
        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)

    def forward(self, z):
        # reshape z -> (batch, height, width, channel) and flatten
        z = z.permute(0, 2, 3, 1).contiguous()
        z_flattened = z.view(-1, self.emb_dim)

        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
            2 * torch.matmul(z_flattened, self.embedding.weight.t())

        mean_distance = torch.mean(d)
        # find closest encodings
        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
        # [0-1], higher score, higher confidence
        min_encoding_scores = torch.exp(-min_encoding_scores/10)

        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
        min_encodings.scatter_(1, min_encoding_indices, 1)

        # get quantized latent vectors
        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
        # compute loss for embedding
        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
        # preserve gradients
        z_q = z + (z_q - z).detach()

        # perplexity
        e_mean = torch.mean(min_encodings, dim=0)
        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
        # reshape back to match original input shape
        z_q = z_q.permute(0, 3, 1, 2).contiguous()

        return z_q, loss, {
            "perplexity": perplexity,
            "min_encodings": min_encodings,
            "min_encoding_indices": min_encoding_indices,
            "min_encoding_scores": min_encoding_scores,
            "mean_distance": mean_distance
            }

    def get_codebook_feat(self, indices, shape):
        # input indices: batch*token_num -> (batch*token_num)*1
        # shape: batch, height, width, channel
        indices = indices.view(-1,1)
        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
        min_encodings.scatter_(1, indices, 1)
        # get quantized latent vectors
        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)

        if shape is not None:  # reshape back to match original input shape
            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()

        return z_q


class GumbelQuantizer(nn.Module):
    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
        super().__init__()
        self.codebook_size = codebook_size  # number of embeddings
        self.emb_dim = emb_dim  # dimension of embedding
        self.straight_through = straight_through
        self.temperature = temp_init
        self.kl_weight = kl_weight
        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
        self.embed = nn.Embedding(codebook_size, emb_dim)

    def forward(self, z):
        hard = self.straight_through if self.training else True

        logits = self.proj(z)

        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)

        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)

        # + kl divergence to the prior loss
        qy = F.softmax(logits, dim=1)
        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
        min_encoding_indices = soft_one_hot.argmax(dim=1)

        return z_q, diff, {
            "min_encoding_indices": min_encoding_indices
        }


class Downsample(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)

    def forward(self, x):
        pad = (0, 1, 0, 1)
        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
        x = self.conv(x)
        return x


class Upsample(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
        x = self.conv(x)

        return x


class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels=None):
        super(ResBlock, self).__init__()
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels
        self.norm1 = normalize(in_channels)
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.norm2 = normalize(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        if self.in_channels != self.out_channels:
            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x_in):
        x = x_in
        x = self.norm1(x)
        x = swish(x)
        x = self.conv1(x)
        x = self.norm2(x)
        x = swish(x)
        x = self.conv2(x)
        if self.in_channels != self.out_channels:
            x_in = self.conv_out(x_in)

        return x + x_in


class AttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = q.reshape(b, c, h*w)
        q = q.permute(0, 2, 1)
        k = k.reshape(b, c, h*w)
        w_ = torch.bmm(q, k)
        w_ = w_ * (int(c)**(-0.5))
        w_ = F.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h*w)
        w_ = w_.permute(0, 2, 1)
        h_ = torch.bmm(v, w_)
        h_ = h_.reshape(b, c, h, w)

        h_ = self.proj_out(h_)

        return x+h_


class Encoder(nn.Module):
    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
        super().__init__()
        self.nf = nf
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.attn_resolutions = attn_resolutions

        curr_res = self.resolution
        in_ch_mult = (1,)+tuple(ch_mult)

        blocks = []
        # initial convultion
        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))

        # residual and downsampling blocks, with attention on smaller res (16x16)
        for i in range(self.num_resolutions):
            block_in_ch = nf * in_ch_mult[i]
            block_out_ch = nf * ch_mult[i]
            for _ in range(self.num_res_blocks):
                blocks.append(ResBlock(block_in_ch, block_out_ch))
                block_in_ch = block_out_ch
                if curr_res in attn_resolutions:
                    blocks.append(AttnBlock(block_in_ch))

            if i != self.num_resolutions - 1:
                blocks.append(Downsample(block_in_ch))
                curr_res = curr_res // 2

        # non-local attention block
        blocks.append(ResBlock(block_in_ch, block_in_ch))
        blocks.append(AttnBlock(block_in_ch))
        blocks.append(ResBlock(block_in_ch, block_in_ch))

        # normalise and convert to latent size
        blocks.append(normalize(block_in_ch))
        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
        self.blocks = nn.ModuleList(blocks)

    def forward(self, x):
        for block in self.blocks:
            x = block(x)

        return x


class Generator(nn.Module):
    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
        super().__init__()
        self.nf = nf
        self.ch_mult = ch_mult
        self.num_resolutions = len(self.ch_mult)
        self.num_res_blocks = res_blocks
        self.resolution = img_size
        self.attn_resolutions = attn_resolutions
        self.in_channels = emb_dim
        self.out_channels = 3
        block_in_ch = self.nf * self.ch_mult[-1]
        curr_res = self.resolution // 2 ** (self.num_resolutions-1)

        blocks = []
        # initial conv
        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))

        # non-local attention block
        blocks.append(ResBlock(block_in_ch, block_in_ch))
        blocks.append(AttnBlock(block_in_ch))
        blocks.append(ResBlock(block_in_ch, block_in_ch))

        for i in reversed(range(self.num_resolutions)):
            block_out_ch = self.nf * self.ch_mult[i]

            for _ in range(self.num_res_blocks):
                blocks.append(ResBlock(block_in_ch, block_out_ch))
                block_in_ch = block_out_ch

                if curr_res in self.attn_resolutions:
                    blocks.append(AttnBlock(block_in_ch))

            if i != 0:
                blocks.append(Upsample(block_in_ch))
                curr_res = curr_res * 2

        blocks.append(normalize(block_in_ch))
        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))

        self.blocks = nn.ModuleList(blocks)


    def forward(self, x):
        for block in self.blocks:
            x = block(x)

        return x


@ARCH_REGISTRY.register()
class VQAutoEncoder(nn.Module):
    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
        super().__init__()
        logger = get_root_logger()
        self.in_channels = 3
        self.nf = nf
        self.n_blocks = res_blocks
        self.codebook_size = codebook_size
        self.embed_dim = emb_dim
        self.ch_mult = ch_mult
        self.resolution = img_size
        self.attn_resolutions = attn_resolutions or [16]
        self.quantizer_type = quantizer
        self.encoder = Encoder(
            self.in_channels,
            self.nf,
            self.embed_dim,
            self.ch_mult,
            self.n_blocks,
            self.resolution,
            self.attn_resolutions
        )
        if self.quantizer_type == "nearest":
            self.beta = beta #0.25
            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
        elif self.quantizer_type == "gumbel":
            self.gumbel_num_hiddens = emb_dim
            self.straight_through = gumbel_straight_through
            self.kl_weight = gumbel_kl_weight
            self.quantize = GumbelQuantizer(
                self.codebook_size,
                self.embed_dim,
                self.gumbel_num_hiddens,
                self.straight_through,
                self.kl_weight
            )
        self.generator = Generator(
            self.nf,
            self.embed_dim,
            self.ch_mult,
            self.n_blocks,
            self.resolution,
            self.attn_resolutions
        )

        if model_path is not None:
            chkpt = torch.load(model_path, map_location='cpu')
            if 'params_ema' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
            elif 'params' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
                logger.info(f'vqgan is loaded from: {model_path} [params]')
            else:
                raise ValueError('Wrong params!')


    def forward(self, x):
        x = self.encoder(x)
        quant, codebook_loss, quant_stats = self.quantize(x)
        x = self.generator(quant)
        return x, codebook_loss, quant_stats



# patch based discriminator
@ARCH_REGISTRY.register()
class VQGANDiscriminator(nn.Module):
    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
        super().__init__()

        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
        ndf_mult = 1
        ndf_mult_prev = 1
        for n in range(1, n_layers):  # gradually increase the number of filters
            ndf_mult_prev = ndf_mult
            ndf_mult = min(2 ** n, 8)
            layers += [
                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
                nn.BatchNorm2d(ndf * ndf_mult),
                nn.LeakyReLU(0.2, True)
            ]

        ndf_mult_prev = ndf_mult
        ndf_mult = min(2 ** n_layers, 8)

        layers += [
            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(ndf * ndf_mult),
            nn.LeakyReLU(0.2, True)
        ]

        layers += [
            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
        self.main = nn.Sequential(*layers)

        if model_path is not None:
            chkpt = torch.load(model_path, map_location='cpu')
            if 'params_d' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
            elif 'params' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
            else:
                raise ValueError('Wrong params!')

    def forward(self, x):
        return self.main(x)

VQGAN은 VQ-VAE(벡터 양자화된 자동 인코더)와 Gumbel-Softmax 양자화를 기반으로 한 이미지 생성 모델입니다. 주요 구성 요소는 다음과 같습니다.

VectorQuantizer: VQ-VAE의 양자화 모듈입니다. 임베딩 크기와 임베딩 개수를 인자로 받으며, 임베딩을 위한 Embedding 층을 포함합니다.

GumbelQuantizer: Gumbel-Softmax 양자화 모듈입니다. 임베딩 크기와 임베딩 개수를 인자로 받으며, 프로젝션 층과 Embedding 층을 포함합니다.

Downsample 및 Upsample: 이미지를 다운샘플링하고 업샘플링하는 모듈입니다.

ResBlock 및 AttnBlock: VQGAN의 인코더와 디코더에서 사용되는 잔차 블록과 어텐션 블록입니다.

Encoder: VQGAN의 인코더 모듈로, 임베딩된 특성을 생성합니다.

Generator: VQGAN의 디코더 모듈로, 임베딩된 특성을 사용하여 이미지를 생성합니다.

VQAutoEncoder: VQGAN의 전체 모델 클래스로, 인코더와 디코더를 포함하고 양자화 모듈을 사용하여 이미지를 생성합니다.

VQGANDiscriminator: VQGAN의 판별자 모듈로, 이미지의 진위 여부를 판단합니다.