import torch from torch import nn from torch.nn import functional as F import torch.distributed as distributed from models.vqvae.vqvae import ResBlock, Quantize from trainer.networks import register_model from utils.util import checkpoint, opt_get # Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper. class UpsampleConv(nn.Module): def __init__(self, in_filters, out_filters, kernel_size, padding): super().__init__() self.conv = nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding) def forward(self, x): up = torch.nn.functional.interpolate(x, scale_factor=2) return self.conv(up) class Encoder(nn.Module): def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride): super().__init__() if stride == 4: blocks = [ nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), nn.LeakyReLU(inplace=True), nn.Conv2d(channel // 2, channel, 5, stride=2, padding=2), nn.LeakyReLU(inplace=True), nn.Conv2d(channel, channel, 3, padding=1), ] elif stride == 2: blocks = [ nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), nn.LeakyReLU(inplace=True), nn.Conv2d(channel // 2, channel, 3, padding=1), ] for i in range(n_res_block): blocks.append(ResBlock(channel, n_res_channel)) blocks.append(nn.LeakyReLU(inplace=True)) self.blocks = nn.Sequential(*blocks) def forward(self, input): return self.blocks(input) class Decoder(nn.Module): def __init__( self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride ): super().__init__() blocks = [nn.Conv2d(in_channel, channel, 3, padding=1)] for i in range(n_res_block): blocks.append(ResBlock(channel, n_res_channel)) blocks.append(nn.LeakyReLU(inplace=True)) if stride == 4: blocks.extend( [ UpsampleConv(channel, channel // 2, 5, padding=2), nn.LeakyReLU(inplace=True), UpsampleConv( channel // 2, out_channel, 5, padding=2 ), ] ) elif stride == 2: blocks.append( UpsampleConv(channel, out_channel, 5, padding=2) ) self.blocks = nn.Sequential(*blocks) def forward(self, input): return self.blocks(input) class VQVAE3(nn.Module): def __init__( self, in_channel=3, channel=128, n_res_block=2, n_res_channel=32, codebook_dim=64, codebook_size=512, decay=0.99, ): super().__init__() self.initial_conv = nn.Sequential(*[nn.Conv2d(in_channel, 32, 3, padding=1), nn.LeakyReLU(inplace=True)]) self.enc_b = Encoder(32, channel, n_res_block, n_res_channel, stride=4) self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2) self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1) self.quantize_t = Quantize(codebook_dim, codebook_size) self.dec_t = Decoder( codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2 ) self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1) self.quantize_b = Quantize(codebook_dim, codebook_size) self.upsample_t = UpsampleConv( codebook_dim, codebook_dim, 5, padding=2 ) self.dec = Decoder( codebook_dim + codebook_dim, 32, channel, n_res_block, n_res_channel, stride=4, ) self.final_conv = nn.Conv2d(32, in_channel, 3, padding=1) def forward(self, input): quant_t, quant_b, diff, _, _ = self.encode(input) dec = self.decode(quant_t, quant_b) return dec, diff def encode(self, input): fea = self.initial_conv(input) enc_b = checkpoint(self.enc_b, fea) enc_t = checkpoint(self.enc_t, enc_b) quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1) quant_t, diff_t, id_t = self.quantize_t(quant_t) quant_t = quant_t.permute(0, 3, 1, 2) diff_t = diff_t.unsqueeze(0) dec_t = checkpoint(self.dec_t, quant_t) enc_b = torch.cat([dec_t, enc_b], 1) quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1) quant_b, diff_b, id_b = self.quantize_b(quant_b) quant_b = quant_b.permute(0, 3, 1, 2) diff_b = diff_b.unsqueeze(0) return quant_t, quant_b, diff_t + diff_b, id_t, id_b def decode(self, quant_t, quant_b): upsample_t = self.upsample_t(quant_t) quant = torch.cat([upsample_t, quant_b], 1) dec = checkpoint(self.dec, quant) dec = checkpoint(self.final_conv, dec) return dec def decode_code(self, code_t, code_b): quant_t = self.quantize_t.embed_code(code_t) quant_t = quant_t.permute(0, 3, 1, 2) quant_b = self.quantize_b.embed_code(code_b) quant_b = quant_b.permute(0, 3, 1, 2) dec = self.decode(quant_t, quant_b) return dec @register_model def register_vqvae3(opt_net, opt): kw = opt_get(opt_net, ['kwargs'], {}) return VQVAE3(**kw) if __name__ == '__main__': v = VQVAE3() print(v(torch.randn(1,3,128,128))[0].shape)