From bc20b4739ea410e50b02742cf066875d6c008aa2 Mon Sep 17 00:00:00 2001 From: James Betker Date: Fri, 29 Jan 2021 15:24:26 -0700 Subject: [PATCH] vqvae3 Changes VQVAE as so: - Reverts back to smaller codebook - Adds an additional conv layer at the highest resolution for both the encoder & decoder - Uses LeakyReLU on trunk --- codes/models/vqvae/vqvae_3.py | 180 ++++++++++++++++++++++++++++++++++ 1 file changed, 180 insertions(+) create mode 100644 codes/models/vqvae/vqvae_3.py diff --git a/codes/models/vqvae/vqvae_3.py b/codes/models/vqvae/vqvae_3.py new file mode 100644 index 00000000..3c040775 --- /dev/null +++ b/codes/models/vqvae/vqvae_3.py @@ -0,0 +1,180 @@ +import torch +from kornia import filter2D +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_vqvae_normalized(opt_net, opt): + kw = opt_get(opt_net, ['kwargs'], {}) + return VQVAE(**kw) + + +if __name__ == '__main__': + v = VQVAE3() + print(v(torch.randn(1,3,128,128))[0].shape)