From f5f91850fddc26f56795ca30d1171854f074fe49 Mon Sep 17 00:00:00 2001 From: James Betker Date: Tue, 2 Feb 2021 21:00:04 -0700 Subject: [PATCH] hardswitch variant of vqvae3 --- codes/models/vqvae/vqvae_3_hardswitch.py | 196 +++++++++++++++++++++++ 1 file changed, 196 insertions(+) create mode 100644 codes/models/vqvae/vqvae_3_hardswitch.py diff --git a/codes/models/vqvae/vqvae_3_hardswitch.py b/codes/models/vqvae/vqvae_3_hardswitch.py new file mode 100644 index 00000000..c070f8df --- /dev/null +++ b/codes/models/vqvae/vqvae_3_hardswitch.py @@ -0,0 +1,196 @@ +import torch +from torch import nn + +from models.switched_conv.switched_conv_hard_routing import SwitchedConvHardRouting, \ + convert_conv_net_state_dict_to_switched_conv +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 = SwitchedConvHardRouting(in_filters, out_filters, kernel_size, breadth=16, include_coupler=True, + coupler_mode='standard', coupler_dim_in=in_filters, dropout_rate=0.4) + + 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), + SwitchedConvHardRouting(channel // 2, channel, 5, breadth=16, stride=2, include_coupler=True, + coupler_mode='standard', coupler_dim_in=channel // 2, dropout_rate=0.4), + nn.LeakyReLU(inplace=True), + SwitchedConvHardRouting(channel, channel, 3, breadth=16, include_coupler=True, coupler_mode='standard', + coupler_dim_in=channel, dropout_rate=0.4), + ] + + elif stride == 2: + blocks = [ + nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), + nn.LeakyReLU(inplace=True), + SwitchedConvHardRouting(channel // 2, channel, 3, breadth=16, include_coupler=True, coupler_mode='standard', + coupler_dim_in=channel // 2, dropout_rate=0.4), + ] + + 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 = [SwitchedConvHardRouting(in_channel, channel, 3, breadth=16, include_coupler=True, coupler_mode='standard', + coupler_dim_in=in_channel, dropout_rate=0.4)] + + 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 VQVAE3HardSwitch(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 + + + +def convert_weights(weights_file): + sd = torch.load(weights_file) + from models.vqvae.vqvae_3 import VQVAE3 + std_model = VQVAE3() + std_model.load_state_dict(sd) + nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 16, ['quantize_conv_t', 'quantize_conv_b', + 'enc_b.blocks.0', 'enc_t.blocks.0', + 'conv.1', 'conv.3', 'initial_conv', 'final_conv']) + torch.save(nsd, "converted.pth") + + +@register_model +def register_vqvae3_hard_switch(opt_net, opt): + kw = opt_get(opt_net, ['kwargs'], {}) + return VQVAE3HardSwitch(**kw) + + +if __name__ == '__main__': + #v = VQVAE3HardSwitch() + #print(v(torch.randn(1,3,128,128))[0].shape) + convert_weights("../../../experiments/test_vqvae3.pth") \ No newline at end of file