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

@@ -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)