hardswitch variant of vqvae3

2021-02-02 21:00:04 -07:00 · 2021-02-02 21:00:04 -07:00 · f5f91850fd
commit f5f91850fd
parent 320edbaa3c
1 changed files with 196 additions and 0 deletions
--- a/codes/models/vqvae/vqvae_3_hardswitch.py
+++ 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")