forked from mrq/DL-Art-School
196 lines
6.9 KiB
Python
196 lines
6.9 KiB
Python
import torch
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from torch import nn
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from models.switched_conv.switched_conv_hard_routing import SwitchedConvHardRouting, \
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convert_conv_net_state_dict_to_switched_conv
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from models.vqvae.vqvae import ResBlock, Quantize
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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# Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper.
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class UpsampleConv(nn.Module):
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def __init__(self, in_filters, out_filters, kernel_size, padding):
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super().__init__()
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self.conv = SwitchedConvHardRouting(in_filters, out_filters, kernel_size, breadth=16, include_coupler=True,
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coupler_mode='standard', coupler_dim_in=in_filters, dropout_rate=0.4)
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def forward(self, x):
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up = torch.nn.functional.interpolate(x, scale_factor=2)
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return self.conv(up)
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class Encoder(nn.Module):
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def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride):
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super().__init__()
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if stride == 4:
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blocks = [
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nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
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nn.LeakyReLU(inplace=True),
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SwitchedConvHardRouting(channel // 2, channel, 5, breadth=16, stride=2, include_coupler=True,
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coupler_mode='standard', coupler_dim_in=channel // 2, dropout_rate=0.4),
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nn.LeakyReLU(inplace=True),
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SwitchedConvHardRouting(channel, channel, 3, breadth=16, include_coupler=True, coupler_mode='standard',
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coupler_dim_in=channel, dropout_rate=0.4),
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]
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elif stride == 2:
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blocks = [
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nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
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nn.LeakyReLU(inplace=True),
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SwitchedConvHardRouting(channel // 2, channel, 3, breadth=16, include_coupler=True, coupler_mode='standard',
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coupler_dim_in=channel // 2, dropout_rate=0.4),
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]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel))
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blocks.append(nn.LeakyReLU(inplace=True))
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class Decoder(nn.Module):
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def __init__(
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self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride
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):
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super().__init__()
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blocks = [SwitchedConvHardRouting(in_channel, channel, 3, breadth=16, include_coupler=True, coupler_mode='standard',
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coupler_dim_in=in_channel, dropout_rate=0.4)]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel))
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blocks.append(nn.LeakyReLU(inplace=True))
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if stride == 4:
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blocks.extend(
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[
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UpsampleConv(channel, channel // 2, 5, padding=2),
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nn.LeakyReLU(inplace=True),
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UpsampleConv(
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channel // 2, out_channel, 5, padding=2
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),
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]
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)
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elif stride == 2:
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blocks.append(
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UpsampleConv(channel, out_channel, 5, padding=2)
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)
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class VQVAE3HardSwitch(nn.Module):
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def __init__(
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self,
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in_channel=3,
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channel=128,
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n_res_block=2,
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n_res_channel=32,
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codebook_dim=64,
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codebook_size=512,
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decay=0.99,
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):
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super().__init__()
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self.initial_conv = nn.Sequential(*[nn.Conv2d(in_channel, 32, 3, padding=1),
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nn.LeakyReLU(inplace=True)])
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self.enc_b = Encoder(32, channel, n_res_block, n_res_channel, stride=4)
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self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2)
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self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1)
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self.quantize_t = Quantize(codebook_dim, codebook_size)
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self.dec_t = Decoder(
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codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2
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)
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self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
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self.quantize_b = Quantize(codebook_dim, codebook_size)
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self.upsample_t = UpsampleConv(
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codebook_dim, codebook_dim, 5, padding=2
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)
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self.dec = Decoder(
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codebook_dim + codebook_dim,
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32,
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channel,
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n_res_block,
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n_res_channel,
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stride=4,
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)
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self.final_conv = nn.Conv2d(32, in_channel, 3, padding=1)
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def forward(self, input):
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quant_t, quant_b, diff, _, _ = self.encode(input)
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dec = self.decode(quant_t, quant_b)
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return dec, diff
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def encode(self, input):
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fea = self.initial_conv(input)
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enc_b = checkpoint(self.enc_b, fea)
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enc_t = checkpoint(self.enc_t, enc_b)
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quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
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quant_t, diff_t, id_t = self.quantize_t(quant_t)
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quant_t = quant_t.permute(0, 3, 1, 2)
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diff_t = diff_t.unsqueeze(0)
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dec_t = checkpoint(self.dec_t, quant_t)
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enc_b = torch.cat([dec_t, enc_b], 1)
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quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1)
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quant_b, diff_b, id_b = self.quantize_b(quant_b)
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quant_b = quant_b.permute(0, 3, 1, 2)
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diff_b = diff_b.unsqueeze(0)
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return quant_t, quant_b, diff_t + diff_b, id_t, id_b
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def decode(self, quant_t, quant_b):
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upsample_t = self.upsample_t(quant_t)
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quant = torch.cat([upsample_t, quant_b], 1)
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dec = checkpoint(self.dec, quant)
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dec = checkpoint(self.final_conv, dec)
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return dec
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def decode_code(self, code_t, code_b):
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quant_t = self.quantize_t.embed_code(code_t)
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quant_t = quant_t.permute(0, 3, 1, 2)
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quant_b = self.quantize_b.embed_code(code_b)
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quant_b = quant_b.permute(0, 3, 1, 2)
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dec = self.decode(quant_t, quant_b)
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return dec
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def convert_weights(weights_file):
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sd = torch.load(weights_file)
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from models.vqvae.vqvae_3 import VQVAE3
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std_model = VQVAE3()
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std_model.load_state_dict(sd)
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nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 16, ['quantize_conv_t', 'quantize_conv_b',
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'enc_b.blocks.0', 'enc_t.blocks.0',
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'conv.1', 'conv.3', 'initial_conv', 'final_conv'])
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torch.save(nsd, "converted.pth")
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@register_model
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def register_vqvae3_hard_switch(opt_net, opt):
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kw = opt_get(opt_net, ['kwargs'], {})
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return VQVAE3HardSwitch(**kw)
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if __name__ == '__main__':
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#v = VQVAE3HardSwitch()
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#print(v(torch.randn(1,3,128,128))[0].shape)
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convert_weights("../../../experiments/test_vqvae3.pth") |