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Configuration error
| import math | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| import torch.nn.functional as F | |
| def nonlinearity(x): | |
| return x*torch.sigmoid(x) | |
| class SpatialNorm(nn.Module): | |
| def __init__( | |
| self, f_channels, zq_channels=None, norm_layer=nn.GroupNorm, freeze_norm_layer=False, add_conv=False, **norm_layer_params | |
| ): | |
| super().__init__() | |
| self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params) | |
| if zq_channels is not None: | |
| if freeze_norm_layer: | |
| for p in self.norm_layer.parameters: | |
| p.requires_grad = False | |
| self.add_conv = add_conv | |
| if self.add_conv: | |
| self.conv = nn.Conv2d(zq_channels, zq_channels, kernel_size=3, stride=1, padding=1) | |
| self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0) | |
| self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0) | |
| def forward(self, f, zq=None): | |
| norm_f = self.norm_layer(f) | |
| if zq is not None: | |
| f_size = f.shape[-2:] | |
| zq = torch.nn.functional.interpolate(zq.float(), size=f_size, mode="nearest").to(zq.dtype) | |
| if self.add_conv: | |
| zq = self.conv(zq) | |
| norm_f = norm_f * self.conv_y(zq) + self.conv_b(zq) | |
| return norm_f | |
| def Normalize(in_channels, zq_ch=None, add_conv=None): | |
| return SpatialNorm( | |
| in_channels, zq_ch, norm_layer=nn.GroupNorm, | |
| freeze_norm_layer=False, add_conv=add_conv, num_groups=32, eps=1e-6, affine=True | |
| ) | |
| class Upsample(nn.Module): | |
| def __init__(self, in_channels, with_conv): | |
| super().__init__() | |
| self.with_conv = with_conv | |
| if self.with_conv: | |
| self.conv = torch.nn.Conv2d(in_channels, | |
| in_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| def forward(self, x): | |
| x = torch.nn.functional.interpolate(x.float(), scale_factor=2.0, mode="nearest").to(x.dtype) | |
| if self.with_conv: | |
| x = self.conv(x) | |
| return x | |
| class Downsample(nn.Module): | |
| def __init__(self, in_channels, with_conv): | |
| super().__init__() | |
| self.with_conv = with_conv | |
| if self.with_conv: | |
| self.conv = torch.nn.Conv2d(in_channels, | |
| in_channels, | |
| kernel_size=3, | |
| stride=2, | |
| padding=0) | |
| def forward(self, x): | |
| if self.with_conv: | |
| pad = (0,1,0,1) | |
| x = torch.nn.functional.pad(x, pad, mode="constant", value=0) | |
| x = self.conv(x) | |
| else: | |
| x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) | |
| return x | |
| class ResnetBlock(nn.Module): | |
| def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, | |
| dropout, temb_channels=512, zq_ch=None, add_conv=False): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| out_channels = in_channels if out_channels is None else out_channels | |
| self.out_channels = out_channels | |
| self.use_conv_shortcut = conv_shortcut | |
| self.norm1 = Normalize(in_channels, zq_ch, add_conv=add_conv) | |
| self.conv1 = torch.nn.Conv2d(in_channels, | |
| out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| if temb_channels > 0: | |
| self.temb_proj = torch.nn.Linear(temb_channels, | |
| out_channels) | |
| self.norm2 = Normalize(out_channels, zq_ch, add_conv=add_conv) | |
| self.dropout = torch.nn.Dropout(dropout) | |
| self.conv2 = torch.nn.Conv2d(out_channels, | |
| out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| if self.in_channels != self.out_channels: | |
| if self.use_conv_shortcut: | |
| self.conv_shortcut = torch.nn.Conv2d(in_channels, | |
| out_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| else: | |
| self.nin_shortcut = torch.nn.Conv2d(in_channels, | |
| out_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| def forward(self, x, temb, zq=None): | |
| h = x | |
| h = self.norm1(h, zq) | |
| h = nonlinearity(h) | |
| h = self.conv1(h) | |
| if temb is not None: | |
| h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] | |
| h = self.norm2(h, zq) | |
| h = nonlinearity(h) | |
| h = self.dropout(h) | |
| h = self.conv2(h) | |
| if self.in_channels != self.out_channels: | |
| if self.use_conv_shortcut: | |
| x = self.conv_shortcut(x) | |
| else: | |
| x = self.nin_shortcut(x) | |
| return x+h | |
| class AttnBlock(nn.Module): | |
| def __init__(self, in_channels, zq_ch=None, add_conv=False): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv) | |
| self.q = torch.nn.Conv2d(in_channels, | |
| in_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| self.k = torch.nn.Conv2d(in_channels, | |
| in_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| self.v = torch.nn.Conv2d(in_channels, | |
| in_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| self.proj_out = torch.nn.Conv2d(in_channels, | |
| in_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| def forward(self, x, zq=None): | |
| h_ = x | |
| h_ = self.norm(h_, zq) | |
| q = self.q(h_) | |
| k = self.k(h_) | |
| v = self.v(h_) | |
| # compute attention | |
| b,c,h,w = q.shape | |
| q = q.reshape(b,c,h*w) | |
| q = q.permute(0,2,1) # b,hw,c | |
| k = k.reshape(b,c,h*w) # b,c,hw | |
| w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] | |
| w_ = w_ * (int(c)**(-0.5)) | |
| w_ = torch.nn.functional.softmax(w_, dim=2) | |
| # attend to values | |
| v = v.reshape(b,c,h*w) | |
| w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) | |
| h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] | |
| h_ = h_.reshape(b,c,h,w) | |
| h_ = self.proj_out(h_) | |
| return x+h_ | |
| class Encoder(nn.Module): | |
| def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, | |
| attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, | |
| resolution, z_channels, double_z=True, **ignore_kwargs): | |
| super().__init__() | |
| self.ch = ch | |
| self.temb_ch = 0 | |
| self.num_resolutions = len(ch_mult) | |
| self.num_res_blocks = num_res_blocks | |
| self.resolution = resolution | |
| self.in_channels = in_channels | |
| # downsampling | |
| self.conv_in = torch.nn.Conv2d(in_channels, | |
| self.ch, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| curr_res = resolution | |
| in_ch_mult = (1,)+tuple(ch_mult) | |
| self.down = nn.ModuleList() | |
| for i_level in range(self.num_resolutions): | |
| block = nn.ModuleList() | |
| attn = nn.ModuleList() | |
| block_in = ch*in_ch_mult[i_level] | |
| block_out = ch*ch_mult[i_level] | |
| for i_block in range(self.num_res_blocks): | |
| block.append(ResnetBlock(in_channels=block_in, | |
| out_channels=block_out, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout)) | |
| block_in = block_out | |
| if curr_res in attn_resolutions: | |
| attn.append(AttnBlock(block_in)) | |
| down = nn.Module() | |
| down.block = block | |
| down.attn = attn | |
| if i_level != self.num_resolutions-1: | |
| down.downsample = Downsample(block_in, resamp_with_conv) | |
| curr_res = curr_res // 2 | |
| self.down.append(down) | |
| # middle | |
| self.mid = nn.Module() | |
| self.mid.block_1 = ResnetBlock(in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout) | |
| self.mid.attn_1 = AttnBlock(block_in) | |
| self.mid.block_2 = ResnetBlock(in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout) | |
| # end | |
| self.norm_out = Normalize(block_in) | |
| self.conv_out = torch.nn.Conv2d(block_in, | |
| 2*z_channels if double_z else z_channels, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| def forward(self, x): | |
| temb = None | |
| # downsampling | |
| hs = [self.conv_in(x)] | |
| for i_level in range(self.num_resolutions): | |
| for i_block in range(self.num_res_blocks): | |
| h = self.down[i_level].block[i_block](hs[-1], temb) | |
| if len(self.down[i_level].attn) > 0: | |
| h = self.down[i_level].attn[i_block](h) | |
| hs.append(h) | |
| if i_level != self.num_resolutions-1: | |
| hs.append(self.down[i_level].downsample(hs[-1])) | |
| # middle | |
| h = hs[-1] | |
| h = self.mid.block_1(h, temb) | |
| h = self.mid.attn_1(h) | |
| h = self.mid.block_2(h, temb) | |
| # end | |
| h = self.norm_out(h) | |
| h = nonlinearity(h) | |
| h = self.conv_out(h) | |
| return h | |
| class Decoder(nn.Module): | |
| def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, | |
| attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, | |
| resolution, z_channels, give_pre_end=False, zq_ch=None, add_conv=False, **ignorekwargs): | |
| super().__init__() | |
| self.ch = ch | |
| self.temb_ch = 0 | |
| self.num_resolutions = len(ch_mult) | |
| self.num_res_blocks = num_res_blocks | |
| self.resolution = resolution | |
| self.in_channels = in_channels | |
| self.give_pre_end = give_pre_end | |
| # compute in_ch_mult, block_in and curr_res at lowest res | |
| in_ch_mult = (1,)+tuple(ch_mult) | |
| block_in = ch*ch_mult[self.num_resolutions-1] | |
| curr_res = resolution // 2**(self.num_resolutions-1) | |
| self.z_shape = (1,z_channels,curr_res,curr_res) | |
| # z to block_in | |
| self.conv_in = torch.nn.Conv2d(z_channels, | |
| block_in, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| # middle | |
| self.mid = nn.Module() | |
| self.mid.block_1 = ResnetBlock(in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| zq_ch=zq_ch, | |
| add_conv=add_conv) | |
| self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv) | |
| self.mid.block_2 = ResnetBlock(in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| zq_ch=zq_ch, | |
| add_conv=add_conv) | |
| # upsampling | |
| self.up = nn.ModuleList() | |
| for i_level in reversed(range(self.num_resolutions)): | |
| block = nn.ModuleList() | |
| attn = nn.ModuleList() | |
| block_out = ch*ch_mult[i_level] | |
| for i_block in range(self.num_res_blocks+1): | |
| block.append(ResnetBlock(in_channels=block_in, | |
| out_channels=block_out, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| zq_ch=zq_ch, | |
| add_conv=add_conv)) | |
| block_in = block_out | |
| if curr_res in attn_resolutions: | |
| attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv)) | |
| up = nn.Module() | |
| up.block = block | |
| up.attn = attn | |
| if i_level != 0: | |
| up.upsample = Upsample(block_in, resamp_with_conv) | |
| curr_res = curr_res * 2 | |
| self.up.insert(0, up) # prepend to get consistent order | |
| # end | |
| self.norm_out = Normalize(block_in, zq_ch, add_conv=add_conv) | |
| self.conv_out = torch.nn.Conv2d(block_in, | |
| out_ch, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| def forward(self, z, zq): | |
| #assert z.shape[1:] == self.z_shape[1:] | |
| self.last_z_shape = z.shape | |
| # timestep embedding | |
| temb = None | |
| # z to block_in | |
| h = self.conv_in(z) | |
| # middle | |
| h = self.mid.block_1(h, temb, zq) | |
| h = self.mid.attn_1(h, zq) | |
| h = self.mid.block_2(h, temb, zq) | |
| # upsampling | |
| for i_level in reversed(range(self.num_resolutions)): | |
| for i_block in range(self.num_res_blocks+1): | |
| h = self.up[i_level].block[i_block](h, temb, zq) | |
| if len(self.up[i_level].attn) > 0: | |
| h = self.up[i_level].attn[i_block](h, zq) | |
| if i_level != 0: | |
| h = self.up[i_level].upsample(h) | |
| # end | |
| if self.give_pre_end: | |
| return h | |
| h = self.norm_out(h, zq) | |
| h = nonlinearity(h) | |
| h = self.conv_out(h) | |
| return h | |
| class MoVQ(nn.Module): | |
| def __init__(self, generator_params): | |
| super().__init__() | |
| z_channels = generator_params["z_channels"] | |
| self.encoder = Encoder(**generator_params) | |
| self.quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1) | |
| self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1) | |
| self.decoder = Decoder(zq_ch=z_channels, **generator_params) | |
| def encode(self, x): | |
| h = self.encoder(x) | |
| h = self.quant_conv(h) | |
| return h | |
| def decode(self, quant): | |
| decoder_input = self.post_quant_conv(quant) | |
| decoded = self.decoder(decoder_input, quant) | |
| return decoded | |