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Configuration error
| import torch | |
| import numpy as np | |
| from torch import nn | |
| def Normalize(in_channels, num_groups=32): | |
| return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) | |
| def nonlinearity(x): # swish | |
| return x*torch.sigmoid(x) | |
| class AutoencoderKL(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.embed_dim = config["embed_dim"] | |
| self.encoder = Encoder(**config) | |
| self.decoder = Decoder(**config) | |
| assert config["double_z"] | |
| self.quant_conv = torch.nn.Conv2d(2*config["z_channels"], 2*self.embed_dim, 1) | |
| self.post_quant_conv = torch.nn.Conv2d(self.embed_dim, config["z_channels"], 1) | |
| def encode(self, x): | |
| h = self.encoder(x) | |
| moments = self.quant_conv(h) | |
| posterior = DiagonalGaussianDistribution(moments) | |
| return posterior.sample() | |
| def decode(self, z): | |
| z = self.post_quant_conv(z) | |
| dec = self.decoder(z) | |
| return dec | |
| def forward(self, input, sample_posterior=True): | |
| posterior = self.encode(input) | |
| if sample_posterior: | |
| z = posterior.sample() | |
| else: | |
| z = posterior.mode() | |
| dec = self.decode(z) | |
| return dec, posterior | |
| 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, use_linear_attn=False, attn_type="vanilla", | |
| **ignore_kwargs): | |
| super().__init__() | |
| if use_linear_attn: attn_type = "linear" | |
| 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.in_ch_mult = in_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(make_attn(block_in, attn_type=attn_type)) | |
| 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 = make_attn(block_in, attn_type=attn_type) | |
| 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): | |
| # timestep embedding | |
| 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, tanh_out=False, use_linear_attn=False, | |
| attn_type="vanilla", post_quant_conv=None, **ignorekwargs): | |
| super().__init__() | |
| if use_linear_attn: attn_type = "linear" | |
| 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 | |
| self.tanh_out = tanh_out | |
| self.post_quant_conv = post_quant_conv | |
| # 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) | |
| print("Working with z of shape {} = {} dimensions.".format( | |
| self.z_shape, np.prod(self.z_shape))) | |
| # 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) | |
| self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) | |
| self.mid.block_2 = ResnetBlock(in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout) | |
| # 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)) | |
| block_in = block_out | |
| if curr_res in attn_resolutions: | |
| attn.append(make_attn(block_in, attn_type=attn_type)) | |
| 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) | |
| self.conv_out = torch.nn.Conv2d(block_in, | |
| out_ch, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| def forward(self, z): | |
| #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) | |
| h = self.mid.attn_1(h) | |
| h = self.mid.block_2(h, temb) | |
| # 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) | |
| if len(self.up[i_level].attn) > 0: | |
| h = self.up[i_level].attn[i_block](h) | |
| if i_level != 0: | |
| h = self.up[i_level].upsample(h) | |
| # end | |
| if self.give_pre_end: | |
| return h | |
| h = self.norm_out(h) | |
| h = nonlinearity(h) | |
| h = self.conv_out(h) | |
| if self.tanh_out: | |
| h = torch.tanh(h) | |
| return h | |
| class DiagonalGaussianDistribution(object): | |
| def __init__(self, parameters, deterministic=False): | |
| self.parameters = parameters | |
| self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) | |
| self.logvar = torch.clamp(self.logvar, -30.0, 20.0) | |
| self.deterministic = deterministic | |
| self.std = torch.exp(0.5 * self.logvar) | |
| self.var = torch.exp(self.logvar) | |
| if self.deterministic: | |
| self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) | |
| def sample(self): | |
| x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) | |
| return x | |
| def kl(self, other=None): | |
| if self.deterministic: | |
| return torch.Tensor([0.]) | |
| else: | |
| if other is None: | |
| return 0.5 * torch.sum(torch.pow(self.mean, 2) | |
| + self.var - 1.0 - self.logvar, | |
| dim=[1, 2, 3]) | |
| else: | |
| return 0.5 * torch.sum( | |
| torch.pow(self.mean - other.mean, 2) / other.var | |
| + self.var / other.var - 1.0 - self.logvar + other.logvar, | |
| dim=[1, 2, 3]) | |
| def nll(self, sample, dims=[1,2,3]): | |
| if self.deterministic: | |
| return torch.Tensor([0.]) | |
| logtwopi = np.log(2.0 * np.pi) | |
| return 0.5 * torch.sum( | |
| logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, | |
| dim=dims) | |
| def mode(self): | |
| return self.mean | |
| 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): | |
| # BF16 fix | |
| xh = x.to(torch.float32) | |
| xh = torch.nn.functional.interpolate(xh, scale_factor=2.0, mode="nearest") | |
| x = xh.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: | |
| # no asymmetric padding in torch conv, must do it ourselves | |
| 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): | |
| 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) | |
| 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) | |
| 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): | |
| h = x | |
| h = self.norm1(h) | |
| 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) | |
| 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): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.norm = Normalize(in_channels) | |
| 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): | |
| h_ = x | |
| h_ = self.norm(h_) | |
| 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_ = w_ * (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_ | |
| def make_attn(in_channels, attn_type="vanilla"): | |
| assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' | |
| print(f"making attention of type '{attn_type}' with {in_channels} in_channels") | |
| if attn_type == "vanilla": | |
| return AttnBlock(in_channels) | |
| elif attn_type == "none": | |
| return nn.Identity(in_channels) | |
| else: | |
| return LinAttnBlock(in_channels) | |