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smthemex
@@ -0,0 +1,357 @@
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# pytorch_diffusion + derived encoder decoder
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import math
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import torch
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import torch.nn as nn
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import numpy as np
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def get_timestep_embedding(timesteps, embedding_dim):
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"""
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This matches the implementation in Denoising Diffusion Probabilistic Models:
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From Fairseq.
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Build sinusoidal embeddings.
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This matches the implementation in tensor2tensor, but differs slightly
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from the description in Section 3.5 of "Attention Is All You Need".
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"""
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assert len(timesteps.shape) == 1
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half_dim = embedding_dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
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emb = emb.to(device=timesteps.device)
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emb = timesteps.float()[:, None] * emb[None, :]
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
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if embedding_dim % 2 == 1: # zero pad
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emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
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return emb
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def nonlinearity(x):
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# swish
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return x * torch.sigmoid(x)
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class SpatialNorm(nn.Module):
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def __init__(
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self,
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f_channels,
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zq_channels,
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norm_layer=nn.GroupNorm,
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freeze_norm_layer=False,
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add_conv=False,
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**norm_layer_params
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):
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super().__init__()
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self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
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if freeze_norm_layer:
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for p in self.norm_layer.parameters:
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p.requires_grad = False
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self.add_conv = add_conv
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if self.add_conv:
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self.conv = nn.Conv2d(
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zq_channels, zq_channels, kernel_size=3, stride=1, padding=1
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)
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self.conv_y = nn.Conv2d(
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zq_channels, f_channels, kernel_size=1, stride=1, padding=0
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)
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self.conv_b = nn.Conv2d(
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zq_channels, f_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, f, zq):
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f_size = f.shape[-2:]
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zq = torch.nn.functional.interpolate(zq, size=f_size, mode="nearest")
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if self.add_conv:
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zq = self.conv(zq)
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norm_f = self.norm_layer(f)
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new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
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return new_f
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def Normalize(in_channels, zq_ch, add_conv):
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return SpatialNorm(
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in_channels,
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zq_ch,
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norm_layer=nn.GroupNorm,
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freeze_norm_layer=False,
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add_conv=add_conv,
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num_groups=32,
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eps=1e-6,
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affine=True,
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)
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class Upsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=1, padding=1
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)
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def forward(self, x):
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x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
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if self.with_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=2, padding=0
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)
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def forward(self, x):
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if self.with_conv:
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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else:
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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return x
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class ResnetBlock(nn.Module):
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def __init__(
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self,
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*,
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in_channels,
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out_channels=None,
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conv_shortcut=False,
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dropout,
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temb_channels=512,
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zq_ch=None,
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add_conv=False
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):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.norm1 = Normalize(in_channels, zq_ch, add_conv=add_conv)
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self.conv1 = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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if temb_channels > 0:
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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self.norm2 = Normalize(out_channels, zq_ch, add_conv=add_conv)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1
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)
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else:
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self.nin_shortcut = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x, temb, zq):
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h = x
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h = self.norm1(h, zq)
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h = nonlinearity(h)
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h = self.conv1(h)
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if temb is not None:
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h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
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h = self.norm2(h, zq)
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h = nonlinearity(h)
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h = self.dropout(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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x = self.conv_shortcut(x)
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else:
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x = self.nin_shortcut(x)
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return x + h
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class AttnBlock(nn.Module):
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def __init__(self, in_channels, zq_ch=None, add_conv=False):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x, zq):
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h_ = x
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h_ = self.norm(h_, zq)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = q.reshape(b, c, h * w)
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q = q.permute(0, 2, 1) # b,hw,c
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k = k.reshape(b, c, h * w) # b,c,hw
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w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w_ = w_ * (int(c) ** (-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b, c, h * w)
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w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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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]
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h_ = h_.reshape(b, c, h, w)
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h_ = self.proj_out(h_)
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return x + h_
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class MOVQDecoder(nn.Module):
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def __init__(
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self,
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*,
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ch,
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out_ch,
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ch_mult=(1, 2, 4, 8),
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num_res_blocks,
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attn_resolutions,
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dropout=0.0,
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resamp_with_conv=True,
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in_channels,
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resolution,
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z_channels,
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give_pre_end=False,
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zq_ch=None,
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add_conv=False,
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**ignorekwargs
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):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.give_pre_end = give_pre_end
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# compute in_ch_mult, block_in and curr_res at lowest res
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in_ch_mult = (1,) + tuple(ch_mult)
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2 ** (self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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print(
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"Working with z of shape {} = {} dimensions.".format(
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self.z_shape, np.prod(self.z_shape)
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)
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)
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# z to block_in
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self.conv_in = torch.nn.Conv2d(
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z_channels, block_in, kernel_size=3, stride=1, padding=1
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)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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zq_ch=zq_ch,
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add_conv=add_conv,
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)
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self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv)
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self.mid.block_2 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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zq_ch=zq_ch,
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add_conv=add_conv,
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)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks + 1):
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block.append(
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ResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout,
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zq_ch=zq_ch,
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add_conv=add_conv,
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)
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)
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv))
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in, resamp_with_conv)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = Normalize(block_in, zq_ch, add_conv=add_conv)
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self.conv_out = torch.nn.Conv2d(
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block_in, out_ch, kernel_size=3, stride=1, padding=1
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)
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def forward(self, z, zq):
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# assert z.shape[1:] == self.z_shape[1:]
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self.last_z_shape = z.shape
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# timestep embedding
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temb = None
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# z to block_in
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h = self.conv_in(z)
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# middle
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h = self.mid.block_1(h, temb, zq)
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h = self.mid.attn_1(h, zq)
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h = self.mid.block_2(h, temb, zq)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks + 1):
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h = self.up[i_level].block[i_block](h, temb, zq)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h, zq)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
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if self.give_pre_end:
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return h
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h = self.norm_out(h, zq)
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h = nonlinearity(h)
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h = self.conv_out(h)
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return h
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@@ -0,0 +1,148 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from torch import einsum
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from einops import rearrange
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class VectorQuantizer(nn.Module):
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"""
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Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
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avoids costly matrix multiplications and allows for post-hoc remapping of indices.
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"""
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# NOTE: due to a bug the beta term was applied to the wrong term. for
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# backwards compatibility we use the buggy version by default, but you can
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# specify legacy=False to fix it.
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def __init__(
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self,
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n_e,
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e_dim,
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beta,
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remap=None,
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unknown_index="random",
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sane_index_shape=False,
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legacy=True,
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):
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super().__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.legacy = legacy
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed + 1
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print(
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f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices."
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)
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else:
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self.re_embed = n_e
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self.sane_index_shape = sane_index_shape
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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match = (inds[:, :, None] == used[None, None, ...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2) < 1
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if self.unknown_index == "random":
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new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(
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device=new.device
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)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds >= self.used.shape[0]] = 0 # simply set to zero
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back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
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return back.reshape(ishape)
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def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
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assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
|
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assert rescale_logits == False, "Only for interface compatible with Gumbel"
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assert return_logits == False, "Only for interface compatible with Gumbel"
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# reshape z -> (batch, height, width, channel) and flatten
|
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z = rearrange(z, "b c h w -> b h w c").contiguous()
|
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z_flattened = z.view(-1, self.e_dim)
|
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
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|
||||
d = (
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torch.sum(z_flattened**2, dim=1, keepdim=True)
|
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+ torch.sum(self.embedding.weight**2, dim=1)
|
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- 2
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* torch.einsum(
|
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"bd,dn->bn", z_flattened, rearrange(self.embedding.weight, "n d -> d n")
|
||||
)
|
||||
)
|
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|
||||
min_encoding_indices = torch.argmin(d, dim=1)
|
||||
z_q = self.embedding(min_encoding_indices).view(z.shape)
|
||||
perplexity = None
|
||||
min_encodings = None
|
||||
|
||||
# compute loss for embedding
|
||||
if not self.legacy:
|
||||
loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean(
|
||||
(z_q - z.detach()) ** 2
|
||||
)
|
||||
else:
|
||||
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
|
||||
(z_q - z.detach()) ** 2
|
||||
)
|
||||
|
||||
# preserve gradients
|
||||
z_q = z + (z_q - z).detach()
|
||||
|
||||
# reshape back to match original input shape
|
||||
z_q = rearrange(z_q, "b h w c -> b c h w").contiguous()
|
||||
|
||||
if self.remap is not None:
|
||||
min_encoding_indices = min_encoding_indices.reshape(
|
||||
z.shape[0], -1
|
||||
) # add batch axis
|
||||
min_encoding_indices = self.remap_to_used(min_encoding_indices)
|
||||
min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
|
||||
|
||||
if self.sane_index_shape:
|
||||
min_encoding_indices = min_encoding_indices.reshape(
|
||||
z_q.shape[0], z_q.shape[2], z_q.shape[3]
|
||||
)
|
||||
|
||||
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
|
||||
|
||||
def get_codebook_entry(self, indices, shape):
|
||||
# shape specifying (batch, height, width, channel)
|
||||
if self.remap is not None:
|
||||
indices = indices.reshape(shape[0], -1) # add batch axis
|
||||
indices = self.unmap_to_all(indices)
|
||||
indices = indices.reshape(-1) # flatten again
|
||||
|
||||
# get quantized latent vectors
|
||||
z_q = self.embedding(indices)
|
||||
|
||||
if shape is not None:
|
||||
z_q = z_q.view(shape)
|
||||
# reshape back to match original input shape
|
||||
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return z_q
|
||||
@@ -0,0 +1,499 @@
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
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([0.0])
|
||||
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([0.0])
|
||||
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 LinearAttention(nn.Module):
|
||||
def __init__(self, dim, heads=4, dim_head=32):
|
||||
super().__init__()
|
||||
self.heads = heads
|
||||
hidden_dim = dim_head * heads
|
||||
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
|
||||
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h, w = x.shape
|
||||
qkv = self.to_qkv(x)
|
||||
q, k, v = rearrange(
|
||||
qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
|
||||
)
|
||||
k = k.softmax(dim=-1)
|
||||
context = torch.einsum("bhdn,bhen->bhde", k, v)
|
||||
out = torch.einsum("bhde,bhdn->bhen", context, q)
|
||||
out = rearrange(
|
||||
out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
|
||||
)
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
def nonlinearity(x):
|
||||
# swish
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
def Normalize(in_channels, num_groups=32):
|
||||
return torch.nn.GroupNorm(
|
||||
num_groups=num_groups, num_channels=in_channels, 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, scale_factor=2.0, mode="nearest")
|
||||
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 LinAttnBlock(LinearAttention):
|
||||
"""to match AttnBlock usage"""
|
||||
|
||||
def __init__(self, in_channels):
|
||||
super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
|
||||
|
||||
|
||||
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_ = 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)
|
||||
|
||||
|
||||
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",
|
||||
**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
|
||||
|
||||
# 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
|
||||
Reference in New Issue
Block a user