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# ------------------------------------------------------------------------------------ | |
# Modified from VQGAN (https://github.com/CompVis/taming-transformers) | |
# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer. All Rights Reserved. | |
# ------------------------------------------------------------------------------------ | |
import torch | |
import torch.nn as nn | |
from typing import Tuple, Optional | |
def nonlinearity(x): | |
# swish | |
return x*torch.sigmoid(x) | |
def Normalize(in_channels): | |
return torch.nn.GroupNorm(num_groups=32, | |
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): | |
assert temb_channels == 0 | |
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) | |
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=None): | |
assert temb is None | |
h = x | |
h = self.norm1(h) | |
h = nonlinearity(h) | |
h = self.conv1(h) | |
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_ = 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, | |
*, # forced to use named arguments | |
ch: int, | |
out_ch: int, | |
ch_mult: Tuple[int] = (1, 2, 4, 8), | |
num_res_blocks: int, | |
attn_resolutions: Tuple[int], | |
pdrop: float = 0.0, | |
resamp_with_conv: bool = True, | |
in_channels: int, | |
resolution: int, | |
z_channels: int, | |
double_z: Optional[bool] = None) -> None: | |
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=pdrop)) | |
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=pdrop) | |
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=pdrop) | |
# 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): | |
assert x.shape[2] == x.shape[3] == self.resolution, \ | |
"{}, {}".format(x.shape, self.resolution) | |
# downsampling | |
h = 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](h) | |
if len(self.down[i_level].attn) > 0: | |
h = self.down[i_level].attn[i_block](h) | |
if i_level != self.num_resolutions-1: | |
h = self.down[i_level].downsample(h) | |
# middle | |
h = self.mid.block_1(h) | |
h = self.mid.attn_1(h) | |
h = self.mid.block_2(h) | |
# end | |
h = self.norm_out(h) | |
h = nonlinearity(h) | |
h = self.conv_out(h) | |
return h | |
class Decoder(nn.Module): | |
def __init__(self, | |
*, # forced to use named arguments | |
ch: int, | |
out_ch: int, | |
ch_mult: Tuple[int] = (1, 2, 4, 8), | |
num_res_blocks: int, | |
attn_resolutions: Tuple[int], | |
pdrop: float = 0.0, | |
resamp_with_conv: bool = True, | |
in_channels: int, | |
resolution: int, | |
z_channels: int, | |
double_z: bool) -> None: | |
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 | |
# compute in_ch_mult, block_in and curr_res at lowest res | |
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=pdrop) | |
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=pdrop) | |
# 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=pdrop)) | |
block_in = block_out | |
if curr_res in attn_resolutions: | |
attn.append(AttnBlock(block_in)) | |
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 | |
# z to block_in | |
h = self.conv_in(z) | |
# middle | |
h = self.mid.block_1(h) | |
h = self.mid.attn_1(h) | |
h = self.mid.block_2(h) | |
# 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) | |
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) | |
h = self.norm_out(h) | |
h = nonlinearity(h) | |
h = self.conv_out(h) | |
return h | |