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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.distributions.distributions import DiagonalGaussianDistribution
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 Upsample2d(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 Upsample1d(Upsample2d):
def __init__(self, in_channels, with_conv):
super().__init__(in_channels, with_conv)
if self.with_conv:
self.conv = torch.nn.Conv1d(
in_channels, in_channels, kernel_size=3, stride=1, padding=1
)
class Downsample2d(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
)
self.pad = (0, 1, 0, 1)
else:
self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x):
if self.with_conv: # bp: check self.avgpool and self.pad
x = torch.nn.functional.pad(x, self.pad, mode="constant", value=0)
x = self.conv(x)
else:
x = self.avg_pool(x)
return x
class Downsample1d(Downsample2d):
def __init__(self, in_channels, with_conv):
super().__init__(in_channels, with_conv)
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
# TODO: can we replace it just with conv2d with padding 1?
self.conv = torch.nn.Conv1d(
in_channels, in_channels, kernel_size=3, stride=2, padding=0
)
self.pad = (1, 1)
else:
self.avg_pool = nn.AvgPool1d(kernel_size=2, stride=2)
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout):
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):
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 ResnetBlock1d(ResnetBlock):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout,
temb_channels=512
):
super().__init__(
in_channels=in_channels,
out_channels=out_channels,
conv_shortcut=conv_shortcut,
dropout=dropout,
)
self.conv1 = torch.nn.Conv1d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
self.conv2 = torch.nn.Conv1d(
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.Conv1d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
else:
self.nin_shortcut = torch.nn.Conv1d(
in_channels, out_channels, kernel_size=1, stride=1, padding=0
)
class Encoder2d(nn.Module):
def __init__(
self,
*,
ch,
ch_mult=(1, 2, 4, 8),
num_res_blocks,
dropout=0.0,
resamp_with_conv=True,
in_channels,
z_channels,
double_z=True,
**ignore_kwargs
):
super().__init__()
self.ch = ch
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(
in_channels, self.ch, kernel_size=3, stride=1, padding=1
)
in_ch_mult = (1,) + tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = 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, dropout=dropout
)
)
block_in = block_out
down = nn.Module()
down.block = block
if i_level != self.num_resolutions - 1:
down.downsample = Downsample2d(block_in, resamp_with_conv)
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(
in_channels=block_in, out_channels=block_in, dropout=dropout
)
self.mid.block_2 = ResnetBlock(
in_channels=block_in, out_channels=block_in, 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):
# 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])
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)
h = self.mid.block_2(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
# TODO: Encoder1d
class Encoder1d(Encoder2d):
...
class Decoder2d(nn.Module):
def __init__(
self,
*,
ch,
out_ch,
ch_mult=(1, 2, 4, 8),
num_res_blocks,
dropout=0.0,
resamp_with_conv=True,
in_channels,
z_channels,
give_pre_end=False,
**ignorekwargs
):
super().__init__()
self.ch = ch
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
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]
# 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, dropout=dropout
)
self.mid.block_2 = ResnetBlock(
in_channels=block_in, out_channels=block_in, 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, dropout=dropout
)
)
block_in = block_out
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample2d(block_in, resamp_with_conv)
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):
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.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 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)
return h
# TODO: decoder1d
class Decoder1d(Decoder2d):
...
class AutoencoderKL(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.encoder = Encoder2d(
ch=cfg.ch,
ch_mult=cfg.ch_mult,
num_res_blocks=cfg.num_res_blocks,
in_channels=cfg.in_channels,
z_channels=cfg.z_channels,
double_z=cfg.double_z,
)
self.decoder = Decoder2d(
ch=cfg.ch,
ch_mult=cfg.ch_mult,
num_res_blocks=cfg.num_res_blocks,
out_ch=cfg.out_ch,
z_channels=cfg.z_channels,
in_channels=None,
)
assert self.cfg.double_z
self.quant_conv = torch.nn.Conv2d(2 * cfg.z_channels, 2 * cfg.z_channels, 1)
self.post_quant_conv = torch.nn.Conv2d(cfg.z_channels, cfg.z_channels, 1)
self.embed_dim = cfg.z_channels
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
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
def get_last_layer(self):
return self.decoder.conv_out.weight
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