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| import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.normalization import GroupNorm
def get_norm(norm, num_channels, num_groups):
if norm == "in":
return nn.InstanceNorm2d(num_channels, affine=True)
elif norm == "bn":
return nn.BatchNorm2d(num_channels)
elif norm == "gn":
return nn.GroupNorm(num_groups, num_channels)
elif norm is None:
return nn.Identity()
else:
raise ValueError("unknown normalization type")
class PositionalEmbedding(nn.Module):
__doc__ = r"""Computes a positional embedding of timesteps.
Input:
x: tensor of shape (N)
Output:
tensor of shape (N, dim)
Args:
dim (int): embedding dimension
scale (float): linear scale to be applied to timesteps. Default: 1.0
"""
def __init__(self, dim, scale=1.0):
super().__init__()
assert dim % 2 == 0
self.dim = dim
self.scale = scale
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / half_dim
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = torch.outer(x * self.scale, emb)
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class Downsample(nn.Module):
__doc__ = r"""Downsamples a given tensor by a factor of 2. Uses strided convolution. Assumes even height and width.
Input:
x: tensor of shape (N, in_channels, H, W)
time_emb: ignored
y: ignored
Output:
tensor of shape (N, in_channels, H // 2, W // 2)
Args:
in_channels (int): number of input channels
"""
def __init__(self, in_channels):
super().__init__()
self.downsample = nn.Conv2d(in_channels, in_channels, 3, stride=2, padding=1)
def forward(self, x, time_emb, y):
if x.shape[2] % 2 == 1:
raise ValueError("downsampling tensor height should be even")
if x.shape[3] % 2 == 1:
raise ValueError("downsampling tensor width should be even")
return self.downsample(x)
class Upsample(nn.Module):
__doc__ = r"""Upsamples a given tensor by a factor of 2. Uses resize convolution to avoid checkerboard artifacts.
Input:
x: tensor of shape (N, in_channels, H, W)
time_emb: ignored
y: ignored
Output:
tensor of shape (N, in_channels, H * 2, W * 2)
Args:
in_channels (int): number of input channels
"""
def __init__(self, in_channels):
super().__init__()
self.upsample = nn.Sequential(
nn.Upsample(scale_factor=2, mode="nearest"),
nn.Conv2d(in_channels, in_channels, 3, padding=1),
)
def forward(self, x, time_emb, y):
return self.upsample(x)
class AttentionBlock(nn.Module):
__doc__ = r"""Applies QKV self-attention with a residual connection.
Input:
x: tensor of shape (N, in_channels, H, W)
norm (string or None): which normalization to use (instance, group, batch, or none). Default: "gn"
num_groups (int): number of groups used in group normalization. Default: 32
Output:
tensor of shape (N, in_channels, H, W)
Args:
in_channels (int): number of input channels
"""
def __init__(self, in_channels, norm="gn", num_groups=32):
super().__init__()
self.in_channels = in_channels
self.norm = get_norm(norm, in_channels, num_groups)
self.to_qkv = nn.Conv2d(in_channels, in_channels * 3, 1)
self.to_out = nn.Conv2d(in_channels, in_channels, 1)
def forward(self, x):
b, c, h, w = x.shape
q, k, v = torch.split(self.to_qkv(self.norm(x)), self.in_channels, dim=1)
q = q.permute(0, 2, 3, 1).view(b, h * w, c)
k = k.view(b, c, h * w)
v = v.permute(0, 2, 3, 1).view(b, h * w, c)
dot_products = torch.bmm(q, k) * (c ** (-0.5))
assert dot_products.shape == (b, h * w, h * w)
attention = torch.softmax(dot_products, dim=-1)
out = torch.bmm(attention, v)
assert out.shape == (b, h * w, c)
out = out.view(b, h, w, c).permute(0, 3, 1, 2)
return self.to_out(out) + x
class ResidualBlock(nn.Module):
__doc__ = r"""Applies two conv blocks with resudual connection. Adds time and class conditioning by adding bias after first convolution.
Input:
x: tensor of shape (N, in_channels, H, W)
time_emb: time embedding tensor of shape (N, time_emb_dim) or None if the block doesn't use time conditioning
y: classes tensor of shape (N) or None if the block doesn't use class conditioning
Output:
tensor of shape (N, out_channels, H, W)
Args:
in_channels (int): number of input channels
out_channels (int): number of output channels
time_emb_dim (int or None): time embedding dimension or None if the block doesn't use time conditioning. Default: None
num_classes (int or None): number of classes or None if the block doesn't use class conditioning. Default: None
activation (function): activation function. Default: torch.nn.functional.relu
norm (string or None): which normalization to use (instance, group, batch, or none). Default: "gn"
num_groups (int): number of groups used in group normalization. Default: 32
use_attention (bool): if True applies AttentionBlock to the output. Default: False
"""
def __init__(
self,
in_channels,
out_channels,
dropout,
time_emb_dim=None,
num_classes=None,
activation=F.relu,
norm="gn",
num_groups=32,
use_attention=False,
):
super().__init__()
self.activation = activation
self.norm_1 = get_norm(norm, in_channels, num_groups)
self.conv_1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
self.norm_2 = get_norm(norm, out_channels, num_groups)
self.conv_2 = nn.Sequential(
nn.Dropout(p=dropout),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
)
self.time_bias = nn.Linear(time_emb_dim, out_channels) if time_emb_dim is not None else None
self.class_bias = nn.Embedding(num_classes, out_channels) if num_classes is not None else None
self.residual_connection = nn.Conv2d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity()
self.attention = nn.Identity() if not use_attention else AttentionBlock(out_channels, norm, num_groups)
def forward(self, x, time_emb=None, y=None):
out = self.activation(self.norm_1(x))
out = self.conv_1(out)
if self.time_bias is not None:
if time_emb is None:
raise ValueError("time conditioning was specified but time_emb is not passed")
out += self.time_bias(self.activation(time_emb))[:, :, None, None]
if self.class_bias is not None:
if y is None:
raise ValueError("class conditioning was specified but y is not passed")
out += self.class_bias(y)[:, :, None, None]
out = self.activation(self.norm_2(out))
out = self.conv_2(out) + self.residual_connection(x)
out = self.attention(out)
return out
class UNet(nn.Module):
__doc__ = """UNet model used to estimate noise.
Input:
x: tensor of shape (N, in_channels, H, W)
time_emb: time embedding tensor of shape (N, time_emb_dim) or None if the block doesn't use time conditioning
y: classes tensor of shape (N) or None if the block doesn't use class conditioning
Output:
tensor of shape (N, out_channels, H, W)
Args:
img_channels (int): number of image channels
base_channels (int): number of base channels (after first convolution)
channel_mults (tuple): tuple of channel multiplers. Default: (1, 2, 4, 8)
time_emb_dim (int or None): time embedding dimension or None if the block doesn't use time conditioning. Default: None
time_emb_scale (float): linear scale to be applied to timesteps. Default: 1.0
num_classes (int or None): number of classes or None if the block doesn't use class conditioning. Default: None
activation (function): activation function. Default: torch.nn.functional.relu
dropout (float): dropout rate at the end of each residual block
attention_resolutions (tuple): list of relative resolutions at which to apply attention. Default: ()
norm (string or None): which normalization to use (instance, group, batch, or none). Default: "gn"
num_groups (int): number of groups used in group normalization. Default: 32
initial_pad (int): initial padding applied to image. Should be used if height or width is not a power of 2. Default: 0
"""
def __init__(
self,
img_channels,
base_channels,
channel_mults=(1, 2, 4, 8),
num_res_blocks=2,
time_emb_dim=None,
time_emb_scale=1.0,
num_classes=None,
activation=F.relu,
dropout=0.1,
attention_resolutions=(),
norm="gn",
num_groups=32,
initial_pad=0,
):
super().__init__()
self.activation = activation
self.initial_pad = initial_pad
self.num_classes = num_classes
self.time_mlp = nn.Sequential(
PositionalEmbedding(base_channels, time_emb_scale),
nn.Linear(base_channels, time_emb_dim),
nn.SiLU(),
nn.Linear(time_emb_dim, time_emb_dim),
) if time_emb_dim is not None else None
self.init_conv = nn.Conv2d(img_channels, base_channels, 3, padding=1)
self.downs = nn.ModuleList()
self.ups = nn.ModuleList()
channels = [base_channels]
now_channels = base_channels
for i, mult in enumerate(channel_mults):
out_channels = base_channels * mult
for _ in range(num_res_blocks):
self.downs.append(ResidualBlock(
now_channels,
out_channels,
dropout,
time_emb_dim=time_emb_dim,
num_classes=num_classes,
activation=activation,
norm=norm,
num_groups=num_groups,
use_attention=i in attention_resolutions,
))
now_channels = out_channels
channels.append(now_channels)
if i != len(channel_mults) - 1:
self.downs.append(Downsample(now_channels))
channels.append(now_channels)
self.mid = nn.ModuleList([
ResidualBlock(
now_channels,
now_channels,
dropout,
time_emb_dim=time_emb_dim,
num_classes=num_classes,
activation=activation,
norm=norm,
num_groups=num_groups,
use_attention=True,
),
ResidualBlock(
now_channels,
now_channels,
dropout,
time_emb_dim=time_emb_dim,
num_classes=num_classes,
activation=activation,
norm=norm,
num_groups=num_groups,
use_attention=False,
),
])
for i, mult in reversed(list(enumerate(channel_mults))):
out_channels = base_channels * mult
for _ in range(num_res_blocks + 1):
self.ups.append(ResidualBlock(
channels.pop() + now_channels,
out_channels,
dropout,
time_emb_dim=time_emb_dim,
num_classes=num_classes,
activation=activation,
norm=norm,
num_groups=num_groups,
use_attention=i in attention_resolutions,
))
now_channels = out_channels
if i != 0:
self.ups.append(Upsample(now_channels))
assert len(channels) == 0
self.out_norm = get_norm(norm, base_channels, num_groups)
self.out_conv = nn.Conv2d(base_channels, img_channels, 3, padding=1)
def forward(self, x, time=None, y=None):
ip = self.initial_pad
if ip != 0:
x = F.pad(x, (ip,) * 4)
if self.time_mlp is not None:
if time is None:
raise ValueError("time conditioning was specified but tim is not passed")
time_emb = self.time_mlp(time)
else:
time_emb = None
if self.num_classes is not None and y is None:
raise ValueError("class conditioning was specified but y is not passed")
x = self.init_conv(x)
skips = [x]
for layer in self.downs:
x = layer(x, time_emb, y)
skips.append(x)
for layer in self.mid:
x = layer(x, time_emb, y)
for layer in self.ups:
if isinstance(layer, ResidualBlock):
x = torch.cat([x, skips.pop()], dim=1)
x = layer(x, time_emb, y)
x = self.activation(self.out_norm(x))
x = self.out_conv(x)
if self.initial_pad != 0:
return x[:, :, ip:-ip, ip:-ip]
else:
return x
|
