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models.py
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models.py
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import numpy as np
import torch
import torch.nn as nn
from transformer import SpatialTransformer
DEVICE = "cuda"
class GaussianFourierProjection(nn.Module):
def __init__(self, embed_dim, scale=30.):
super().__init__()
self.W = nn.Parameter(torch.randn(embed_dim // 2) * scale, requires_grad=False)
def forward(self, x):
x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
class Dense(nn.Module):
def __init__(self, input_dim, output_dim) -> None:
super().__init__()
self.dense = nn.Linear(input_dim, output_dim)
def forward(self, x):
return self.dense(x)[..., None, None]
class UNet(nn.Module):
def __init__(self, marginal_prob_std, channels=[32, 64, 128, 256], embed_dim=256):
super().__init__()
self.time_embed = nn.Sequential(
GaussianFourierProjection(embed_dim=embed_dim),
nn.Linear(embed_dim, embed_dim)
)
self.conv1 = nn.Conv2d(1, channels[0], 3, stride=1, bias=False)
self.dense1 = Dense(embed_dim, channels[0])
self.gnorm1 = nn.GroupNorm(4, num_channels=channels[0])
self.conv2 = nn.Conv2d(channels[0], channels[1], 3, stride=2, bias=False)
self.dense2 = Dense(embed_dim, channels[1])
self.gnorm2 = nn.GroupNorm(32, num_channels=channels[1])
self.conv3 = nn.Conv2d(channels[1], channels[2], 3, stride=2, bias=False)
self.dense3 = Dense(embed_dim, channels[2])
self.gnorm3 = nn.GroupNorm(32, num_channels=channels[2])
self.conv4 = nn.Conv2d(channels[2], channels[3], 3, stride=2, bias=False)
self.dense4 = Dense(embed_dim, channels[3])
self.gnorm4 = nn.GroupNorm(32, num_channels=channels[3])
self.tconv4 = nn.ConvTranspose2d(channels[3], channels[2], 3, stride=2, bias=False)
self.dense5 = Dense(embed_dim, channels[2])
self.tgnorm4 = nn.GroupNorm(32, num_channels=channels[2])
self.tconv3 = nn.ConvTranspose2d(channels[2] + channels[2], channels[1], 3, stride=2, bias=False, output_padding=1)
self.dense6 = Dense(embed_dim, channels[1])
self.tgnorm3 = nn.GroupNorm(32, num_channels=channels[1])
self.tconv2 = nn.ConvTranspose2d(channels[1] + channels[1], channels[0], 3, stride=2, bias=False, output_padding=1)
self.dense7 = Dense(embed_dim, channels[0])
self.tgnorm2 = nn.GroupNorm(32, num_channels=channels[0])
self.tconv1 = nn.ConvTranspose2d(channels[0] + channels[0], 1, 3, stride=1)
self.act = lambda x: x * torch.sigmoid(x)
self.marginal_prob_std = marginal_prob_std
def forward(self, x, t, y=None):
embed = self.act(self.time_embed(t))
h1 = self.conv1(x) + self.dense1(embed)
h1 = self.act(self.gnorm1(h1))
h2 = self.conv2(h1) + self.dense2(embed)
h2 = self.act(self.gnorm2(h2))
h3 = self.conv3(h2) + self.dense3(embed)
h3 = self.act(self.gnorm3(h3))
h4 = self.conv4(h3) + self.dense4(embed)
h4 = self.act(self.gnorm4(h4))
h = self.tconv4(h4)
h += self.dense5(embed)
h = self.act(self.tgnorm4(h))
h = self.tconv3(torch.cat([h, h3], dim=1))
h += self.dense6(embed)
h = self.act(self.tgnorm3(h))
h = self.tconv2(torch.cat([h, h2], dim=1))
h += self.dense7(embed)
h = self.act(self.tgnorm2(h))
h = self.tconv1(torch.cat([h, h1], dim=1))
h = h / self.marginal_prob_std(t)[:, None, None, None]
return h
class UNet_res(nn.Module):
def __init__(self, marginal_prob_std, channels=[32, 64, 128, 256], embed_dim=256):
super().__init__()
self.time_embed = nn.Sequential(
GaussianFourierProjection(embed_dim=embed_dim),
nn.Linear(embed_dim, embed_dim)
)
self.conv1 = nn.Conv2d(1, channels[0], 3, stride=1, bias=False)
self.dense1 = Dense(embed_dim, channels[0])
self.gnorm1 = nn.GroupNorm(4, num_channels=channels[0])
self.conv2 = nn.Conv2d(channels[0], channels[1], 3, stride=2, bias=False)
self.dense2 = Dense(embed_dim, channels[1])
self.gnorm2 = nn.GroupNorm(32, num_channels=channels[1])
self.conv3 = nn.Conv2d(channels[1], channels[2], 3, stride=2, bias=False)
self.dense3 = Dense(embed_dim, channels[2])
self.gnorm3 = nn.GroupNorm(32, num_channels=channels[2])
self.conv4 = nn.Conv2d(channels[2], channels[3], 3, stride=2, bias=False)
self.dense4 = Dense(embed_dim, channels[3])
self.gnorm4 = nn.GroupNorm(32, num_channels=channels[3])
self.tconv4 = nn.ConvTranspose2d(channels[3], channels[2], 3, stride=2, bias=False)
self.dense5 = Dense(embed_dim, channels[2])
self.tgnorm4 = nn.GroupNorm(32, num_channels=channels[2])
self.tconv3 = nn.ConvTranspose2d(channels[2], channels[1], 3, stride=2, bias=False, output_padding=1)
self.dense6 = Dense(embed_dim, channels[1])
self.tgnorm3 = nn.GroupNorm(32, num_channels=channels[1])
self.tconv2 = nn.ConvTranspose2d(channels[1], channels[0], 3, stride=2, bias=False, output_padding=1)
self.dense7 = Dense(embed_dim, channels[0])
self.tgnorm2 = nn.GroupNorm(32, num_channels=channels[0])
self.tconv1 = nn.ConvTranspose2d(channels[0], 1, 3, stride=1)
self.act = lambda x: x * torch.sigmoid(x)
self.marginal_prob_std = marginal_prob_std
def forward(self, x, t, y=None):
embed = self.act(self.time_embed(t))
h1 = self.conv1(x) + self.dense1(embed)
h1 = self.act(self.gnorm1(h1))
h2 = self.conv2(h1) + self.dense2(embed)
h2 = self.act(self.gnorm2(h2))
h3 = self.conv3(h2) + self.dense3(embed)
h3 = self.act(self.gnorm3(h3))
h4 = self.conv4(h3) + self.dense4(embed)
h4 = self.act(self.gnorm4(h4))
h = self.tconv4(h4)
h += self.dense5(embed)
h = self.act(self.tgnorm4(h))
h = self.tconv3(h + h3)
h += self.dense6(embed)
h = self.act(self.tgnorm3(h))
h = self.tconv2(h + h2)
h += self.dense7(embed)
h = self.act(self.tgnorm2(h))
h = self.tconv1(h + h1)
h = h / self.marginal_prob_std(t)[:, None, None, None]
return h
import torch.nn as nn
class UNet_Transformer(nn.Module):
def __init__(self, marginal_prob_std, dataset_name="mnist", channels=[32, 64, 128, 256], embed_dim=256, text_dim=256):
super().__init__()
self.dataset_name = dataset_name
input_channels = 1 if self.dataset_name == 'mnist' else 3
output_channels = 1 if self.dataset_name == 'mnist' else 3
padding = 0 if self.dataset_name == 'mnist' else 1
nClass = 10 if self.dataset_name in ['mnist', 'cifar10'] else 100
self.time_embed = nn.Sequential(
GaussianFourierProjection(embed_dim=embed_dim),
nn.Linear(embed_dim, embed_dim)
)
self.conv1 = nn.Conv2d(input_channels, channels[0], 3, stride=1, padding=padding, bias=False)
self.dense1 = Dense(embed_dim, channels[0])
self.gnorm1 = nn.GroupNorm(4, num_channels=channels[0])
self.conv2 = nn.Conv2d(channels[0], channels[1], 3, stride=2, padding=padding, bias=False)
self.dense2 = Dense(embed_dim, channels[1])
self.gnorm2 = nn.GroupNorm(32, num_channels=channels[1])
self.conv3 = nn.Conv2d(channels[1], channels[2], 3, stride=2, padding=padding, bias=False)
self.dense3 = Dense(embed_dim, channels[2])
self.gnorm3 = nn.GroupNorm(32, num_channels=channels[2])
self.attn3 = SpatialTransformer(channels[2], text_dim)
self.conv4 = nn.Conv2d(channels[2], channels[3], 3, stride=2, padding=padding, bias=False)
self.dense4 = Dense(embed_dim, channels[3])
self.gnorm4 = nn.GroupNorm(32, num_channels=channels[3])
self.attn4 = SpatialTransformer(channels[3], text_dim)
self.tconv4 = nn.ConvTranspose2d(channels[3], channels[2], 3, stride=2, padding=padding, output_padding=1 if dataset_name != 'mnist' else 0, bias=False)
self.dense5 = Dense(embed_dim, channels[2])
self.tgnorm4 = nn.GroupNorm(32, num_channels=channels[2])
self.tconv3 = nn.ConvTranspose2d(channels[2], channels[1], 3, stride=2, padding=padding, output_padding=1, bias=False)
self.dense6 = Dense(embed_dim, channels[1])
self.tgnorm3 = nn.GroupNorm(32, num_channels=channels[1])
self.tconv2 = nn.ConvTranspose2d(channels[1], channels[0], 3, stride=2, padding=padding, output_padding=1, bias=False)
self.dense7 = Dense(embed_dim, channels[0])
self.tgnorm2 = nn.GroupNorm(32, num_channels=channels[0])
self.tconv1 = nn.ConvTranspose2d(channels[0], output_channels, 3, stride=1, padding=padding)
self.act = nn.SiLU()
self.marginal_prob_std = marginal_prob_std
self.cond_embed = nn.Embedding(nClass, text_dim)
def forward(self, x, t, y=None):
embed = self.act(self.time_embed(t))
y_embed = self.cond_embed(y).unsqueeze(1)
h1 = self.conv1(x) + self.dense1(embed)
h1 = self.act(self.gnorm1(h1))
h2 = self.conv2(h1) + self.dense2(embed)
h2 = self.act(self.gnorm2(h2))
h3 = self.conv3(h2) + self.dense3(embed)
h3 = self.act(self.gnorm3(h3))
h3 = self.attn3(h3, y_embed)
h4 = self.conv4(h3) + self.dense4(embed)
h4 = self.act(self.gnorm4(h4))
h4 = self.attn4(h4, y_embed)
h = self.tconv4(h4) + self.dense5(embed)
h = self.act(self.tgnorm4(h))
h = self.tconv3(h + h3) + self.dense6(embed)
h = self.act(self.tgnorm3(h))
h = self.tconv2(h + h2) + self.dense7(embed)
h = self.act(self.tgnorm2(h))
h = self.tconv1(h + h1)
h = h / self.marginal_prob_std(t)[:, None, None, None]
return h
def marginal_prob_std(t, ro):
t = torch.tensor(t, device=DEVICE)
return torch.sqrt((ro**(2*t) - 1.) / 2. / np.log(ro))
def diffusion_coeff(t, ro):
return torch.tensor(ro**t, device=DEVICE)