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DDPM_model.py
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'''
This file implement Denoising Diffusion Model.
Code is tailored to the LIDC images and is based on listed below sources:
This code is modified from,
https://github.com/cloneofsimo/minDiffusion
Diffusion model is based on DDPM,
https://arxiv.org/abs/2006.11239
The conditioning idea is taken from 'Classifier-Free Diffusion Guidance',
https://arxiv.org/abs/2207.12598
This technique also features in ImageGen 'Photorealistic Text-to-Image Diffusion Modelswith Deep Language Understanding',
https://arxiv.org/abs/2205.11487
'''
from typing import Dict, Tuple
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import models, transforms
from LIDC_Dataset import LIDC_Dataset
from torchvision.utils import save_image, make_grid
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
import numpy as np
class ResidualConvBlock(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, is_res: bool = False
) -> None:
super().__init__()
'''
standard ResNet style convolutional block
'''
self.same_channels = in_channels==out_channels
self.is_res = is_res
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 1, 1),
nn.BatchNorm2d(out_channels),
nn.GELU(),
)
self.conv2 = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, 1, 1),
nn.BatchNorm2d(out_channels),
nn.GELU(),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.is_res:
x1 = self.conv1(x)
x2 = self.conv2(x1)
# this adds on correct residual in case channels have increased
if self.same_channels:
out = x + x2
else:
out = x1 + x2
return out / 1.414
else:
x1 = self.conv1(x)
x2 = self.conv2(x1)
return x2
class UnetDown(nn.Module):
def __init__(self, in_channels, out_channels):
super(UnetDown, self).__init__()
'''
process and downscale the image feature maps
'''
layers = [ResidualConvBlock(in_channels, out_channels), nn.MaxPool2d(2)]
self.model = nn.Sequential(*[ResidualConvBlock(in_channels, out_channels), nn.MaxPool2d(2)])
def forward(self, x):
return self.model(x)
class UnetUp(nn.Module):
def __init__(self, in_channels, out_channels):
super(UnetUp, self).__init__()
'''
process and upscale the image feature maps
'''
layers = [
nn.ConvTranspose2d(in_channels, out_channels, 2, 2),
ResidualConvBlock(out_channels, out_channels),
ResidualConvBlock(out_channels, out_channels),
]
self.model = nn.Sequential(*layers)
def forward(self, x, skip):
x = torch.cat((x, skip), 1)
x = self.model(x)
return x
class EmbedFC(nn.Module):
def __init__(self, input_dim, emb_dim):
super(EmbedFC, self).__init__()
'''
generic one layer FC NN for embedding things
'''
self.input_dim = input_dim
layers = [
nn.Linear(input_dim, emb_dim),
nn.GELU(),
nn.Linear(emb_dim, emb_dim),
]
self.model = nn.Sequential(*layers)
def forward(self, x):
x = x.view(-1, self.input_dim)
return self.model(x)
class ContextUnet(nn.Module):
def __init__(self, in_channels, n_feat = 256, n_classes=2):
super(ContextUnet, self).__init__()
self.in_channels = in_channels
self.n_feat = n_feat
self.n_classes = n_classes
self.init_conv = ResidualConvBlock(in_channels, n_feat, is_res=True)
self.down1 = UnetDown(n_feat, n_feat)
self.down2 = UnetDown(n_feat, 2 * n_feat)
self.to_vec = nn.Sequential(nn.AvgPool2d(7), nn.GELU())
self.timeembed1 = EmbedFC(1, 2*n_feat)
self.timeembed2 = EmbedFC(1, 1*n_feat)
self.contextembed1 = EmbedFC(n_classes, 2*n_feat)
self.contextembed2 = EmbedFC(n_classes, 1*n_feat)
self.up0 = nn.Sequential(
# nn.ConvTranspose2d(6 * n_feat, 2 * n_feat, 7, 7), # when concat temb and cemb end up w 6*n_feat
# WARNING: Had to change the following line
#nn.ConvTranspose2d(2 * n_feat, 2 * n_feat, 7, 7), # otherwise just have 2*n_feat
#to this, to have correct dimensions for LIDC
# hope that doesn't break anything
nn.ConvTranspose2d(2 * n_feat, 2 * n_feat, 8, 8), # otherwise just have 2*n_feat
nn.GroupNorm(8, 2 * n_feat),
nn.ReLU(),
)
self.up1 = UnetUp(4 * n_feat, n_feat)
self.up2 = UnetUp(2 * n_feat, n_feat)
self.out = nn.Sequential(
nn.Conv2d(2 * n_feat, n_feat, 3, 1, 1),
nn.GroupNorm(8, n_feat),
nn.ReLU(),
nn.Conv2d(n_feat, self.in_channels, 3, 1, 1),
)
def forward(self, x, c, t, context_mask):
# x is (noisy) image, c is context label, t is timestep,
# context_mask says which samples to blockc the context on
x = self.init_conv(x)
down1 = self.down1(x)
down2 = self.down2(down1)
hiddenvec = self.to_vec(down2)
# convert context to one hot embedding
c = nn.functional.one_hot(c, num_classes=self.n_classes).type(torch.float)
# mask out context if context_mask == 1
context_mask = context_mask[:, None]
context_mask = context_mask.repeat(1,self.n_classes)
context_mask = (-1*(1-context_mask)) # need to flip 0 <-> 1
c = c * context_mask
# embed context, time step
cemb1 = self.contextembed1(c).view(-1, self.n_feat * 2, 1, 1)
temb1 = self.timeembed1(t).view(-1, self.n_feat * 2, 1, 1)
cemb2 = self.contextembed2(c).view(-1, self.n_feat, 1, 1)
temb2 = self.timeembed2(t).view(-1, self.n_feat, 1, 1)
# could concatenate the context embedding here instead of adaGN
# hiddenvec = torch.cat((hiddenvec, temb1, cemb1), 1)
up1 = self.up0(hiddenvec)
# up2 = self.up1(up1, down2) # if want to avoid add and multiply embeddings
up2 = self.up1(cemb1*up1+ temb1, down2) # add and multiply embeddings
up3 = self.up2(cemb2*up2+ temb2, down1)
out = self.out(torch.cat((up3, x), 1))
return out
def ddpm_schedules(beta1, beta2, T):
"""
Returns pre-computed schedules for DDPM sampling, training process.
"""
assert beta1 < beta2 < 1.0, "beta1 and beta2 must be in (0, 1)"
beta_t = (beta2 - beta1) * torch.arange(0, T + 1, dtype=torch.float32) / T + beta1
sqrt_beta_t = torch.sqrt(beta_t)
alpha_t = 1 - beta_t
log_alpha_t = torch.log(alpha_t)
alphabar_t = torch.cumsum(log_alpha_t, dim=0).exp()
sqrtab = torch.sqrt(alphabar_t)
oneover_sqrta = 1 / torch.sqrt(alpha_t)
sqrtmab = torch.sqrt(1 - alphabar_t)
mab_over_sqrtmab_inv = (1 - alpha_t) / sqrtmab
return {
"alpha_t": alpha_t, # \alpha_t
"oneover_sqrta": oneover_sqrta, # 1/\sqrt{\alpha_t}
"sqrt_beta_t": sqrt_beta_t, # \sqrt{\beta_t}
"alphabar_t": alphabar_t, # \bar{\alpha_t}
"sqrtab": sqrtab, # \sqrt{\bar{\alpha_t}}
"sqrtmab": sqrtmab, # \sqrt{1-\bar{\alpha_t}}
"mab_over_sqrtmab": mab_over_sqrtmab_inv, # (1-\alpha_t)/\sqrt{1-\bar{\alpha_t}}
}
class DDPM(nn.Module):
def __init__(self, nn_model, betas, n_T, device, drop_prob=0.1, n_classes=2):
super(DDPM, self).__init__()
self.nn_model = nn_model.to(device)
# register_buffer allows accessing dictionary produced by ddpm_schedules
# e.g. can access self.sqrtab later
for k, v in ddpm_schedules(betas[0], betas[1], n_T).items():
self.register_buffer(k, v)
self.n_T = n_T
self.device = device
self.drop_prob = drop_prob
self.loss_mse = nn.MSELoss()
self.n_classes = n_classes
def forward(self, x, c):
"""
this method is used in training, so samples t and noise randomly
"""
_ts = torch.randint(1, self.n_T, (x.shape[0],)).to(self.device) # t ~ Uniform(0, n_T)
noise = torch.randn_like(x) # eps ~ N(0, 1)
x_t = (
self.sqrtab[_ts, None, None, None] * x
+ self.sqrtmab[_ts, None, None, None] * noise
) # This is the x_t, which is sqrt(alphabar) x_0 + sqrt(1-alphabar) * eps
# We should predict the "error term" from this x_t. Loss is what we return.
# dropout context with some probability
context_mask = torch.bernoulli(torch.zeros_like(c)+self.drop_prob).to(self.device)
# return MSE between added noise, and our predicted noise
return self.loss_mse(noise, self.nn_model(x_t, c, _ts / self.n_T, context_mask))
def sample(self, n_sample, size, device, guide_w = 0.0):
# we follow the guidance sampling scheme described in 'Classifier-Free Diffusion Guidance'
# to make the fwd passes efficient, we concat two versions of the dataset,
# one with context_mask=0 and the other context_mask=1
# we then mix the outputs with the guidance scale, w
# where w>0 means more guidance
x_i = torch.randn(n_sample, *size).to(device) # x_T ~ N(0, 1), sample initial noise
c_i = torch.arange(0, self.n_classes).to(device) # context for us just cycles throught the LIDC labels
c_i = c_i.repeat(int(n_sample/c_i.shape[0]))
# don't drop context at test time
context_mask = torch.zeros_like(c_i).to(device)
# double the batch
c_i = c_i.repeat(2)
context_mask = context_mask.repeat(2)
context_mask[n_sample:] = 1. # makes second half of batch context free
x_i_store = [] # keep track of generated steps in case want to plot something
print()
for i in range(self.n_T, 0, -1):
print(f'sampling timestep {i}',end='\r')
t_is = torch.tensor([i / self.n_T]).to(device)
t_is = t_is.repeat(n_sample,1,1,1)
# double batch
x_i = x_i.repeat(2,1,1,1)
t_is = t_is.repeat(2,1,1,1)
z = torch.randn(n_sample, *size).to(device) if i > 1 else 0
# split predictions and compute weighting
eps = self.nn_model(x_i, c_i, t_is, context_mask)
eps1 = eps[:n_sample]
eps2 = eps[n_sample:]
eps = (1+guide_w)*eps1 - guide_w*eps2
x_i = x_i[:n_sample]
x_i = (
self.oneover_sqrta[i] * (x_i - eps * self.mab_over_sqrtmab[i])
+ self.sqrt_beta_t[i] * z
)
if i%20==0 or i==self.n_T or i<8:
x_i_store.append(x_i.detach().cpu().numpy())
x_i_store = np.array(x_i_store)
return x_i, c_i[:n_sample], x_i_store
def DDPM_model(n_feat, n_T, device):
ddpm = DDPM(
nn_model=ContextUnet(in_channels=1, n_feat=n_feat, n_classes=2),
betas=(1e-4, 0.02),
n_T=n_T,
device=device,
drop_prob=0.1,
n_classes = 2)
return ddpm