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%% Cell type:markdown id: tags:
<h1 ><big><center>MSO 3.4 - Deep Structured Learning</center></big></h1>
<h2><big><center> BE 2 - GANs and cGAN </center></big></h2>
<h2><big><center> BE 2 - GANs and Diffusion </center></big></h2>
<h5><big><center>Adapted from <i>Projet d'Option</i> of : Mhamed Jabri, Martin Chauvin, Ahmed Sahraoui, Zakariae Moustaïne and Taoufik Bouchikhi
<h5><center>GANs section adapted from the <i>Projet d'Option</i> of : Mhamed Jabri, Martin Chauvin, Ahmed Sahraoui, Zakariae Moustaïne and Taoufik Bouchikhi
<h5><center>Diffusion section implemented by Bruno Machado<br>
<p align="center">
<img height=300px src="https://cdn-images-1.medium.com/max/1080/0*tJRy5Chmk4XymxwN.png"/></p>
<p align="center"></p>
%% Cell type:markdown id: tags:
The aim of this assignment is to discover GANs, understand how they are implemented and then explore one specific architecture of GANs that allows us to perform image to image translation (which corresponds to the picture that you can see above this text ! )
The aim of this assignment is to discover generative models for images, understand how they are implemented and then explore some specific architectures and training strategies which allows us to perform image generation with and without conditioning (the picture above exemplifies image generation conditioned by another image)
Before starting the exploration of the world of GANs, here's what students should do and send back for this assignement :
* In the "tutorial" parts of this assignement that focus on explaining new concepts, you'll find <font color='red'>**questions**</font> that aim to test your understanding of those concepts.
* In some of the code cells, you'll have to complete the code and you'll find a "TO DO" explaining what you should implement.
The deliverable for this assignment is this notebook, with the required modifications :
* In this notebook you'll find <font color='red'>**questions**</font> that aim to test your understanding of the concepts studied. You should answer them by editing the Markdown cells (or adding new ones).
* In some of the code cells, you'll have to complete the code and you'll find a "TODO" explaining what you should implement.
* There are also some <font color='green'>**bonus**</font> exercises. Those exercises are **optional** and should be performed only **AFTER** all the rest of the assignment is complete.
%% Cell type:markdown id: tags:
# Part1: DC-GAN
# Part 1: DC-GAN
%% Cell type:markdown id: tags:
In this part, we aim to learn and understand the basic concepts of **Generative Adversarial Networks** through a DCGAN and generate new celebrities from the learned network after showing it real celebrities. For this purpose, please study the tutorial here: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
In this part, we aim to learn and understand the basic concepts of **Generative Adversarial Networks** through a DCGAN. For this purpose, please study the following tutorial: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
%% Cell type:markdown id: tags:
##Work to do
Now we want to generate handwritten digits using the MNIST dataset. It is available within torvision package (https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST)
Please re-train the DCGAN and display some automatically generated handwritten digits.
We want to generate handwritten digits using the MNIST dataset. It is available within the torvision package (https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST)
Adapt the code given in the tutorial in order to use it with the MNIST dataset. Present the loss curves in function of the gradient steps for the generator and discriminator and also compare the generated images with the images from the dataset.
%% Cell type:code id: tags:
``` python
#TO DO: your code here to adapt the code from the tutorial to experiment on MNIST dataset
# TODO: adapt the code from the tutorial to experiment on the MNIST dataset
```
%% Cell type:markdown id: tags:
# Part2: Conditional GAN (cGAN)
The results for some images might be convincing, but probably you can spot some bad results as well. We will see how we can use different architectures and training objectives to get better results. More importantly, we generate images directly from noise, not knowing what number (if any) will come out on the output.
%% Cell type:markdown id: tags:
<font color='red'>**Question**</font>
How could we change the architecture above to control which number the generator should output ?
%% Cell type:markdown id: tags:
Let's take the example of the set described in the next picture.
![Map to satellite picture](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/map_streetview.png)
<font color='green'>**Bonus**</font>
**Copy** the code you implemented above into a new Python cell and adapt it to implement your idea to control the number the generator outputs. Plot the training loss curves and a few examples of your model's outputs (e.g. generate(number=7) # Generates a handwritten 7). Note that we still want to be able to generate different images of the same class ! (i.e. generate(number=4) shoud not generate always the same handwritten 4, but a different one at each time)
%% Cell type:markdown id: tags:
# Part 2: Conditional GAN (cGAN)
%% Cell type:markdown id: tags:
Let's take the example of the set described in the following picture:
<p align="center">
<img height=300px src="https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/map_streetview.png"/></p>
<p align="center"></p>
We have a picture of a map (from Google Maps) and we want to create an image of what the satellite view may look like.
As we are not only trying to generate a random picture but a mapping between a picture to another one, we can't use the standard GAN architecture. We will then use a cGAN.
Therefore, we do not only want to generate a picture from random noise, but rather generate a picture from another picture. For this purpose we will use a cGAN instead of a vanilla GAN, which was introduced in this [paper](https://arxiv.org/pdf/1611.07004.pdf).
A cGAN is a supervised GAN aiming at mapping a label picture to a real one or a real picture to a label one. As you can see in the diagram below, the discriminator will take as input a pair of images and try to predict if the pair was generated or not. The generator will not only generate an image from noise but will also use an image (label or real) to generate another one (real or label).
![Diagram of how a cGan works](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/cgan_map.png)
A cGAN is a supervised GAN aiming at mapping a label picture to a real one or a real picture to a label one. As you can see in the diagram below, the discriminator will take as input a pair of images and try to predict if the pair was generated or not. The generator will not generate an image from noise but will intead use an image (label or real) to generate another one (real or label).
<p align="center">
<img height=300px src="https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/cgan_map.png"/></p>
<p align="center"></p>
%% Cell type:markdown id: tags:
### Generator
In the cGAN architecture, the generator chosen is a U-Net.
![U-Net](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/unet.png)
<p align="center">
<img height=300px src="https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/unet.png"/></p>
<p align="center"></p>
A U-Net takes as input an image, and outputs another image.
It can be divided into 2 subparts : an encoder and a decoder.
* The encoder takes the input image and reduces its dimension to encode the main features into a vector.
* The decoder takes this vector and map the features stored into an image.
A U-Net architecture is different from a classic encoder-decoder in that every layer of the decoder takes as input the previous decoded output as well as the output vector from the encoder layers of the same level. It allows the decoder to map low frequencies information encoded during the descent as well as high frequencies from the original picture.
A U-Net architecture is different from a vanilla convolutional encoder-decoder in that every layer of the decoder takes as input the previous decoded output as well as the output feature map from the encoder layers of the same level. This allows the decoder to map low frequencies information encoded during the descent as well as high frequencies from the original picture.
![U-Net](https://www.researchgate.net/profile/Baris_Turkbey/publication/315514772/figure/fig2/AS:485824962797569@1492841105670/U-net-architecture-Each-box-corresponds-to-a-multi-channel-features-maps-The-number-of.png)
<p align="center">
<img height=300px src="https://www.researchgate.net/profile/Baris_Turkbey/publication/315514772/figure/fig2/AS:485824962797569@1492841105670/U-net-architecture-Each-box-corresponds-to-a-multi-channel-features-maps-The-number-of.png"/></p>
<p align="center"></p>
%% Cell type:markdown id: tags:
The architecture we will implement is the following (the number in the square is the number of filters used).
![UNet Architecture](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/unet_architecture.png)
<p align="center">
<img height=300px src="https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/unet_architecture.png"/></p>
<p align="center"></p>
The encoder will take as input a colored picture (3 channels: RGB), it will pass through a series of convolution layers to encode the features of the picture. It will then be decoded by the decoder using transposed convolutional layers. These layers will take as input the previous decoded vector AND the encoded features of the same level.
%% Cell type:markdown id: tags:
Now, let's create or cGAN to generate facades from a template image. For this purpose, we will use the "Facade" dataset available at http://cmp.felk.cvut.cz/~tylecr1/facade/.
For this part the objective is to use a cGAN to generate facades from a template image. For this purpose, we will use the "Facade" dataset.
<p align="center">
<img height=250px src="https://storage.googleapis.com/kaggle-datasets-images/926166/1567332/1fd1b7ec805d92b48a1227c376044d0b/dataset-cover.png"/></p>
<p align="center"></p>
%% Cell type:markdown id: tags:
Let's first create a few classes describing the layers we will use in the U-Net.
Let's start by creating a few classes describing the layers we will use in the U-Net.
%% Cell type:code id: tags:
``` python
# Importing all the libraries needed
import matplotlib.pyplot as plt
import imageio
import glob
import random
import os
import numpy as np
import math
import itertools
import time
import datetime
import cv2
from pathlib import Path
from PIL import Image
import glob
import torch
import kagglehub
from torch.utils.data import Dataset, DataLoader
import numpy as np
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from torchvision.utils import save_image, make_grid
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch import nn
from PIL import Image
from torch.utils.data import Dataset, DataLoader
```
%% Cell type:code id: tags:
``` python
# code adapted from https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py
# Input layer
class inconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(inconv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),
nn.LeakyReLU(negative_slope=0.2, inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
# Encoder layer
class down(nn.Module):
def __init__(self, in_ch, out_ch):
super(down, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),
nn.BatchNorm2d(out_ch),
nn.LeakyReLU(negative_slope=0.2, inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
# Decoder layer
class up(nn.Module):
def __init__(self, in_ch, out_ch, dropout=False):
super(up, self).__init__()
if dropout :
self.conv = nn.Sequential(
nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),
nn.BatchNorm2d(out_ch),
nn.Dropout(0.5, inplace=True),
nn.ReLU(inplace=True)
)
else:
self.conv = nn.Sequential(
nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x1, x2):
x1 = self.conv(x1)
x = torch.cat([x1, x2], dim=1)
return x
# Output layer
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Sequential(
nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),
nn.Tanh()
)
def forward(self, x):
x = self.conv(x)
return x
```
%% Cell type:markdown id: tags:
Now let's create the U-Net using the helper classes defined previously.
%% Cell type:code id: tags:
``` python
class U_Net(nn.Module):
class U_Net(nn.Module):
'''
Ck denotes a Convolution-BatchNorm-ReLU layer with k filters.
CDk denotes a Convolution-BatchNorm-Dropout-ReLU layer with a dropout rate of 50%
Encoder:
C64 - C128 - C256 - C512 - C512 - C512 - C512 - C512
Decoder:
CD512 - CD1024 - CD1024 - C1024 - C1024 - C512 - C256 - C128
'''
def __init__(self, n_channels, n_classes):
super(U_Net, self).__init__()
# Encoder
self.inc = inconv(n_channels, 64) # 64 filters
# TO DO :
# TODO :
# Create the 7 encoder layers called "down1" to "down7" following this sequence
# C64 - C128 - C256 - C512 - C512 - C512 - C512 - C512
# The first one has already been implemented
# Decoder
# TO DO :
# TODO :
# Create the 7 decoder layers called up1 to up7 following this sequence :
# CD512 - CD1024 - CD1024 - C1024 - C1024 - C512 - C256 - C128
# The last layer has already been defined
self.outc = outconv(128, n_classes) # 128 filters
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x6 = self.down5(x5)
x7 = self.down6(x6)
x8 = self.down7(x7)
# At this stage x8 is our encoded vector, we will now decode it
x = self.up7(x8, x7)
x = self.up6(x, x6)
x = self.up5(x, x5)
x = self.up4(x, x4)
x = self.up3(x, x3)
x = self.up2(x, x2)
x = self.up1(x, x1)
x = self.outc(x)
return x
```
%% Cell type:code id: tags:
``` python
# We take images that have 3 channels (RGB) as input and output an image that also have 3 channels (RGB)
generator=U_Net(3,3)
generator = U_Net(3, 3)
# Check that the architecture is as expected
generator
print(generator)
```
%% Cell type:markdown id: tags:
You should now have a working U-Net.
%% Cell type:markdown id: tags:
<font color='red'>**Question 1**</font>
Knowing the input and output images will be 256x256, what will be the dimension of the encoded vector x8 ?
<font color='red'>**Question**</font>
Knowing the input and output images will have the shape 256x256 with 3 channels, what will be the dimension of the feature map "x8" ?
<font color='red'>**Question 2**</font>
As you can see, U-net has an encoder-decoder architecture with skip connections. Explain why it works better than a traditional encoder-decoder.
<font color='red'>**Question**</font>
As you can see, U-net has an encoder-decoder architecture with skip connections. Explain what is the point of the skip connections.
%% Cell type:markdown id: tags:
### Discriminator
In the cGAN architecture, the chosen discriminator is a Patch GAN. It is a convolutional discriminator which enables to produce a map of the input pictures where each pixel represents a patch of size NxN of the input.
In the cGAN architecture, the chosen discriminator is a Patch GAN. Instead of classifying if the whole image is fake or not (binary classification), this discriminator tries to classify if each N × N patch in an image is real or fake.
![patch GAN](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/patchGAN.png)
The size N is given by the depth of the net. According to this table :
| Number of layers | N |
| ---- | ---- |
| 1 | 16 |
| 2 | 34 |
| 3 | 70 |
| 4 | 142 |
| 5 | 286 |
| 6 | 574 |
The number of layers actually means the number of layers with `kernel=(4,4)`, `padding=(1,1)` and `stride=(2,2)`. These layers are followed by 2 layers with `kernel=(4,4)`, `padding=(1,1)` and `stride=(1,1)`.
In our case we are going to create a 70x70 PatchGAN.
%% Cell type:markdown id: tags:
Let's first create a few helping classes.
%% Cell type:code id: tags:
``` python
class conv_block(nn.Module):
def __init__(self, in_ch, out_ch, use_batchnorm=True, stride=2):
super(conv_block, self).__init__()
if use_batchnorm:
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=stride),
nn.BatchNorm2d(out_ch),
nn.LeakyReLU(negative_slope=0.2, inplace=True)
)
else:
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=stride),
nn.LeakyReLU(negative_slope=0.2, inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class out_block(nn.Module):
def __init__(self, in_ch, out_ch):
super(out_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, 1, kernel_size=4, padding=1, stride=1),
nn.Sigmoid()
)
def forward(self, x):
x = self.conv(x)
return x
```
%% Cell type:markdown id: tags:
Now let's create the Patch GAN discriminator.
As we want a 70x70 Patch GAN, the architecture will be as follows :
```
1. C64 - K4, P1, S2
2. C128 - K4, P1, S2
3. C256 - K4, P1, S2
4. C512 - K4, P1, S1
5. C1 - K4, P1, S1 (output)
```
Where Ck denotes a convolution block with k filters, Kk a kernel of size k, Pk is the padding size and Sk the stride applied.
*Note :* For the first layer, we do not use batchnorm.
%% Cell type:markdown id: tags:
<font color='red'>**Question 3**</font>
Knowing input images will be 256x256 with 3 channels each, how many parameters are there to learn ?
<font color='red'>**Question**</font>
Knowing input images will be 256x256 with 3 channels each, how many learnable parameters this neural network has ?
%% Cell type:code id: tags:
``` python
class PatchGAN(nn.Module):
def __init__(self, n_channels, n_classes):
super(PatchGAN, self).__init__()
# TODO :
# create the 4 first layers named conv1 to conv4
# TODO : create the 4 first layers named conv1 to conv4
self.conv1 =
self.conv2 =
self.conv3 =
self.conv4 =
# output layer
self.out = out_block(512, n_classes)
def forward(self, x1, x2):
x = torch.cat([x2, x1], dim=1)
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.out(x)
return x
```
%% Cell type:code id: tags:
``` python
# We have 6 input channels as we concatenate 2 images (with 3 channels each)
discriminator = PatchGAN(6,1)
discriminator
discriminator = PatchGAN(6, 1)
print(discriminator)
```
%% Cell type:markdown id: tags:
You should now have a working discriminator.
%% Cell type:markdown id: tags:
### Loss functions
As we have seen in the choice of the various architectures for this GAN, the issue is to map both low and high frequencies.
To tackle this problem, this GAN rely on the architecture to map the high frequencies (U-Net + PatchGAN) and the loss function to learn low frequencies features. The global loss function will indeed be made of 2 parts :
* the first part to map hight frequencies, will try to optimize the mean squared error of the GAN.
* the second part to map low frequencies, will minimize the $\mathcal{L}_1$ norm of the generated picture.
So the loss can be defined as $$ G^* = arg\ \underset{G}{min}\ \underset{D}{max}\ \mathcal{L}_{cGAN}(G,D) + \lambda \mathcal{L}_1(G)$$
The global loss function will be made of 2 parts :
* The vanilla GAN loss, in which the discriminator tries to maximize the probability it correctly classifies reals and fakes and the generator tries to minimize the probability that the discriminator will predict its outputs are fake.
* An auxiliary image reconstruction objective, in which the generator not only has to fool the discriminator, but also generate an image that is near to the ground truth image.
Therefore, the loss can be defined as: $$ G^* = arg\ \underset{G}{min}\ \underset{D}{max}\ \mathcal{L}_{cGAN}(G, D) + \lambda \mathcal{L}_{L1}(G)$$
In which
$$ \mathcal{L}_{cGAN}(G, D) = \mathbb{E}_{x, y}\big[logD(x, y)\big] + \mathbb{E}_{x, z}\big[log(1 - D(x, G(x, z)))\big] $$
$$ \mathcal{L}_{L1}(G) = \mathbb{E}_{x, y, z}\big[||y - G(x, z)||_1\big] $$
%% Cell type:code id: tags:
``` python
# Loss functions
criterion_GAN = torch.nn.MSELoss()
criterion_GAN = torch.nn.BCELoss()
criterion_pixelwise = torch.nn.L1Loss()
# Loss weight of L1 pixel-wise loss between translated image and real image
lambda_pixel = 100
```
%% Cell type:markdown id: tags:
### Training and evaluating models
%% Cell type:code id: tags:
``` python
# parameters
epoch = 0 # epoch to start training from
n_epoch = 200 # number of epochs of training
batch_size =10 # size of the batches
lr = 0.0002 # adam: learning rate
b1 =0.5 # adam: decay of first order momentum of gradient
b2 = 0.999 # adam: decay of first order momentum of gradient
decay_epoch = 100 # epoch from which to start lr decay
img_height = 256 # size of image height
img_width = 256 # size of image width
channels = 3 # number of image channels
sample_interval = 500 # interval between sampling of images from generators
checkpoint_interval = -1 # interval between model checkpoints
cuda = True if torch.cuda.is_available() else False # do you have cuda ?
num_epochs = 200 # number of epochs of training
batch_size = 16 # size of the batches
lr = 2e-4 # learning rate
b1 = 0.5 # decay of first order momentum of gradient
b2 = 0.999 # decay of second order momentum of gradient
img_height = 256 # size of image height
img_width = 256 # size of image width
channels = 3 # number of image channels
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
```
%% Cell type:markdown id: tags:
Download the dataset.
Download the Facades dataset
%% Cell type:code id: tags:
``` python
import urllib.request
from tqdm import tqdm
import os
import zipfile
dataset_path = kagglehub.dataset_download("kokeyehya/cmp-facade-db-base")
def download_hook(t):
"""Wraps tqdm instance.
Don't forget to close() or __exit__()
the tqdm instance once you're done with it (easiest using `with` syntax).
Example
-------
>>> with tqdm(...) as t:
... reporthook = my_hook(t)
... urllib.request.urlretrieve(..., reporthook=reporthook)
"""
last_b = [0]
def update_to(b=1, bsize=1, tsize=None):
"""
b : int, optional
Number of blocks transferred so far [default: 1].
bsize : int, optional
Size of each block (in tqdm units) [default: 1].
tsize : int, optional
Total size (in tqdm units). If [default: None] remains unchanged.
"""
if tsize is not None:
t.total = tsize
t.update((b - last_b[0]) * bsize)
last_b[0] = b
return update_to
def download(url, save_dir):
filename = url.split('/')[-1]
with tqdm(unit = 'B', unit_scale = True, unit_divisor = 1024, miniters = 1, desc = filename) as t:
urllib.request.urlretrieve(url, filename = os.path.join(save_dir, filename), reporthook = download_hook(t), data = None)
if __name__ == '__main__':
# Download ground truth
if not os.path.exists("CMP_facade_DB_base.zip"):
download("http://cmp.felk.cvut.cz/~tylecr1/facade/CMP_facade_DB_base.zip", "./")
# Extract in the correct folder
with zipfile.ZipFile("CMP_facade_DB_base.zip", 'r') as zip_ref:
zip_ref.extractall("./facades")
os.rename("./facades/base", "./facades/train")
# Download ground truth
if not os.path.exists("CMP_facade_DB_extended.zip"):
download("http://cmp.felk.cvut.cz/~tylecr1/facade/CMP_facade_DB_extended.zip", "./")
# Extract in the correct folder
with zipfile.ZipFile("CMP_facade_DB_extended.zip", 'r') as zip_ref:
zip_ref.extractall("./facades")
os.rename("./facades/extended", "./facades/val")
print("Path to dataset files:", dataset_path)
```
%% Cell type:markdown id: tags:
Configure the dataloader
%% Cell type:code id: tags:
``` python
class ImageDataset(Dataset):
class FacadeDataset(Dataset):
def __init__(self, root, transforms_=None, mode='train'):
self.transform = transforms.Compose(transforms_)
self.images_path = sorted(glob.glob(root + '/base/*.jpg'))
self.files_img = sorted(glob.glob(os.path.join(root, mode) + '/*.jpg'))
if mode == 'val':
self.files_img.extend(
sorted(glob.glob(os.path.join(root, 'val') + '/*.jpg')))
self.files_mask = sorted(glob.glob(os.path.join(root, mode) + '/*.png'))
if mode == 'val':
self.files_mask.extend(
sorted(glob.glob(os.path.join(root, 'val') + '/*.png')))
if mode == 'train':
self.images_path = self.images_path[:int(len(self.images_path) * 0.95)]
elif mode == 'val':
self.images_path = self.images_path[int(len(self.images_path) * 0.95):]
else:
raise Exception('Invalid mode! It must be either train or val')
assert len(self.files_img) == len(self.files_mask)
self.masks_path = [image.split(".jpg")[0] + ".png" for image in self.images_path]
assert len(self.images_path) == len(self.masks_path), "Number of images and masks must be the same"
def __getitem__(self, index):
img = Image.open(self.files_img[index % len(self.files_img)])
mask = Image.open(self.files_mask[index % len(self.files_img)])
def __getitem__(self, index):
img = Image.open(self.images_path[index])
mask = Image.open(self.masks_path[index])
mask = mask.convert('RGB')
img = self.transform(img)
mask = self.transform(mask)
return img, mask
def __len__(self):
return len(self.files_img)
return len(self.images_path)
# Configure dataloaders
transforms_ = [transforms.Resize((img_height, img_width), Image.BICUBIC),
transforms.ToTensor()] # transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
transforms.ToTensor()]
dataloader = DataLoader(ImageDataset("facades", transforms_=transforms_),
batch_size=16, shuffle=True)
dataloader = DataLoader(FacadeDataset(dataset_path, transforms_=transforms_),
batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4)
val_dataloader = DataLoader(ImageDataset("facades", transforms_=transforms_, mode='val'),
batch_size=8, shuffle=False)
# Tensor type
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
val_dataloader = DataLoader(FacadeDataset(dataset_path, transforms_=transforms_, mode='val'),
batch_size=batch_size, shuffle=False)
```
%% Cell type:markdown id: tags:
Check the loading works and a few helper functions
Check if the loading works and add few helper functions
%% Cell type:code id: tags:
``` python
def plot2x2Array(image, mask):
f, axarr = plt.subplots(1, 2)
axarr[0].imshow(image)
axarr[1].imshow(mask)
axarr[0].set_title('Image')
axarr[1].set_title('Mask')
def reverse_transform(image):
image = image.numpy().transpose((1, 2, 0))
image = np.clip(image, 0, 1)
image = (image * 255).astype(np.uint8)
return image
def plot2x3Array(image, mask,predict):
f, axarr = plt.subplots(1,3,figsize=(15,15))
axarr[0].imshow(image)
axarr[1].imshow(mask)
axarr[2].imshow(predict)
axarr[0].set_title('input')
axarr[1].set_title('real')
axarr[2].set_title('fake')
```
%% Cell type:code id: tags:
``` python
image, mask = next(iter(dataloader))
image = reverse_transform(image[0])
mask = reverse_transform(mask[0])
plot2x2Array(image, mask)
images, masks = next(iter(dataloader))
for i in range(5):
image = reverse_transform(images[i])
mask = reverse_transform(masks[i])
plot2x2Array(image, mask)
```
%% Cell type:markdown id: tags:
Initialize our GAN
%% Cell type:code id: tags:
``` python
# Calculate output of image discriminator (PatchGAN)
patch = (1, img_height//2**3-2, img_width//2**3-2)
patch_size = (1, img_height//2**3-2, img_width//2**3-2)
if cuda:
generator = generator.cuda()
discriminator = discriminator.cuda()
criterion_GAN.cuda()
criterion_pixelwise.cuda()
generator = generator.to(device)
discriminator = discriminator.to(device)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=lr, betas=(b1, b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))
```
%% Cell type:markdown id: tags:
Start training
Additional auxiliary functions
%% Cell type:code id: tags:
``` python
def save_model(epoch):
# save your work
def save_model(epoch, loss_D, loss_G):
# save your model weights
torch.save({
'epoch': epoch,
'model_state_dict': generator.state_dict(),
'optimizer_state_dict': optimizer_G.state_dict(),
'loss': loss_G,
}, 'generator_'+str(epoch)+'.pth')
torch.save({
'epoch': epoch,
'model_state_dict': discriminator.state_dict(),
'optimizer_state_dict': optimizer_D.state_dict(),
'loss': loss_D,
}, 'discriminator_'+str(epoch)+'.pth')
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
```
%% Cell type:markdown id: tags:
<font color='red'>Complete the loss function </font> in the following training code and train your network:
Train the model !
But first, complete the loss function in the following training loop:
%% Cell type:code id: tags:
``` python
# ----------
# Training
# ----------
losses = []
num_epochs = 200
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
epoch_D = 0
epoch_G = 0
# train the network
discriminator.train()
generator.train()
print_every = 400
for epoch in range(epoch_G, num_epochs):
for epoch in range(num_epochs):
for i, batch in enumerate(dataloader):
# Model inputs
real_A = Variable(batch[0].type(Tensor))
real_B = Variable(batch[1].type(Tensor))
# Adversarial ground truths
valid = Variable(Tensor(np.ones((real_B.size(0), *patch))), requires_grad=False)
fake = Variable(Tensor(np.zeros((real_B.size(0), *patch))), requires_grad=False)
images, masks = batch
images = images.to(device)
masks = masks.to(device)
# Discriminator labels
valid = torch.ones((images.size(0), *patch_size), requires_grad=False).to(device)
fake = torch.zeros((images.size(0), *patch_size), requires_grad=False).to(device)
# ------------------
# Train Generators
# ------------------
optimizer_G.zero_grad()
# GAN loss
# TO DO: Put here your GAN loss
# TODO: Put here your GAN loss
# Pixel-wise loss
# TO DO: Put here your pixel loss
# TODO: Put here your pixel loss
# Total loss
# TO DO: Put here your total loss
# TODO: Put here your total loss for the generator
loss_G.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Real loss
pred_real = discriminator(real_A, real_B)
pred_real = discriminator(images, masks)
loss_real = criterion_GAN(pred_real, valid)
# Fake loss
pred_fake = discriminator(fake_A.detach(), real_B)
pred_fake = discriminator(generated_images.detach(), masks)
loss_fake = criterion_GAN(pred_fake, fake)
# Total loss
loss_D = 0.5 * (loss_real + loss_fake)
loss_D.backward()
optimizer_D.step()
# Print some loss stats
if i % print_every == 0:
# print discriminator and generator loss
print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(
epoch+1, num_epochs, loss_D.item(), loss_G.item()))
## AFTER EACH EPOCH##
# append discriminator loss and generator loss
print(f'Epoch [{epoch+1}/{num_epochs}][{i}/{len(dataloader)}] | d_loss: {loss_D.item():6.4f} | g_loss: {loss_G.item():6.4f}')
# Keep track of discriminator loss and generator loss
losses.append((loss_D.item(), loss_G.item()))
if epoch % 100 == 0:
if (epoch + 1) % 100 == 0:
print('Saving model...')
save_model(epoch)
save_model(epoch+1, loss_D, loss_G)
```
%% Cell type:markdown id: tags:
Observation of the loss along the training
%% Cell type:code id: tags:
``` python
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator')
plt.plot(losses.T[1], label='Generator')
plt.title("Training Losses")
plt.legend()
```
%% Cell type:markdown id: tags:
If the training takes too much time, you can use a pretrained model in the meantime, to evaluate its performance.
It is available at : https://partage.liris.cnrs.fr/index.php/s/xwEFmxn9ANeq4zY
%% Cell type:markdown id: tags:
### Evaluate your cGAN
%% Cell type:code id: tags:
``` python
def load_model(epoch=200):
if 'generator_'+str(epoch)+'.pth' in os.listdir() and 'discriminator_'+str(epoch)+'.pth' in os.listdir():
if cuda:
checkpoint_generator = torch.load('generator_'+str(epoch)+'.pth')
else:
checkpoint_generator = torch.load('generator_'+str(epoch)+'.pth', map_location='cpu')
checkpoint_generator = torch.load('generator_'+str(epoch)+'.pth', map_location=device)
generator.load_state_dict(checkpoint_generator['model_state_dict'])
optimizer_G.load_state_dict(checkpoint_generator['optimizer_state_dict'])
epoch_G = checkpoint_generator['epoch']
loss_G = checkpoint_generator['loss']
if cuda:
checkpoint_discriminator = torch.load('discriminator_'+str(epoch)+'.pth')
else:
checkpoint_discriminator = torch.load('discriminator_'+str(epoch)+'.pth', map_location='cpu')
checkpoint_discriminator = torch.load('discriminator_'+str(epoch)+'.pth', map_location=device)
discriminator.load_state_dict(checkpoint_discriminator['model_state_dict'])
optimizer_D.load_state_dict(checkpoint_discriminator['optimizer_state_dict'])
epoch_D = checkpoint_discriminator['epoch']
loss_D = checkpoint_discriminator['loss']
else:
print('There isn\' a training available with this number of epochs')
```
%% Cell type:code id: tags:
``` python
load_model(epoch=200)
# switching mode
generator.eval()
```
%% Cell type:code id: tags:
``` python
# show a sample evaluation image on the training base
image, mask = next(iter(dataloader))
output = generator(mask.type(Tensor))
output = output.view(16, 3, 256, 256)
output = generator(mask.to(device))
output = output.cpu().detach()
for i in range(8):
image_plot = reverse_transform(image[i])
output_plot = reverse_transform(output[i])
mask_plot = reverse_transform(mask[i])
plot2x3Array(mask_plot,image_plot,output_plot)
```
%% Cell type:code id: tags:
``` python
# show a sample evaluation image on the validation dataset
# show a sample evaluation image on the validation base
image, mask = next(iter(val_dataloader))
output = generator(mask.type(Tensor))
output = output.view(8, 3, 256, 256)
output = generator(mask.to(device))
output = output.cpu().detach()
for i in range(8):
image_plot = reverse_transform(image[i])
output_plot = reverse_transform(output[i])
mask_plot = reverse_transform(mask[i])
plot2x3Array(mask_plot,image_plot,output_plot)
```
%% Cell type:markdown id: tags:
<font color='red'>**Question 4**</font>
Compare results for 100 and 200 epochs
<font color='red'>**Question**</font>
Compare results of your model after 100 and 200 epochs
%% Cell type:code id: tags:
``` python
# TODO : Your code here to load and evaluate with a few samples the 2 checkpoints (100 epochs and 200 epochs)
```
%% Cell type:markdown id: tags:
# Part 3: Diffusion
%% Cell type:markdown id: tags:
Diffusion models are probabilistic generative models which learn to generate data by iteratively refining random noise through a reverse diffusion process. Given a sample of data, noise is progressvely added in small steps until it becomes pure noise. Then, a neural network is trained to reverse this process and generate realistic data from noise.
<p align="center">
<img height=300px src="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2f5d7da7-52db-4104-9742-a0b4555d8dd6_1300x387.png"/></p>
<p align="center"></p>
Diffusion models have gained popularity due to their ability to generate high-quality, diverse, and detailed content, surpassing GANs in the quality of the generated images.
In this assignment we will focus on DDPMs, which were introduced in this [paper](https://arxiv.org/abs/2006.11239) and laid the foundation for generative diffusion models.
%% Cell type:markdown id: tags:
For this part, we will use the MNIST dataset, used in part 1
%% Cell type:code id: tags:
``` python
# TODO: change the variable name to match the one you used in part 1 or reload the dataset
mnist_dataset =
mnist_dataloader =
```
%% Cell type:markdown id: tags:
Auxiliary function for plotting images
%% Cell type:code id: tags:
``` python
def plot1xNArray(images, labels):
f, axarr = plt.subplots(1, len(images))
for image, ax, label in zip(images, axarr, labels):
ax.imshow(image, cmap='gray')
ax.axis('off')
ax.set_title(label)
```
%% Cell type:markdown id: tags:
In order to train the model with the diffusion process, we will use a noise scheduler, which will be in charge of the forward diffusion process. The scheduler takes an image, a sample of random noise and a timestep, and return a noisy image for the corresponding timestep. Noise is progressvely added to the image at each timestep, therefore a noisy image at timestep 0 will have barely any noise while a noisy image at the maximum timestep will be basically just noise.
Let's create a noise scheduler with 1000 max timesteps and visualize some noise images.
We will use the diffusers library, which provides several tools for training and using diffusion models.
%% Cell type:code id: tags:
``` python
from diffusers import DDPMScheduler
# TODO: Create the scheduler
noise_scheduler =
image, _ = mnist_dataset[0]
# TODO: Create a noise tensor sampled from a normal distribution with the same shape as the image
noise =
images, labels = [reverse_transform(image)], ["Original"]
for i in [100, 250, 400, 900]:
timestep = torch.LongTensor([i])
noisy_image = noise_scheduler.add_noise(image, noise, timestep)
images.append(reverse_transform(noisy_image))
labels.append(f"t={i}")
plot1xNArray(images, labels)
```
%% Cell type:markdown id: tags:
For the reverse diffusion process we will use a neural network. Given a noisy image and the corresponding timestep, the goal of the neural network is to predict the noise, which allows for the denoising.
For the model, we will have a similar architecture as we used for the cGAN generator, a 2D UNet with a few modifications. The main difference will be that we have to indicate to the model which timestep is currently being denoised. For that purpose a timestep embedding is added, therefore the model has 2 inputs, the noisy image and the corresponding timestep.
In this exercise, we will use an UNet implementation from the diffusers library, which already has the timestep embedding included.
%% Cell type:code id: tags:
``` python
# TO DO : Your code here to load and evaluate with a few samples
# a model after 100 epochs
from diffusers import UNet2DModel
# TODO: Complete the parameters
diffusion_backbone = UNet2DModel(
block_out_channels=(64, 128, 256, 512),
down_block_types=("DownBlock2D", "DownBlock2D", "DownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D"),
sample_size=,
in_channels=,
out_channels=,
).to(device)
# Optimizer
optimizer = torch.optim.AdamW(diffusion_backbone.parameters(), lr=1e-4)
print(diffusion_backbone)
```
%% Cell type:markdown id: tags:
<font color='red'>Question</font>
What are the differences between the UNet used for the cGAN generator and the one defined above ?
Indicate the differences in the architecture by analyzing both models \_\_str\_\_.
%% Cell type:markdown id: tags:
<font color='green'>**Bonus**</font>
**Extend** the code of the UNet used in part 2 to add the timestep conditioning. Train it in the same way as the UNet2DModel from the diffusers library (using the DDPMScheduler) and compare the results. As in the first bonus exercise, add a label conditioning which allows to control the output of the model (i.e. generate a specific handwritten digit (e.g. "4") instead of a random one). Test the model, showcasing some examples.
%% Cell type:markdown id: tags:
Now, let's train the model
%% Cell type:code id: tags:
``` python
# And finally :
if cuda:
torch.cuda.empty_cache()
# ----------------
# Training Loop
# ----------------
torch.backends.cudnn.deterministic = True
losses = []
num_epochs = 5
print_every = 100
diffusion_backbone.train()
for epoch in range(num_epochs):
for i, batch in enumerate(mnist_dataloader):
# Zero the gradients
optimizer.zero_grad()
# Send input to device
images = batch[0].to(device)
# Generate noisy images, different timestep for each image in the batch
timesteps = torch.randint(noise_scheduler.config.num_train_timesteps, (images.size(0),), device=device)
# TODO: Complete the code
noise =
noisy_images =
# Forward pass
residual = diffusion_backbone(noisy_images, timesteps).sample
# TODO: Compute the loss
loss =
loss.backward()
optimizer.step()
# Print stats
if i % print_every == 0:
print(f'Epoch [{epoch+1}/{num_epochs}][{i}/{len(mnist_dataloader)}] | loss: {loss.item():6.4f}')
losses.append(loss.item())
torch.save(diffusion_backbone.state_dict(), f"diffusion_{epoch+1}.pth")
```
%% Cell type:markdown id: tags:
If the training takes too long, you can download an already trained model from [this link](https://partage.liris.cnrs.fr/index.php/s/AP2t6b3w8SM4Bp5) and use it for inference.
%% Cell type:code id: tags:
``` python
# TODO: Add the path to the model checkpoint for loading the model
diffusion_backbone.load_state_dict(torch.load())
diffusion_backbone.eval()
```
%% Cell type:markdown id: tags:
Time to generate some images.
During training, for each data sample, we take a random timestep and correspondent noisy image to give it as input to our model. With suffitient training, the model should learn how to predict the noise in a noisy image for all possible timesteps.
During inference, to generate an image, we will start from pure noise and step by step predict the noise to go from one noisy image to the next, progressively denoising the image until we reach the timestep 0, in which we should have an image without any noise.
%% Cell type:code id: tags:
``` python
from tqdm import tqdm
# Start the image as random noise
image = torch.randn((10, 1, 64, 64)).to(device)
# Create a list of images and labels for visualization
images, labels = [(image / 2 + 0.5).clamp(0, 1).cpu().permute(0, 2, 3, 1).numpy()], ["Original"]
# Use the scheduler to iterate over timesteps
noise_scheduler.set_timesteps(1000)
for timestep in tqdm(noise_scheduler.timesteps):
with torch.no_grad():
residual = diffusion_backbone(image, timestep).sample
image = noise_scheduler.step(residual, timestep, image).prev_sample
if timestep.item() % 200 == 0:
images.append((image / 2 + 0.5).clamp(0, 1).cpu().permute(0, 2, 3, 1).numpy())
labels.append(f"t={timestep.item()}")
for i in range(images[0].shape[0]):
plot1xNArray([img[i] for img in images], labels)
```
%% Cell type:markdown id: tags:
The diffusers library also provides *Pipeline* classes, which are wrappers around the model that abstracts the inference loop implemented above.
We can create a pipeline, giving it the trained model and the noise scheduler, and use it to generate images. In this case, we will only have access to the final image, generated on the last timestep, but not the intermediary images from the denoising process.
%% Cell type:code id: tags:
``` python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline(diffusion_backbone, noise_scheduler)
generated_images = pipeline(10, output_type="np")
f, axarr = plt.subplots(1, len(generated_images["images"]))
for image, ax in zip(generated_images["images"], axarr):
ax.imshow(image, cmap='gray')
ax.axis('off')
```
%% Cell type:markdown id: tags:
# Part 4: What about those beautiful images ?
%% Cell type:markdown id: tags:
<p align="center">
<img height=300px src="https://huggingface.co/stabilityai/stable-diffusion-3.5-large/media/main/sd3.5_large_demo.png"/></p>
<p align="center"></p>
In this exercise we achieved decent results for very simple datasets. But we are quite far from those beautiful AI generated images we can find online. That is for 2 main reasons:
- Model size: due to the computation and time constrains, we can't really train very large models
- Dataset size: due to the same constrains, we can't use very complex and large datasets, which requires larger models and longer training times.
Fortunatly, even though we can train those large models with the available hardware and time, we can at least use them for inference !
The goal of this part is to learn how to retrive and use a pre-trained diffusion models and also to get creative to come up with some nice prompts to generate cool images.
We are going to use Stable Diffusion 3.5, which is a state of the art open-source text conditioned model. It takes a prompt in natural language and use it to guide the diffusion process. This type of models are trained with image-text pairs, but can generalize beyond the pairs seen during training, being able to mix several different concepts into a single image.
In order to save memory, we will use quantization, which consists into converting the model weights types from float16 into float4. That simply means that each model weight will be stored using only 4 bits instead of 16 bits. That allows us to run the model in GPUs with less VRAM and have faster inference, with a small drop in the quality of the results.
%% Cell type:markdown id: tags:
For this part of the assignment restart the notebook kernel to be sure your GPU memory is empty. The memory usage can be verified using the command `nvidia-smi` in a terminal. If your GPU has 2GB of VRAM or less, the model will probably not fit into memory even with quantization. In that case, use Google Colab for this part or use the smaller model indicated below. If you are not happy with the results and have plenty of VRAM available, feel free to increase the quantization to 8 bits or even load the model without quantization.
%% Cell type:markdown id: tags:
Before downloading the model, got to its [HuggingFace page](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium), log in with your account and accept the terms of use.
%% Cell type:code id: tags:
``` python
from diffusers import BitsAndBytesConfig, SD3Transformer2DModel
from diffusers import StableDiffusion3Pipeline
model_id = "stabilityai/stable-diffusion-3.5-medium"
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16
)
model_nf4 = SD3Transformer2DModel.from_pretrained(
model_id,
subfolder="transformer",
quantization_config=nf4_config,
torch_dtype=torch.float16
)
pipeline = StableDiffusion3Pipeline.from_pretrained(
model_id,
transformer=model_nf4,
torch_dtype=torch.float16
)
pipeline.enable_model_cpu_offload()
# TODO: test different prompts and visualize the generated images
# Once you are happy with the results, you can save 3 differet images as png file with the correspondent prompts in a text file
# Don't forget to add the images and prompts in your gitlab submission!
prompt =
image = pipeline(
prompt=prompt,
num_inference_steps=40,
guidance_scale=4.5,
max_sequence_length=512
).images[0]
image.save("generated_image.png")
```
%% Cell type:markdown id: tags:
If even with the quantization you still run out of GPU memory and you can't use Google Colab, you can use the following code instead, which uses a much smaller model (the results won't be as near as impressive, but it should be able to run even on a CPU, if you have a little patience)
%% Cell type:code id: tags:
``` python
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("OFA-Sys/small-stable-diffusion-v0").to(device)
prompt =
image = pipe(prompt).images[0]
image.save("not_as_good_generated_image.png")
```
%% Cell type:markdown id: tags:
# How to submit your Work ?
This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.
This work must be done individually. The expected output is a private repository named gan-diffusion on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files), a README.md file that explains briefly the successive steps performed, the model checkpoints, and the generated images. Don't forget to add your teacher as developer member of the project. The last commit is due before 11:59 pm on Wednesday, April 9th, 2025. Subsequent commits will not be considered.
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README.md
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# GAN & cGAN tutorial.
# TD 2 : GAN & Diffusion
We recommand to use the notebook (.ipynb) but the Python script (.py) is also provided if more convenient for you.
MSO 3.4 Apprentissage Automatique
---
# How to submit your Work ?
This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.
\ No newline at end of file
This work must be done individually. The expected output is a private repository named gan-diffusion on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. Don't forget to add your teacher as developer member of the project. The last commit is due before 11:59 pm on Wednesday, April 9th, 2025. Subsequent commits will not be considered.