Libraries¶
In [1]:
import pandas as pd; print("Pandas Version: ", pd.__version__)
import numpy as np; print("Numpy Version: ", np.__version__)
import torch; print("PyTorch Version: ", torch.__version__)
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data_utils
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
WANDB = False
Pandas Version: 1.5.3 Numpy Version: 1.24.2 PyTorch Version: 1.13.1+cu117
In [2]:
try :
from google.colab import drive
IN_COLAB = True
drive.mount('/content/drive/')
import os
os.chdir("/content/drive/My Drive/Colab Notebooks/")
if WANDB :
! pip install wandb -qU
except :
IN_COLAB = False
if WANDB :
import wandb
wandb.login()
In [3]:
if IN_COLAB and WANDB:
run = wandb.init(project='font_creation')
Import data¶
In [4]:
# import data from csv file
path = 'dataset/csv/images.csv'
data_input = pd.read_csv('dataset/csv/images.csv')
# convert data to torch tensor
data = torch.from_numpy(data_input.values)
print(data.shape)
torch.Size([1674, 20384])
In [5]:
font_count, size = data.shape
alphabet = 26
image_size = 28
print("font_count: ", font_count)
print("size: ", size)
print("alphabet: ", alphabet)
print("image_size: ", image_size)
# reshape data to 26x28x28 tensor
data = data.reshape(font_count, alphabet, image_size, image_size)
print(data.shape)
font_count: 1674 size: 20384 alphabet: 26 image_size: 28 torch.Size([1674, 26, 28, 28])
Graphic helpers¶
In [6]:
def show_font_tensor( font_tensor: torch.Tensor ) :
assert font_tensor.shape == (alphabet, image_size, image_size), "font_tensor shape is not valid"
# plot font image of 26 alphabets
plt.subplots(1, alphabet, figsize=(image_size, image_size))
for i in range(alphabet) :
plt.subplot(1, 26, i+1)
plt.axis('off')
plt.imshow(font_tensor[i], cmap='gray')
plt.show()
def show_font(font_index = np.random.randint(0, font_count)) :
assert font_count > font_index, "font_index out of range"
# get font image
font = data[font_index]
show_font_tensor(font)
# show random font image
show_font()
In [7]:
mean = data.mean(axis=0)
show_font_tensor(mean)
Hyperparameters¶
In [8]:
class opt :
n_epochs = 6000 if IN_COLAB else 50
batch_size = 64
lr = 0.0002
b1 = 0.5
b2 = 0.999
n_cpu = 8
latent_dim = 100
img_size = 28
channels = 1
sample_interval = 400
channels = 26
img_size = 28
latent_dim = 100
cutoff_threshold = 0.000001
Generator and discriminator architectures¶
In [12]:
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
img_shape = (opt.channels, opt.img_size, opt.img_size)
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*block(opt.latent_dim, 128, normalize=False),
*block(128, 256),
*block(256, 512),
*block(512, 1024),
nn.Linear(1024, int(np.prod(img_shape))),
nn.Tanh()
)
def forward(self, z):
img = self.model(z)
img = img.view(img.size(0), *img_shape)
return img
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(img_shape)), 512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def forward(self, img):
img_flat = img.view(img.size(0), -1)
validity = self.model(img_flat)
return validity
Loss function¶
In [13]:
cuda = True if torch.cuda.is_available() else False
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# Loss function
adversarial_loss = torch.nn.BCELoss()
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
Synthetic data generation¶
In [14]:
# define dataloader from data_2d
data_2d = data
dataloader = torch.utils.data.DataLoader(data_2d, batch_size=opt.batch_size, shuffle=True)
Main training loop¶
In [19]:
losses = []
abort = False
for epoch in range(opt.n_epochs):
if abort:
break
print("Epoch %d/%d" %(epoch,opt.n_epochs))
for i, (imgs) in enumerate(dataloader):
# Adversarial ground truths
valid = Variable(
Tensor(imgs.size(0), 1).uniform_(0.85,1.10), requires_grad=False)
fake = Variable(Tensor(imgs.size(0), 1).uniform_(0.0, 0.2), requires_grad=False)
# Configure input
real_imgs = Variable(imgs.type(Tensor))
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
# Sample noise as generator input
z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))
# Generate a batch of images
gen_imgs = generator(z)
# Loss measures generator's ability to fool the discriminator
g_loss = adversarial_loss(discriminator(gen_imgs), valid)
g_loss.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Measure discriminator's ability to classify real from generated samples
real_loss = adversarial_loss(discriminator(real_imgs), valid)
fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)
d_loss = (real_loss + fake_loss) / 2
d_loss.backward()
optimizer_D.step()
if IN_COLAB and WANDB:
wandb.log({
"D loss":d_loss.item(),
"G loss":g_loss.item(),
"real loss":real_loss.item(),
"fake loss":fake_loss.item()
})
losses.append((d_loss.item(), g_loss.item()))
if d_loss.item() < opt.cutoff_threshold or g_loss.item() < opt.cutoff_threshold:
abort = True
break
batches_done = epoch * len(dataloader) + i
if IN_COLAB and WANDB:
path = 'weights/'
if os.path.isdir(path) == False:
os.mkdir(path)
torch.save(generator.state_dict(), path +'generator.pth')
torch.save(discriminator.state_dict(), path + 'discriminator.pth')
artifact = wandb.Artifact('model', type='model')
artifact.add_file(path +'generator.pth')
artifact.add_file(path+'discriminator.pth')
run.log_artifact(artifact)
run.finish()
Epoch 0/50 Epoch 1/50 Epoch 2/50 Epoch 3/50 Epoch 4/50 Epoch 5/50 Epoch 6/50 Epoch 7/50 Epoch 8/50 Epoch 9/50 Epoch 10/50 Epoch 11/50 Epoch 12/50 Epoch 13/50 Epoch 14/50 Epoch 15/50 Epoch 16/50 Epoch 17/50 Epoch 18/50 Epoch 19/50 Epoch 20/50 Epoch 21/50 Epoch 22/50 Epoch 23/50 Epoch 24/50 Epoch 25/50 Epoch 26/50 Epoch 27/50 Epoch 28/50 Epoch 29/50 Epoch 30/50 Epoch 31/50 Epoch 32/50 Epoch 33/50 Epoch 34/50 Epoch 35/50 Epoch 36/50 Epoch 37/50 Epoch 38/50 Epoch 39/50 Epoch 40/50 Epoch 41/50 Epoch 42/50 Epoch 43/50 Epoch 44/50 Epoch 45/50 Epoch 46/50 Epoch 47/50 Epoch 48/50 Epoch 49/50
Post-training analysis¶
In [20]:
indices = list(range(len(gen_imgs)))
indices = np.random.choice(indices,20)
for i in indices :
show_font_tensor(gen_imgs[i].detach().cpu().numpy())
In [21]:
plt.plot(losses)
plt.legend(['Discriminator', 'Generator'])
plt.show()
In [22]:
# save losses as backup
path = 'dataset/losses/'
if os.path.isdir(path) == False:
os.mkdir(path)
name = "loss_with_noise" + str(time.time())
# save csv file
pd.DataFrame(losses).to_csv(path + name + '.csv', index=False)