[PLSHEEP3] - A DCGAN to Draw a Sheep, using Pytorch Lightning¶
"Draw me a sheep", revisited with a DCGAN, using Pytorch LightningObjectives :¶
- Build and train a DCGAN model with the Quick Draw dataset
- Understanding DCGAN
The Quick draw dataset contains about 50.000.000 drawings, made by real people...
We are using a subset of 117.555 of Sheep drawings
To get the dataset : https://github.com/googlecreativelab/quickdraw-dataset
Datasets in numpy bitmap file : https://console.cloud.google.com/storage/quickdraw_dataset/full/numpy_bitmap
Sheep dataset : https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sheep.npy (94.3 Mo)
What we're going to do :¶
- Have a look to the dataset
- Defining a GAN model
- Build the model
- Train it
- Have a look of the results
In [1]:
import os
import sys
import shutil
import numpy as np
import torch
from lightning import Trainer
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.loggers.tensorboard import TensorBoardLogger
import fidle
from modules.QuickDrawDataModule import QuickDrawDataModule
from modules.GAN import GAN
from modules.WGANGP import WGANGP
from modules.Generators import *
from modules.Discriminators import *
# Init Fidle environment
run_id, run_dir, datasets_dir = fidle.init('PLSHEEP3')
FIDLE - Environment initialization
Version : 2.3.0 Run id : PLSHEEP3_1 Run dir : ./run/PLSHEEP3_1 Datasets dir : /gpfswork/rech/mlh/uja62cb/fidle-project/datasets-fidle Start time : 03/03/24 21:25:02 Hostname : r6i1n1 (Linux) Tensorflow log level : Warning + Error (=1) Update keras cache : False Update torch cache : False Save figs : ./run/PLSHEEP3_1/figs (True) numpy : 1.24.4 sklearn : 1.3.2 yaml : 6.0.1 matplotlib : 3.8.2 pandas : 2.1.3 torch : 2.1.1 torchvision : 0.16.1+fdea156 lightning : 2.1.2 ** run_id has been overrided from PLSHEEP3 to PLSHEEP3_1
Few parameters¶
scale=1, epochs=20 : Need 22' on a V100
In [2]:
latent_dim = 128
gan_name = 'WGANGP'
generator_name = 'Generator_2'
discriminator_name = 'Discriminator_3'
scale = 0.001
epochs = 4
num_workers = 2
lr = 0.0001
b1 = 0.5
b2 = 0.999
lambda_gp = 10
batch_size = 64
num_img = 48
fit_verbosity = 2
dataset_file = datasets_dir+'/QuickDraw/origine/sheep.npy'
data_shape = (28,28,1)
Override parameters (batch mode) - Just forget this cell
In [3]:
fidle.override('latent_dim', 'gan_name', 'generator_name', 'discriminator_name')
fidle.override('epochs', 'lr', 'b1', 'b2', 'batch_size', 'num_img', 'fit_verbosity')
fidle.override('dataset_file', 'data_shape', 'scale', 'num_workers' )
** Overrided parameters : ** gan_name : GAN generator_name : Generator_2 discriminator_name : Discriminator_3 ** Overrided parameters : ** epochs : 30 batch_size : 64 ** Overrided parameters : ** scale : 1 num_workers : 2
Cleaning¶
In [4]:
# You can comment these lines to keep each run...
shutil.rmtree(f'{run_dir}/figs', ignore_errors=True)
shutil.rmtree(f'{run_dir}/models', ignore_errors=True)
shutil.rmtree(f'{run_dir}/tb_logs', ignore_errors=True)
Step 2 - Get some nice data¶
Get a Nice DataModule¶
Our DataModule is defined in ./modules/QuickDrawDataModule.py
This is a LightningDataModule
In [5]:
dm = QuickDrawDataModule(dataset_file, scale, batch_size, num_workers=num_workers)
dm.setup()
---- QuickDrawDataModule initialization ---------------------------- with : scale=1 batch size=64 DataModule Setup : Original dataset shape : (126121, 784) Rescaled dataset shape : (126121, 784) Final dataset shape : torch.Size([126121, 28, 28, 1]) Dataset loaded and ready.
Have a look¶
In [6]:
dl = dm.train_dataloader()
batch_data = next(iter(dl))
fidle.scrawler.images( batch_data.reshape(-1,28,28), indices=range(batch_size), columns=12, x_size=1, y_size=1,
y_padding=0,spines_alpha=0, save_as='01-Sheeps')
Saved: ./run/PLSHEEP3_1/figs/01-Sheeps
Step 3 - Get a nice GAN model¶
Our Generators are defined in ./modules/Generators.py
Our Discriminators are defined in ./modules/Discriminators.py
Our GANs are defined in :
Retrieve class by name¶
To be very flexible, we just specify class names as parameters.
The code below retrieves classes from their names.
In [7]:
module=sys.modules['__main__']
Generator_ = getattr(module, generator_name)
Discriminator_ = getattr(module, discriminator_name)
GAN_ = getattr(module, gan_name)
Basic test - Just to be sure it (could) works... ;-)¶
In [8]:
generator = Generator_( latent_dim=latent_dim, data_shape=data_shape )
discriminator = Discriminator_( latent_dim=latent_dim, data_shape=data_shape )
print('\nFew tests :\n')
z = torch.randn(batch_size, latent_dim)
print('z size : ',z.size())
fake_img = generator.forward(z)
print('fake_img : ', fake_img.size())
p = discriminator.forward(fake_img)
print('pred fake : ', p.size())
print('batch_data : ',batch_data.size())
p = discriminator.forward(batch_data)
print('pred real : ', p.size())
print('\nShow fake images :')
nimg = fake_img.detach().numpy()
fidle.scrawler.images( nimg.reshape(-1,28,28), indices=range(batch_size), columns=12, x_size=1, y_size=1,
y_padding=0,spines_alpha=0, save_as='01-Sheeps')
init generator 2 : 128 to (28, 28, 1) init discriminator 3 : (28, 28, 1) to sigmoid Few tests : z size : torch.Size([64, 128]) fake_img : torch.Size([64, 28, 28, 1]) pred fake : torch.Size([64, 1]) batch_data : torch.Size([64, 28, 28, 1]) pred real : torch.Size([64, 1]) Show fake images :
Saved: ./run/PLSHEEP3_1/figs/01-Sheeps
In [9]:
print('Fake images : ', fake_img.size())
print('Batch size : ', batch_data.size())
e = torch.distributions.uniform.Uniform(0, 1).sample([batch_size,1])
e = e[:None,None,None]
i = fake_img * e + (1-e)*batch_data
print('\ninterpolate images :')
nimg = i.detach().numpy()
fidle.scrawler.images( nimg.reshape(-1,28,28), indices=range(batch_size), columns=12, x_size=1, y_size=1,
y_padding=0,spines_alpha=0, save_as='01-Sheeps')
Fake images : torch.Size([64, 28, 28, 1]) Batch size : torch.Size([64, 28, 28, 1]) interpolate images :
Saved: ./run/PLSHEEP3_1/figs/01-Sheeps
GAN model¶
To simplify our code, the GAN class is defined separately in the module ./modules/GAN.py
Passing the classe names for generator/discriminator by parameter allows to stay modular and to use the PL checkpoints.
In [10]:
gan = GAN_( data_shape = data_shape,
lr = lr,
b1 = b1,
b2 = b2,
lambda_gp = lambda_gp,
batch_size = batch_size,
latent_dim = latent_dim,
generator_name = generator_name,
discriminator_name = discriminator_name)
---- GAN initialization -------------------------------------------- Hyperarameters are : data_shape : (28, 28, 1) latent_dim : 128 lr : 0.0001 b1 : 0.5 b2 : 0.999 batch_size : 64 generator_name : Generator_2 discriminator_name : Discriminator_3 lambda_gp : 10 Submodels : init generator 2 : 128 to (28, 28, 1) init discriminator 3 : (28, 28, 1) to sigmoid
In [11]:
# ---- for tensorboard logs
#
logger = TensorBoardLogger( save_dir = f'{run_dir}',
name = 'tb_logs' )
log_dir = os.path.abspath(f'{run_dir}/tb_logs')
print('To access the logs with tensorboard, use this command line :')
print(f'tensorboard --logdir {log_dir}')
# ---- To save checkpoints
#
callback_checkpoints = ModelCheckpoint( dirpath = f'{run_dir}/models',
filename = 'bestModel',
save_top_k = 1,
save_last = True,
every_n_epochs = 1,
monitor = "g_loss")
To access the logs with tensorboard, use this command line : tensorboard --logdir /gpfsdswork/projects/rech/mlh/uja62cb/fidle-project/fidle/DCGAN.Lightning/run/PLSHEEP3_1/tb_logs
Train it¶
In [12]:
trainer = Trainer(
accelerator = "auto",
max_epochs = epochs,
callbacks = [callback_checkpoints],
log_every_n_steps = batch_size,
logger = logger
)
trainer.fit(gan, dm)
GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs Missing logger folder: ./run/PLSHEEP3_1/tb_logs
DataModule Setup : Original dataset shape : (126121, 784) Rescaled dataset shape : (126121, 784)
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Final dataset shape : torch.Size([126121, 28, 28, 1]) Dataset loaded and ready.
| Name | Type | Params | In sizes | Out sizes ------------------------------------------------------------------------------ 0 | generator | Generator_2 | 780 K | [2, 128] | [2, 28, 28, 1] 1 | discriminator | Discriminator_3 | 401 K | ? | ? ------------------------------------------------------------------------------ 1.2 M Trainable params 0 Non-trainable params 1.2 M Total params 4.728 Total estimated model params size (MB) SLURM auto-requeueing enabled. Setting signal handlers.
Training: | | 0/? [00:00<?, ?it/s]
`Trainer.fit` stopped: `max_epochs=30` reached.
Step 6 - Reload our best model¶
Note :
In [13]:
gan = GAN.load_from_checkpoint(f'{run_dir}/models/bestModel.ckpt')
---- GAN initialization -------------------------------------------- Hyperarameters are : data_shape : (28, 28, 1) latent_dim : 128 lr : 0.0001 b1 : 0.5 b2 : 0.999 batch_size : 64 generator_name : Generator_2 discriminator_name : Discriminator_3 lambda_gp : 10 Submodels : init generator 2 : 128 to (28, 28, 1) init discriminator 3 : (28, 28, 1) to sigmoid
In [14]:
torch.cuda.current_device()
Out[14]:
0
In [15]:
nb_images = 96
z = torch.randn(nb_images, latent_dim)
print('z size : ',z.size())
if torch.cuda.is_available(): z=z.cuda()
fake_img = gan.generator.forward(z)
print('fake_img : ', fake_img.size())
nimg = fake_img.cpu().detach().numpy()
fidle.scrawler.images( nimg.reshape(-1,28,28), indices=range(nb_images), columns=12, x_size=1, y_size=1,
y_padding=0,spines_alpha=0, save_as='01-Sheeps')
z size : torch.Size([96, 128]) fake_img : torch.Size([96, 28, 28, 1])
Saved: ./run/PLSHEEP3_1/figs/01-Sheeps
In [16]:
fidle.end()
End time : 03/03/24 21:54:27
Duration : 00:29:25 348ms
This notebook ends here :-)
https://fidle.cnrs.fr
