{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [SHEEP3] - A DCGAN to Draw a Sheep, using Pytorch Lightning\n",
"<!-- DESC --> \"Draw me a sheep\", revisited with a DCGAN, using Pytorch Lightning\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
" - Build and train a DCGAN model with the Quick Draw dataset\n",
" - Understanding DCGAN\n",
"\n",
"The [Quick draw dataset](https://quickdraw.withgoogle.com/data) contains about 50.000.000 drawings, made by real people... \n",
"We are using a subset of 117.555 of Sheep drawings \n",
"To get the dataset : [https://github.com/googlecreativelab/quickdraw-dataset](https://github.com/googlecreativelab/quickdraw-dataset) \n",
"Datasets in numpy bitmap file : [https://console.cloud.google.com/storage/quickdraw_dataset/full/numpy_bitmap](https://console.cloud.google.com/storage/quickdraw_dataset/full/numpy_bitmap) \n",
"Sheep dataset : [https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sheep.npy](https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sheep.npy) (94.3 Mo)\n",
"\n",
"\n",
"## What we're going to do :\n",
"\n",
" - Have a look to the dataset\n",
" - Defining a GAN model\n",
" - Build the model\n",
" - Train it\n",
" - Have a look of the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - Init and parameters\n",
"#### Python init"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import shutil\n",
"\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from lightning import LightningDataModule, LightningModule, Trainer\n",
"from lightning.pytorch.callbacks.progress.tqdm_progress import TQDMProgressBar\n",
"from lightning.pytorch.callbacks.progress.base import ProgressBarBase\n",
"from lightning.pytorch.callbacks import ModelCheckpoint\n",
"from lightning.pytorch.loggers.tensorboard import TensorBoardLogger\n",
"\n",
"from tqdm import tqdm\n",
"from torch.utils.data import DataLoader\n",
"\n",
"import fidle\n",
"\n",
"from modules.SmartProgressBar import SmartProgressBar\n",
"from modules.QuickDrawDataModule import QuickDrawDataModule\n",
"\n",
"from modules.GAN import GAN\n",
"from modules.WGANGP import WGANGP\n",
"from modules.Generators import *\n",
"from modules.Discriminators import *\n",
"\n",
"# Init Fidle environment\n",
"run_id, run_dir, datasets_dir = fidle.init('SHEEP3')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Few parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"latent_dim = 128\n",
"\n",
"gan_class = 'WGANGP'\n",
"generator_class = 'Generator_2'\n",
"discriminator_class = 'Discriminator_3' \n",
" \n",
"scale = 0.001\n",
"epochs = 3\n",
"lr = 0.0001\n",
"b1 = 0.5\n",
"b2 = 0.999\n",
"batch_size = 32\n",
"num_img = 48\n",
"fit_verbosity = 2\n",
" \n",
"dataset_file = datasets_dir+'/QuickDraw/origine/sheep.npy' \n",
"data_shape = (28,28,1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Cleaning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You can comment these lines to keep each run...\n",
"shutil.rmtree(f'{run_dir}/figs', ignore_errors=True)\n",
"shutil.rmtree(f'{run_dir}/models', ignore_errors=True)\n",
"shutil.rmtree(f'{run_dir}/tb_logs', ignore_errors=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Get some nice data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get a Nice DataModule\n",
"Our DataModule is defined in [./modules/QuickDrawDataModule.py](./modules/QuickDrawDataModule.py) \n",
"This is a [LightningDataModule](https://pytorch-lightning.readthedocs.io/en/stable/data/datamodule.html)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dm = QuickDrawDataModule(dataset_file, scale, batch_size, num_workers=8)\n",
"dm.setup()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Have a look"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dl = dm.train_dataloader()\n",
"batch_data = next(iter(dl))\n",
"\n",
"fidle.scrawler.images( batch_data.reshape(-1,28,28), indices=range(batch_size), columns=12, x_size=1, y_size=1, \n",
" y_padding=0,spines_alpha=0, save_as='01-Sheeps')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - Get a nice GAN model\n",
"\n",
"Our Generators are defined in [./modules/Generators.py](./modules/Generators.py) \n",
"Our Discriminators are defined in [./modules/Discriminators.py](./modules/Discriminators.py) \n",
"\n",
"\n",
"Our GAN is defined in [./modules/GAN.py](./modules/GAN.py) \n",
"\n",
"#### Class loader"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_class(class_name):\n",
" module=sys.modules['__main__']\n",
" class_ = getattr(module, class_name)\n",
" return class_\n",
" \n",
"def get_instance(class_name, **args):\n",
" module=sys.modules['__main__']\n",
" class_ = getattr(module, class_name)\n",
" instance_ = class_(**args)\n",
" return instance_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Basic test - Just to be sure it (could) works... ;-)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# ---- A little piece of black magic to instantiate a class from its name\n",
"#\n",
"def get_classByName(class_name, **args):\n",
" module=sys.modules['__main__']\n",
" class_ = getattr(module, class_name)\n",
" instance_ = class_(**args)\n",
" return instance_\n",
"\n",
"# ----Get it, and play with them\n",
"#\n",
"print('\\nInstantiation :\\n')\n",
"\n",
"Generator_ = get_class(generator_class)\n",
"Discriminator_ = get_class(discriminator_class)\n",
"\n",
"generator = Generator_( latent_dim=latent_dim, data_shape=data_shape)\n",
"discriminator = Discriminator_( latent_dim=latent_dim, data_shape=data_shape)\n",
"\n",
"print('\\nFew tests :\\n')\n",
"z = torch.randn(batch_size, latent_dim)\n",
"print('z size : ',z.size())\n",
"\n",
"fake_img = generator.forward(z)\n",
"print('fake_img : ', fake_img.size())\n",
"\n",
"p = discriminator.forward(fake_img)\n",
"print('pred fake : ', p.size())\n",
"\n",
"print('batch_data : ',batch_data.size())\n",
"\n",
"p = discriminator.forward(batch_data)\n",
"print('pred real : ', p.size())\n",
"\n",
"nimg = fake_img.detach().numpy()\n",
"fidle.scrawler.images( nimg.reshape(-1,28,28), indices=range(batch_size), columns=12, x_size=1, y_size=1, \n",
" y_padding=0,spines_alpha=0, save_as='01-Sheeps')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"print(fake_img.size())\n",
"print(batch_data.size())\n",
"e = torch.distributions.uniform.Uniform(0, 1).sample([32,1])\n",
"e = e[:None,None,None]\n",
"i = fake_img * e + (1-e)*batch_data\n",
"\n",
"\n",
"nimg = i.detach().numpy()\n",
"fidle.scrawler.images( nimg.reshape(-1,28,28), indices=range(batch_size), columns=12, x_size=1, y_size=1, \n",
" y_padding=0,spines_alpha=0, save_as='01-Sheeps')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### GAN model\n",
"To simplify our code, the GAN class is defined separately in the module [./modules/GAN.py](./modules/GAN.py) \n",
"Passing the classe names for generator/discriminator by parameter allows to stay modular and to use the PL checkpoints."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"GAN_ = get_class(gan_class)\n",
"\n",
"gan = GAN_( data_shape = data_shape,\n",
" lr = lr,\n",
" b1 = b1,\n",
" b2 = b2,\n",
" batch_size = batch_size, \n",
" latent_dim = latent_dim, \n",
" generator_class = generator_class, \n",
" discriminator_class = discriminator_class)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5 - Train it !\n",
"#### Instantiate Callbacks, Logger & co.\n",
"More about :\n",
"- [Checkpoints](https://pytorch-lightning.readthedocs.io/en/stable/common/checkpointing_basic.html)\n",
"- [modelCheckpoint](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.ModelCheckpoint.html#pytorch_lightning.callbacks.ModelCheckpoint)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"# ---- for tensorboard logs\n",
"#\n",
"logger = TensorBoardLogger( save_dir = f'{run_dir}',\n",
" name = 'tb_logs' )\n",
"\n",
"log_dir = os.path.abspath(f'{run_dir}/tb_logs')\n",
"print('To access the logs with tensorboard, use this command line :')\n",
"print(f'tensorboard --logdir {log_dir}')\n",
"\n",
"# ---- To save checkpoints\n",
"#\n",
"callback_checkpoints = ModelCheckpoint( dirpath = f'{run_dir}/models', \n",
" filename = 'bestModel', \n",
" save_top_k = 1, \n",
" save_last = True,\n",
" every_n_epochs = 1, \n",
" monitor = \"g_loss\")\n",
"\n",
"# ---- To have a nive progress bar\n",
"#\n",
"callback_progressBar = SmartProgressBar(verbosity=2) # Usable evertywhere\n",
"# progress_bar = TQDMProgressBar(refresh_rate=1) # Usable in real jupyter lab (bug in vscode)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"trainer = Trainer(\n",
" accelerator = \"auto\",\n",
" max_epochs = epochs,\n",
" callbacks = [callback_progressBar, callback_checkpoints],\n",
" log_every_n_steps = batch_size,\n",
" logger = logger\n",
")\n",
"\n",
"trainer.fit(gan, dm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 6 - Reload our best model\n",
"Note : "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gan = WGANGP.load_from_checkpoint('./run/SHEEP3/models/bestModel.ckpt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nb_images = 96\n",
"\n",
"z = torch.randn(nb_images, latent_dim)\n",
"print('z size : ',z.size())\n",
"\n",
"fake_img = gan.generator.forward(z)\n",
"print('fake_img : ', fake_img.size())\n",
"\n",
"nimg = fake_img.detach().numpy()\n",
"fidle.scrawler.images( nimg.reshape(-1,28,28), indices=range(nb_images), columns=12, x_size=1, y_size=1, \n",
" y_padding=0,spines_alpha=0, save_as='01-Sheeps')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fidle.end()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"<img width=\"80px\" src=\"../fidle/img/logo-paysage.svg\"></img>"
]
}
],
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"display_name": "fidle-env",
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