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"source": [
"Variational AutoEncoder (VAE) with CelebA\n",
"=========================================\n",
"---\n",
"Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
"\n",
"## Episode 1 - Train a model\n",
"\n",
" - Defining a VAE model\n",
" - Build the model\n",
" - Train it\n",
" - Follow the learning process with Tensorboard\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - Setup environment\n",
"### 1.1 - Python stuff"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
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"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
"Version : 0.2.8\n",
"Run time : Friday 14 February 2020, 00:07:28\n",
"TensorFlow version : 2.0.0\n",
"Keras version : 2.2.4-tf\n",
"\n",
"FIDLE 2020 - Variational AutoEncoder (VAE)\n",
"TensorFlow version : 2.0.0\n",
"VAE version : 1.28\n",
"\n",
"FIDLE 2020 - DataGenerator\n",
"Version : 0.4.1\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import os,sys\n",
"from importlib import reload\n",
"\n",
"import modules.vae\n",
"import modules.data_generator\n",
"\n",
"reload(modules.data_generator)\n",
"reload(modules.vae)\n",
"\n",
"from modules.vae import VariationalAutoencoder\n",
"from modules.data_generator import DataGenerator\n",
"\n",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"reload(ooo)\n",
"\n",
"ooo.init()\n",
"\n",
"VariationalAutoencoder.about()\n",
"DataGenerator.about()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 - The good place"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Well, we should be at IDRIS !\n",
"We are going to use: /gpfswork/rech/mlh/uja62cb/datasets/celeba\n"
]
}
],
"source": [
"place, dataset_dir = ooo.good_place( { 'GRICAD' : f'{os.getenv(\"SCRATCH_DIR\",\"\")}/PROJECTS/pr-fidle/datasets/celeba',\n",
" 'IDRIS' : f'{os.getenv(\"WORK\",\"\")}/datasets/celeba',\n",
" 'HOME' : f'{os.getenv(\"HOME\",\"\")}/datasets/celeba'} )\n",
"\n",
"# ---- train/test datasets\n",
"\n",
"train_dir = f'{dataset_dir}/clusters-M.train'\n",
"test_dir = f'{dataset_dir}/clusters-M.test'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - DataGenerator and validation data\n",
"Ok, everything's perfect, now let's instantiate our generator for the entire dataset."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data generator : 6250 batchs of 32 images, or 200000 images\n",
"x_test : 2599 images\n"
]
}
],
"source": [
"data_gen = DataGenerator(train_dir, 32, k_size=1)\n",
"x_test = np.load(f'{test_dir}/images-000.npy')\n",
"\n",
"print(f'Data generator : {len(data_gen)} batchs of {data_gen.batch_size} images, or {data_gen.dataset_size} images')\n",
"print(f'x_test : {len(x_test)} images')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - Get VAE model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model initialized.\n",
"Outputs will be in : ./run/CelebA.052-M.810156\n",
"Config saved in : ./run/CelebA.052-M.810156/models/vae_config.json\n"
]
}
],
"source": [
"tag = f'CelebA.052-M.{os.getenv(\"SLURM_JOB_ID\",\"unknown\")}'\n",
"\n",
"input_shape = (192, 160, 3)\n",
"z_dim = 200\n",
"verbose = 0\n",
"\n",
"encoder= [ {'type':'Conv2D', 'filters':32, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" ]\n",
"\n",
"decoder= [ {'type':'Conv2DTranspose', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" {'type':'Conv2DTranspose', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" {'type':'Conv2DTranspose', 'filters':32, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
" {'type':'Conv2DTranspose', 'filters':3, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'sigmoid'}\n",
" ]\n",
"\n",
"vae = modules.vae.VariationalAutoencoder(input_shape = input_shape, \n",
" encoder_layers = encoder, \n",
" decoder_layers = decoder,\n",
" z_dim = z_dim, \n",
" verbose = verbose,\n",
" run_tag = tag)\n",
"vae.save(model=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4 - Compile it"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compiled.\n"
]
}
],
"source": [
"optimizer = tf.keras.optimizers.Adam(1e-4)\n",
"r_loss_factor = 10000\n",
"\n",
"vae.compile(optimizer, r_loss_factor)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5 - Train\n",
"For 10 epochs, adam optimizer : \n",
"- Run time at IDRIS : 1299.77 sec. - 0:21:39\n",
"- Run time at GRICAD : 2092.77 sec. - 0:34:52\n",
"- At IDRIS with medium resolution : Train duration : 6638.61 sec. - 1:50:38"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"epochs = 20\n",
"initial_epoch = 0"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"6250/6250 [==============================] - 342s 55ms/step - loss: 332.4792 - vae_r_loss: 282.9655 - vae_kl_loss: 49.5134 - val_loss: 235.4288 - val_vae_r_loss: 187.4334 - val_vae_kl_loss: 48.1192\n",
"Epoch 2/20\n",
"6250/6250 [==============================] - 337s 54ms/step - loss: 224.0962 - vae_r_loss: 166.4602 - vae_kl_loss: 57.6360 - val_loss: 210.9632 - val_vae_r_loss: 155.0925 - val_vae_kl_loss: 56.0114\n",
"Epoch 4/20\n",
"6250/6250 [==============================] - 333s 53ms/step - loss: 214.5463 - vae_r_loss: 155.7666 - vae_kl_loss: 58.7794 - val_loss: 203.8241 - val_vae_r_loss: 147.3248 - val_vae_kl_loss: 56.5778\n",
"Epoch 7/20\n",
"6250/6250 [==============================] - 327s 52ms/step - loss: 211.1459 - vae_r_loss: 152.4026 - vae_kl_loss: 58.7437 - val_loss: 201.1862 - val_vae_r_loss: 145.6906 - val_vae_kl_loss: 55.6326\n",
"Epoch 10/20\n",
"6250/6250 [==============================] - 335s 54ms/step - loss: 209.7628 - vae_r_loss: 151.0874 - vae_kl_loss: 58.6756 - val_loss: 202.3954 - val_vae_r_loss: 147.0956 - val_vae_kl_loss: 55.4047\n",
"Epoch 12/20\n",
"6250/6250 [==============================] - 333s 53ms/step - loss: 207.9830 - vae_r_loss: 149.3870 - vae_kl_loss: 58.5959 - val_loss: 198.5626 - val_vae_r_loss: 142.5848 - val_vae_kl_loss: 56.0871\n",
"Epoch 16/20\n",
"6250/6250 [==============================] - 330s 53ms/step - loss: 206.6382 - vae_r_loss: 148.0522 - vae_kl_loss: 58.5863 - val_loss: 197.5800 - val_vae_r_loss: 142.6799 - val_vae_kl_loss: 54.9832\n",
"\n",
"Train duration : 6638.61 sec. - 1:50:38\n"
]
}
],
"source": [
"vae.train(data_generator = data_gen,\n",
" x_test = x_test,\n",
" epochs = epochs,\n",
" initial_epoch = initial_epoch\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"That's all folks !"
]
}
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