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"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE5] - Checking the clustered CelebA dataset\n",
"<!-- DESC --> Verification of prepared data from CelebA dataset\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
" - Making sure our clustered dataset is correct\n",
" - Do a little bit of python while waiting to build and train our VAE model.\n",
"\n",
"The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) contains about 200,000 images (202599,218,178,3). \n",
"\n",
"\n",
"## What we're going to do :\n",
"\n",
" - Reload our dataset\n",
" - Check and verify our clustered dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - Import and init\n",
"### 1.2 - Import"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import os,time,sys,json,glob,importlib\n",
"import math, random\n",
"\n",
"import modules.data_generator\n",
"from modules.data_generator import DataGenerator\n",
"\n",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
"ooo.init()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 - Directories and files :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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_dir = f'{dataset_dir}/clusters.train'\n",
"test_dir = f'{dataset_dir}/clusters.test'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Data verification\n",
"What we're going to do:\n",
" - Recover all clusters by normalizing images\n",
" - Make some statistics to be sure we have all the data\n",
" - picking one image per cluster to check that everything is good."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ---- Return a legend from a description \n",
"def get_legend(x_desc,i):\n",
" cols = x_desc.columns\n",
" desc = x_desc.iloc[i]\n",
" legend =[]\n",
" for i,v in enumerate(desc):\n",
" if v==1 : legend.append(cols[i])\n",
" return str('\\n'.join(legend))\n",
"\n",
"start_time = time.time()\n",
"\n",
"# ---- get cluster list\n",
"\n",
"clusters_name = [ os.path.splitext(f)[0] for f in glob.glob( f'{train_dir}/*.npy') ]\n",
"\n",
"# ---- Counters set to 0\n",
"\n",
"imax = len(clusters_name)\n",
"i,n1,n2,s = 0,0,0,0\n",
"imgs,desc = [],[]\n",
"\n",
"# ---- Reload all clusters\n",
"\n",
"ooo.update_progress('Load clusters :',i,imax, redraw=True)\n",
"for cluster_name in clusters_name: \n",
" \n",
" # ---- Reload images and normalize\n",
"\n",
" x_data = np.load(cluster_name+'.npy')\n",
" \n",
" # ---- Reload descriptions\n",
" \n",
" x_desc = pd.read_csv(cluster_name+'.csv', header=0)\n",
" \n",
" # ---- Counters\n",
" \n",
" n1 += len(x_data)\n",
" n2 += len(x_desc.index)\n",
" s += x_data.nbytes\n",
" i += 1\n",
" \n",
" # ---- Get somes images/legends\n",
" \n",
" j=random.randint(0,len(x_data)-1)\n",
" imgs.append( x_data[j].copy() )\n",
" desc.append( get_legend(x_desc,j) )\n",
" x_data=None\n",
" \n",
" # ---- To appear professional\n",
" \n",
" ooo.update_progress('Load clusters :',i,imax, redraw=True)\n",
"\n",
"d=time.time()-start_time\n",
"\n",
"print(f'Loading time : {d:.2f} s or {ooo.hdelay(d)}')\n",
"print(f'Number of cluster : {i}')\n",
"print(f'Number of images : {n1}')\n",
"print(f'Number of desc. : {n2}')\n",
"print(f'Total size of img : {ooo.hsize(s)}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ooo.plot_images(imgs,desc,x_size=2,y_size=2,fontsize=8,columns=7,y_padding=2.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class='nota'>\n",
" <b>Note :</b> With this approach, the use of data is much much more effective !\n",
" <ul>\n",
" <li>Data loading speed : <b>x 10</b> (81 s vs 16 min.)</li>\n",
" </ul>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - How we will read our data during the train session\n",
"We are going to use a \"dataset reader\", which is a [tensorflow.keras.utils.Sequence](https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence) \n",
"The batches will be requested to our DataGenerator, which will read the clusters as they come in.\n",
"\n",
"### 3.1 - An example to understand"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ---- A very small dataset\n",
"\n",
"clusters_dir = f'{dataset_dir}/clusters-xs.train'\n",
"\n",
"# ---- Our DataGenerator\n",
"# with small batch size, debug mode and 50% of the dataset\n",
"\n",
"data_gen = DataGenerator(clusters_dir, 32, debug=True, k_size=1)\n",
"\n",
"# ---- We ask him to retrieve all batchs\n",
"\n",
"batch_sizes=[]\n",
"for i in range( len(data_gen)):\n",
" x,y = data_gen[i]\n",
" batch_sizes.append(len(x))\n",
"\n",
"print(f'\\n\\ntotal number of items : {sum(batch_sizes)}')\n",
"print(f'batch sizes : {batch_sizes}')\n",
"print(f'Last batch shape : {x.shape}')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
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}
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