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Commit c322a913 authored by Jean-Luc Parouty's avatar Jean-Luc Parouty
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VAE Test

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VAE/06-Prepare-CelebA-datasets.ipynb
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%% Cell type:markdown id: tags:
<img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img>
# <!-- TITLE --> [VAE6] - Preparation of the CelebA dataset
<!-- DESC --> Preparation of a clustered dataset, batchable
<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
## Objectives :
- Formatting our dataset in **cluster files**, using batch mode
- Adapting a notebook for batch use
The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) contains about 200,000 images (202599,218,178,3).
## What we're going to do :
- Lire les images
- redimensionner et normaliser celles-ci,
- Constituer des clusters d'images en format npy
%% Cell type:markdown id: tags:
## Step 1 - Import and init
### 1.2 - Import
%% Cell type:code id: tags:
``` python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from skimage import io, transform
import os,pathlib,time,sys,json,glob
import csv
import math, random
from importlib import reload
sys.path.append('..')
import fidle.pwk as pwk
datasets_dir = pwk.init('VAE6')
```
%% Output
**FIDLE 2020 - Practical Work Module**
Version : 0.6.1 DEV
Notebook id : VAE6
Run time : Monday 4 January 2021, 21:17:51
TensorFlow version : 2.4.0
Keras version : 2.4.0
Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets
Run time : Monday 4 January 2021, 23:45:46
TensorFlow version : 2.2.0
Keras version : 2.3.0-tf
Datasets dir : /home/pjluc/datasets/fidle
Run dir : ./run
CI running mode : none
Update keras cache : False
Save figs : True
Path figs : ./run/figs
%% Cell type:markdown id: tags:
### 1.2 - Directories and files :
%% Cell type:code id: tags:
``` python
dataset_csv = f'{datasets_dir}/celeba/origine/list_attr_celeba.csv'
dataset_img = f'{datasets_dir}/celeba/origine/img_align_celeba'
```
%% Cell type:markdown id: tags:
## Step 2 - Read and shuffle filenames catalog
%% Cell type:code id: tags:
``` python
dataset_desc = pd.read_csv(dataset_csv, header=0)
dataset_desc = dataset_desc.reindex(np.random.permutation(dataset_desc.index))
```
%% Cell type:markdown id: tags:
## Step 3 - Save as clusters of n images
%% Cell type:markdown id: tags:
### 4.2 - Cooking function
%% Cell type:code id: tags:
``` python
def read_and_save( dataset_img, dataset_desc, scale=1,
cluster_size=1000, cluster_dir='./dataset_cluster', cluster_name='images',
image_size=(128,128)):
global pwk
def save_cluster(imgs,desc,cols,id):
file_img = f'{cluster_dir}/{cluster_name}-{id:03d}.npy'
file_desc = f'{cluster_dir}/{cluster_name}-{id:03d}.csv'
np.save(file_img, np.array(imgs))
df=pd.DataFrame(data=desc,columns=cols)
df.to_csv(file_desc, index=False)
return [],[],id+1
pwk.chrono_start()
cols = list(dataset_desc.columns)
# ---- Check if cluster files exist
#
if os.path.isfile(f'{cluster_dir}/images-000.npy'):
print('\n*** Oups. There are already clusters in the target folder!\n')
return 0,0
pwk.mkdir(cluster_dir)
# ---- Scale
#
n=int(len(dataset_desc)*scale)
dataset = dataset_desc[:n]
cluster_size = int(cluster_size*scale)
pwk.subtitle('Parameters :')
print(f'Scale is : {scale}')
print(f'Image size is : {image_size}')
print(f'dataset length is : {n}')
print(f'cluster size is : {cluster_size}')
print(f'clusters nb is :',int(n/cluster_size + 1))
print(f'cluster dir is : {cluster_dir}')
# ---- Read and save clusters
#
pwk.subtitle('Running...')
imgs, desc, cluster_id = [],[],0
#
for i,row in dataset.iterrows():
#
filename = f'{dataset_img}/{row.image_id}'
#
# ---- Read image, resize (and normalize)
#
img = io.imread(filename)
img = transform.resize(img, image_size)
#
# ---- Add image and description
#
imgs.append( img )
desc.append( row.values )
#
# ---- Progress bar
#
pwk.update_progress(f'Cluster {cluster_id:03d} :',len(imgs),cluster_size)
#
# ---- Save cluster if full
#
if len(imgs)==cluster_size:
imgs,desc,cluster_id=save_cluster(imgs,desc,cols, cluster_id)
# ---- Save uncomplete cluster
if len(imgs)>0 : imgs,desc,cluster_id=save_cluster(imgs,desc,cols,cluster_id)
duration=pwk.chrono_stop()
return cluster_id,duration
```
%% Cell type:markdown id: tags:
### 4.3 - Cluster building
All the dataset will be use for training
Reading the 200,000 images can take a long time **(>20 minutes)** and a lot of place **(>170 GB)**
Example :
Image Sizes: 128x128 : 74 GB
Image Sizes: 192x160 : 138 GB
You can define theses parameters :
`scale` : 1 mean 100% of the dataset - set 0.05 for tests
`image_size` : images size in the clusters, should be 128x128 or 192,160 (original is 218,178)
`output_dir` : where to write clusters, could be :
- `./data`, for tests purpose
- `<datasets_dir>/celeba/enhanced` to add clusters in your datasets dir.
`cluster_size` : number of images in a cluster, 10000 is fine. (will be adjust by scale)
**Note :** If the target folder is not empty, the construction is blocked.
%% Cell type:code id: tags:
``` python
# ---- Parameters you can change -----------------------------------
# ---- Tests
scale = 0.1
scale = 0.2
image_size = (128,128)
output_dir = './data'
# ---- Full clusters generation, medium size
# scale = 1.
# image_size = (128,128)
# output_dir = f'{datasets_dir}/celeba/enhanced'
# ---- Full clusters generation, large size
# scale = 1.
# image_size = (192,160)
# output_dir = f'{datasets_dir}/celeba/enhanced'
```
%% Cell type:code id: tags:
``` python
# ---- Used for continous integration - Just forget this 3 lines
#
scale = pwk.override('scale', scale)
image_size = pwk.override('image_size', image_size)
output_dir = pwk.override('output_dir', output_dir)
# ---- Build clusters
#
cluster_size = 10000
lx,ly = image_size
cluster_dir = f'{output_dir}/clusters-{lx}x{ly}'
cluster_nb,duration = read_and_save( dataset_img, dataset_desc,
scale = scale,
cluster_size = cluster_size,
cluster_dir = cluster_dir,
image_size = image_size )
# ---- Conclusion...
directory = pathlib.Path(cluster_dir)
s=sum(f.stat().st_size for f in directory.glob('**/*') if f.is_file())
pwk.subtitle('Conclusion :')
print('Duration : ',pwk.hdelay(duration))
print('Size : ',pwk.hsize(s))
```
%% Output
<br>**Parameters :**
Scale is : 1.0
Scale is : 0.2
Image size is : (128, 128)
dataset length is : 202599
cluster size is : 10000
dataset length is : 40519
cluster size is : 2000
clusters nb is : 21
cluster dir is : /gpfswork/rech/mlh/uja62cb/datasets/celeba/enhanced/clusters-128x128
cluster dir is : ./data/clusters-128x128
<br>**Running...**
Cluster 000 : [########################################] 100.0% of 10000
Cluster 001 : [########################################] 100.0% of 10000
Cluster 002 : [########################################] 100.0% of 10000
Cluster 003 : [########################################] 100.0% of 10000
Cluster 004 : [########################################] 100.0% of 10000
Cluster 005 : [########################################] 100.0% of 10000
Cluster 006 : [########################################] 100.0% of 10000
Cluster 007 : [########################################] 100.0% of 10000
Cluster 008 : [########################################] 100.0% of 10000
Cluster 009 : [########################################] 100.0% of 10000
Cluster 010 : [########################################] 100.0% of 10000
Cluster 011 : [########################################] 100.0% of 10000
Cluster 012 : [########################################] 100.0% of 10000
Cluster 013 : [########################################] 100.0% of 10000
Cluster 014 : [########################################] 100.0% of 10000
Cluster 015 : [########################################] 100.0% of 10000
Cluster 016 : [########################################] 100.0% of 10000
Cluster 017 : [########################################] 100.0% of 10000
Cluster 018 : [########################################] 100.0% of 10000
Cluster 019 : [########################################] 100.0% of 10000
Cluster 020 : [##########------------------------------] 25.0% of 10000
Cluster 000 : [########################################] 100.0% of 2000
Cluster 001 : [########################################] 100.0% of 2000
Cluster 002 : [########################################] 100.0% of 2000
Cluster 003 : [########################################] 100.0% of 2000
Cluster 004 : [########################################] 100.0% of 2000
Cluster 005 : [########################################] 100.0% of 2000
Cluster 006 : [########################################] 100.0% of 2000
Cluster 007 : [########################################] 100.0% of 2000
Cluster 008 : [########################################] 100.0% of 2000
Cluster 009 : [########################################] 100.0% of 2000
Cluster 010 : [########################################] 100.0% of 2000
Cluster 011 : [########################################] 100.0% of 2000
Cluster 012 : [########################################] 100.0% of 2000
Cluster 013 : [########################################] 100.0% of 2000
Cluster 014 : [########################################] 100.0% of 2000
Cluster 015 : [########################################] 100.0% of 2000
Cluster 016 : [########################################] 100.0% of 2000
Cluster 017 : [########################################] 100.0% of 2000
Cluster 018 : [########################################] 100.0% of 2000
Cluster 019 : [########################################] 100.0% of 2000
Cluster 020 : [##########------------------------------] 25.0% of 2000
<br>**Conclusion :**
Duration : 0:51:04
Size : 74.2 Go
Duration : 0:05:06
Size : 14.8 Go
%% Cell type:code id: tags:
``` python
pwk.end()
```
%% Output
End time is : Monday 4 January 2021, 22:08:57
Duration is : 00:51:06 647ms
End time is : Monday 4 January 2021, 23:50:54
Duration is : 00:05:08 166ms
This notebook ends here
%% Cell type:markdown id: tags:
---
<img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img>
......
fidle/log/finished.json
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......@@ -126,10 +126,10 @@
"duration": "00:00:10 061ms"
},
"VAE6": {
"path": "/gpfsdswork/projects/rech/mlh/uja62cb/fidle/VAE",
"start": "Monday 4 January 2021, 21:17:51",
"end": "Monday 4 January 2021, 22:08:57",
"duration": "00:51:06 647ms"
"path": "/home/pjluc/dev/fidle/VAE",
"start": "Monday 4 January 2021, 23:45:46",
"end": "Monday 4 January 2021, 23:50:54",
"duration": "00:05:08 166ms"
},
"GTS1": {
"path": "/home/pjluc/dev/fidle/GTSRB",
......@@ -150,8 +150,8 @@
"duration": "00:00:08 736ms"
},
"VAE8": {
"path": "/gpfsdswork/projects/rech/mlh/uja62cb/fidle/VAE",
"start": "Monday 4 January 2021, 22:27:20",
"path": "/home/pjluc/dev/fidle/VAE",
"start": "Monday 4 January 2021, 23:53:16",
"end": "",
"duration": "Unfinished..."
}
......
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