diff --git a/README.md b/README.md
index ff43f757ccc491747c1c5893388b0e0add87b67d..cf5182b2d5cda0810a0702ed2303d07cf281be6c 100644
--- a/README.md
+++ b/README.md
@@ -29,54 +29,74 @@ Useful information is also available in the [wiki](https://gricad-gitlab.univ-gr
<!-- DO NOT REMOVE THIS TAG !!! -->
<!-- INDEX -->
<!-- INDEX_BEGIN -->
-1. [[NP1] - A short introduction to Numpy](Prerequisites/Numpy.ipynb)<br>
- Numpy is an essential tool for the Scientific Python.
-1. [[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br>
- Direct determination of linear regression
-1. [[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br>
- An example of gradient descent in the simple case of a linear regression.
-1. [[FIT1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br>
- Illustration of the problem of complexity with the polynomial regression
-1. [[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)<br>
- Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
-1. [[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)<br>
- Example of classification with a fully connected neural network
-1. [[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)<br>
- A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
-1. [[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)<br>
- More advanced example of DNN network code - BHPD dataset
-1. [[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br>
- Episode 1: Data analysis and creation of a usable dataset
-1. [[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br>
- Episode 2 : First convolutions and first results
-1. [[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br>
- Episode 3: Monitoring and analysing training, managing checkpoints
-1. [[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br>
- Episode 4: Improving the results with data augmentation
-1. [[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br>
- Episode 5: A lot of models, a lot of datasets and a lot of results.
-1. [[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)<br>
- Episode 6 : Run Full convolution notebook as a batch
-1. [[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb)<br>
- Displaying the reports of the different jobs
-1. [[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)<br>
- 4 ways to use Tensorboard from the Jupyter environment
-1. [[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br>
- A very classical example of word embedding for text classification (sentiment analysis)
-1. [[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br>
- Example of reusing a previously saved model
-1. [[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br>
- Still the same problem, but with a network combining embedding and LSTM
+[[NP1] - A short introduction to Numpy](Prerequisites/Numpy.ipynb)
+ Numpy is an essential tool for the Scientific Python.
+[[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
+ Direct determination of linear regression
+[[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
+ An example of gradient descent in the simple case of a linear regression.
+[[FIT1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
+ Illustration of the problem of complexity with the polynomial regression
+[[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)
+ Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
+[[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
+ Example of classification with a fully connected neural network
+[[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
+ A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
+[[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
+ More advanced example of DNN network code - BHPD dataset
+[[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
+ Episode 1: Data analysis and creation of a usable dataset
+[[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)
+ Episode 2 : First convolutions and first results
+[[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)
+ Episode 3: Monitoring and analysing training, managing checkpoints
+[[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
+ Episode 4: Improving the results with data augmentation
+[[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)
+ Episode 5: A lot of models, a lot of datasets and a lot of results.
+[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)
+ Episode 6 : Run Full convolution notebook as a batch
+[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb)
+ Displaying the reports of the different jobs
+[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)
+ 4 ways to use Tensorboard from the Jupyter environment
+[[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)
+ A very classical example of word embedding for text classification (sentiment analysis)
+[[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)
+ Example of reusing a previously saved model
+[[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)
+ Still the same problem, but with a network combining embedding and LSTM
+[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)
+ First generative network experience with the MNIST dataset
+[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)
+ Use of the previously trained model, analysis of the results
+[[VAE3] - About the CelebA dataset](VAE/03-Prepare-CelebA.ipynb)
+ New VAE experience, but with a larger and more fun dataset
+[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb)
+ Preparation of a clustered dataset, batchable
+[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)
+ Verification of prepared data from CelebA dataset
+[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb)
+ VAE with a more fun and realistic dataset - small resolution and batchable
+[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb)
+ VAE with a more fun and realistic dataset - medium resolution and batchable
+[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/12-VAE-withCelebA-post.ipynb)
+ Use of the previously trained model with CelebA, analysis of the results
+[[BASH1] - OAR batch script](VAE/batch-oar.sh)
+ Bash script for OAR batch submission of a notebook
+[[BASH2] - SLURM batch script](VAE/batch-slurm.sh)
+ Bash script for SLURM batch submission of a notebook
<!-- INDEX_END -->
-
## Installation
A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/howto-jupyter)**.
## Licence
+[<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/)
\[en\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
\[Fr\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
diff --git a/README.md.old b/README.md.old
deleted file mode 100644
index e2364c96ecdfa07c0075fb988a894bc30ad351b5..0000000000000000000000000000000000000000
--- a/README.md.old
+++ /dev/null
@@ -1,81 +0,0 @@
-[<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#)
-
-## A propos
-
-This repository contains all the documents and links of the **Fidle Training**.
-
-The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, are :
- - Understanding the **bases of deep learning** neural networks (Deep Learning)
- - Develop a **first experience** through simple and representative examples
- - Understand the different types of networks, their **architectures** and their **use cases**.
- - Understanding **Tensorflow/Keras and Jupyter lab** technologies on the GPU
- - Apprehend the **academic computing environments** Tier-2 (meso) and/or Tier-1 (national)
-
-## Course materials
-**[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>
-Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/z7XZA36xKkMcaTS)**
-
-
-
-<!--  -->
-Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
-
-
-## Jupyter notebooks
-
-[](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Ftalks%2Fdeeplearning.git/master?urlpath=lab/tree/index.ipynb)
-
-
-<!-- DO NOT REMOVE THIS TAG !!! -->
-<!-- INDEX -->
-<!-- INDEX_BEGIN -->
-1. [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br>
- Direct determination of linear regression
-1. [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br>
- An example of gradient descent in the simple case of a linear regression.
-1. [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br>
- Illustration of the problem of complexity with the polynomial regression
-1. [Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)<br>
- Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
-1. [[REG1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)<br>
- A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
-1. [[REG2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)<br>
- More advanced example of DNN network code - BHPD dataset
-1. [[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br>
- Episode 1: Data analysis and creation of a usable dataset
-1. [[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br>
- Episode 2 : First convolutions and first results
-1. [[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br>
- Episode 3: Monitoring and analysing training, managing checkpoints
-1. [CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br>
- Episode 4: Improving the results with data augmentation
-1. [CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br>
- Episode 5: A lot of models, a lot of datasets and a lot of results.
-1. [CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)<br>
- Episode 6 : Run Full convolution notebook as a batch
-1. [Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)<br>
- 4 ways to use Tensorboard from the Jupyter environment
-1. [Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br>
- A very classical example of word embedding for text classification (sentiment analysis)
-1. [Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br>
- Example of reusing a previously saved model
-1. [Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br>
- Still the same problem, but with a network combining embedding and LSTM
-<!-- INDEX_END -->
-
-
-
-## Installation
-
-A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/howto-jupyter)**.
-
-## Licence
-
-\[en\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
-\[Fr\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
-See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
-See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
-
-
-----
-[<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img>](#)
\ No newline at end of file
diff --git a/VAE/01-VAE-with-MNIST.ipynb b/VAE/01-VAE-with-MNIST.ipynb
index aa2cef1de622137a1abefc83420e58b223c80384..576132a2982362149e3f63c5902a4587c1d7ee0d 100644
--- a/VAE/01-VAE-with-MNIST.ipynb
+++ b/VAE/01-VAE-with-MNIST.ipynb
@@ -4,16 +4,25 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Variational AutoEncoder (VAE) with MNIST\n",
- "========================================\n",
- "---\n",
- "Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [VAE1] - Variational AutoEncoder (VAE) with MNIST\n",
+ "<!-- DESC --> First generative network experience with the MNIST dataset\n",
+ "\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Understanding and implementing a variational autoencoder neurals network (VAE)\n",
+ " - Understanding a more advanced programming model\n",
+ "\n",
+ "The calculation needs being important, it is preferable to use a very simple dataset such as MNIST to start with.\n",
+ "\n",
+ "## What we're going to do :\n",
"\n",
- "## Episode 1 - Train a model\n",
" - Defining a VAE model\n",
" - Build the model\n",
" - Train it\n",
- " - Follow the learning process with Tensorboard\n"
+ " - Follow the learning process with Tensorboard"
]
},
{
@@ -150,18 +159,12 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": []
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
}
],
"metadata": {
@@ -180,7 +183,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/VAE/02-VAE-with-MNIST-post.ipynb b/VAE/02-VAE-with-MNIST-post.ipynb
index 61d141d9bc9fbfcd205747948cc95f688daaee19..eb3f02043144aa7b2cb342e3236f64d733a19597 100644
--- a/VAE/02-VAE-with-MNIST-post.ipynb
+++ b/VAE/02-VAE-with-MNIST-post.ipynb
@@ -4,12 +4,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Variational AutoEncoder (VAE) with MNIST\n",
- "========================================\n",
- "---\n",
- "Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis\n",
+ "<!-- DESC --> Use of the previously trained model, analysis of the results\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - First data generation from latent space \n",
+ " - Understanding of underlying principles\n",
+ " - Model management\n",
+ "\n",
+ "Here, we don't consume data anymore, but we generate them ! ;-)\n",
+ "\n",
+ "## What we're going to do :\n",
"\n",
- "## Episode 2 - Analyse our trained model\n",
" - Load a saved model\n",
" - Reconstruct some images\n",
" - Latent space visualization\n",
@@ -319,8 +328,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "----\n",
- "That's all folks !"
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
}
],
@@ -340,7 +349,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/VAE/03-Prepare-CelebA.ipynb b/VAE/03-Prepare-CelebA.ipynb
index 2326f69c00c9c221c599be2e94ce56ba54fbd94e..1a5393d53ec69c138fa2c6c4e94dad589f41c73d 100644
--- a/VAE/03-Prepare-CelebA.ipynb
+++ b/VAE/03-Prepare-CelebA.ipynb
@@ -1,29 +1,21 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Celeb Faces Dataset (CelebA)\n",
- "=================================================\n",
- "---\n",
- "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "We'll do the same thing again but with a more interesting dataset: CelebFaces \n",
- "About this dataset : http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
+ "# <!-- TITLE --> [VAE3] - About the CelebA dataset\n",
+ "<!-- DESC --> New VAE experience, but with a larger and more fun dataset\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
- "## Episode 1 : Preparation of data\n",
+ "## Objectives :\n",
+ " - Data analysis and preparation\n",
+ " - Problems related to the use of more real datasets\n",
"\n",
- " - Understanding the dataset\n",
- " - Preparing and formatting enhanced data\n",
- " - Save as clusters of n images\n"
+ "We'll do the same thing again but with a more interesting dataset: **CelebFaces** \n",
+ "\"[CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations.\""
]
},
{
@@ -79,8 +71,8 @@
"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
- "Version : 0.2.8\n",
- "Run time : Wednesday 12 February 2020, 17:09:15\n",
+ "Version : 0.2.9\n",
+ "Run time : Friday 21 February 2020, 11:44:05\n",
"TensorFlow version : 2.0.0\n",
"Keras version : 2.2.4-tf\n"
]
@@ -730,8 +722,11 @@
"source": [
"<div class=warn>\n",
"Fine ! :-)<br>But how can we effectively use this dataset, considering its size and the number of files ?<br>\n",
- "We're talking about a 10' of loading time and 170 GB of data... ;-(<br>\n",
- "...and on top of that, the size of the pictures will make life difficult for us !\n",
+ "We're talking about a 10' to 20' of loading time and 170 GB of data... ;-(<br><br>\n",
+ "The only solution will be to:\n",
+ "<ul>\n",
+ " <li>group images into clusters, to limit the number of files,\n",
+ " <li>read the data gradually, because not all of it can be stored in memory\n",
"<div/>"
]
},
@@ -739,560 +734,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 4 - Save as clusters of n images\n",
- "\n",
- "In order to avoid reading multiple files of very small size, we will group our images in large npy files. \n",
- "We will proceed as follows: \n",
- " - we're gonna shuffle our catalog,\n",
- " - we're going to create an save clusters of n resized images,\n",
- " - 80% of these clusters will be used for training,\n",
- " - 20 % for test/validation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.1 - Shuffle catalog "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>image_id</th>\n",
- " <th>5_o_Clock_Shadow</th>\n",
- " <th>Arched_Eyebrows</th>\n",
- " <th>Attractive</th>\n",
- " <th>Bags_Under_Eyes</th>\n",
- " <th>Bald</th>\n",
- " <th>Bangs</th>\n",
- " <th>Big_Lips</th>\n",
- " <th>Big_Nose</th>\n",
- " <th>Black_Hair</th>\n",
- " <th>...</th>\n",
- " <th>Sideburns</th>\n",
- " <th>Smiling</th>\n",
- " <th>Straight_Hair</th>\n",
- " <th>Wavy_Hair</th>\n",
- " <th>Wearing_Earrings</th>\n",
- " <th>Wearing_Hat</th>\n",
- " <th>Wearing_Lipstick</th>\n",
- " <th>Wearing_Necklace</th>\n",
- " <th>Wearing_Necktie</th>\n",
- " <th>Young</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>165385</th>\n",
- " <td>165386.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>113624</th>\n",
- " <td>113625.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>110009</th>\n",
- " <td>110010.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
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- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>...</td>\n",
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- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>97337</th>\n",
- " <td>097338.jpg</td>\n",
- " <td>-1</td>\n",
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- " <td>1</td>\n",
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- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4543</th>\n",
- " <td>004544.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
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- " <td>-1</td>\n",
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- " <td>-1</td>\n",
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- " <td>-1</td>\n",
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- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>134983</th>\n",
- " <td>134984.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>153296</th>\n",
- " <td>153297.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>114005</th>\n",
- " <td>114006.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>86357</th>\n",
- " <td>086358.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>153028</th>\n",
- " <td>153029.jpg</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>...</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>10 rows × 41 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " image_id 5_o_Clock_Shadow Arched_Eyebrows Attractive \\\n",
- "165385 165386.jpg -1 -1 1 \n",
- "113624 113625.jpg -1 -1 -1 \n",
- "110009 110010.jpg -1 -1 1 \n",
- "97337 097338.jpg -1 1 1 \n",
- "4543 004544.jpg -1 -1 -1 \n",
- "134983 134984.jpg -1 -1 -1 \n",
- "153296 153297.jpg -1 -1 -1 \n",
- "114005 114006.jpg -1 1 1 \n",
- "86357 086358.jpg -1 1 1 \n",
- "153028 153029.jpg -1 -1 -1 \n",
- "\n",
- " Bags_Under_Eyes Bald Bangs Big_Lips Big_Nose Black_Hair ... \\\n",
- "165385 1 -1 1 -1 -1 -1 ... \n",
- "113624 1 -1 1 -1 -1 -1 ... \n",
- "110009 -1 -1 1 -1 -1 1 ... \n",
- "97337 -1 -1 -1 -1 -1 -1 ... \n",
- "4543 -1 -1 -1 -1 1 -1 ... \n",
- "134983 -1 -1 -1 -1 -1 1 ... \n",
- "153296 1 1 -1 -1 -1 -1 ... \n",
- "114005 -1 -1 -1 -1 -1 -1 ... \n",
- "86357 -1 -1 -1 -1 -1 -1 ... \n",
- "153028 -1 -1 -1 -1 -1 1 ... \n",
- "\n",
- " Sideburns Smiling Straight_Hair Wavy_Hair Wearing_Earrings \\\n",
- "165385 -1 -1 -1 -1 -1 \n",
- "113624 -1 1 -1 1 1 \n",
- "110009 -1 -1 -1 -1 -1 \n",
- "97337 -1 -1 -1 -1 -1 \n",
- "4543 -1 -1 -1 -1 -1 \n",
- "134983 -1 -1 1 -1 -1 \n",
- "153296 -1 1 -1 -1 -1 \n",
- "114005 -1 1 -1 -1 -1 \n",
- "86357 -1 -1 -1 1 -1 \n",
- "153028 -1 -1 -1 -1 -1 \n",
- "\n",
- " Wearing_Hat Wearing_Lipstick Wearing_Necklace Wearing_Necktie \\\n",
- "165385 -1 -1 -1 -1 \n",
- "113624 -1 1 1 -1 \n",
- "110009 -1 1 -1 -1 \n",
- "97337 -1 1 -1 -1 \n",
- "4543 -1 -1 -1 -1 \n",
- "134983 -1 -1 1 -1 \n",
- "153296 -1 -1 -1 -1 \n",
- "114005 1 -1 -1 -1 \n",
- "86357 -1 1 -1 -1 \n",
- "153028 -1 1 -1 -1 \n",
- "\n",
- " Young \n",
- "165385 1 \n",
- "113624 -1 \n",
- "110009 1 \n",
- "97337 1 \n",
- "4543 -1 \n",
- "134983 1 \n",
- "153296 -1 \n",
- "114005 1 \n",
- "86357 1 \n",
- "153028 1 \n",
- "\n",
- "[10 rows x 41 columns]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "dataset_desc = dataset_desc.reindex(np.random.permutation(dataset_desc.index))\n",
- "display(dataset_desc.head(10))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - A nice function :\n",
- "Who will read the .jpg images and build the clusters files, with :\n",
- " - **dataset_img** : The folder where the .jgp images are located.\n",
- " - **dataset_desc** : A Pandas DataFrame with the list of images and associated attributes\n",
- " - **cluster_size** : Cluster size (ie number of images per cluster)\n",
- " - **cluster_dir** : Where to put the cluster files\n",
- " - **cluster_name** : Cluster files name"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "def read_and_save( dataset_img, dataset_desc, \n",
- " cluster_size=1000, cluster_dir='./dataset_cluster', cluster_name='images',\n",
- " image_size=(128,128)):\n",
- " \n",
- " def save_cluster(imgs,desc,cols,id):\n",
- " file_img = f'{cluster_dir}/{cluster_name}-{id:03d}.npy'\n",
- " file_desc = f'{cluster_dir}/{cluster_name}-{id:03d}.csv'\n",
- " np.save(file_img, np.array(imgs))\n",
- " df=pd.DataFrame(data=desc,columns=cols)\n",
- " df.to_csv(file_desc, index=False)\n",
- " return [],[],id+1\n",
- " \n",
- " start_time = time.time()\n",
- " cols = list(dataset_desc.columns)\n",
- "\n",
- " # ---- Check if cluster files exist\n",
- " #\n",
- " if os.path.isfile(f'{cluster_dir}/images-000.npy'):\n",
- " print('\\n*** Oops. There are already clusters in the target folder!\\n')\n",
- " return 0,0\n",
- " \n",
- " # ---- Create cluster_dir\n",
- " #\n",
- " os.makedirs(cluster_dir, mode=0o750, exist_ok=True)\n",
- " \n",
- " # ---- Read and save clusters\n",
- " #\n",
- " imgs, desc, cluster_id = [],[],0\n",
- " #\n",
- " for i,row in dataset_desc.iterrows():\n",
- " #\n",
- " filename = f'{dataset_img}/{row.image_id}'\n",
- " #\n",
- " # ---- Read image, resize (and normalize)\n",
- " #\n",
- " img = io.imread(filename)\n",
- " img = transform.resize(img, image_size)\n",
- " #\n",
- " # ---- Add image and description\n",
- " #\n",
- " imgs.append( img )\n",
- " desc.append( row.values )\n",
- " #\n",
- " # ---- Progress bar\n",
- " #\n",
- " ooo.update_progress(f'Cluster {cluster_id:03d} :',len(imgs),cluster_size)\n",
- " #\n",
- " # ---- Save cluster if full\n",
- " #\n",
- " if len(imgs)==cluster_size:\n",
- " imgs,desc,cluster_id=save_cluster(imgs,desc,cols, cluster_id)\n",
- "\n",
- " # ---- Save uncomplete cluster\n",
- " if len(imgs)>0 : imgs,desc,cluster_id=save_cluster(imgs,desc,cols,cluster_id)\n",
- "\n",
- " duration=time.time()-start_time\n",
- " return cluster_id,duration\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.3 - Cluster building\n",
- "Reading the 200,000 images can take up to 20 minutes. \n",
- "190,000 images will be used for training (x_train), the rest for validation (x_test) \n",
- "The 21 clusters will represent about 21 GB. \n",
- "If the target folder is not empty, the construction is blocked."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Cluster 000 : [########################################] 100.0% of 10000\n",
- "Cluster 001 : [########################################] 100.0% of 10000\n",
- "Cluster 002 : [########################################] 100.0% of 10000\n",
- "Cluster 003 : [########################################] 100.0% of 10000\n",
- "Cluster 004 : [########################################] 100.0% of 10000\n",
- "Cluster 005 : [########################################] 100.0% of 10000\n",
- "Cluster 006 : [########################################] 100.0% of 10000\n",
- "Cluster 007 : [########################################] 100.0% of 10000\n",
- "Cluster 008 : [########################################] 100.0% of 10000\n",
- "Cluster 009 : [########################################] 100.0% of 10000\n",
- "Cluster 010 : [########################################] 100.0% of 10000\n",
- "Cluster 011 : [########################################] 100.0% of 10000\n",
- "Cluster 012 : [########################################] 100.0% of 10000\n",
- "Cluster 013 : [########################################] 100.0% of 10000\n",
- "Cluster 014 : [########################################] 100.0% of 10000\n",
- "Cluster 015 : [########################################] 100.0% of 10000\n",
- "Cluster 016 : [########################################] 100.0% of 10000\n",
- "Cluster 017 : [########################################] 100.0% of 10000\n",
- "Cluster 018 : [########################################] 100.0% of 10000\n",
- "Cluster 019 : [########################################] 100.0% of 10000\n",
- "Cluster 000 : [##########------------------------------] 25.0% of 10000\n",
- "\n",
- "Duration : 1579.87 s or 0:26:19\n",
- "Train clusters : /gpfswork/rech/mlh/uja62cb/datasets/celeba/clusters.train\n",
- "Test clusters : /gpfswork/rech/mlh/uja62cb/datasets/celeba/clusters.test\n"
- ]
- }
- ],
- "source": [
- "# ---- Cluster size\n",
- "\n",
- "cluster_size_train = 10000\n",
- "cluster_size_test = 10000\n",
- "image_size = (128,128)\n",
- "\n",
- "# ---- Clusters location\n",
- "\n",
- "train_dir = f'{dataset_dir}/clusters.train'\n",
- "test_dir = f'{dataset_dir}/clusters.test'\n",
- "\n",
- "# ---- x_train, x_test\n",
- "#\n",
- "n1,d1 = read_and_save(dataset_img, dataset_desc[:200000],\n",
- " cluster_size = cluster_size_train, \n",
- " cluster_dir = train_dir,\n",
- " image_size = image_size )\n",
- "\n",
- "n2,d2 = read_and_save(dataset_img, dataset_desc[200000:],\n",
- " cluster_size = cluster_size_test, \n",
- " cluster_dir = test_dir,\n",
- " image_size = image_size )\n",
- " \n",
- "print(f'\\n\\nDuration : {d1+d2:.2f} s or {ooo.hdelay(d1+d2)}')\n",
- "print(f'Train clusters : {train_dir}')\n",
- "print(f'Test clusters : {test_dir}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----\n",
- "That's all folks !"
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -1311,7 +755,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/VAE/03.1-Batch.nbconvert.ipynb b/VAE/03.1-Batch.nbconvert.ipynb
deleted file mode 100644
index a17e29958866788b01d3d11e79508b3d5edfa9bc..0000000000000000000000000000000000000000
--- a/VAE/03.1-Batch.nbconvert.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Celeb Faces Dataset (CelebA)\n",
- "=================================================\n",
- "---\n",
- "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
- "\n",
- "We'll do the same thing again but with a more interesting dataset: CelebFaces \n",
- "About this dataset : http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
- "\n",
- "## Episode 1 : Preparation of data - Batch mode\n",
- "\n",
- " - Save enhanced datasets in h5 file format\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init\n",
- "### 1.2 - Import"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "FIDLE 2020 - Practical Work Module\n",
- "Version : 0.2.8\n",
- "Run time : Thursday 13 February 2020, 23:50:25\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "from skimage import io, transform\n",
- "\n",
- "import os,time,sys,json,glob\n",
- "import csv\n",
- "import math, random\n",
- "\n",
- "from importlib import reload\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": 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",
- "\n",
- "dataset_csv = f'{dataset_dir}/list_attr_celeba.csv'\n",
- "dataset_img = f'{dataset_dir}/img_align_celeba'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Read filenames catalog"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "dataset_desc = pd.read_csv(dataset_csv, header=0)\n",
- "dataset_desc = dataset_desc.reindex(np.random.permutation(dataset_desc.index))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Save as clusters of n images"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Cooking function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def read_and_save( dataset_img, dataset_desc, \n",
- " cluster_size=1000, cluster_dir='./dataset_cluster', cluster_name='images',\n",
- " image_size=(128,128)):\n",
- " \n",
- " def save_cluster(imgs,desc,cols,id):\n",
- " file_img = f'{cluster_dir}/{cluster_name}-{id:03d}.npy'\n",
- " file_desc = f'{cluster_dir}/{cluster_name}-{id:03d}.csv'\n",
- " np.save(file_img, np.array(imgs))\n",
- " df=pd.DataFrame(data=desc,columns=cols)\n",
- " df.to_csv(file_desc, index=False)\n",
- " return [],[],id+1\n",
- " \n",
- " start_time = time.time()\n",
- " cols = list(dataset_desc.columns)\n",
- "\n",
- " # ---- Check if cluster files exist\n",
- " #\n",
- " if os.path.isfile(f'{cluster_dir}/images-000.npy'):\n",
- " print('\\n*** Oops. There are already clusters in the target folder!\\n')\n",
- " return 0,0\n",
- " \n",
- " # ---- Create cluster_dir\n",
- " #\n",
- " os.makedirs(cluster_dir, mode=0o750, exist_ok=True)\n",
- " \n",
- " # ---- Read and save clusters\n",
- " #\n",
- " imgs, desc, cluster_id = [],[],0\n",
- " #\n",
- " for i,row in dataset_desc.iterrows():\n",
- " #\n",
- " filename = f'{dataset_img}/{row.image_id}'\n",
- " #\n",
- " # ---- Read image, resize (and normalize)\n",
- " #\n",
- " img = io.imread(filename)\n",
- " img = transform.resize(img, image_size)\n",
- " #\n",
- " # ---- Add image and description\n",
- " #\n",
- " imgs.append( img )\n",
- " desc.append( row.values )\n",
- " #\n",
- " # ---- Progress bar\n",
- " #\n",
- " ooo.update_progress(f'Cluster {cluster_id:03d} :',len(imgs),cluster_size)\n",
- " #\n",
- " # ---- Save cluster if full\n",
- " #\n",
- " if len(imgs)==cluster_size:\n",
- " imgs,desc,cluster_id=save_cluster(imgs,desc,cols, cluster_id)\n",
- "\n",
- " # ---- Save uncomplete cluster\n",
- " if len(imgs)>0 : imgs,desc,cluster_id=save_cluster(imgs,desc,cols,cluster_id)\n",
- "\n",
- " duration=time.time()-start_time\n",
- " return cluster_id,duration\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.3 - Cluster building"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "*** Oops. There are already clusters in the target folder!\n",
- "\n",
- "\n",
- "*** Oops. There are already clusters in the target folder!\n",
- "\n",
- "\n",
- "\n",
- "Duration : 0.00 s or 0:00:00\n",
- "Train clusters : /gpfswork/rech/mlh/uja62cb/datasets/celeba/clusters-M.train\n",
- "Test clusters : /gpfswork/rech/mlh/uja62cb/datasets/celeba/clusters-M.test\n"
- ]
- }
- ],
- "source": [
- "# ---- Cluster size\n",
- "\n",
- "cluster_size_train = 10000\n",
- "cluster_size_test = 10000\n",
- "image_size = (192,160)\n",
- "\n",
- "# ---- Clusters location\n",
- "\n",
- "train_dir = f'{dataset_dir}/clusters-M.train'\n",
- "test_dir = f'{dataset_dir}/clusters-M.test'\n",
- "\n",
- "# ---- x_train, x_test\n",
- "#\n",
- "n1,d1 = read_and_save(dataset_img, dataset_desc[:200000],\n",
- " cluster_size = cluster_size_train, \n",
- " cluster_dir = train_dir,\n",
- " image_size = image_size )\n",
- "\n",
- "n2,d2 = read_and_save(dataset_img, dataset_desc[200000:],\n",
- " cluster_size = cluster_size_test, \n",
- " cluster_dir = test_dir,\n",
- " image_size = image_size )\n",
- " \n",
- "print(f'\\n\\nDuration : {d1+d2:.2f} s or {ooo.hdelay(d1+d2)}')\n",
- "print(f'Train clusters : {train_dir}')\n",
- "print(f'Test clusters : {test_dir}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "----\n",
- "That's all folks !"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/VAE/04-Check-CelebA.ipynb b/VAE/04-Check-CelebA.ipynb
deleted file mode 100644
index 19772f4e52d72648c10348295cd8a0b4513705f9..0000000000000000000000000000000000000000
--- a/VAE/04-Check-CelebA.ipynb
+++ /dev/null
@@ -1,343 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Celeb Faces Dataset (CelebA)\n",
- "=================================================\n",
- "---\n",
- "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
- "\n",
- "## Episode 2 : Check clustered dataset\n",
- "\n",
- " - Reload our dataset\n",
- " - Check and verify\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init\n",
- "### 1.2 - Import"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "FIDLE 2020 - Practical Work Module\n",
- "Version : 0.2.8\n",
- "Run time : Tuesday 11 February 2020, 15:49:00\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n"
- ]
- }
- ],
- "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": 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",
- "\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": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Load clusters : [####################] 100.0% of 20\n",
- "Loading time : 33.41 s or 0:00:33\n",
- "Number of cluster : 20\n",
- "Number of images : 200000\n",
- "Number of desc. : 200000\n",
- "Total size of img : 73.2 Go\n"
- ]
- }
- ],
- "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": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x972 with 20 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "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": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "FIDLE 2020 - DataGenerator\n",
- "Version : 0.4\n",
- "\n",
- "Clusters nb : 0 files\n",
- "Dataset size : 0\n",
- "Batch size : 32\n",
- "\n",
- "[shuffle!]\n"
- ]
- },
- {
- "ename": "IndexError",
- "evalue": "list index out of range",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-5-91fcbf642617>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# with small batch size, debug mode and 50% of the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdata_gen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclusters_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# ---- We ask him to retrieve all batchs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfsdswork/projects/rech/mlh/uja62cb/fidle/VAE/modules/data_generator.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, clusters_dir, batch_size, debug, k_size)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclusters_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_next_cluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfsdswork/projects/rech/mlh/uja62cb/fidle/VAE/modules/data_generator.py\u001b[0m in \u001b[0;36mread_next_cluster\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;31m# ---- Read it (images still normalized)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclusters_name\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.npy'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;31m# ---- Remember all of that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mIndexError\u001b[0m: list index out of range"
- ]
- }
- ],
- "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",
- "That's all folks !"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/VAE/03.1-Batch.ipynb b/VAE/04-Prepare-CelebA-batch.ipynb
similarity index 80%
rename from VAE/03.1-Batch.ipynb
rename to VAE/04-Prepare-CelebA-batch.ipynb
index ce16a361f062f8f4cddaac801edb2a26323616cd..5ecbb8c6b9abfd87bf9565f32ab69b657830e369 100644
--- a/VAE/03.1-Batch.ipynb
+++ b/VAE/04-Prepare-CelebA-batch.ipynb
@@ -4,17 +4,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Celeb Faces Dataset (CelebA)\n",
- "=================================================\n",
- "---\n",
- "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [VAE4] - Preparation of the CelebA dataset\n",
+ "<!-- DESC --> Preparation of a clustered dataset, batchable\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Formatting our dataset in cluster files, using batch mode\n",
+ " - Adapting a notebook for batch use\n",
"\n",
- "We'll do the same thing again but with a more interesting dataset: CelebFaces \n",
- "About this dataset : http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\n",
"\n",
- "## Episode 1 : Preparation of data - Batch mode\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",
- " - Save enhanced datasets in h5 file format\n"
+ "\n",
+ "## What we're going to do :\n",
+ " - Lire les images\n",
+ " - redimensionner et normaliser celles-ci,\n",
+ " - Constituer des clusters d'images en format npy\n"
]
},
{
@@ -72,7 +79,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 2 - Read filenames catalog"
+ "## Step 2 - Read and shuffle filenames catalog"
]
},
{
@@ -168,7 +175,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 4.3 - Cluster building"
+ "### 4.3 - Cluster building\n",
+ "Reading the 200,000 images can take a long time (>20 minutes)\n",
+ "200,000 images will be used for training (x_train), the rest for validation (x_test) \n",
+ "If the target folder is not empty, the construction is blocked. \n",
+ "\n",
+ "Image Sizes: 128x128 : 74 GB \n",
+ "Image Sizes: 192x160 : 138 GB"
]
},
{
@@ -181,12 +194,12 @@
"\n",
"cluster_size_train = 10000\n",
"cluster_size_test = 10000\n",
- "image_size = (192,160)\n",
+ "image_size = (128,128)\n",
"\n",
"# ---- Clusters location\n",
"\n",
- "train_dir = f'{dataset_dir}/clusters-M.train'\n",
- "test_dir = f'{dataset_dir}/clusters-M.test'\n",
+ "train_dir = f'{dataset_dir}/clusters.train'\n",
+ "test_dir = f'{dataset_dir}/clusters.test'\n",
"\n",
"# ---- x_train, x_test\n",
"#\n",
@@ -209,16 +222,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "----\n",
- "That's all folks !"
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -237,7 +243,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/VAE/05-Check-CelebA.ipynb b/VAE/05-Check-CelebA.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7ff3faf1370615e440452a494af2569f3e1b090f
--- /dev/null
+++ b/VAE/05-Check-CelebA.ipynb
@@ -0,0 +1,242 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "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",
+ "\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>"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/VAE/05-VAE-with-CelebA.ipynb b/VAE/05-VAE-with-CelebA.ipynb
deleted file mode 100644
index c88cea44b4681a87cc366cd61ea87040a5dee081..0000000000000000000000000000000000000000
--- a/VAE/05-VAE-with-CelebA.ipynb
+++ /dev/null
@@ -1,414 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "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": 8,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "FIDLE 2020 - Practical Work Module\n",
- "Version : 0.2.8\n",
- "Run time : Thursday 13 February 2020, 21:38:51\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 : 1.27\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": 9,
- "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",
- "\n",
- "# ---- train/test datasets\n",
- "\n",
- "train_dir = f'{dataset_dir}/clusters.train'\n",
- "test_dir = f'{dataset_dir}/clusters.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": 10,
- "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": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model initialized.\n",
- "Outputs will be in : ./run/CelebA.test\n",
- "\n",
- " ---------- Encoder -------------------------------------------------- \n",
- "\n",
- "Model: \"model_1\"\n",
- "__________________________________________________________________________________________________\n",
- "Layer (type) Output Shape Param # Connected to \n",
- "==================================================================================================\n",
- "encoder_input (InputLayer) [(None, 128, 128, 3) 0 \n",
- "__________________________________________________________________________________________________\n",
- "conv2d (Conv2D) (None, 64, 64, 32) 896 encoder_input[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout (Dropout) (None, 64, 64, 32) 0 conv2d[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "conv2d_1 (Conv2D) (None, 32, 32, 64) 18496 dropout[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 32, 32, 64) 0 conv2d_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "conv2d_2 (Conv2D) (None, 16, 16, 64) 36928 dropout_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 16, 16, 64) 0 conv2d_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "conv2d_3 (Conv2D) (None, 8, 8, 64) 36928 dropout_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout_3 (Dropout) (None, 8, 8, 64) 0 conv2d_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "flatten (Flatten) (None, 4096) 0 dropout_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "mu (Dense) (None, 200) 819400 flatten[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "log_var (Dense) (None, 200) 819400 flatten[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "encoder_output (Lambda) (None, 200) 0 mu[0][0] \n",
- " log_var[0][0] \n",
- "==================================================================================================\n",
- "Total params: 1,732,048\n",
- "Trainable params: 1,732,048\n",
- "Non-trainable params: 0\n",
- "__________________________________________________________________________________________________\n",
- "\n",
- " ---------- Encoder -------------------------------------------------- \n",
- "\n",
- "Model: \"model_2\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "decoder_input (InputLayer) [(None, 200)] 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 4096) 823296 \n",
- "_________________________________________________________________\n",
- "reshape (Reshape) (None, 8, 8, 64) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_transpose (Conv2DTran (None, 16, 16, 64) 36928 \n",
- "_________________________________________________________________\n",
- "dropout_4 (Dropout) (None, 16, 16, 64) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_transpose_1 (Conv2DTr (None, 32, 32, 64) 36928 \n",
- "_________________________________________________________________\n",
- "dropout_5 (Dropout) (None, 32, 32, 64) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_transpose_2 (Conv2DTr (None, 64, 64, 32) 18464 \n",
- "_________________________________________________________________\n",
- "dropout_6 (Dropout) (None, 64, 64, 32) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_transpose_3 (Conv2DTr (None, 128, 128, 3) 867 \n",
- "=================================================================\n",
- "Total params: 916,483\n",
- "Trainable params: 916,483\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n",
- "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n",
- "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n",
- "Config saved in : ./run/CelebA.test/models/vae_config.json\n"
- ]
- }
- ],
- "source": [
- "tag = 'CelebA.test'\n",
- "\n",
- "input_shape = (128, 128, 3)\n",
- "z_dim = 200\n",
- "verbose = 1\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": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Compiled.\n"
- ]
- }
- ],
- "source": [
- "optimizer = tf.keras.optimizers.Adam(1e-4)\n",
- "# optimizer = 'adam'\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"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "epochs = 10\n",
- "initial_epoch = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "6250/6250 [==============================] - 175s 28ms/step - loss: 349.6490 - vae_r_loss: 301.2042 - vae_kl_loss: 48.4450 - val_loss: 236.8924 - val_vae_r_loss: 189.8669 - val_vae_kl_loss: 47.1441\n",
- "Epoch 2/10\n",
- "6250/6250 [==============================] - 128s 20ms/step - loss: 241.0380 - vae_r_loss: 187.2187 - vae_kl_loss: 53.8191 - val_loss: 215.4507 - val_vae_r_loss: 162.7656 - val_vae_kl_loss: 52.8317\n",
- "Epoch 3/10\n",
- "3141/6250 [==============>...............] - ETA: 1:02 - loss: 230.1868 - vae_r_loss: 175.1927 - vae_kl_loss: 54.9941"
- ]
- }
- ],
- "source": [
- "vae.train(data_generator = data_gen,\n",
- " x_test = x_test,\n",
- " epochs = epochs,\n",
- " initial_epoch = initial_epoch\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/VAE/05.1-Variant.ipynb b/VAE/06-VAE-with-CelebA-s.ipynb
similarity index 86%
rename from VAE/05.1-Variant.ipynb
rename to VAE/06-VAE-with-CelebA-s.ipynb
index 8d299a19e629736e9d1b4beaccb94c6dac81e72f..d0b01824f6fbf2cab88108b4bcddf6cb58d5ba70 100644
--- a/VAE/05.1-Variant.ipynb
+++ b/VAE/06-VAE-with-CelebA-s.ipynb
@@ -4,12 +4,19 @@
"cell_type": "markdown",
"metadata": {},
"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",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [VAE6] - Variational AutoEncoder (VAE) with CelebA (small)\n",
+ "<!-- DESC --> VAE with a more fun and realistic dataset - small resolution and batchable\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Build and train a VAE model with a large dataset in small resolution(>70 GB)\n",
+ " - Understanding a more advanced programming model with data generator\n",
"\n",
- "## Episode 1 - Train a model\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",
+ "## What we're going to do :\n",
"\n",
" - Defining a VAE model\n",
" - Build the model\n",
@@ -111,11 +118,11 @@
"metadata": {},
"outputs": [],
"source": [
- "tag = 'CelebA.004'\n",
+ "tag = f'CelebA.006-S.{os.getenv(\"SLURM_JOB_ID\",\"unknown\")}'\n",
"\n",
"input_shape = (128, 128, 3)\n",
"z_dim = 200\n",
- "verbose = 0\n",
+ "verbose = 1\n",
"\n",
"encoder= [ {'type':'Conv2D', 'filters':32, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Dropout', 'rate':0.25},\n",
@@ -199,18 +206,12 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": []
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
}
],
"metadata": {
@@ -229,7 +230,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/VAE/05.2-Variant.ipynb b/VAE/07-VAE-with-CelebA-m.ipynb
similarity index 86%
rename from VAE/05.2-Variant.ipynb
rename to VAE/07-VAE-with-CelebA-m.ipynb
index c8605150144ded7c8d3642142df4d8d6d3c2dde4..6d078afdb0189eef45a61d49bfde44ab816f7226 100644
--- a/VAE/05.2-Variant.ipynb
+++ b/VAE/07-VAE-with-CelebA-m.ipynb
@@ -4,17 +4,24 @@
"cell_type": "markdown",
"metadata": {},
"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",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)\n",
+ "<!-- DESC --> VAE with a more fun and realistic dataset - medium resolution and batchable\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Build and train a VAE model with a large dataset in small resolution(>140 GB)\n",
+ " - Understanding a more advanced programming model with data generator\n",
"\n",
- "## Episode 1 - Train a model\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",
+ "## What we're going to do :\n",
"\n",
" - Defining a VAE model\n",
" - Build the model\n",
" - Train it\n",
- " - Follow the learning process with Tensorboard\n"
+ " - Follow the learning process with Tensorboard"
]
},
{
@@ -111,7 +118,7 @@
"metadata": {},
"outputs": [],
"source": [
- "tag = f'CelebA.052-M.{os.getenv(\"SLURM_JOB_ID\",\"unknown\")}'\n",
+ "tag = f'CelebA.007-M.{os.getenv(\"SLURM_JOB_ID\",\"unknown\")}'\n",
"\n",
"input_shape = (192, 160, 3)\n",
"z_dim = 200\n",
@@ -201,8 +208,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "----\n",
- "That's all folks !"
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
}
],
@@ -222,7 +229,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/VAE/05.2-Variant.nbconvert.ipynb b/VAE/08-VAE-with-CelebA-m.nbconvert.ipynb
similarity index 100%
rename from VAE/05.2-Variant.nbconvert.ipynb
rename to VAE/08-VAE-with-CelebA-m.nbconvert.ipynb
diff --git a/VAE/06-VAE-withCelebA-post.ipynb b/VAE/12-VAE-withCelebA-post.ipynb
similarity index 99%
rename from VAE/06-VAE-withCelebA-post.ipynb
rename to VAE/12-VAE-withCelebA-post.ipynb
index 6dcc958133a6f74d37f4917d4ed949eeebeab299..2b66c85fdee2d4e14a18e4fe83a06ed35be37714 100644
--- a/VAE/06-VAE-withCelebA-post.ipynb
+++ b/VAE/12-VAE-withCelebA-post.ipynb
@@ -4,16 +4,28 @@
"cell_type": "markdown",
"metadata": {},
"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",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis\n",
+ "<!-- DESC --> Use of the previously trained model with CelebA, analysis of the results\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - New data generation from latent space\n",
+ " - Understanding of underlying principles\n",
+ " - Guided image generation (latent morphing)\n",
+ " - Model management\n",
+ " \n",
+ "Here again, we don't consume data anymore, but we generate them ! ;-)\n",
"\n",
- "## Episode 2 - Analyse our trained 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",
+ "...But our data is now in the imagination of our network!\n",
+ "\n",
+ "## What we're going to do :\n",
" - Load a saved model\n",
- " - Reconstruct some images\n",
- " - Latent space visualization\n",
- " - Matrix of generated images\n"
+ " - Reconstruct some images from latent space\n",
+ " - Matrix of generated images"
]
},
{
@@ -702,8 +714,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "----\n",
- "That's all folks !"
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
}
],
diff --git a/VAE/batch-oar.sh b/VAE/batch-oar.sh
index 06dec0a0025cff85f6c840aeced1e247da826439..3d95d27a2e8f85c2330ecad8f56b6468c097a8d2 100755
--- a/VAE/batch-oar.sh
+++ b/VAE/batch-oar.sh
@@ -17,23 +17,26 @@
# | '_ \ / _` | __/ __| '_ \
# | |_) | (_| | || (__| | | |
# |_.__/ \__,_|\__\___|_| |_|
-# VAE CelebA at GRICAD
+# Fidle at GRICAD
# -----------------------------------------------
#
+# <!-- TITLE --> [BASH1] - OAR batch script
+# <!-- DESC --> Bash script for OAR batch submission of a notebook
+# <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
-CONDA_ENV=deeplearning2
+CONDA_ENV=fidle
RUN_DIR=~/fidle/VAE
-RUN_IPYNB=05.1-Batch-01.ipynb
+RUN_IPYNB='06-VAE-with-CelebA-s.ipynb'
# ---- Cuda Conda initialization
-#
+
echo '------------------------------------------------------------'
echo "Start : $0"
echo '------------------------------------------------------------'
-#
+
source /applis/environments/cuda_env.sh dahu 10.0
source /applis/environments/conda.sh
-#
+
conda activate "$CONDA_ENV"
# ---- Run it...
diff --git a/VAE/batch-slurm.sh b/VAE/batch-slurm.sh
index aae9db2fb7c86e02e870dec19791dc6942991782..6612e29a9d8ffbed1a975573f68d7f7df3bd6d7a 100755
--- a/VAE/batch-slurm.sh
+++ b/VAE/batch-slurm.sh
@@ -17,13 +17,16 @@
# | '_ \ / _` | __/ __| '_ \
# | |_) | (_| | || (__| | | |
# |_.__/ \__,_|\__\___|_| |_|
-# VAE CelebA at IDRIS
+# Fidle at IDRIS
# -----------------------------------------------
#
+# <!-- TITLE --> [BASH2] - SLURM batch script
+# <!-- DESC --> Bash script for SLURM batch submission of a notebook
+# <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
MODULE_ENV="tensorflow-gpu/py3/2.0.0"
RUN_DIR="$WORK/fidle/VAE"
-RUN_IPYNB="05.2-Variant.ipynb"
+RUN_IPYNB="06-VAE-with-CelebA-s.ipynb"
# ---- Welcome...
diff --git a/fidle/img/00-Fidle-header-01.png b/fidle/img/00-Fidle-header-01.png
deleted file mode 100755
index bfba6ea8d904880b5890e62f9ac3346f274973b4..0000000000000000000000000000000000000000
Binary files a/fidle/img/00-Fidle-header-01.png and /dev/null differ
diff --git a/fidle/img/00-Fidle-header-01.svg b/fidle/img/00-Fidle-header-01.svg
index 01d8b3f9ae4a73b6044947da078e201639e348f4..29519477f235b9a067dfff4b2de50dcbc9efcf1f 100755
--- a/fidle/img/00-Fidle-header-01.svg
+++ b/fidle/img/00-Fidle-header-01.svg
@@ -1 +1 @@
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