From 622e54df5e5feab55a2806b7b4e0d946762251ad Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@simap.grenoble-inp.fr>
Date: Wed, 6 Oct 2021 17:31:24 +0200
Subject: [PATCH] Works on DCGAN...
---
.gitignore | 1 +
DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb | 263 ++++++++++++++++++++++
DCGAN/modules/callbacks/ImagesCallback.py | 46 ++++
DCGAN/modules/callbacks/__init__.py | 1 +
DCGAN/modules/models/DCGAN.py | 247 ++++++++++++++++++++
DCGAN/modules/models/__init__.py | 1 +
fidle/config.py | 2 +-
7 files changed, 560 insertions(+), 1 deletion(-)
create mode 100644 DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
create mode 100644 DCGAN/modules/callbacks/ImagesCallback.py
create mode 100644 DCGAN/modules/callbacks/__init__.py
create mode 100644 DCGAN/modules/models/DCGAN.py
create mode 100644 DCGAN/modules/models/__init__.py
diff --git a/.gitignore b/.gitignore
index 504a663..4daa354 100755
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
.ipynb_checkpoints
+.vscode
*/.ipynb_checkpoints/*
__pycache__
*/__pycache__/*
diff --git a/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb b/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
new file mode 100644
index 0000000..efb7ed4
--- /dev/null
+++ b/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [DCGAN01] - A first DCGAN to Draw a Sheep\n",
+ "<!-- DESC --> Episode 1 : Draw me a sheep, revisited with a DCGAN\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Build and train a DCGAN model with the Quick Draw dataset\n",
+ " - Understanding DCGAN\n",
+ "\n",
+ "The [Quick draw dataset](https://quickdraw.withgoogle.com/data) contains about 50.000.000 drawings, made by real people... \n",
+ "We are using a subset of 117.555 of Sheep drawings \n",
+ "To get the dataset : [https://github.com/googlecreativelab/quickdraw-dataset](https://github.com/googlecreativelab/quickdraw-dataset) \n",
+ "To get the subdataset in numpy bitmap file : [https://console.cloud.google.com/storage/quickdraw_dataset/full/numpy_bitmap](https://console.cloud.google.com/storage/quickdraw_dataset/full/numpy_bitmap) (94.3 Mo)\n",
+ "\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ " - Have a look to the dataset\n",
+ " - Defining a VAE model\n",
+ " - Build the model\n",
+ " - Train it\n",
+ " - Follow the learning process with Tensorboard\n",
+ "\n",
+ "## Acknowledgements :\n",
+ "Thanks to **François Chollet** who is at the base of this example. \n",
+ "See : [https://keras.io/examples/](https://keras.io/examples/)\n"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 1 - Init python stuff"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.stats\n",
+ "import sys\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras import layers\n",
+ "from tensorflow.keras.callbacks import TensorBoard\n",
+ "\n",
+ "from modules.models import DCGAN\n",
+ "from modules.callbacks import ImagesCallback\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "run_dir = './run/DCGAN.001' # Output directory\n",
+ "datasets_dir = pwk.init('DCGAN01', run_dir)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 2 - Parameters"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "latent_dim = 128"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 3 - Load dataset and have a look \n",
+ "Load sheeps as numpy bitmaps..."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "x_data = np.load(datasets_dir+'/QuickDraw/origine/full_numpy_bitmap_sheep.npy')\n",
+ "x_data = x_data/255\n",
+ "x_data=x_data.reshape(-1,28,28,1)\n",
+ "x_data.shape"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "...and have a look : \n",
+ "Note : These sheep are sheep drawn ... by real humans!"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "pwk.plot_images(x_data.reshape(-1,28,28), indices=range(72), columns=12, x_size=1, y_size=1, y_padding=0,spines_alpha=0, save_as='01-Sheeps')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 4 - Create a discriminator"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "discriminator = keras.Sequential(\n",
+ " [\n",
+ " keras.Input(shape=(28, 28, 1)),\n",
+ " layers.Conv2D(64, kernel_size=4, strides=2, padding=\"same\"),\n",
+ " layers.LeakyReLU(alpha=0.2),\n",
+ " layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
+ " layers.LeakyReLU(alpha=0.2),\n",
+ " layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
+ " layers.LeakyReLU(alpha=0.2),\n",
+ " layers.Flatten(),\n",
+ " layers.Dropout(0.2),\n",
+ " layers.Dense(1, activation=\"sigmoid\"),\n",
+ " ],\n",
+ " name=\"discriminator\",\n",
+ ")\n",
+ "discriminator.summary()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 5 - Create a generator"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "generator = keras.Sequential(\n",
+ " [\n",
+ " keras.Input(shape=(latent_dim,)),\n",
+ " layers.Dense(7 * 7 * 64),\n",
+ " layers.Reshape((7, 7, 64)),\n",
+ " layers.Conv2DTranspose(128, kernel_size=3, strides=2, padding=\"same\"),\n",
+ " layers.LeakyReLU(alpha=0.2),\n",
+ " layers.Conv2DTranspose(256, kernel_size=3, strides=2, padding=\"same\"),\n",
+ " layers.LeakyReLU(alpha=0.2),\n",
+ " layers.Conv2D(1, kernel_size=5, padding=\"same\", activation=\"sigmoid\"),\n",
+ " ],\n",
+ " name=\"generator\",\n",
+ ")\n",
+ "generator.summary()\n"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 6 - Create our DCGAN !"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "gan = DCGAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "gan.compile(\n",
+ " discriminator_optimizer = keras.optimizers.Adam(learning_rate=0.0001),\n",
+ " generator_optimizer = keras.optimizers.Adam(learning_rate=0.0001),\n",
+ " loss_function = keras.losses.BinaryCrossentropy(),\n",
+ ")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "gan.fit( x_data, epochs=1, batch_size=32, callbacks=[ImagesCallback(num_img=10, latent_dim=latent_dim)]\n",
+ ")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "a=np.array([[1,2,3], [1,2,3],])\n",
+ "a.shape"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [],
+ "outputs": [],
+ "metadata": {}
+ }
+ ],
+ "metadata": {
+ "orig_nbformat": 4,
+ "language_info": {
+ "name": "python",
+ "version": "3.8.10",
+ "mimetype": "text/x-python",
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "pygments_lexer": "ipython3",
+ "nbconvert_exporter": "python",
+ "file_extension": ".py"
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.8.10 64-bit ('fidle': conda)"
+ },
+ "interpreter": {
+ "hash": "0d4eaa39e9afca7e4a329c71e6d11fd895be2163d24367e6af7052bf3ebf4932"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/DCGAN/modules/callbacks/ImagesCallback.py b/DCGAN/modules/callbacks/ImagesCallback.py
new file mode 100644
index 0000000..d418a0e
--- /dev/null
+++ b/DCGAN/modules/callbacks/ImagesCallback.py
@@ -0,0 +1,46 @@
+
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+class ImagesCallback(keras.callbacks.Callback):
+
+ def __init__(self,
+ num_img = 3,
+ latent_dim = 100,
+ filename = 'image-{epoch:03d}-{i:02d}.jpg',
+ run_dir = './run'):
+ self.num_img = num_img
+ self.latent_dim = latent_dim
+
+
+
+ def save_images(self, images, epoch):
+ '''Save images as <filename>'''
+
+ for i,image in enumerate(images):
+
+ image = image.squeeze() # Squeeze it if monochrome : (lx,ly,1) -> (lx,ly)
+
+ filenamei = self.filename.format(epoch=epoch,i=i)
+
+ if len(image.shape) == 2:
+ plt.imsave(filenamei, image, cmap='gray_r')
+ else:
+ plt.imsave(filenamei, image)
+
+
+
+ def on_epoch_end(self, epoch, logs=None):
+
+ # ---- Get some points from latent space
+ random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))
+
+ # ---- Get fake images from generator
+ generated_images = self.model.generator(random_latent_vectors)
+
+ # ---- Save them
+ self.save_images(generated_images, epoch)
diff --git a/DCGAN/modules/callbacks/__init__.py b/DCGAN/modules/callbacks/__init__.py
new file mode 100644
index 0000000..fa10321
--- /dev/null
+++ b/DCGAN/modules/callbacks/__init__.py
@@ -0,0 +1 @@
+from modules.callbacks.ImagesCallback import ImagesCallback
\ No newline at end of file
diff --git a/DCGAN/modules/models/DCGAN.py b/DCGAN/modules/models/DCGAN.py
new file mode 100644
index 0000000..027e05d
--- /dev/null
+++ b/DCGAN/modules/models/DCGAN.py
@@ -0,0 +1,247 @@
+# ------------------------------------------------------------------
+# _____ _ _ _
+# | ___(_) __| | | ___
+# | |_ | |/ _` | |/ _ \
+# | _| | | (_| | | __/
+# |_| |_|\__,_|_|\___| DCGAN Example
+# ------------------------------------------------------------------
+# Formation Introduction au Deep Learning (FIDLE)
+# CNRS/SARI/DEVLOG 2020 - S. Arias, E. Maldonado, JL. Parouty
+# ------------------------------------------------------------------
+# by JL Parouty (dec 2020), based on François Chollet example
+#
+# Thanks to François Chollet example : https://keras.io/examples
+
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from IPython.display import display,Markdown
+import os
+
+class DCGAN(keras.Model):
+ '''
+ A DCGAN model, built from given generator and discriminator
+ '''
+
+ version = '1.0'
+
+ def __init__(self, discriminator=None, generator=None, latent_dim=100, **kwargs):
+ '''
+ DCGAN instantiation with a given discriminator and generator
+ args :
+ discriminator : discriminator model
+ generator : generator model
+ latent_dim : latent space dimension
+ return:
+ None
+ '''
+ super(DCGAN, self).__init__(**kwargs)
+ self.discriminator = discriminator
+ self.generator = generator
+ self.latent_dim = latent_dim
+ print(f'Fidle DCGAN is ready :-) latent dim = {latent_dim}')
+
+
+ # def call(self, inputs):
+ # '''
+ # When we use our model
+ # args:
+ # inputs : Model inputs
+ # return:
+ # output : Output of the model
+ # '''
+ # z_mean, z_log_var, z = self.encoder(inputs)
+ # output = self.decoder(z)
+ # return output
+
+
+ def compile(self,
+ discriminator_optimizer = keras.optimizers.Adam(),
+ generator_optimizer = keras.optimizers.Adam(),
+ loss_function = keras.losses.BinaryCrossentropy() ):
+ super(DCGAN, self).compile()
+ self.d_optimizer = discriminator_optimizer
+ self.g_optimizer = generator_optimizer
+ self.loss_fn = loss_function
+ self.d_loss_metric = keras.metrics.Mean(name="d_loss")
+ self.g_loss_metric = keras.metrics.Mean(name="g_loss")
+
+
+ @property
+ def metrics(self):
+ return [self.d_loss_metric, self.g_loss_metric]
+
+
+
+ def train_step(self, input):
+ '''
+ Implementation of the training update.
+ Receive some real images.
+ This will compute loss, get gradients and update weights for generator and discriminator
+ Return metrics.
+ args:
+ real_images : real images
+ return:
+ d_loss : discriminator loss
+ g_loss : generator loss
+ '''
+
+ # ---- Prepare data for discriminator ----------------------
+ # ----------------------------------------------------------
+ #
+ # ---- Get the input we need, specified in the .fit()
+ #
+ if isinstance(input, tuple):
+ real_images = input[0]
+
+ batch_size=tf.shape(real_images)[0]
+
+ print('batch size = ', batch_size)
+ print('real images',real_images)
+
+ # Get some random points in the latent space
+ # batch_size = tf.shape(real_images)[0]
+ # random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
+ random_latent_vectors = np.random.uniform( size=(32, self.latent_dim) )
+
+
+ # Generate fake images with the generator
+ generated_images = self.generator(random_latent_vectors)
+
+ print('generated images shape =',generated_images.shape)
+
+ # Combine them with real images
+ combined_images = tf.concat([generated_images, real_images], axis=0)
+
+ # Creation of labels corresponding to real or fake images
+ labels = tf.concat(
+ [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0
+ )
+ # Add random noise to the labels - important trick!
+ labels += 0.05 * tf.random.uniform(tf.shape(labels))
+
+ # ---- Train the discriminator -----------------------------
+ # ----------------------------------------------------------
+ #
+ # ---- Forward pass
+ # Run the forward pass and record operations with the GradientTape.
+ #
+ with tf.GradientTape() as tape:
+
+ # Get predictions from discriminator
+ predictions = self.discriminator(combined_images)
+
+ # Get loss
+ d_loss = self.loss_fn(labels, predictions)
+
+ # ---- Backward pass
+ # Retrieve gradients from gradient_tape and run one step
+ # of gradient descent to optimize trainable weights
+ #
+ grads = tape.gradient(d_loss, self.discriminator.trainable_weights)
+ self.d_optimizer.apply_gradients( zip(grads, self.discriminator.trainable_weights) )
+
+ # ---- Prepare data for generator ----------------------
+ # ----------------------------------------------------------
+ #
+ # Sample random points in the latent space
+ random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
+
+ # Assemble labels that say "all real images"
+ misleading_labels = tf.zeros((batch_size, 1))
+
+ # ---- Train the generator ---------------------------------
+ # ----------------------------------------------------------
+ # We should *not* update the weights of the discriminator!
+ #
+ # ---- Forward pass
+ # Run the forward pass and record operations with the GradientTape.
+ #
+ with tf.GradientTape() as tape:
+
+ # Get fake images from generator
+ fake_images = self.generator(random_latent_vectors)
+
+ # Get predictions from discriminator
+ predictions = self.discriminator(fake_images)
+
+ # Get loss
+ g_loss = self.loss_fn(misleading_labels, predictions)
+
+ # ---- Backward pass (only for generator)
+ # Retrieve gradients from gradient_tape and run one step
+ # of gradient descent to optimize trainable weights
+ #
+ grads = tape.gradient(g_loss, self.generator.trainable_weights)
+ self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))
+
+ # ---- Update and return metrics ---------------------------
+ # ----------------------------------------------------------
+ #
+ self.d_loss_metric.update_state(d_loss)
+ self.g_loss_metric.update_state(g_loss)
+
+ return {
+ "d_loss": self.d_loss_metric.result(),
+ "g_loss": self.g_loss_metric.result(),
+ }
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ def predict(self,inputs):
+ '''Our predict function...'''
+ z_mean, z_var, z = self.encoder.predict(inputs)
+ outputs = self.decoder.predict(z)
+ return outputs
+
+
+ def save(self,filename):
+ '''Save model in 2 part'''
+ filename, extension = os.path.splitext(filename)
+ self.encoder.save(f'{filename}-encoder.h5')
+ self.decoder.save(f'{filename}-decoder.h5')
+
+
+ def reload(self,filename):
+ '''Reload a 2 part saved model.'''
+ filename, extension = os.path.splitext(filename)
+ self.encoder = keras.models.load_model(f'{filename}-encoder.h5', custom_objects={'SamplingLayer': SamplingLayer})
+ self.decoder = keras.models.load_model(f'{filename}-decoder.h5')
+ print('Reloaded.')
+
+
+ @classmethod
+ def about(cls):
+ '''Basic whoami method'''
+ display(Markdown('<br>**FIDLE 2021 - VAE**'))
+ print('Version :', cls.version)
+ print('TensorFlow version :', tf.__version__)
+ print('Keras version :', tf.keras.__version__)
diff --git a/DCGAN/modules/models/__init__.py b/DCGAN/modules/models/__init__.py
new file mode 100644
index 0000000..e679ea6
--- /dev/null
+++ b/DCGAN/modules/models/__init__.py
@@ -0,0 +1 @@
+from modules.models.DCGAN import DCGAN
\ No newline at end of file
diff --git a/fidle/config.py b/fidle/config.py
index 9bc2e51..8b11294 100644
--- a/fidle/config.py
+++ b/fidle/config.py
@@ -14,7 +14,7 @@
# ---- Version -----------------------------------------------------
#
-VERSION = '2.0.23'
+VERSION = '2.0.24'
# ---- Default notebook name ---------------------------------------
#
--
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