import os
os.environ['KERAS_BACKEND'] = 'torch'

import keras

import numpy as np
from skimage import io
import random

from keras import layers
from keras.callbacks import ModelCheckpoint, TensorBoard

import os
from importlib import reload

from modules.MNIST          import MNIST
from modules.ImagesCallback import ImagesCallback

import fidle

# Init Fidle environment
run_id, run_dir, datasets_dir = fidle.init('K3AE2')

Version              : 2.3.2
Run id               : K3AE2
Run dir              : ./run/K3AE2
Datasets dir         : /lustre/fswork/projects/rech/mlh/uja62cb/fidle-project/datasets-fidle
Start time           : 22/12/24 21:24:22
Hostname             : r3i6n0 (Linux)
Tensorflow log level : Info + Warning + Error  (=0)
Update keras cache   : False
Update torch cache   : False
Save figs            : ./run/K3AE2/figs (True)
keras                : 3.7.0
numpy                : 2.1.2
sklearn              : 1.5.2
yaml                 : 6.0.2
skimage              : 0.24.0
matplotlib           : 3.9.2
pandas               : 2.2.3
torch                : 2.5.0

prepared_dataset = './data/mnist-noisy.h5'
dataset_seed     = 123

scale            = .1

latent_dim       = 10

train_prop       = .8
batch_size       = 128
epochs           = 20
fit_verbosity    = 1

fidle.override('prepared_dataset', 'dataset_seed', 'scale', 'latent_dim')
fidle.override('train_prop', 'batch_size', 'epochs', 'fit_verbosity')

** Overrided parameters : **
scale                : 1
latent_dim           : 10
** Overrided parameters : **
epochs               : 20
fit_verbosity        : 2

clean_train,clean_test, noisy_train,noisy_test, _,_ = MNIST.reload_prepared_dataset(scale      = scale, 
                                                                                    train_prop = train_prop,
                                                                                    seed       = dataset_seed,
                                                                                    shuffle    = True,
                                                                                    filename=prepared_dataset )

Loaded.
rescaled (1).
Seeded (123)

Shuffled.
splited (0.8).

clean_train shape is :  (56000, 28, 28, 1)
clean_test  shape is :  (14000, 28, 28, 1)
noisy_train shape is :  (56000, 28, 28, 1)
noisy_test  shape is :  (14000, 28, 28, 1)
class_train shape is :  (56000,)
class_test  shape is :  (14000,)
Blake2b digest is    :  3e97fec95d853b5a2615

inputs    = keras.Input(shape=(28, 28, 1))
x         = layers.Conv2D(32, 3, activation="relu", strides=2, padding="same")(inputs)
x         = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x         = layers.Flatten()(x)
x         = layers.Dense(16, activation="relu")(x)
z         = layers.Dense(latent_dim)(x)

encoder = keras.Model(inputs, z, name="encoder")
# encoder.summary()

inputs  = keras.Input(shape=(latent_dim,))
x       = layers.Dense(7 * 7 * 64, activation="relu")(inputs)
x       = layers.Reshape((7, 7, 64))(x)
x       = layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x       = layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)
outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)

decoder = keras.Model(inputs, outputs, name="decoder")
# decoder.summary()

inputs    = keras.Input(shape=(28, 28, 1))

latents   = encoder(inputs)
outputs   = decoder(latents)

ae = keras.Model(inputs,outputs, name="ae")

ae.compile(optimizer=keras.optimizers.Adam(), loss='binary_crossentropy')

# ---- Callback : Images
#
fidle.utils.mkdir( run_dir + '/images')
filename = run_dir + '/images/image-{epoch:03d}-{i:02d}.jpg'
callback_images = ImagesCallback(filename, x=clean_test[:5], encoder=encoder,decoder=decoder)

chrono = fidle.Chrono()
chrono.start()

history = ae.fit(noisy_train, clean_train,
                 batch_size      = batch_size,
                 epochs          = epochs,
                 verbose         = fit_verbosity,
                 validation_data = (noisy_test, clean_test),
                 callbacks       = [ callback_images ] )

chrono.show()

Epoch 1/20

/lustre/fswork/projects/rech/mlh/uja62cb/local/fidle-k3/lib/python3.12/site-packages/keras/src/backend/common/backend_utils.py:91: UserWarning: You might experience inconsistencies across backends when calling conv transpose with kernel_size=3, stride=2, dilation_rate=1, padding=same, output_padding=1.
  warnings.warn(

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step


1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step


1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step

438/438 - 5s - 13ms/step - loss: 0.2672 - val_loss: 0.2139

Epoch 2/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step


1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step

os.makedirs(f'{run_dir}/models', exist_ok=True)

encoder.save(f'{run_dir}/models/encoder.keras')
decoder.save(f'{run_dir}/models/decoder.keras')

fidle.scrawler.history(history,  plot={'loss':['loss','val_loss']}, save_as='01-history')

imgs=[]
for epoch in range(0,epochs,2):
    for i in range(5):
        filename = run_dir + '/images/image-{epoch:03d}-{i:02d}.jpg'.format(epoch=epoch, i=i)
        img      = io.imread(filename)
        imgs.append(img)      

fidle.utils.subtitle('Real images (clean_test) :')
fidle.scrawler.images(clean_test[:5], None, indices='all', columns=5, x_size=2,y_size=2, interpolation=None, save_as='02-original-real')

fidle.utils.subtitle('Noisy images (noisy_test) :')
fidle.scrawler.images(noisy_test[:5], None, indices='all', columns=5, x_size=2,y_size=2, interpolation=None, save_as='03-original-noisy')

fidle.utils.subtitle('Evolution during the training period (denoised_test) :')
fidle.scrawler.images(imgs, None, indices='all', columns=5, x_size=2,y_size=2, interpolation=None, y_padding=0.1, save_as='04-learning')

fidle.utils.subtitle('Noisy images (noisy_test) :')
fidle.scrawler.images(noisy_test[:5], None, indices='all', columns=5, x_size=2,y_size=2, interpolation=None, save_as=None)

fidle.utils.subtitle('Real images (clean_test) :')
fidle.scrawler.images(clean_test[:5], None, indices='all', columns=5, x_size=2,y_size=2, interpolation=None, save_as=None)

encoder = keras.models.load_model(f'{run_dir}/models/encoder.keras')
decoder = keras.models.load_model(f'{run_dir}/models/decoder.keras')

inputs    = keras.Input(shape=(28, 28, 1))

latents   = encoder(inputs)
outputs   = decoder(latents)

ae_reloaded = keras.Model(inputs,outputs, name="ae")

denoised_test = ae_reloaded.predict(noisy_test, verbose=0)

print('Denoised images   (denoised_test) shape : ',denoised_test.shape)

Denoised images   (denoised_test) shape :  (14000, 28, 28, 1)

i=random.randint(0,len(denoised_test)-8)
j=i+8

fidle.utils.subtitle('Noisy test images (input):')
fidle.scrawler.images(noisy_test[i:j], None, indices='all', columns=8, x_size=2,y_size=2, interpolation=None, save_as='05-test-noisy')

fidle.utils.subtitle('Denoised images (output):')
fidle.scrawler.images(denoised_test[i:j], None, indices='all', columns=8, x_size=2,y_size=2, interpolation=None, save_as='06-test-predict')

fidle.utils.subtitle('Real test images :')
fidle.scrawler.images(clean_test[i:j], None, indices='all', columns=8, x_size=2,y_size=2, interpolation=None, save_as='07-test-real')

fidle.end()

[K3AE2] - Building and training an AE denoiser model¶

Objectives :¶

What we're going to do :¶

Data Terminology :¶

Step 1 - Init python stuff¶

1.1 - Init¶

1.2 - Parameters¶

Step 2 - Retrieve dataset¶

Step 3 - Build models¶

Encoder¶

Decoder¶

AE¶

Step 4 - Train¶

Step 5 - History¶

Step 6 - Denoising progress¶

Step 7 - Evaluation¶

7.1 - Reload model¶

7.2 - Let's make a prediction¶

7.3 - Denoised images¶