From b587b08f9cde60c366c4cacad359a4d02bfbfac3 Mon Sep 17 00:00:00 2001 From: oekk Date: Fri, 22 Nov 2019 12:02:03 +0100 Subject: [PATCH] Initial update --- .gitignore | 182 +++++ CNN/lenet/README.md | 12 + CNN/lenet/gen_scripts/lenet_keras2.2.4.py | 103 +++ .../gen_scripts/squeezenet_keras2.2.4.py | 230 +++++++ LICENSE | 626 ++++++++++++++++++ README.md | 31 + converters/keras2tflite.py | 30 + 7 files changed, 1214 insertions(+) create mode 100644 .gitignore create mode 100644 CNN/lenet/README.md create mode 100644 CNN/lenet/gen_scripts/lenet_keras2.2.4.py create mode 100644 CNN/squeezenet/gen_scripts/squeezenet_keras2.2.4.py create mode 100644 LICENSE create mode 100644 README.md create mode 100644 converters/keras2tflite.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f5aaec4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,182 @@ +# ---> Python +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# ---> Emacs +# -*- mode: gitignore; -*- +*~ +\#*\# +/.emacs.desktop +/.emacs.desktop.lock +*.elc +auto-save-list +tramp +.\#* + +# Org-mode +.org-id-locations +*_archive + +# flymake-mode +*_flymake.* + +# eshell files +/eshell/history +/eshell/lastdir + +# elpa packages +/elpa/ + +# reftex files +*.rel + +# AUCTeX auto folder +/auto/ + +# cask packages +.cask/ +dist/ + +# Flycheck +flycheck_*.el + +# server auth directory +/server/ + +# projectiles files +.projectile + +# directory configuration +.dir-locals.el + +# ---> Vim +# Swap +[._]*.s[a-v][a-z] +[._]*.sw[a-p] +[._]s[a-rt-v][a-z] +[._]ss[a-gi-z] +[._]sw[a-p] + +# Session +Session.vim + +# Temporary +.netrwhist +*~ +# Auto-generated tag files +tags +# Persistent undo +[._]*.un~ + diff --git a/CNN/lenet/README.md b/CNN/lenet/README.md new file mode 100644 index 0000000..d6e10a1 --- /dev/null +++ b/CNN/lenet/README.md @@ -0,0 +1,12 @@ +# LeNet - Handwritten Digit Recognition + +## Description + +This model predicts handwritten digits using a convolutional neural network (CNN). + +## Model + +### Dataset + +This model has been trained on the popular [MNIST datset](http://yann.lecun.com/exdb/mnist/), imported directly form keras.datasets + diff --git a/CNN/lenet/gen_scripts/lenet_keras2.2.4.py b/CNN/lenet/gen_scripts/lenet_keras2.2.4.py new file mode 100644 index 0000000..fd1826b --- /dev/null +++ b/CNN/lenet/gen_scripts/lenet_keras2.2.4.py @@ -0,0 +1,103 @@ +import numpy as np +import pandas as pd +from time import time +from sklearn.model_selection import train_test_split +import keras +import keras.layers as layers +from keras import optimizers +from keras.preprocessing.image import ImageDataGenerator +from keras.models import Model +from keras.layers import Input, Activation, Concatenate +from keras.layers import Flatten, Dropout +from keras.layers import Convolution2D, MaxPooling2D +from keras.layers import GlobalAveragePooling2D + +# Training variables +EPOCHS = 30 +BATCH_SIZE = 480 + +# dataset +def prepare_mnist_data(rows=28, cols=28, nb_classes=10, categorical=False, padding = True, debug = True): + """ Get MNIST data """ + + from keras.datasets import mnist + from keras.utils import np_utils + (X_train, y_train), (X_test, y_test) = mnist.load_data() + X_train = X_train.reshape(X_train.shape[0], rows, cols, 1) + X_test = X_test.reshape(X_test.shape[0], rows, cols, 1) + X_train = X_train.astype('float32') + X_test = X_test.astype('float32') + X_train /= 255 + X_test /= 255 + X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2, random_state=0) + if padding: + # Pad images with 0s + X_train = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant') + X_test = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant') + X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant') + + if categorical: + # convert class vectors to binary class matrices + y_train = np_utils.to_categorical(y_train, nb_classes) + y_test = np_utils.to_categorical(y_test, nb_classes) + y_validation = np_utils.to_categorical(y_validation, nb_classes) + + if debug: + print('X_train shape:', X_train.shape) + print(X_train.shape[0], 'train samples') + print(X_test.shape[0], 'test samples') + print(X_validation.shape[0], 'validation samples') + if not categorical: + train_labels_count = np.unique(y_train, return_counts=True) + dataframe_train_labels = pd.DataFrame({'Label':train_labels_count[0], 'Count':train_labels_count[1]}) + print(dataframe_train_labels) + + return X_train, X_test, X_validation, y_train, y_test, y_validation + + +# SqueezeNet +def LeNet(): + model = keras.Sequential() + + model.add(layers.Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(32,32,1))) + model.add(layers.AveragePooling2D()) + + model.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu')) + model.add(layers.AveragePooling2D()) + + model.add(layers.Flatten()) + + model.add(layers.Dense(units=120, activation='relu')) + + model.add(layers.Dense(units=84, activation='relu')) + + model.add(layers.Dense(units=10, activation = 'softmax')) + return model + +# Get dataset +X_train, X_test, X_validation, y_train, y_test, y_validation = prepare_mnist_data(categorical = True, debug=True) + +# Check +model = LeNet() +model.summary() + +# Preparation +model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(), metrics=['accuracy']) + +# Trainer and validator +train_generator = ImageDataGenerator().flow(X_train, y_train, batch_size=BATCH_SIZE) +validation_generator = ImageDataGenerator().flow(X_validation, y_validation, batch_size=BATCH_SIZE) + +# Training +from keras.callbacks import TensorBoard +steps_per_epoch = X_train.shape[0]//BATCH_SIZE +validation_steps = X_validation.shape[0]//BATCH_SIZE +tensorboard = TensorBoard(log_dir="logs/{}".format(time())) + +model.fit_generator(train_generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, + validation_data=validation_generator, validation_steps=validation_steps, + shuffle=True, callbacks=[tensorboard]) + +# Saving the model in keras format (.h5) +model.save('lenet_keras_v2.2.4.h5') +print(">>> Model saved!") diff --git a/CNN/squeezenet/gen_scripts/squeezenet_keras2.2.4.py b/CNN/squeezenet/gen_scripts/squeezenet_keras2.2.4.py new file mode 100644 index 0000000..ee271e8 --- /dev/null +++ b/CNN/squeezenet/gen_scripts/squeezenet_keras2.2.4.py @@ -0,0 +1,230 @@ +import numpy as np +import pandas as pd +from time import time +from sklearn.model_selection import train_test_split +import keras +import keras.layers as layers +from keras import optimizers +from keras.preprocessing.image import ImageDataGenerator +from keras.models import Model +from keras.layers import Input, Activation, Concatenate +from keras.layers import Flatten, Dropout +from keras.layers import Convolution2D, MaxPooling2D +from keras.layers import GlobalAveragePooling2D + +# Training variables +EPOCHS = 30 +BATCH_SIZE = 480 + +# dataset +def prepare_mnist_data(rows=28, cols=28, nb_classes=10, categorical=False, padding = True, debug = True): + """ Get MNIST data """ + + from keras.datasets import mnist + from keras.utils import np_utils + (X_train, y_train), (X_test, y_test) = mnist.load_data() + X_train = X_train.reshape(X_train.shape[0], 1, rows, cols) + X_test = X_test.reshape(X_test.shape[0], 1, rows, cols) + X_train = X_train.astype('float32') + X_test = X_test.astype('float32') + X_train /= 255 + X_test /= 255 + X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2, random_state=0) + if padding: + # Pad images with 0s + X_train = np.pad(X_train, ((0,0),(0,0),(2,2),(2,2)), 'constant') + X_test = np.pad(X_test, ((0,0),(0,0),(2,2),(2,2)), 'constant') + X_validation = np.pad(X_validation, ((0,0),(0,0),(2,2),(2,2)), 'constant') + + if categorical: + # convert class vectors to binary class matrices + y_train = np_utils.to_categorical(y_train, nb_classes) + y_test = np_utils.to_categorical(y_test, nb_classes) + y_validation = np_utils.to_categorical(y_validation, nb_classes) + + if debug: + print('X_train shape:', X_train.shape) + print(X_train.shape[0], 'train samples') + print(X_test.shape[0], 'test samples') + print(X_validation.shape[0], 'validation samples') + if not categorical: + train_labels_count = np.unique(y_train, return_counts=True) + dataframe_train_labels = pd.DataFrame({'Label':train_labels_count[0], 'Count':train_labels_count[1]}) + print(dataframe_train_labels) + + return X_train, X_test, X_validation, y_train, y_test, y_validation + + +# SqueezeNet +def SqueezeNet(nb_classes, inputs=(1, 32, 32)): + """ Keras Implementation of SqueezeNet(arXiv 1602.07360) + @param nb_classes: total number of final categories + Arguments: + inputs -- shape of the input images (channel, cols, rows) + """ + + input_img = Input(shape=inputs) + conv1 = Convolution2D( + 96, (7, 7), activation='relu', kernel_initializer='glorot_uniform', + strides=(2, 2), padding='same', name='conv1', + data_format="channels_first")(input_img) + maxpool1 = MaxPooling2D( + pool_size=(3, 3), strides=(2, 2), name='maxpool1', + data_format="channels_first")(conv1) + fire2_squeeze = Convolution2D( + 16, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire2_squeeze', + data_format="channels_first")(maxpool1) + fire2_expand1 = Convolution2D( + 64, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire2_expand1', + data_format="channels_first")(fire2_squeeze) + fire2_expand2 = Convolution2D( + 64, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire2_expand2', + data_format="channels_first")(fire2_squeeze) + merge2 = Concatenate(axis=1)([fire2_expand1, fire2_expand2]) + + fire3_squeeze = Convolution2D( + 16, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire3_squeeze', + data_format="channels_first")(merge2) + fire3_expand1 = Convolution2D( + 64, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire3_expand1', + data_format="channels_first")(fire3_squeeze) + fire3_expand2 = Convolution2D( + 64, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire3_expand2', + data_format="channels_first")(fire3_squeeze) + merge3 = Concatenate(axis=1)([fire3_expand1, fire3_expand2]) + + fire4_squeeze = Convolution2D( + 32, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire4_squeeze', + data_format="channels_first")(merge3) + fire4_expand1 = Convolution2D( + 128, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire4_expand1', + data_format="channels_first")(fire4_squeeze) + fire4_expand2 = Convolution2D( + 128, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire4_expand2', + data_format="channels_first")(fire4_squeeze) + merge4 = Concatenate(axis=1)([fire4_expand1, fire4_expand2]) + maxpool4 = MaxPooling2D( + pool_size=(3, 3), strides=(2, 2), name='maxpool4', + data_format="channels_first")(merge4) + + fire5_squeeze = Convolution2D( + 32, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire5_squeeze', + data_format="channels_first")(maxpool4) + fire5_expand1 = Convolution2D( + 128, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire5_expand1', + data_format="channels_first")(fire5_squeeze) + fire5_expand2 = Convolution2D( + 128, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire5_expand2', + data_format="channels_first")(fire5_squeeze) + merge5 = Concatenate(axis=1)([fire5_expand1, fire5_expand2]) + + fire6_squeeze = Convolution2D( + 48, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire6_squeeze', + data_format="channels_first")(merge5) + fire6_expand1 = Convolution2D( + 192, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire6_expand1', + data_format="channels_first")(fire6_squeeze) + fire6_expand2 = Convolution2D( + 192, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire6_expand2', + data_format="channels_first")(fire6_squeeze) + merge6 = Concatenate(axis=1)([fire6_expand1, fire6_expand2]) + + fire7_squeeze = Convolution2D( + 48, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire7_squeeze', + data_format="channels_first")(merge6) + fire7_expand1 = Convolution2D( + 192, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire7_expand1', + data_format="channels_first")(fire7_squeeze) + fire7_expand2 = Convolution2D( + 192, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire7_expand2', + data_format="channels_first")(fire7_squeeze) + merge7 = Concatenate(axis=1)([fire7_expand1, fire7_expand2]) + + fire8_squeeze = Convolution2D( + 64, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire8_squeeze', + data_format="channels_first")(merge7) + fire8_expand1 = Convolution2D( + 256, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire8_expand1', + data_format="channels_first")(fire8_squeeze) + fire8_expand2 = Convolution2D( + 256, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire8_expand2', + data_format="channels_first")(fire8_squeeze) + merge8 = Concatenate(axis=1)([fire8_expand1, fire8_expand2]) + + maxpool8 = MaxPooling2D( + pool_size=(3, 3), strides=(2, 2), name='maxpool8', + data_format="channels_first")(merge8) + fire9_squeeze = Convolution2D( + 64, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire9_squeeze', + data_format="channels_first")(maxpool8) + fire9_expand1 = Convolution2D( + 256, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire9_expand1', + data_format="channels_first")(fire9_squeeze) + fire9_expand2 = Convolution2D( + 256, (3, 3), activation='relu', kernel_initializer='glorot_uniform', + padding='same', name='fire9_expand2', + data_format="channels_first")(fire9_squeeze) + merge9 = Concatenate(axis=1)([fire9_expand1, fire9_expand2]) + + fire9_dropout = Dropout(0.5, name='fire9_dropout')(merge9) + conv10 = Convolution2D( + nb_classes, (1, 1), activation='relu', kernel_initializer='glorot_uniform', + padding='valid', name='conv10', + data_format="channels_first")(fire9_dropout) + + global_avgpool10 = GlobalAveragePooling2D(data_format='channels_first')(conv10) + softmax = Activation("softmax", name='softmax')(global_avgpool10) + + return Model(inputs=input_img, outputs=softmax) + + +# Get dataset +X_train, X_test, X_validation, y_train, y_test, y_validation = prepare_mnist_data(categorical = True) + +# Check +model = SqueezeNet(10) +model.summary() + +# Preparation +model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(), metrics=['accuracy']) + +# Trainer and validator +train_generator = ImageDataGenerator().flow(X_train, y_train, batch_size=BATCH_SIZE) +validation_generator = ImageDataGenerator().flow(X_validation, y_validation, batch_size=BATCH_SIZE) + +# Training +from keras.callbacks import TensorBoard +steps_per_epoch = X_train.shape[0]//BATCH_SIZE +validation_steps = X_validation.shape[0]//BATCH_SIZE +tensorboard = TensorBoard(log_dir="logs/{}".format(time())) + +model.fit_generator(train_generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, + validation_data=validation_generator, validation_steps=validation_steps, + shuffle=True, callbacks=[tensorboard]) + +# Saving the model in keras format (.h5) +model.save('squeezenet_keras_v2.2.4.h5') +print(">>> Model saved!") diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8015026 --- /dev/null +++ b/LICENSE @@ -0,0 +1,626 @@ +GNU GENERAL PUBLIC LICENSE + +Version 3, 29 June 2007 + +Copyright © 2007 Free Software Foundation, Inc. + +Everyone is permitted to copy and distribute verbatim copies of this license +document, but changing it is not allowed. + +Preamble + +The GNU General Public License is a free, copyleft license for software and +other kinds of works. + +The licenses for most software and other practical works are designed to take +away your freedom to share and change the 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But first, please read . diff --git a/README.md b/README.md new file mode 100644 index 0000000..752b276 --- /dev/null +++ b/README.md @@ -0,0 +1,31 @@ +# Oekk Model Zoo + +A collection of pre-trained, state-of-the-art models in different formats with scripts to generate them and converter tools. + +## Models + +#### Vision + +- CNN (Convolutional Neural Networks) + - Object Detection & Image Segmentation + - MNIST models + +### CNN + +| Model Class | Reference | Description | +|-------------|-----------|-------------| + | [U-Net]() | [Olaf Ronneberger et al.](https://arxiv.org/abs/1505.04597) | CNN with very good performance on very different biomedical segmentation applications. + +#### Object Detection & Image Segmentation + + +#### MNIST models + +This collection of models take 32x32x1 images (or variations) as input, then classifies the images into 10 number categories. Solvers of the MNIST dataset, frequently used as guinea pigs. + +| Model Class | Reference | Description | +|-------------|-----------|-------------| +| [LeNet](https://coolneng.duckdns.org/gitea/oekk/models/src/branch/master/CNN/lenet/gen_scripts) | [Yann LeCun et al.](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf) | LeNet is one of the first CNN for image recognition. | +| [SqueezeNet](https://coolneng.duckdns.org/gitea/oekk/models/src/branch/master/CNN/squeezenet/gen_scripts)| [Iandola et al.](https://arxiv.org/abs/1602.07360) | A light-weight CNN model providing AlexNet level accuracy with 50x fewer parameters. Top-5 error from paper - ~20% | + + diff --git a/converters/keras2tflite.py b/converters/keras2tflite.py new file mode 100644 index 0000000..471c374 --- /dev/null +++ b/converters/keras2tflite.py @@ -0,0 +1,30 @@ +import sys +import os + +def explain(): + print('This converter can be used to transform keras models in the .h5 format into a tflite flatbuffer model .tflite\n') + print('\tIt must be called like in this exampple: python keras2tflite.py /path/to/input_model.h5 ./output_model.tflite\n') + print('\tArguments expected: 2\n\tArguments received: ' + str(len(sys.argv))-1) + versions() + +def versions(k=''): + print(k + 'Python version >= 3.x') + print(k + 'Keras model version >= 2.2.4') + print(k + 'TensorFlow Lite version >= 1.11') + print(k + 'TensorFlow version >= 1.14.x') + +if len(sys.argv) != 3: + explain() + +else: + # Converting a tf.Keras model to a TensorFlow Lite model. + try: + os.system('tflite_convert \ + --keras_model_file='+sys.argv[1]+' \ + --output_file='+sys.argv[2]) + except Exception as e: + print('\nError in the conversion to TFLite:\n' + str(e) + '\n') + except IOError as e: + print('\nError when trying to save the model:\n' + str(e) +'\n') + else: + print('\nModel saved!\n')