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"cell_type": "markdown",
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"source": [
"German Traffic Sign Recognition Benchmark (GTSRB)\n",
"=================================================\n",
"---\n",
"Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
"\n",
"## Episode 5 : Full Convolutions\n",
"\n",
"Our main steps:\n",
" - Try n models with n datasets\n",
" - Save a Pandas/h5 report\n",
" - Write to be run in batch mode\n",
"\n",
"## 1/ Import"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"\n",
"import numpy as np\n",
"import h5py\n",
"import os,time,json\n",
"\n",
"from IPython.display import display\n",
"\n",
"VERSION='1.2'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2/ Init and start"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('\\nFull Convolutions Notebook')\n",
"print(' Version : {}'.format(VERSION))\n",
"print(' Run time : {}'.format(time.strftime(\"%A %-d %B %Y, %H:%M:%S\")))\n",
"print(' TensorFlow version :',tf.__version__)\n",
"print(' Keras version :',tf.keras.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3/ Dataset loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def read_dataset(name):\n",
" '''Reads h5 dataset from ./data\n",
"\n",
" Arguments: dataset name, without .h5\n",
" Returns: x_train,y_train,x_test,y_test data'''\n",
" # ---- Read dataset\n",
" filename='./data/'+name+'.h5'\n",
" with h5py.File(filename) as f:\n",
" x_train = f['x_train'][:]\n",
" y_train = f['y_train'][:]\n",
" x_test = f['x_test'][:]\n",
" y_test = f['y_test'][:]\n",
"\n",
" return x_train,y_train,x_test,y_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4/ Models collection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# A basic model\n",
"#\n",
"def get_model_v1(lx,ly,lz):\n",
" \n",
" model = keras.models.Sequential()\n",
" \n",
" model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
" model.add( keras.layers.MaxPooling2D((2, 2)))\n",
" model.add( keras.layers.Dropout(0.2))\n",
"\n",
" model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D((2, 2)))\n",
" model.add( keras.layers.Dropout(0.2))\n",
"\n",
" model.add( keras.layers.Flatten()) \n",
" model.add( keras.layers.Dense(1500, activation='relu'))\n",
" model.add( keras.layers.Dropout(0.5))\n",
"\n",
" model.add( keras.layers.Dense(43, activation='softmax'))\n",
" return model\n",
" \n",
"# A more sophisticated model\n",
"#\n",
"def get_model_v2(lx,ly,lz):\n",
" model = keras.models.Sequential()\n",
"\n",
" model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
" model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
" model.add( keras.layers.Dropout(0.2))\n",
"\n",
" model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
" model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
" model.add( keras.layers.Dropout(0.2))\n",
"\n",
" model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
" model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
" model.add( keras.layers.Dropout(0.2))\n",
"\n",
" model.add( keras.layers.Flatten())\n",
" model.add( keras.layers.Dense(512, activation='relu'))\n",
" model.add( keras.layers.Dropout(0.5))\n",
" model.add( keras.layers.Dense(43, activation='softmax'))\n",
" return model\n",
"\n",
"# My sphisticated model, but small and fast\n",
"#\n",
"def get_model_v3(lx,ly,lz):\n",
" model = keras.models.Sequential()\n",
" model.add( keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
" model.add( keras.layers.MaxPooling2D((2, 2)))\n",
" model.add( keras.layers.Dropout(0.5))\n",
"\n",
" model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D((2, 2)))\n",
" model.add( keras.layers.Dropout(0.5))\n",
"\n",
" model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D((2, 2)))\n",
" model.add( keras.layers.Dropout(0.5))\n",
"\n",
" model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
" model.add( keras.layers.MaxPooling2D((2, 2)))\n",
" model.add( keras.layers.Dropout(0.5))\n",
"\n",
" model.add( keras.layers.Flatten()) \n",
" model.add( keras.layers.Dense(1152, activation='relu'))\n",
" model.add( keras.layers.Dropout(0.5))\n",
"\n",
" model.add( keras.layers.Dense(43, activation='softmax'))\n",
" return model\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5/ Multiple datasets, multiple models ;-)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def multi_run(datasets, models, batch_size=64, epochs=16):\n",
"\n",
" # ---- Columns of report\n",
" #\n",
" report={}\n",
" report['Dataset']=[]\n",
" report['Size'] =[]\n",
" for m in models:\n",
" report[m+' Accuracy'] = []\n",
" report[m+' Duration'] = []\n",
"\n",
" # ---- Let's go\n",
" #\n",
" for d_name in datasets:\n",
" print(\"\\nDataset : \",d_name)\n",
"\n",
" # ---- Read dataset\n",
" x_train,y_train,x_test,y_test = read_dataset(d_name)\n",
" d_size=os.path.getsize('./data/'+d_name+'.h5')/(1024*1024)\n",
" report['Dataset'].append(d_name)\n",
" report['Size'].append(d_size)\n",
" \n",
" # ---- Get the shape\n",
" (n,lx,ly,lz) = x_train.shape\n",
"\n",
" # ---- For each model\n",
" for m_name,m_function in models.items():\n",
" print(\" Run model {} : \".format(m_name), end='')\n",
" # ---- get model\n",
" try:\n",
" model=m_function(lx,ly,lz)\n",
" # ---- Compile it\n",
" model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
" # ---- Callbacks tensorboard\n",
" log_dir = \"./run/logs/tb_{}_{}\".format(d_name,m_name)\n",
" tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
" # ---- Callbacks bestmodel\n",
" save_dir = \"./run/models/model_{}_{}.h5\".format(d_name,m_name)\n",
" bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
" # ---- Train\n",
" start_time = time.time()\n",
" history = model.fit( x_train, y_train,\n",
" batch_size = batch_size,\n",
" epochs = epochs,\n",
" verbose = 0,\n",
" validation_data = (x_test, y_test),\n",
" callbacks = [tensorboard_callback, bestmodel_callback])\n",
" # ---- Result\n",
" end_time = time.time()\n",
" duration = end_time-start_time\n",
" accuracy = max(history.history[\"val_accuracy\"])*100\n",
" #\n",
" report[m_name+' Accuracy'].append(accuracy)\n",
" report[m_name+' Duration'].append(duration)\n",
" print(\"Accuracy={:.2f} and Duration={:.2f})\".format(accuracy,duration))\n",
" except:\n",
" report[m_name+' Accuracy'].append('-')\n",
" report[m_name+' Duration'].append('-')\n",
" print('-')\n",
" return report"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6/ Run\n",
"### 6.1/ Clean"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"/bin/rm -r ./run/logs 2>/dev/null\n",
"/bin/rm -r ./run/models 2>/dev/null\n",
"/bin/mkdir -p -m 755 ./run/logs\n",
"/bin/mkdir -p -m 755 ./run/models\n",
"echo -e \"\\nReset directories : ./run/logs and ./run/models .\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6.2/ run and save report"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"print('\\n---- Run','-'*50)\n",
"\n",
"# ---- Datasets and models list\n",
"\n",
"# For tests\n",
"# datasets = ['set-24x24-L', 'set-24x24-RGB']\n",
"# models = {'v1':get_model_v1, 'v3':get_model_v3}\n",
"\n",
"# The real one\n",
"datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
"models = {'v1':get_model_v1, 'v2':get_model_v2, 'v3':get_model_v3}\n",
"\n",
"# ---- Report name\n",
"\n",
"report_name='./run/report-{}.json'.format(time.strftime(\"%Y-%m-%d_%Hh%Mm%Ss\"))\n",
"\n",
"# ---- Run\n",
"\n",
"out = multi_run(datasets, models, batch_size=64, epochs=2)\n",
"\n",
"# ---- Save report\n",
"\n",
"with open(report_name, 'w') as outfile:\n",
" json.dump(out, outfile)\n",
"\n",
"print('\\nReport saved as ',report_name)\n",
"print('-'*59)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7/ That's all folks.."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('\\n{}'.format(time.strftime(\"%A %-d %B %Y, %H:%M:%S\")))\n",
"print(\"The work is done.\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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