TP-reseaux-profond/TP6.ipynb
2023-06-22 20:35:38 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Kd4SVfDxz5aw"
},
"source": [
"<h1>Localisation et détection d'objet</h1>\n",
"\n",
"Dans ce TP, nous allons mettre en pratique certaines des méthodes présentées en cours pour localiser des objets dans une image.\n",
"\n",
"En localisation et détection, on cherche à déterminer la position d'un objet, ainsi que sa classe, sous la forme d'une boîte englobante de largeur $b_w$ et hauteur $b_h$, et dont le centre a pour coordonnées le point $(b_x, b_y)$. \n",
"\n",
"<center> <img src=\"https://drive.google.com/uc?id=1_jHHv6ZDe-3Xz25jIZ6o177laBEfmMRR\" style=\"width:500;height:300px;\"></center>\n",
"<caption><center> Figure 1: Modèle de boîte englobante utilisé pour la localisation </center></caption>\n",
"\n",
"Le problème de localisation considère qu'un seul objet est présent sur l'image, alors que le problème de détection cherche à déterminer l'ensemble des objets présents sur l'image.\n",
"\n",
"\n",
"\n",
"Pour commencer, récupérez les images de la base de données :\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "2ZjveWpbuNeV"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fatal: le chemin de destination 'mangeoires_loc' existe déjà et n'est pas un répertoire vide.\n"
]
}
],
"source": [
"!git clone https://github.com/axelcarlier/mangeoires_loc.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LF6aRZLE2-Yl"
},
"source": [
"La base de données consiste en des photographies prises par une caméra reliée à une Raspberry Pi, cachée dans une mangeoire. Plusieurs mangeoires sont disséminées dans la nature en Occitanie, et l'objectif de [ce projet](https://econect.cnrs.fr/) est la reconnaissance des espèces et le comptage des individus qui viennent se poser devant le caméra, afin de suivre l'évolution des populations d'oiseaux et ainsi monitorer la biodiversité.\n",
"\n",
"La base de données qui vous est fournie regroupe 11 espèces d'animaux, majoritairement des oiseaux, désignés par un code : \n",
"\n",
"1. Mésange charbonnière (**MESCHA**)\n",
"2. Verdier d'Europe (**VEREUR**)\n",
"3. Écureuil roux (**ECUROU**)\n",
"4. Pie bavarde (**PIEBAV**)\n",
"5. Sittelle torchepot (**SITTOR**)\n",
"6. Pinson des arbres (**PINARB**)\n",
"7. Mésange noire (**MESNOI**)\n",
"8. Mésange nonnette (**MESNON**)\n",
"9. Mésange bleue (**MESBLE**)\n",
"10. Rouge-gorge (**ROUGOR**)\n",
"11. Accenteur mouchet (**ACCMOU**)\n",
"\n",
"\n",
"\n",
"<center> <img src=\"https://drive.google.com/uc?id=1Eit86N4D0Ea7ai1rBa0o-GmTwckhP9i0\" width=200>\n",
"<img src=\"https://drive.google.com/uc?id=1lC7WL93UqDT_KV2m21yx99N5dkf98nCY\" width=200>\n",
"<img src=\"https://drive.google.com/uc?id=10tzJORcSrSckDWmBDBtc8FUirxdbRxri\" width=200>\n",
"<img src=\"https://drive.google.com/uc?id=1IHZp_B6bc8bADtAReRHhcyOjDQZAXDBQ\" width=200></center>\n",
"<caption><center> Figure 2: Exemples d'images de la base de données </center></caption>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kxZ6cVouz9Lp"
},
"source": [
"# Localisation et classification d'objet\n",
"\n",
"Dans cette partie, nous allons nous concentrer sur le problème de la localisation d'un seul objet par classe. La base de données a été épurée pour se concentrer uniquement sur ce cas.\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "SBA3fa8RSDpt"
},
"outputs": [],
"source": [
"import PIL\n",
"from PIL import Image\n",
"import csv\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import tensorflow as tf\n",
"\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras import models\n",
"from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, Input\n",
"from tensorflow.keras.models import Model, Sequential\n",
"from tensorflow.keras.optimizers import Adam\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HKRm5oT-_Qsw"
},
"source": [
"## Préparation des données\n",
"\n",
"Le code ci-dessous permet de charger les données et les formater pour la classification. Prenez le temps de regarder un peu le format des labels $y$.\n",
"Notez que les images sont rendues carrées lors du chargement."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "Q54zSuMvGM-5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n"
]
}
],
"source": [
"# Lecture du CSV contenant les informations relatives à la base de données\n",
"dataset = []\n",
"with open('mangeoires_loc/bd_mangeoires_equilibre.csv', newline='') as csvfile:\n",
"\tfilereader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
"\tfor row in filereader:\n",
"\t\tdata = row[0].split(',')\n",
"\t\tif data[0] != 'Data':\n",
"\t\t\tbox = [float(data[5]), float(data[6]), float(data[7]), float(data[8])]\n",
"\t\t\tnew_entry = {'type': data[0], 'specie': data[1], 'path': data[2], 'shape': [float(data[3]), float(data[4])], 'box': box}\n",
"\t\t\tdataset.append(new_entry)\n",
"\n",
"# Nombre de classes de la base de données et intitulé des classes\n",
"class_labels = list(dict.fromkeys([item['specie'] for item in dataset]))\n",
"num_classes = len(class_labels)\n",
"\n",
"# Extraction des données d'apprentissage et de test \n",
"dataset_train = [item for item in dataset if item['type']=='TRAIN']\n",
"dataset_test = [item for item in dataset if item['type']=='TEST']\n",
"\n",
"print(class_labels)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "JPzqdJBVJLWJ"
},
"outputs": [],
"source": [
"def build_localization_tensors(image_size, dataset, num_classes):\n",
" # Préparation des structures de données pour x et y\n",
" x = np.zeros((len(dataset), image_size, image_size, 3))\n",
" y = np.empty((len(dataset), num_classes + 5)) # 1 + 4 + num_classes : présence / boîte englobante / classes\n",
"\n",
" # Compteur de parcours du dataset\n",
" i = 0\n",
"\n",
" for item in dataset:\n",
" # Lecture de l'image\n",
" img = Image.open('mangeoires_loc/' + item['path'])\n",
" # Mise à l'échelle de l'image\n",
" img = img.resize((image_size,image_size), Image.ANTIALIAS)\n",
" # Remplissage de la variable x\n",
" x[i] = np.asarray(img)\n",
"\n",
" y[i, 0] = 1 # Un objet est toujours présent !\n",
"\n",
" # Coordonnées de boîte englobante\n",
" img_shape = item['shape']\n",
" box = item['box']\n",
" bx = (box[0] + (box[2] - box[0])/2)/img_shape[0]\n",
" by = (box[1] + (box[3] - box[1])/2)/img_shape[1]\n",
" bw = (box[2] - box[0])/img_shape[0]\n",
" bh = (box[3] - box[1])/img_shape[1]\n",
" y[i, 1] = bx\n",
" y[i, 2] = by\n",
" y[i, 3] = bw\n",
" y[i, 4] = bh\n",
"\n",
" # Probabilités de classe, sous la forme d'une one-hot vector\n",
" label = class_labels.index(item['specie'])\n",
" classes_probabilities = keras.utils.to_categorical(label, num_classes=num_classes)\n",
" y[i, 5:] = classes_probabilities\n",
"\n",
" i = i+1\n",
"\n",
" return x, y\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fnlljZWc_i1L"
},
"source": [
"Séparation des données d'entraînement pour extraire un ensemble de validation, et pré-traitement des données."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "FJLRiuFX_VPL"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_3171614/3333676307.py:13: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n",
" img = img.resize((image_size,image_size), Image.ANTIALIAS)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"\n",
"# Pour la suite du TP on considèrera des images de taille 64x64x3\n",
"# Augmenter cette valeur donnerait de meilleurs résultats mais nécessiterait des calculs plus long.\n",
"IMAGE_SIZE = 64\n",
"\n",
"# Lecture des données d'entraînement et de test\n",
"x, y = build_localization_tensors(IMAGE_SIZE, dataset_train, num_classes)\n",
"x_test, y_test = build_localization_tensors(IMAGE_SIZE, dataset_test, num_classes)\n",
"\n",
"#Extraction d'un ensemble de validation\n",
"x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.10, random_state=42)\n",
"\n",
"# Pour améliorer l'entraînement, on peut centrer-réduire les coordonnées des bounding boxes...\n",
"y_std = np.std(y_train, axis=0)\n",
"y_mean = np.mean(y_train, axis=0)\n",
"y_train[...,1:5] = (y_train[...,1:5] - y_mean[1:5])/y_std[1:5]\n",
"y_val[...,1:5] = (y_val[...,1:5] - y_mean[1:5])/y_std[1:5]\n",
"y_test[...,1:5] = (y_test[...,1:5] - y_mean[1:5])/y_std[1:5]\n",
"\n",
"# ... et normaliser les valeurs de couleur\n",
"x_train = x_train/255\n",
"x_val = x_val/255\n",
"x_test = x_test/255"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DE4wQYq3AKnA"
},
"source": [
"## Fonctions utiles"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n6NBpMtaM-C0"
},
"source": [
"Une fonction de calcul de l'intersection sur union, qui nous sera utile pour les métriques d'évaluation de nos méthodes :\n",
"\n",
"$$ IoU (R_1, R_2) = \\frac{\\mathcal{A} (R_1 \\cap R_2)}{\\mathcal{A} (R_1 \\cup R_2)} = \\frac{\\mathcal{A} (R_1 \\cap R_2)}{\\mathcal{A} (R_1) + \\mathcal{A} (R_2) - \\mathcal{A} (R_1 \\cap R_2)} $$ \n",
"\n",
"<center>\n",
"<img src=\"https://drive.google.com/uc?id=1BQx2kDOCltZ_5gcnKVWTVUVNmGk-7qBC\" width=500>\n",
"</center>\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "rr-O1XuwLoL3"
},
"outputs": [],
"source": [
"### A COMPLETER\n",
"def intersection_sur_union(box1, box2):\n",
" \"\"\"\n",
" Calcul de l'intersection sur union entre deux rectangles box1 et box2\n",
"\n",
" Arguments:\n",
" box1, box2 -- les coordonnées des deux rectangles, chacun sous la forme [cx, cy, w, h]\n",
" où (cx, cy) désigne les coordonnées du centre du rectangle, \n",
" w sa largeur et h sa hauteur\n",
"\n",
" Retourne :\n",
" iou -- la valeur d'intersection sur union entre les deux rectangles \n",
" \"\"\"\n",
"\n",
" # unpacking\n",
" cx1, cy1, w1, h1 = box1\n",
" cx2, cy2, w2, h2 = box2\n",
"\n",
" # haut gauche R1\n",
" x1 = cx1 - w1/2\n",
" y1 = cy1 - h1/2\n",
" \n",
" # bas droite R1\n",
" x2 = cx1 + w1/2\n",
" y2 = cy1 + h1/2\n",
"\n",
" # haut gauche R2\n",
" x3 = cx2 - w2/2\n",
" y3 = cy2 - h2/2\n",
" \n",
" # bas droite R2\n",
" x4 = cx2 + w2/2\n",
" y4 = cy2 + 2/2\n",
"\n",
" # haut gauche intersection\n",
" xi1 = max(x1, x3)\n",
" yi1 = max(y1, y3)\n",
"\n",
" # bas droite intersection\n",
" xi2 = min(x2, x4)\n",
" yi2 = min(y2, y4)\n",
"\n",
" # dimension de l'intersection\n",
" wi = abs(xi2 - xi1)\n",
" hi = abs(yi2 - yi1)\n",
"\n",
" # calcul des aires\n",
" aR1iR2 = wi * hi\n",
" aR1 = w1 * h1\n",
" aR2 = w2 * h2\n",
"\n",
" return aR1iR2 / (aR1 + aR2 - aR1iR2)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "SdqAFWFxRx8l"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.2\n",
"0.0\n"
]
}
],
"source": [
"print(intersection_sur_union([2.5, 2, 1, 4], [2, 3, 4, 2])) # Résultat attendu : 0.2\n",
"print(intersection_sur_union([2.5, 2, 1, 4], [5, 3, 4, 2])) # Résultat attendu : 0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RLpMJGi3QC9i"
},
"source": [
"Calcul des différentes métriques : \n",
"\n",
"$$ P = \\frac{TP}{TP + FP} $$\n",
"\n",
"$$ R = \\frac{TP}{TP + FN} $$\n",
"\n",
"$$ F1 = \\frac{2}{\\frac{1}{P} + \\frac{1}{R}} $$\n",
"\n",
"où $TP$ désigne le nombre de vrais positifs, $FP$ le nombre de faux positifs, $FN$ le nombre de faux négatifs, $P$ la précision, $R$ le rappel et $F1$ le F1-score.\n",
"\n",
"On considère souvent qu'une détection est correcte si la classification est valide et que l'intersection sur union entre vérité terrain et prédiction est supérieure à 0.5 (on utilisera un seuil modifiable *iou_thres*)."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"id": "5e_aIvjXLq32"
},
"outputs": [],
"source": [
"# A COMPLETER\n",
"def global_accuracy(y_true, y_pred, iou_thres=0.5):\n",
" \"\"\"\n",
" Calcul, pour chaque classe de la précision, du rappel et du F1-score ainsi \n",
" que du pourcentage global de bonnes détections.\n",
"\n",
" Arguments:\n",
" y_true -- les labels de la vérité terrain, de dimension (M, 1+4+N) où M désigne\n",
" le nombre d'éléments du dataset et N le nombre de classes (11 dans notre cas)\n",
" y_pred -- les labels prédits par un modèle, de dimension (M, 1+4+N) \n",
" iou_thres -- seuil d'intersection sur union entre une boîte \"vérité-terrain\" et \n",
" une boite prédite au-dessus duquel on considère que la prédiction est correcte \n",
"\n",
" Retourne :\n",
" class_res -- liste de longueur N contenant des dictionnaires sous la forme \n",
" {\"Précision\": p, \"Rappel\": r, \"F-score\": f} résumant les métriques\n",
" précision, rappel et F1-score pour chacune des classes.\n",
" accuracy -- pourcentage global de bonnes détections\n",
" \"\"\"\n",
" # Initialisation des métriques : nombre de vrais positifs (TP), faux positifs (FP)\n",
" # et faux négatifs (FN) pour chaque classe\n",
" class_metrics = []\n",
" for i in range(num_classes):\n",
" class_metrics.append({'TP': 0, 'FP': 0, 'FN': 0})\n",
"\n",
" # Nombres de détections correctes et de détections incorrectes\n",
" total_correct_detections = 0\n",
" total_incorrect_detections = 0\n",
" for i in range(y_true.shape[0]):\n",
" # Labels vérité-terrain et prédits\n",
" groundtruth_label = np.argmax(y_true[i,5:])\n",
" predicted_label = np.argmax(y_pred[i,5:])\n",
"\n",
" # Coordonnées de boîtes englobantes réelles et prédites\n",
" bx_true = (y_true[i,1]*y_std[1] + y_mean[1])\n",
" by_true = (y_true[i,2]*y_std[2] + y_mean[2])\n",
" bw_true = (y_true[i,3]*y_std[3] + y_mean[3])\n",
" bh_true = (y_true[i,4]*y_std[4] + y_mean[4]) \n",
" bx_pred = (y_pred[i,1]*y_std[1] + y_mean[1])\n",
" by_pred = (y_pred[i,2]*y_std[2] + y_mean[2])\n",
" bw_pred = (y_pred[i,3]*y_std[3] + y_mean[3])\n",
" bh_pred = (y_pred[i,4]*y_std[4] + y_mean[4]) \n",
"\n",
" # Calcul de l'intersection sur union\n",
" iou = intersection_sur_union([bx_true, by_true, bw_true, bh_true], [bx_pred, by_pred, bw_pred, bh_pred])\n",
" \n",
" # Si la détection est correcte : \n",
" if groundtruth_label == predicted_label and iou > iou_thres:\n",
" total_correct_detections += 1\n",
" class_metrics[predicted_label][\"TP\"] += 1\n",
" else:\n",
" total_incorrect_detections += 1\n",
" class_metrics[predicted_label][\"FP\"] += 1\n",
" class_metrics[groundtruth_label][\"FN\"] += 1\n",
"\n",
"# for i in range(num_classes):\n",
"# print(class_metrics[i])\n",
"\n",
" class_res = []\n",
" for i in range(num_classes):\n",
" TP = class_metrics[i][\"TP\"]\n",
" FP = class_metrics[i][\"FP\"]\n",
" FN = class_metrics[i][\"FN\"]\n",
" if (TP or FP) and (TP or FN): \n",
" P = TP / (TP + FP)\n",
" R = TP / (TP + FN)\n",
" F_score = 2 / ( 1/P + 1/R )\n",
" else:\n",
" P = 0\n",
" R = 0\n",
" F_score = 0\n",
" class_res.append({'Precision': P, 'Rappel': R, 'F-score': F_score})\n",
"\n",
" accuracy = total_correct_detections / (total_correct_detections + total_incorrect_detections)\n",
" return class_res, accuracy"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"id": "AOOWhuk4VBZE"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La précision globale est de 66.7%\n",
"\n",
"--------------------------------------------\n",
"| Classe | Précision | Rappel | F1-score |\n",
"--------------------------------------------\n",
"| Classe 1 | 1.00 | 1.00 | 1.00 |\n",
"--------------------------------------------\n",
"| Classe 2 | 0.00 | 0.00 | 0.00 |\n",
"--------------------------------------------\n",
"| Classe 3 | 0.50 | 1.00 | 0.67 |\n",
"--------------------------------------------\n"
]
}
],
"source": [
"num_class_test = 3\n",
"class_labels_test = ['Classe 1', 'Classe 2', 'Classe 3']\n",
"y_true_test = np.ones((num_class_test,8))\n",
"y_true_test[0,:2] = [0.5, 0.5]\n",
"y_true_test[0, 5:] = [1, 0, 0]\n",
"y_true_test[1,:2] = [0.5, 0.5]\n",
"y_true_test[1, 5:] = [0, 1, 0]\n",
"y_true_test[2,:2] = [0.5, 0.5]\n",
"y_true_test[2, 5:] = [0, 0, 1]\n",
"y_pred_test = np.ones((num_class_test,8))\n",
"y_pred_test[0,:2] = [0.6, 0.6]\n",
"y_pred_test[0, 5:] = [1, 0, 0]\n",
"y_pred_test[1,:2] = [2.5, 2.5]\n",
"y_pred_test[1, 5:] = [0, 0, 1]\n",
"y_pred_test[2,:2] = [0.6, 0.6]\n",
"y_pred_test[2, 5:] = [0, 0, 1]\n",
"\n",
"class_res_test, acc_test = global_accuracy(y_true_test, y_pred_test)\n",
"\n",
"print(f\"La précision globale est de {100 * acc_test:.1f}%\")\n",
"\n",
"print()\n",
"print(\"--------------------------------------------\")\n",
"print(\"| Classe | Précision | Rappel | F1-score |\")\n",
"print(\"--------------------------------------------\")\n",
"for i in range(num_class_test):\n",
" print(f\"| {class_labels_test[i]:9s}| {class_res_test[i]['Precision']:.2f} | {class_res_test[i]['Rappel']:.2f} | {class_res_test[i]['F-score']:.2f} |\")\n",
" print(\"--------------------------------------------\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q1Tg4VFVVB-w"
},
"source": [
"**Affichage attendu :**\n",
"```\n",
"La précision globale est de 66.7%\n",
"\n",
"--------------------------------------------\n",
"| Classe | Précision | Rappel | F1-score |\n",
"--------------------------------------------\n",
"| Classe 1 | 1.00 | 1.00 | 1.00 |\n",
"--------------------------------------------\n",
"| Classe 2 | 0.00 | 0.00 | 0.00 |\n",
"--------------------------------------------\n",
"| Classe 3 | 0.50 | 1.00 | 0.67 |\n",
"--------------------------------------------\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cMntCEgkANMg"
},
"source": [
"La fonction ci-dessous permet de calculer l'intersection sur union sur des tenseurs (et non des tableaux numpy), elle sera donc utilisable comme métrique pendant l'entraînement."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"id": "tVk9cB1WAMUK"
},
"outputs": [],
"source": [
"def compute_iou(y_true, y_pred):\n",
" ### \"Dénormalisation\" des coordonnées des boîtes englobantes\n",
" pred_box_xy = y_pred[..., 0:2]* y_std[0:2] + y_mean[0:2]\n",
" true_box_xy = y_true[..., 0:2]* y_std[0:2] + y_mean[0:2]\n",
"\n",
" ### \"Dénormalisation\" des largeur et hauteur des boîtes englobantes\n",
" pred_box_wh = y_pred[..., 2:4] * y_std[2:4] + y_mean[2:4]\n",
" true_box_wh = y_true[..., 2:4] * y_std[2:4] + y_mean[2:4]\n",
" \n",
" # Calcul des coordonnées minimales et maximales des boiptes englobantes réelles\n",
" true_wh_half = true_box_wh / 2.\n",
" true_mins = true_box_xy - true_wh_half\n",
" true_maxes = true_box_xy + true_wh_half\n",
" \n",
" # Calcul des coordonnées minimales et maximales des boiptes englobantes prédites\n",
" pred_wh_half = pred_box_wh / 2.\n",
" pred_mins = pred_box_xy - pred_wh_half\n",
" pred_maxes = pred_box_xy + pred_wh_half \n",
" \n",
" # Détermination de l'intersection des boîtes englobantes\n",
" intersect_mins = tf.maximum(pred_mins, true_mins)\n",
" intersect_maxes = tf.minimum(pred_maxes, true_maxes)\n",
" intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)\n",
" intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]\n",
" \n",
" # Aire des boîtes englobantes prédites et réelles\n",
" true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]\n",
" pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n",
"\n",
" # Aire de l'union des boîtes prédites et réelles\n",
" union_areas = pred_areas + true_areas - intersect_areas\n",
"\n",
" iou_scores = tf.truediv(intersect_areas, union_areas)\n",
" return iou_scores\n",
"\n",
"def iou():\n",
" def iou_metrics(y_true, y_pred):\n",
" return compute_iou(y_true, y_pred)\n",
" iou_metrics.__name__= \"IoU\"\n",
" return iou_metrics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vidE3XHlAkst"
},
"source": [
"Visualisation des données et labels"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"id": "8yotZHKgAiV1"
},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Si seuls x et y sont indiqués, on tire au hasard un numéro d'image et on affiche le label y associé à l'image\n",
"# Si un 2e y, nommé y_pred, est indiqué, alors les deux labels sont affichés côte à côte, afin de pouvoir les comparer\n",
"# Enfin on peut également indiquer l'id de l'image que l'on souhaite visualiser.\n",
"def print_data_localisation(x, y, y_pred=[], id=None, image_size=64):\n",
" if id==None:\n",
" # Tirage aléatoire d'une image dans la base\n",
" num_img = np.random.randint(x.shape[0]-1)\n",
" else:\n",
" num_img = id\n",
"\n",
" img = x[num_img]\n",
" lab = y[num_img]\n",
"\n",
" colors = [\"blue\", \"yellow\", \"red\", \"orange\", \"coral\", \"gold\", \"ivory\", \"fuchsia\", \"purple\", \"cyan\", \"navy\"] # Différentes couleurs pour les différentes classes\n",
" classes = ['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n",
"\n",
" if np.any(y_pred):\n",
" plt.subplot(1, 2, 1)\n",
"\n",
" # Affichage de l'image\n",
" plt.imshow(img)\n",
" # Détermination de la classe\n",
" class_id = np.argmax(lab[5:])\n",
"\n",
" # Détermination des coordonnées de la boîte englobante dans le repère image\n",
" ax = (lab[1]*y_std[1] + y_mean[1]) * image_size\n",
" ay = (lab[2]*y_std[2] + y_mean[2]) * image_size\n",
" width = (lab[3]*y_std[3] + y_mean[3]) * image_size\n",
" height = (lab[4]*y_std[4] + y_mean[4]) * image_size\n",
" #print(\"x: {}, y: {}, w: {}, h:{}\".format(ax,ay,width, height))\n",
" # Détermination des extrema de la boîte englobante\n",
" p_x = [ax-width/2, ax+width/2]\n",
" p_y = [ay-height/2, ay+height/2]\n",
" # Affichage de la boîte englobante, dans la bonne couleur\n",
" plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n",
" plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n",
" plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n",
" plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id])\n",
" plt.title(\"Vérité Terrain : Image {} - {}\".format(num_img, classes[class_id]))\n",
"\n",
" if np.any(y_pred):\n",
" plt.subplot(1, 2, 2)\n",
" # Affichage de l'image\n",
" plt.imshow(img)\n",
" lab = y_pred[num_img]\n",
" # Détermination de la classe\n",
" class_id = np.argmax(lab[5:])\n",
"\n",
" # Détermination des coordonnées de la boîte englobante dans le repère image\n",
" ax = (lab[1]*y_std[1] + y_mean[1]) * image_size\n",
" ay = (lab[2]*y_std[2] + y_mean[2]) * image_size\n",
" width = (lab[3]*y_std[3] + y_mean[3]) * image_size\n",
" height = (lab[4]*y_std[4] + y_mean[4]) * image_size\n",
" #print(\"x: {}, y: {}, w: {}, h:{}\".format(ax,ay,width, height))\n",
" # Détermination des extrema de la boîte englobante\n",
" p_x = [ax-width/2, ax+width/2]\n",
" p_y = [ay-height/2, ay+height/2]\n",
" # Affichage de la boîte englobante, dans la bonne couleur\n",
" plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n",
" plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n",
" plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n",
" plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id])\n",
" plt.title(\"Prédiction : Image {} - {}\".format(num_img, classes[class_id]))\n",
"\n",
" plt.show()\n",
"\n",
"for i in range(10):#x.shape[0]):\n",
" print_data_localisation(x_train, y_train, image_size=IMAGE_SIZE, id=i)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mE9_B3yuR4OH"
},
"source": [
"Fonction d'affichage des courbes d'apprentissage et de validation"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"id": "-6O7R3qnCtlN"
},
"outputs": [],
"source": [
"def plot_training_analysis(history, metric='loss'): \n",
"\n",
" loss = history.history[metric]\n",
" val_loss = history.history['val_' + metric]\n",
"\n",
" epochs = range(len(loss))\n",
"\n",
" plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training ' + metric)\n",
" plt.plot(epochs, val_loss, 'g', label='Validation ' + metric)\n",
" plt.title('Training and validation ' + metric)\n",
" plt.legend()\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8F_l_yNlDapa"
},
"source": [
"## Travail à faire\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E6D31NGPNyJp"
},
"source": [
"<center> <img src=\"https://drive.google.com/uc?id=1YzCZe4pgnjJDVGklAaCHZ7HhlPJsadg9\" width=500></center>\n",
"<caption><center> Figure 3: Illustration de l'architecture du réseau à construire. </center></caption>\n",
"\n",
"Complétez les codes qui vous sont fournis pour obtenir un algorithme de localisation. \n",
"Vous pouvez utiliser n'importe quelle base convolutive de votre choix (**commencez par exemple par réutiliser celle que vous avez implémenté au TP3**), en revanche vous devrez porter une attention particulière à la couche de sortie.\n",
"\n",
"Vous allez en fait produire 3 sorties différentes : une caractérisant la présence d'un objet, une autre fournissant les coordonnées de la boîte englobante, et enfin une dernière effectuant la classification."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"id": "vPOXiJ7hDcbr"
},
"outputs": [],
"source": [
"def create_model_localisation(input_shape=(64, 64, 3)):\n",
"\n",
" input_layer = Input(shape=input_shape)\n",
"\n",
" conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_layer)\n",
" conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n",
" pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
"\n",
" conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n",
" conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n",
" pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
"\n",
" flat = Flatten()(pool2)\n",
" dense = Dense(128, activation = 'relu')(flat)\n",
"\n",
" prev_layer = dense\n",
"\n",
" output_p = Dense(1, activation='sigmoid', name='p')(prev_layer) # Sortie caractérisant la présence d'un objet\n",
" output_coord = Dense(4, activation='linear', name='coord')(prev_layer) # Sortie caractérisant les coordonnées de boîte englobante\n",
" output_class = Dense(len(class_labels), activation='softmax', name='classes')(prev_layer) # Sortie caractérisant les probabilités de classe\n",
" \n",
" output= [output_p, output_coord, output_class]\n",
" model = Model(input_layer, output)\n",
"\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MYoVYfQpotjy"
},
"source": [
"<center> <img src=\"https://drive.google.com/uc?id=1bnh8zU7Os-w-5TT8hV4xDoThKQc-Ywc2\" width=500></center>\n",
"<caption><center> Figure 4: Illustration des fonctions de coût à utiliser pour l'entraînement. </center></caption>\n",
"\n",
"Pour entraîner votre réseau, vous allez donc devoir associer une fonction de coût à chacune des sorties du réseau. La fonction de coût totale sera la somme des trois fonctions de coût précédemment définies, pondérées par des poids définis dans la variable *loss_weights*.\n",
"\n",
"**Prenez le temps de tester différentes valeurs de *loss_weights* en fonction de l'évolution des métriques que vous observerez pendant l'entraînement.**\n",
"\n",
"Vous évaluerez vos résultats de manière qualitative (en affichant les boîtes englobantes prédites et réelles) mais aussi quantitatives grâce aux fonctions définies dans la section précédente. **N'hésitez pas à modifier un peu le paramètre iou_threshold positionné par défaut à 0.5** (une valeur de 0.4 vous permettra d'obtenir de meilleurs résultats !)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"id": "s_ewlCn5Rovm"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model_6\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_7 (InputLayer) [(None, 64, 64, 3)] 0 [] \n",
" \n",
" conv2d_22 (Conv2D) (None, 64, 64, 64) 1792 ['input_7[0][0]'] \n",
" \n",
" conv2d_23 (Conv2D) (None, 64, 64, 64) 36928 ['conv2d_22[0][0]'] \n",
" \n",
" max_pooling2d_11 (MaxPooling2D (None, 32, 32, 64) 0 ['conv2d_23[0][0]'] \n",
" ) \n",
" \n",
" conv2d_24 (Conv2D) (None, 32, 32, 128) 73856 ['max_pooling2d_11[0][0]'] \n",
" \n",
" conv2d_25 (Conv2D) (None, 32, 32, 128) 147584 ['conv2d_24[0][0]'] \n",
" \n",
" max_pooling2d_12 (MaxPooling2D (None, 16, 16, 128) 0 ['conv2d_25[0][0]'] \n",
" ) \n",
" \n",
" flatten_3 (Flatten) (None, 32768) 0 ['max_pooling2d_12[0][0]'] \n",
" \n",
" dense_5 (Dense) (None, 128) 4194432 ['flatten_3[0][0]'] \n",
" \n",
" p (Dense) (None, 1) 129 ['dense_5[0][0]'] \n",
" \n",
" coord (Dense) (None, 4) 516 ['dense_5[0][0]'] \n",
" \n",
" classes (Dense) (None, 11) 1419 ['dense_5[0][0]'] \n",
" \n",
"==================================================================================================\n",
"Total params: 4,456,656\n",
"Trainable params: 4,456,656\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n",
"Epoch 1/50\n",
"118/118 [==============================] - 15s 104ms/step - loss: 1.5293 - p_loss: 0.0235 - coord_loss: 1.1148 - classes_loss: 0.3911 - p_accuracy: 1.0000 - coord_IoU: 0.4255 - classes_accuracy: 0.1787 - val_loss: 0.8456 - val_p_loss: 0.0010 - val_coord_loss: 0.5451 - val_classes_loss: 0.2995 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5131 - val_classes_accuracy: 0.2810\n",
"Epoch 2/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.7562 - p_loss: 0.0018 - coord_loss: 0.4862 - classes_loss: 0.2681 - p_accuracy: 1.0000 - coord_IoU: 0.5129 - classes_accuracy: 0.4040 - val_loss: 0.7039 - val_p_loss: 8.6872e-04 - val_coord_loss: 0.4485 - val_classes_loss: 0.2545 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5107 - val_classes_accuracy: 0.4048\n",
"Epoch 3/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.6387 - p_loss: 6.8169e-04 - coord_loss: 0.4034 - classes_loss: 0.2346 - p_accuracy: 1.0000 - coord_IoU: 0.5332 - classes_accuracy: 0.5032 - val_loss: 0.6252 - val_p_loss: 0.0010 - val_coord_loss: 0.3901 - val_classes_loss: 0.2341 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5327 - val_classes_accuracy: 0.4762\n",
"Epoch 4/50\n",
"118/118 [==============================] - 10s 87ms/step - loss: 0.5672 - p_loss: 5.0588e-04 - coord_loss: 0.3520 - classes_loss: 0.2147 - p_accuracy: 1.0000 - coord_IoU: 0.5354 - classes_accuracy: 0.5292 - val_loss: 0.5895 - val_p_loss: 2.2709e-04 - val_coord_loss: 0.3826 - val_classes_loss: 0.2066 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5358 - val_classes_accuracy: 0.5000\n",
"Epoch 5/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.4793 - p_loss: 2.0704e-04 - coord_loss: 0.2845 - classes_loss: 0.1946 - p_accuracy: 1.0000 - coord_IoU: 0.5772 - classes_accuracy: 0.5610 - val_loss: 0.5538 - val_p_loss: 2.4165e-04 - val_coord_loss: 0.3575 - val_classes_loss: 0.1961 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5256 - val_classes_accuracy: 0.5286\n",
"Epoch 6/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.4431 - p_loss: 1.6858e-04 - coord_loss: 0.2604 - classes_loss: 0.1825 - p_accuracy: 1.0000 - coord_IoU: 0.6001 - classes_accuracy: 0.5732 - val_loss: 0.5183 - val_p_loss: 1.1366e-04 - val_coord_loss: 0.3382 - val_classes_loss: 0.1800 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5732 - val_classes_accuracy: 0.5333\n",
"Epoch 7/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.3859 - p_loss: 1.0205e-04 - coord_loss: 0.2163 - classes_loss: 0.1695 - p_accuracy: 1.0000 - coord_IoU: 0.6207 - classes_accuracy: 0.5944 - val_loss: 0.5467 - val_p_loss: 1.3684e-04 - val_coord_loss: 0.3726 - val_classes_loss: 0.1740 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5229 - val_classes_accuracy: 0.5762\n",
"Epoch 8/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.3621 - p_loss: 9.5203e-05 - coord_loss: 0.1990 - classes_loss: 0.1630 - p_accuracy: 1.0000 - coord_IoU: 0.6330 - classes_accuracy: 0.6108 - val_loss: 0.4850 - val_p_loss: 6.6984e-05 - val_coord_loss: 0.3162 - val_classes_loss: 0.1688 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5435 - val_classes_accuracy: 0.5476\n",
"Epoch 9/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.3252 - p_loss: 8.0633e-05 - coord_loss: 0.1702 - classes_loss: 0.1549 - p_accuracy: 1.0000 - coord_IoU: 0.6402 - classes_accuracy: 0.6257 - val_loss: 0.4909 - val_p_loss: 5.5699e-05 - val_coord_loss: 0.3324 - val_classes_loss: 0.1584 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5603 - val_classes_accuracy: 0.5762\n",
"Epoch 10/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2911 - p_loss: 5.4550e-05 - coord_loss: 0.1445 - classes_loss: 0.1466 - p_accuracy: 1.0000 - coord_IoU: 0.6725 - classes_accuracy: 0.6495 - val_loss: 0.4471 - val_p_loss: 4.2831e-05 - val_coord_loss: 0.2914 - val_classes_loss: 0.1556 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5763 - val_classes_accuracy: 0.5714\n",
"Epoch 11/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2748 - p_loss: 4.0582e-05 - coord_loss: 0.1337 - classes_loss: 0.1411 - p_accuracy: 1.0000 - coord_IoU: 0.6778 - classes_accuracy: 0.6617 - val_loss: 0.5055 - val_p_loss: 2.8487e-05 - val_coord_loss: 0.3523 - val_classes_loss: 0.1532 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5665 - val_classes_accuracy: 0.5571\n",
"Epoch 12/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.2577 - p_loss: 3.7545e-05 - coord_loss: 0.1205 - classes_loss: 0.1371 - p_accuracy: 1.0000 - coord_IoU: 0.6899 - classes_accuracy: 0.6691 - val_loss: 0.4531 - val_p_loss: 1.9267e-05 - val_coord_loss: 0.3026 - val_classes_loss: 0.1505 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5872 - val_classes_accuracy: 0.6048\n",
"Epoch 13/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2374 - p_loss: 3.0101e-05 - coord_loss: 0.1064 - classes_loss: 0.1309 - p_accuracy: 1.0000 - coord_IoU: 0.7021 - classes_accuracy: 0.6909 - val_loss: 0.4378 - val_p_loss: 2.6943e-05 - val_coord_loss: 0.2922 - val_classes_loss: 0.1455 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5917 - val_classes_accuracy: 0.6238\n",
"Epoch 14/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2291 - p_loss: 3.2337e-05 - coord_loss: 0.1018 - classes_loss: 0.1273 - p_accuracy: 1.0000 - coord_IoU: 0.7118 - classes_accuracy: 0.7052 - val_loss: 0.4363 - val_p_loss: 2.6610e-05 - val_coord_loss: 0.2911 - val_classes_loss: 0.1452 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5864 - val_classes_accuracy: 0.6238\n",
"Epoch 15/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.2152 - p_loss: 2.7226e-05 - coord_loss: 0.0912 - classes_loss: 0.1239 - p_accuracy: 1.0000 - coord_IoU: 0.7252 - classes_accuracy: 0.7169 - val_loss: 0.4317 - val_p_loss: 3.0600e-05 - val_coord_loss: 0.2906 - val_classes_loss: 0.1411 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5881 - val_classes_accuracy: 0.6381\n",
"Epoch 16/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.2030 - p_loss: 2.6618e-05 - coord_loss: 0.0832 - classes_loss: 0.1198 - p_accuracy: 1.0000 - coord_IoU: 0.7442 - classes_accuracy: 0.7397 - val_loss: 0.4165 - val_p_loss: 1.5708e-05 - val_coord_loss: 0.2784 - val_classes_loss: 0.1381 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5916 - val_classes_accuracy: 0.6238\n",
"Epoch 17/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2058 - p_loss: 3.1942e-05 - coord_loss: 0.0886 - classes_loss: 0.1172 - p_accuracy: 1.0000 - coord_IoU: 0.7303 - classes_accuracy: 0.7386 - val_loss: 0.4309 - val_p_loss: 6.8455e-05 - val_coord_loss: 0.2904 - val_classes_loss: 0.1405 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5980 - val_classes_accuracy: 0.6190\n",
"Epoch 18/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1907 - p_loss: 2.9955e-05 - coord_loss: 0.0776 - classes_loss: 0.1130 - p_accuracy: 1.0000 - coord_IoU: 0.7450 - classes_accuracy: 0.7609 - val_loss: 0.4112 - val_p_loss: 1.6949e-05 - val_coord_loss: 0.2780 - val_classes_loss: 0.1332 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5937 - val_classes_accuracy: 0.6571\n",
"Epoch 19/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1740 - p_loss: 2.3655e-05 - coord_loss: 0.0654 - classes_loss: 0.1086 - p_accuracy: 1.0000 - coord_IoU: 0.7620 - classes_accuracy: 0.7662 - val_loss: 0.4295 - val_p_loss: 7.7834e-06 - val_coord_loss: 0.2988 - val_classes_loss: 0.1308 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5796 - val_classes_accuracy: 0.6905\n",
"Epoch 20/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1670 - p_loss: 2.2241e-05 - coord_loss: 0.0623 - classes_loss: 0.1047 - p_accuracy: 1.0000 - coord_IoU: 0.7619 - classes_accuracy: 0.7821 - val_loss: 0.4024 - val_p_loss: 2.5984e-05 - val_coord_loss: 0.2725 - val_classes_loss: 0.1298 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6069 - val_classes_accuracy: 0.6810\n",
"Epoch 21/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1687 - p_loss: 2.1902e-05 - coord_loss: 0.0664 - classes_loss: 0.1023 - p_accuracy: 1.0000 - coord_IoU: 0.7641 - classes_accuracy: 0.7884 - val_loss: 0.4067 - val_p_loss: 9.9703e-06 - val_coord_loss: 0.2822 - val_classes_loss: 0.1245 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5956 - val_classes_accuracy: 0.7000\n",
"Epoch 22/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1523 - p_loss: 1.5818e-05 - coord_loss: 0.0550 - classes_loss: 0.0973 - p_accuracy: 1.0000 - coord_IoU: 0.7689 - classes_accuracy: 0.7990 - val_loss: 0.4198 - val_p_loss: 1.6219e-05 - val_coord_loss: 0.2917 - val_classes_loss: 0.1281 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5777 - val_classes_accuracy: 0.7095\n",
"Epoch 23/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1517 - p_loss: 1.6976e-05 - coord_loss: 0.0574 - classes_loss: 0.0944 - p_accuracy: 1.0000 - coord_IoU: 0.7685 - classes_accuracy: 0.8070 - val_loss: 0.3936 - val_p_loss: 1.5322e-05 - val_coord_loss: 0.2671 - val_classes_loss: 0.1265 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5896 - val_classes_accuracy: 0.7048\n",
"Epoch 24/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1395 - p_loss: 1.3186e-05 - coord_loss: 0.0486 - classes_loss: 0.0909 - p_accuracy: 1.0000 - coord_IoU: 0.7819 - classes_accuracy: 0.8261 - val_loss: 0.4031 - val_p_loss: 1.2730e-05 - val_coord_loss: 0.2800 - val_classes_loss: 0.1230 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6027 - val_classes_accuracy: 0.7524\n",
"Epoch 25/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1389 - p_loss: 1.2866e-05 - coord_loss: 0.0517 - classes_loss: 0.0872 - p_accuracy: 1.0000 - coord_IoU: 0.7672 - classes_accuracy: 0.8399 - val_loss: 0.4047 - val_p_loss: 2.1689e-05 - val_coord_loss: 0.2824 - val_classes_loss: 0.1223 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5614 - val_classes_accuracy: 0.7286\n",
"Epoch 26/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1326 - p_loss: 1.3932e-05 - coord_loss: 0.0485 - classes_loss: 0.0841 - p_accuracy: 1.0000 - coord_IoU: 0.7796 - classes_accuracy: 0.8478 - val_loss: 0.4003 - val_p_loss: 9.8319e-06 - val_coord_loss: 0.2825 - val_classes_loss: 0.1178 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6046 - val_classes_accuracy: 0.7524\n",
"Epoch 27/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1250 - p_loss: 1.0477e-05 - coord_loss: 0.0449 - classes_loss: 0.0801 - p_accuracy: 1.0000 - coord_IoU: 0.7908 - classes_accuracy: 0.8579 - val_loss: 0.3921 - val_p_loss: 6.6715e-06 - val_coord_loss: 0.2763 - val_classes_loss: 0.1158 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5834 - val_classes_accuracy: 0.7476\n",
"Epoch 28/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1239 - p_loss: 1.2673e-05 - coord_loss: 0.0470 - classes_loss: 0.0769 - p_accuracy: 1.0000 - coord_IoU: 0.7836 - classes_accuracy: 0.8680 - val_loss: 0.3843 - val_p_loss: 1.1446e-05 - val_coord_loss: 0.2687 - val_classes_loss: 0.1156 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5992 - val_classes_accuracy: 0.7524\n",
"Epoch 29/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1191 - p_loss: 1.0588e-05 - coord_loss: 0.0452 - classes_loss: 0.0739 - p_accuracy: 1.0000 - coord_IoU: 0.7808 - classes_accuracy: 0.8818 - val_loss: 0.3907 - val_p_loss: 1.1466e-05 - val_coord_loss: 0.2760 - val_classes_loss: 0.1148 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6082 - val_classes_accuracy: 0.7429\n",
"Epoch 30/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1193 - p_loss: 1.0438e-05 - coord_loss: 0.0486 - classes_loss: 0.0707 - p_accuracy: 1.0000 - coord_IoU: 0.7825 - classes_accuracy: 0.8881 - val_loss: 0.3991 - val_p_loss: 1.1293e-05 - val_coord_loss: 0.2860 - val_classes_loss: 0.1131 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5731 - val_classes_accuracy: 0.7762\n",
"Epoch 31/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1125 - p_loss: 8.7969e-06 - coord_loss: 0.0445 - classes_loss: 0.0680 - p_accuracy: 1.0000 - coord_IoU: 0.7844 - classes_accuracy: 0.8940 - val_loss: 0.3903 - val_p_loss: 5.9613e-06 - val_coord_loss: 0.2771 - val_classes_loss: 0.1132 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5902 - val_classes_accuracy: 0.7714\n",
"Epoch 32/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1079 - p_loss: 8.5401e-06 - coord_loss: 0.0434 - classes_loss: 0.0644 - p_accuracy: 1.0000 - coord_IoU: 0.7930 - classes_accuracy: 0.9062 - val_loss: 0.3907 - val_p_loss: 3.9751e-06 - val_coord_loss: 0.2794 - val_classes_loss: 0.1113 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6132 - val_classes_accuracy: 0.7810\n",
"Epoch 33/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1041 - p_loss: 6.3921e-06 - coord_loss: 0.0421 - classes_loss: 0.0619 - p_accuracy: 1.0000 - coord_IoU: 0.7967 - classes_accuracy: 0.9168 - val_loss: 0.3941 - val_p_loss: 9.8752e-06 - val_coord_loss: 0.2780 - val_classes_loss: 0.1160 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.7333\n",
"Epoch 34/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.0981 - p_loss: 8.5369e-06 - coord_loss: 0.0395 - classes_loss: 0.0585 - p_accuracy: 1.0000 - coord_IoU: 0.8023 - classes_accuracy: 0.9226 - val_loss: 0.3971 - val_p_loss: 3.0896e-06 - val_coord_loss: 0.2855 - val_classes_loss: 0.1116 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.7524\n",
"Epoch 35/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0926 - p_loss: 5.3509e-06 - coord_loss: 0.0377 - classes_loss: 0.0549 - p_accuracy: 1.0000 - coord_IoU: 0.8007 - classes_accuracy: 0.9348 - val_loss: 0.3745 - val_p_loss: 6.3115e-06 - val_coord_loss: 0.2608 - val_classes_loss: 0.1137 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6280 - val_classes_accuracy: 0.7619\n",
"Epoch 36/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0863 - p_loss: 4.5172e-06 - coord_loss: 0.0340 - classes_loss: 0.0523 - p_accuracy: 1.0000 - coord_IoU: 0.8094 - classes_accuracy: 0.9380 - val_loss: 0.3905 - val_p_loss: 1.8461e-06 - val_coord_loss: 0.2769 - val_classes_loss: 0.1136 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5988 - val_classes_accuracy: 0.7476\n",
"Epoch 37/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0808 - p_loss: 3.7010e-06 - coord_loss: 0.0318 - classes_loss: 0.0489 - p_accuracy: 1.0000 - coord_IoU: 0.8073 - classes_accuracy: 0.9507 - val_loss: 0.3798 - val_p_loss: 4.9560e-06 - val_coord_loss: 0.2730 - val_classes_loss: 0.1068 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5770 - val_classes_accuracy: 0.8000\n",
"Epoch 38/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0803 - p_loss: 3.8230e-06 - coord_loss: 0.0338 - classes_loss: 0.0465 - p_accuracy: 1.0000 - coord_IoU: 0.8084 - classes_accuracy: 0.9560 - val_loss: 0.3729 - val_p_loss: 2.7241e-06 - val_coord_loss: 0.2624 - val_classes_loss: 0.1105 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6056 - val_classes_accuracy: 0.7857\n",
"Epoch 39/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0754 - p_loss: 3.1915e-06 - coord_loss: 0.0323 - classes_loss: 0.0431 - p_accuracy: 1.0000 - coord_IoU: 0.8181 - classes_accuracy: 0.9581 - val_loss: 0.3831 - val_p_loss: 4.8808e-06 - val_coord_loss: 0.2767 - val_classes_loss: 0.1064 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6058 - val_classes_accuracy: 0.7952\n",
"Epoch 40/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.0741 - p_loss: 3.3675e-06 - coord_loss: 0.0332 - classes_loss: 0.0409 - p_accuracy: 1.0000 - coord_IoU: 0.8164 - classes_accuracy: 0.9661 - val_loss: 0.3762 - val_p_loss: 2.6131e-06 - val_coord_loss: 0.2668 - val_classes_loss: 0.1094 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6179 - val_classes_accuracy: 0.7952\n",
"Epoch 41/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.0717 - p_loss: 2.9842e-06 - coord_loss: 0.0330 - classes_loss: 0.0388 - p_accuracy: 1.0000 - coord_IoU: 0.8165 - classes_accuracy: 0.9708 - val_loss: 0.3781 - val_p_loss: 2.0115e-06 - val_coord_loss: 0.2716 - val_classes_loss: 0.1064 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6109 - val_classes_accuracy: 0.8048\n",
"Epoch 42/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0667 - p_loss: 2.6782e-06 - coord_loss: 0.0311 - classes_loss: 0.0356 - p_accuracy: 1.0000 - coord_IoU: 0.8156 - classes_accuracy: 0.9719 - val_loss: 0.3922 - val_p_loss: 2.0544e-06 - val_coord_loss: 0.2870 - val_classes_loss: 0.1051 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5667 - val_classes_accuracy: 0.8000\n",
"Epoch 43/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.0719 - p_loss: 2.8606e-06 - coord_loss: 0.0370 - classes_loss: 0.0349 - p_accuracy: 1.0000 - coord_IoU: 0.7998 - classes_accuracy: 0.9756 - val_loss: 0.3827 - val_p_loss: 3.2613e-06 - val_coord_loss: 0.2734 - val_classes_loss: 0.1093 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6156 - val_classes_accuracy: 0.8048\n",
"Epoch 44/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0692 - p_loss: 2.8178e-06 - coord_loss: 0.0368 - classes_loss: 0.0324 - p_accuracy: 1.0000 - coord_IoU: 0.8033 - classes_accuracy: 0.9814 - val_loss: 0.3922 - val_p_loss: 1.6154e-06 - val_coord_loss: 0.2802 - val_classes_loss: 0.1120 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5931 - val_classes_accuracy: 0.7952\n",
"Epoch 45/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0639 - p_loss: 2.7500e-06 - coord_loss: 0.0331 - classes_loss: 0.0308 - p_accuracy: 1.0000 - coord_IoU: 0.8090 - classes_accuracy: 0.9783 - val_loss: 0.3965 - val_p_loss: 1.2561e-06 - val_coord_loss: 0.2813 - val_classes_loss: 0.1152 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5977 - val_classes_accuracy: 0.7810\n",
"Epoch 46/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.0593 - p_loss: 2.0790e-06 - coord_loss: 0.0309 - classes_loss: 0.0284 - p_accuracy: 1.0000 - coord_IoU: 0.8117 - classes_accuracy: 0.9852 - val_loss: 0.3921 - val_p_loss: 9.9823e-07 - val_coord_loss: 0.2795 - val_classes_loss: 0.1125 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5989 - val_classes_accuracy: 0.8048\n",
"Epoch 47/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.0582 - p_loss: 2.2083e-06 - coord_loss: 0.0316 - classes_loss: 0.0266 - p_accuracy: 1.0000 - coord_IoU: 0.8052 - classes_accuracy: 0.9857 - val_loss: 0.4020 - val_p_loss: 1.3179e-06 - val_coord_loss: 0.2929 - val_classes_loss: 0.1091 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6036 - val_classes_accuracy: 0.8048\n",
"Epoch 48/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0585 - p_loss: 2.2486e-06 - coord_loss: 0.0332 - classes_loss: 0.0253 - p_accuracy: 1.0000 - coord_IoU: 0.8175 - classes_accuracy: 0.9878 - val_loss: 0.3783 - val_p_loss: 8.4552e-07 - val_coord_loss: 0.2659 - val_classes_loss: 0.1125 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6222 - val_classes_accuracy: 0.7905\n",
"Epoch 49/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0535 - p_loss: 1.7950e-06 - coord_loss: 0.0302 - classes_loss: 0.0233 - p_accuracy: 1.0000 - coord_IoU: 0.8218 - classes_accuracy: 0.9894 - val_loss: 0.3774 - val_p_loss: 2.5507e-06 - val_coord_loss: 0.2659 - val_classes_loss: 0.1115 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6127 - val_classes_accuracy: 0.7857\n",
"Epoch 50/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.0472 - p_loss: 1.3065e-06 - coord_loss: 0.0260 - classes_loss: 0.0212 - p_accuracy: 1.0000 - coord_IoU: 0.8263 - classes_accuracy: 0.9931 - val_loss: 0.3771 - val_p_loss: 1.0610e-06 - val_coord_loss: 0.2671 - val_classes_loss: 0.1100 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6085 - val_classes_accuracy: 0.8095\n"
]
}
],
"source": [
"batch_size=16\n",
"model = create_model_localisation()\n",
"model.summary()\n",
"opt = Adam(learning_rate=3e-4) \n",
"\n",
"# Ici mettre, dans l'ordre, les fonctions de coût associées à chacune des sorties\n",
"loss=['binary_crossentropy', 'mean_squared_error', 'binary_crossentropy']\n",
"# On va associer une métrique à chaque sortie : l'accuracy pour les deux classifications, \n",
"# et l'IoU définie plus tôt pour la qualité des boîtes englobantes. \n",
"metrics=[ ['accuracy'], [iou()], ['accuracy']]\n",
"loss_weights = [1, 1, 1]\n",
"\n",
"model.compile(loss=loss,\n",
" optimizer=opt,\n",
" metrics=metrics,\n",
" loss_weights=loss_weights\n",
")\n",
"history = model.fit(\n",
" x_train,\n",
" [y_train[:,0], y_train[:,1:5], y_train[:,5:]],\n",
" epochs=50,\n",
" batch_size=batch_size, \n",
" validation_data=(x_val, [y_val[:,0], y_val[:,1:5], y_val[:,5:]])\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"id": "Jc-sGe8LNzA-"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Analyse des résultats : courbes d'évolution de la fonction de perte, et de l'IoU des boîtes englobantes ainsi que de la précision des classes prédites\n",
"plot_training_analysis(history, metric='loss')\n",
"plot_training_analysis(history, metric='coord_IoU')\n",
"plot_training_analysis(history, metric='classes_accuracy')\n",
"\n",
"# Prédiction des données de test\n",
"y_pred_presence, y_pred_coords, y_pred_classes = model.predict(x_test)\n",
"y_pred = np.zeros(y_test.shape)\n",
"for i in range(y_pred.shape[0]):\n",
" y_pred[i, 0] = y_pred_presence[i]\n",
" y_pred[i, 1:5] = y_pred_coords[i]\n",
" y_pred[i, 5:] = y_pred_classes[i]\n",
"\n",
"# Affichage des résultats sur plusieurs images\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=1, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=2, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=3, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=4, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=5, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=6, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=7, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=8, image_size=IMAGE_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"id": "UVU1bR_YEIMs"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La précision globale est de 56.2%\n",
"\n",
"------------------------------------------\n",
"| Classe | Précision | Rappel | F1-score |\n",
"------------------------------------------\n",
"| MESCHA | 0.14 | 0.09 | 0.11 |\n",
"------------------------------------------\n",
"| VEREUR | 0.52 | 0.59 | 0.55 |\n",
"------------------------------------------\n",
"| ECUROU | 0.86 | 0.86 | 0.86 |\n",
"------------------------------------------\n",
"| PIEBAV | 0.91 | 0.91 | 0.91 |\n",
"------------------------------------------\n",
"| SITTOR | 0.55 | 0.27 | 0.36 |\n",
"------------------------------------------\n",
"| PINARB | 0.71 | 0.77 | 0.74 |\n",
"------------------------------------------\n",
"| MESNOI | 0.36 | 0.57 | 0.44 |\n",
"------------------------------------------\n",
"| MESNON | 0.29 | 0.36 | 0.32 |\n",
"------------------------------------------\n",
"| MESBLE | 0.30 | 0.27 | 0.29 |\n",
"------------------------------------------\n",
"| ROUGOR | 0.71 | 0.71 | 0.71 |\n",
"------------------------------------------\n",
"| ACCMOU | 0.71 | 0.77 | 0.74 |\n",
"------------------------------------------\n"
]
}
],
"source": [
"class_res, accuracy = global_accuracy(y_test, y_pred)\n",
"\n",
"print(f\"La précision globale est de {100 * accuracy:.1f}%\")\n",
"\n",
"print()\n",
"print(\"------------------------------------------\")\n",
"print(\"| Classe | Précision | Rappel | F1-score |\")\n",
"print(\"------------------------------------------\")\n",
"for i in range(num_classes):\n",
" print(f\"| {class_labels[i]:7s}| {class_res[i]['Precision']:.2f} | {class_res[i]['Rappel']:.2f} | {class_res[i]['F-score']:.2f} |\")\n",
" print(\"------------------------------------------\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eiFO6fBns-OI"
},
"source": [
"En pratique, il est délicat de trouver une bonne combinaison des fonctions de perte tel que vous l'avez fait sur les cellules précédentes. L'entropie croisée et l'erreur quadratique moyenne donnent des valeurs trop différentes pour être combinables efficacement.\n",
"\n",
"Une variante, peut-être plus simple à faire fonctionner, est d'utiliser uniquement l'erreur quadratique moyenne comme perte pour toutes les sorties. C'est cette variante qui est implémentée dans l'algorithme YOLO, dont nous implémenterons une version simplifiée dans le prochain TP.\n",
"Testez cette solution ci-dessous. Comme sur l'exercice précédent, n'hésitez pas à faire varier le poids des différents éléments de la fonction de coût."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"id": "ttGCeqz0Dc3y"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n",
"118/118 [==============================] - 11s 88ms/step - loss: 1.4310 - p_loss: 0.0151 - coord_loss: 1.3283 - classes_loss: 0.0876 - p_accuracy: 0.9989 - coord_IoU: 0.4143 - classes_accuracy: 0.0859 - val_loss: 0.6519 - val_p_loss: 5.9735e-04 - val_coord_loss: 0.5653 - val_classes_loss: 0.0860 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.4729 - val_classes_accuracy: 0.1286\n",
"Epoch 2/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.5781 - p_loss: 0.0011 - coord_loss: 0.4942 - classes_loss: 0.0828 - p_accuracy: 1.0000 - coord_IoU: 0.5069 - classes_accuracy: 0.1729 - val_loss: 0.5192 - val_p_loss: 5.8288e-04 - val_coord_loss: 0.4374 - val_classes_loss: 0.0812 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5086 - val_classes_accuracy: 0.2143\n",
"Epoch 3/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.4808 - p_loss: 7.0716e-04 - coord_loss: 0.4026 - classes_loss: 0.0774 - p_accuracy: 1.0000 - coord_IoU: 0.5380 - classes_accuracy: 0.2964 - val_loss: 0.4908 - val_p_loss: 9.9283e-04 - val_coord_loss: 0.4128 - val_classes_loss: 0.0770 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5183 - val_classes_accuracy: 0.3143\n",
"Epoch 4/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.4210 - p_loss: 4.7277e-04 - coord_loss: 0.3479 - classes_loss: 0.0726 - p_accuracy: 1.0000 - coord_IoU: 0.5572 - classes_accuracy: 0.3881 - val_loss: 0.4603 - val_p_loss: 2.6880e-04 - val_coord_loss: 0.3883 - val_classes_loss: 0.0718 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.4756 - val_classes_accuracy: 0.3476\n",
"Epoch 5/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.3553 - p_loss: 2.5311e-04 - coord_loss: 0.2874 - classes_loss: 0.0676 - p_accuracy: 1.0000 - coord_IoU: 0.5813 - classes_accuracy: 0.4316 - val_loss: 0.3913 - val_p_loss: 1.5167e-04 - val_coord_loss: 0.3239 - val_classes_loss: 0.0672 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5942 - val_classes_accuracy: 0.3952\n",
"Epoch 6/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.3121 - p_loss: 2.1208e-04 - coord_loss: 0.2483 - classes_loss: 0.0636 - p_accuracy: 1.0000 - coord_IoU: 0.6031 - classes_accuracy: 0.4708 - val_loss: 0.3978 - val_p_loss: 1.8132e-04 - val_coord_loss: 0.3330 - val_classes_loss: 0.0647 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5796 - val_classes_accuracy: 0.4429\n",
"Epoch 7/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2712 - p_loss: 1.4645e-04 - coord_loss: 0.2110 - classes_loss: 0.0600 - p_accuracy: 1.0000 - coord_IoU: 0.6217 - classes_accuracy: 0.5048 - val_loss: 0.3656 - val_p_loss: 9.9904e-05 - val_coord_loss: 0.3042 - val_classes_loss: 0.0613 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6001 - val_classes_accuracy: 0.4762\n",
"Epoch 8/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.2488 - p_loss: 1.8330e-04 - coord_loss: 0.1911 - classes_loss: 0.0576 - p_accuracy: 1.0000 - coord_IoU: 0.6303 - classes_accuracy: 0.5223 - val_loss: 0.3516 - val_p_loss: 2.8725e-04 - val_coord_loss: 0.2916 - val_classes_loss: 0.0597 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6061 - val_classes_accuracy: 0.4952\n",
"Epoch 9/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.2226 - p_loss: 1.1546e-04 - coord_loss: 0.1673 - classes_loss: 0.0552 - p_accuracy: 1.0000 - coord_IoU: 0.6481 - classes_accuracy: 0.5451 - val_loss: 0.3517 - val_p_loss: 9.4986e-05 - val_coord_loss: 0.2937 - val_classes_loss: 0.0579 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5699 - val_classes_accuracy: 0.5048\n",
"Epoch 10/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.2028 - p_loss: 1.1011e-04 - coord_loss: 0.1493 - classes_loss: 0.0533 - p_accuracy: 1.0000 - coord_IoU: 0.6572 - classes_accuracy: 0.5551 - val_loss: 0.3346 - val_p_loss: 6.5773e-05 - val_coord_loss: 0.2787 - val_classes_loss: 0.0558 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6171 - val_classes_accuracy: 0.5571\n",
"Epoch 11/50\n",
"118/118 [==============================] - 10s 85ms/step - loss: 0.1809 - p_loss: 7.7123e-05 - coord_loss: 0.1293 - classes_loss: 0.0515 - p_accuracy: 1.0000 - coord_IoU: 0.6801 - classes_accuracy: 0.5870 - val_loss: 0.3483 - val_p_loss: 3.8490e-05 - val_coord_loss: 0.2935 - val_classes_loss: 0.0547 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.5333\n",
"Epoch 12/50\n",
"118/118 [==============================] - 10s 86ms/step - loss: 0.1652 - p_loss: 7.5254e-05 - coord_loss: 0.1155 - classes_loss: 0.0496 - p_accuracy: 1.0000 - coord_IoU: 0.6897 - classes_accuracy: 0.5960 - val_loss: 0.3261 - val_p_loss: 7.5853e-05 - val_coord_loss: 0.2731 - val_classes_loss: 0.0529 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6050 - val_classes_accuracy: 0.5619\n",
"Epoch 13/50\n",
" 86/118 [====================>.........] - ETA: 2s - loss: 0.1487 - p_loss: 5.7952e-05 - coord_loss: 0.0999 - classes_loss: 0.0488 - p_accuracy: 1.0000 - coord_IoU: 0.6981 - classes_accuracy: 0.6054"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/laurent/Documents/Cours/ENSEEIHT/S8 - Réseau Profond/TP6.ipynb Cell 33'\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=6'>7</a>\u001b[0m loss_weights \u001b[39m=\u001b[39m [\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m]\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=8'>9</a>\u001b[0m model\u001b[39m.\u001b[39mcompile(loss\u001b[39m=\u001b[39mloss,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=9'>10</a>\u001b[0m optimizer\u001b[39m=\u001b[39mopt,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=10'>11</a>\u001b[0m metrics\u001b[39m=\u001b[39mmetrics,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=11'>12</a>\u001b[0m loss_weights\u001b[39m=\u001b[39mloss_weights\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=12'>13</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=13'>14</a>\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(x_train, [y_train[:,\u001b[39m0\u001b[39;49m], y_train[:,\u001b[39m1\u001b[39;49m:\u001b[39m5\u001b[39;49m], y_train[:,\u001b[39m5\u001b[39;49m:]],\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=14'>15</a>\u001b[0m epochs\u001b[39m=\u001b[39;49m\u001b[39m50\u001b[39;49m,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=15'>16</a>\u001b[0m batch_size\u001b[39m=\u001b[39;49mbatch_size, \n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=16'>17</a>\u001b[0m validation_data\u001b[39m=\u001b[39;49m(x_val, [y_val[:,\u001b[39m0\u001b[39;49m], y_val[:,\u001b[39m1\u001b[39;49m:\u001b[39m5\u001b[39;49m], y_val[:,\u001b[39m5\u001b[39;49m:]]))\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=61'>62</a>\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=62'>63</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=63'>64</a>\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=64'>65</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=65'>66</a>\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py:1389\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1386'>1387</a>\u001b[0m logs \u001b[39m=\u001b[39m tmp_logs \u001b[39m# No error, now safe to assign to logs.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1387'>1388</a>\u001b[0m end_step \u001b[39m=\u001b[39m step \u001b[39m+\u001b[39m data_handler\u001b[39m.\u001b[39mstep_increment\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1388'>1389</a>\u001b[0m callbacks\u001b[39m.\u001b[39;49mon_train_batch_end(end_step, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1389'>1390</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstop_training:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1390'>1391</a>\u001b[0m \u001b[39mbreak\u001b[39;00m\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:438\u001b[0m, in \u001b[0;36mCallbackList.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=430'>431</a>\u001b[0m \u001b[39m\"\"\"Calls the `on_train_batch_end` methods of its callbacks.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=431'>432</a>\u001b[0m \n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=432'>433</a>\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=433'>434</a>\u001b[0m \u001b[39m batch: Integer, index of batch within the current epoch.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=434'>435</a>\u001b[0m \u001b[39m logs: Dict. Aggregated metric results up until this batch.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=435'>436</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=436'>437</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_call_train_batch_hooks:\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=437'>438</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook(ModeKeys\u001b[39m.\u001b[39;49mTRAIN, \u001b[39m'\u001b[39;49m\u001b[39mend\u001b[39;49m\u001b[39m'\u001b[39;49m, batch, logs\u001b[39m=\u001b[39;49mlogs)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:297\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=294'>295</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_batch_begin_hook(mode, batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=295'>296</a>\u001b[0m \u001b[39melif\u001b[39;00m hook \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mend\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=296'>297</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_end_hook(mode, batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=297'>298</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=298'>299</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=299'>300</a>\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mUnrecognized hook: \u001b[39m\u001b[39m{\u001b[39;00mhook\u001b[39m}\u001b[39;00m\u001b[39m. Expected values are [\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbegin\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m'\u001b[39m)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:318\u001b[0m, in \u001b[0;36mCallbackList._call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=314'>315</a>\u001b[0m batch_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_start_time\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=315'>316</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times\u001b[39m.\u001b[39mappend(batch_time)\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=317'>318</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook_helper(hook_name, batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=319'>320</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times) \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_batches_for_timing_check:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=320'>321</a>\u001b[0m end_hook_name \u001b[39m=\u001b[39m hook_name\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:356\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=353'>354</a>\u001b[0m \u001b[39mfor\u001b[39;00m callback \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallbacks:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=354'>355</a>\u001b[0m hook \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(callback, hook_name)\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=355'>356</a>\u001b[0m hook(batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=357'>358</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timing:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=358'>359</a>\u001b[0m \u001b[39mif\u001b[39;00m hook_name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_hook_times:\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:1034\u001b[0m, in \u001b[0;36mProgbarLogger.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1032'>1033</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mon_train_batch_end\u001b[39m(\u001b[39mself\u001b[39m, batch, logs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1033'>1034</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_batch_update_progbar(batch, logs)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:1106\u001b[0m, in \u001b[0;36mProgbarLogger._batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1101'>1102</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mseen \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m add_seen\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1103'>1104</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1104'>1105</a>\u001b[0m \u001b[39m# Only block async when verbose = 1.\u001b[39;00m\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1105'>1106</a>\u001b[0m logs \u001b[39m=\u001b[39m tf_utils\u001b[39m.\u001b[39;49msync_to_numpy_or_python_type(logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1106'>1107</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprogbar\u001b[39m.\u001b[39mupdate(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mseen, \u001b[39mlist\u001b[39m(logs\u001b[39m.\u001b[39mitems()), finalize\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py:563\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=559'>560</a>\u001b[0m \u001b[39mreturn\u001b[39;00m t\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=560'>561</a>\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mitem() \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39mndim(t) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39melse\u001b[39;00m t\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=562'>563</a>\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39;49mnest\u001b[39m.\u001b[39;49mmap_structure(_to_single_numpy_or_python_type, tensors)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py:914\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=909'>910</a>\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=910'>911</a>\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=912'>913</a>\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=913'>914</a>\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=914'>915</a>\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py:914\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=909'>910</a>\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=910'>911</a>\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=912'>913</a>\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=913'>914</a>\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39;49mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=914'>915</a>\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py:557\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type.<locals>._to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=553'>554</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_to_single_numpy_or_python_type\u001b[39m(t):\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=554'>555</a>\u001b[0m \u001b[39m# Don't turn ragged or sparse tensors to NumPy.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=555'>556</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(t, tf\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=556'>557</a>\u001b[0m t \u001b[39m=\u001b[39m t\u001b[39m.\u001b[39;49mnumpy()\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=557'>558</a>\u001b[0m \u001b[39m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=558'>559</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(t, (np\u001b[39m.\u001b[39mndarray, np\u001b[39m.\u001b[39mgeneric)):\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1223\u001b[0m, in \u001b[0;36m_EagerTensorBase.numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1199'>1200</a>\u001b[0m \u001b[39m\"\"\"Copy of the contents of this Tensor into a NumPy array or scalar.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1200'>1201</a>\u001b[0m \n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1201'>1202</a>\u001b[0m \u001b[39mUnlike NumPy arrays, Tensors are immutable, so this method has to copy\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1219'>1220</a>\u001b[0m \u001b[39m NumPy dtype.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1220'>1221</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1221'>1222</a>\u001b[0m \u001b[39m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[39;00m\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1222'>1223</a>\u001b[0m maybe_arr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_numpy() \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1223'>1224</a>\u001b[0m \u001b[39mreturn\u001b[39;00m maybe_arr\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(maybe_arr, np\u001b[39m.\u001b[39mndarray) \u001b[39melse\u001b[39;00m maybe_arr\n",
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1189\u001b[0m, in \u001b[0;36m_EagerTensorBase._numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1186'>1187</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_numpy\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1187'>1188</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1188'>1189</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_numpy_internal()\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1189'>1190</a>\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1190'>1191</a>\u001b[0m \u001b[39mraise\u001b[39;00m core\u001b[39m.\u001b[39m_status_to_exception(e) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"batch_size=16\n",
"model = create_model_localisation()\n",
"opt = Adam(learning_rate=3e-4) \n",
"\n",
"loss = ['mean_squared_error', \"mean_squared_error\", 'mean_squared_error']\n",
"metrics =[ ['accuracy'], [iou()], ['accuracy']]\n",
"loss_weights = [1, 1, 1]\n",
"\n",
"model.compile(loss=loss,\n",
" optimizer=opt,\n",
" metrics=metrics,\n",
" loss_weights=loss_weights\n",
" )\n",
"history = model.fit(x_train, [y_train[:,0], y_train[:,1:5], y_train[:,5:]],\n",
" epochs=50,\n",
" batch_size=batch_size, \n",
" validation_data=(x_val, [y_val[:,0], y_val[:,1:5], y_val[:,5:]]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gKlN9-clJMrI"
},
"outputs": [],
"source": [
"# Analyse des résultats : courbes d'évolution de la fonction de perte, et de l'IoU des boîtes englobantes ainsi que de la précision des classes prédites\n",
"plot_training_analysis(history, metric='loss')\n",
"plot_training_analysis(history, metric='coord_IoU')\n",
"plot_training_analysis(history, metric='classes_accuracy')\n",
"\n",
"# Prédiction des données de test\n",
"y_pred_presence, y_pred_coords, y_pred_classes = model.predict(x_test)\n",
"y_pred = np.zeros(y_test.shape)\n",
"for i in range(y_pred.shape[0]):\n",
" y_pred[i, 0] = y_pred_presence[i]\n",
" y_pred[i, 1:5] = y_pred_coords[i]\n",
" y_pred[i, 5:] = y_pred_classes[i]\n",
"\n",
"# Affichage des résultats sur plusieurs images\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=1, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=2, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=3, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=4, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=5, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=6, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=7, image_size=IMAGE_SIZE)\n",
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=8, image_size=IMAGE_SIZE)\n",
"\n",
"\n",
"class_res, accuracy = global_accuracy(y_test, y_pred, iou_thres=0.4)\n",
"\n",
"print(f\"La précision globale est de {100 * accuracy:.1f}%\")\n",
"\n",
"print()\n",
"print(\"------------------------------------------\")\n",
"print(\"| Classe | Précision | Rappel | F1-score |\")\n",
"print(\"------------------------------------------\")\n",
"for i in range(num_classes):\n",
" print(f\"| {class_labels[i]:7s}| {class_res[i]['Precision']:.2f} | {class_res[i]['Rappel']:.2f} | {class_res[i]['F-score']:.2f} |\")\n",
" print(\"------------------------------------------\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "stQpmnmAt_bf"
},
"source": [
"Compte-tenu de la taille réduite de la base de données, les résultats ne sont pas mal du tout ! On observe quelques confusions entre certaines classes mais les prédictions sont souvent intéressantes.\n",
"\n",
"Il devrait cependant subsister un fort surapprentissage à ce stade. Comme nous l'avons vu dans de précédents TPs, vous avez plusieurs possibilités qui s'offrent à vous pour le corriger : \n",
"\n",
"* Augmentation de la base de données. Vous pouvez pour cela vous appuyer sur l'exemple fourni en TP précédent, avec une classe *Sequence* et l'utilisation de la librairie *Albumentation*. Je vous fournis une Sequence qui fonctionne ci-dessous.\n",
"* Utilisation de *transfer learning* : partant d'un réseau entraîné sur ImageNet (qui contient de nombreuses classes d'animaux), vous bénéficieriez de filtres très généraux qui aiderait à limiter le surapprentissage. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Jp6jwZidJgx"
},
"source": [
"# Code fourni pour vous aider à mettre en place l'augmentation de données"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jKbK2AEkUF3M"
},
"source": [
"Il y a des problèmes avec les versions d'openCV et d'Albumentation, voici comment les résoudre :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eTq-d6uadJgx"
},
"outputs": [],
"source": [
"!pip uninstall opencv-python-headless==4.5.5.62\n",
"!pip install opencv-python-headless==4.1.2.30\n",
"!pip install -q -U albumentations\n",
"!echo \"$(pip freeze | grep albumentations) is successfully installed\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B5WuA43ZULxP"
},
"source": [
"Définition des augmentations (uniquement colorimétriques ici)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WS5wxAXvdJgx"
},
"outputs": [],
"source": [
"from albumentations import (Compose, RandomBrightness, RandomContrast, RandomGamma, ShiftScaleRotate, RandomSizedBBoxSafeCrop)\n",
"import albumentations as A\n",
"\n",
"AUGMENTATIONS_TRAIN = Compose([\n",
" RandomContrast(limit=0.2, p=0.5),\n",
" RandomGamma(gamma_limit=(80, 120), p=0.5),\n",
" RandomBrightness(limit=0.2, p=0.5)\n",
"], bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sND_uaWPURfE"
},
"source": [
"Une sequence qui permet d'implanter l'augmentation de données :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x9GXnEyYdJgy"
},
"outputs": [],
"source": [
"from tensorflow.keras.utils import Sequence\n",
"\n",
"class MangeoireSequence(Sequence):\n",
" # Initialisation de la séquence avec différents paramètres\n",
" def __init__(self, x_set, y_set, batch_size,augmentations):\n",
" self.x, self.y = x_set, y_set\n",
" self.classes = ['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n",
" self.batch_size = batch_size\n",
" self.augment = augmentations\n",
" self.indices1 = np.arange(x_set.shape[0]) \n",
" np.random.shuffle(self.indices1) # Les indices permettent d'accéder\n",
" # aux données et sont randomisés à chaque epoch pour varier la composition\n",
" # des batches au cours de l'entraînement\n",
"\n",
" # Fonction calculant le nombre de pas de descente du gradient par epoch\n",
" def __len__(self):\n",
" return int(np.ceil(len(self.x) / float(self.batch_size)))\n",
"\n",
" # Il y a des problèmes d'arrondi dans les conversions de boîtes englobantes \n",
" # internes à la librairie Albumentations\n",
" # Pour les contourner, si les boîtes sont trop proches des bords, on les érode un peu\n",
" def erode_bounding_box(self, box):\n",
" epsilon = 0.01\n",
" \n",
" xmin = max(box[0] - box[2]/2, epsilon)\n",
" ymin = max(box[1] - box[3]/2, epsilon)\n",
" xmax = min(box[0] + box[2]/2, 1-epsilon)\n",
" ymax = min(box[1] + box[3]/2, 1-epsilon)\n",
" \n",
" cx = xmin + (xmax - xmin)/2\n",
" cy = ymin + (ymax - ymin)/2\n",
" width = xmax - xmin\n",
" height = ymax - ymin\n",
" \n",
" return np.array([cx, cy, width, height])\n",
" \n",
" # Application de l'augmentation de données à chaque image du batch et aux\n",
" # boîtes englobantes associées\n",
" def apply_augmentation(self, bx, by):\n",
"\n",
" batch_x = np.zeros((bx.shape[0], IMAGE_SIZE, IMAGE_SIZE, 3))\n",
" batch_y = by\n",
" \n",
" # Pour chaque image du batch\n",
" for i in range(len(bx)):\n",
" bboxes = []\n",
" box = by[i,1:5]\n",
" # Dénormalisation des coordonnées de boites englobantes\n",
" box[0] = (box[0]*y_std[1] + y_mean[1])\n",
" box[1] = (box[1]*y_std[2] + y_mean[2])\n",
" box[2] = (box[2]*y_std[3] + y_mean[3])\n",
" box[3] = (box[3]*y_std[4] + y_mean[4])\n",
" box = self.erode_bounding_box(box)\n",
" bboxes.append(box)\n",
" \n",
" class_labels = []\n",
" class_id = np.argmax(by[i, 5:])\n",
" class_labels.append(self.classes[class_id])\n",
"\n",
" img = bx[i]\n",
"\n",
" # Application de l'augmentation à l'image et aux masques\n",
" transformed = self.augment(image=img.astype('float32'), bboxes=bboxes, class_labels=class_labels)\n",
" batch_x[i] = transformed['image']\n",
" batch_y_transformed = transformed['bboxes']\n",
" \n",
" # Renormalisation des coordonnées de boîte englobante transformée\n",
" batch_y[i, 1] = (batch_y_transformed[0][0] - y_mean[1])/y_std[1]\n",
" batch_y[i, 2] = (batch_y_transformed[0][1] - y_mean[2])/y_std[2]\n",
" batch_y[i, 3] = (batch_y_transformed[0][2] - y_mean[3])/y_std[3]\n",
" batch_y[i, 4] = (batch_y_transformed[0][3] - y_mean[4])/y_std[4]\n",
"\n",
" return batch_x, batch_y\n",
"\n",
" # Fonction appelée à chaque nouveau batch : sélection et augmentation des données\n",
" def __getitem__(self, idx):\n",
" batch_x = self.x[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n",
" batch_y = self.y[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n",
" batch_x, batch_y = self.apply_augmentation(batch_x, batch_y)\n",
" \n",
" batch_y = np.array(batch_y)\n",
" return np.array(batch_x), [batch_y[:,0], batch_y[:,1:5], batch_y[:,5:]]\n",
"\n",
" # Fonction appelée à la fin d'un epoch ; on randomise les indices d'accès aux données\n",
" def on_epoch_end(self):\n",
" np.random.shuffle(self.indices1)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BUbm6gTMdJgy"
},
"outputs": [],
"source": [
"# Instanciation d'une Sequence\n",
"train_gen = MangeoireSequence(x_train, y_train, 16, augmentations=AUGMENTATIONS_TRAIN)\n",
"\n",
"# Pour tester la séquence, nous sélectionnons les éléments du premier batch et les affichons\n",
"batch_x, batch_y = train_gen.__getitem__(0)\n",
"\n",
"y_batch = np.zeros((batch_y[0].shape[0],1+4+num_classes))\n",
"\n",
"for i in range(batch_y[0].shape[0]):\n",
" y_batch[i, 0] = batch_y[0][i]\n",
" y_batch[i, 1:5] = batch_y[1][i]\n",
" y_batch[i, 5:] = batch_y[2][i]\n",
"\n",
"print_data_localisation(batch_x, y_batch, image_size=IMAGE_SIZE)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "TP6 - Localisation d'objet - Sujet.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3.10.6 64-bit",
"language": "python",
"name": "python3"
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"language_info": {
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"name": "python",
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